Tag: EN

Article

Apr 22, 2022

9min read

How to Create a Modern Data Foundation for Experimentation

AB Tasty

Staying ahead of the game to deliver seamless brand experiences for your customers is crucial in today’s experience economy. Today we’ll dip our toe into the “how” by looking at the underlying foundation upon which all of your experiences, optimization and experimentation efforts will be built: data.

Data is the foundation experimentation is built on (Source)

Data is the technology that can power the experiences you build for your customers by first understanding what they want and how it’ll best serve your business to deliver this. It’s the special sauce that helps connect the dots between your interpretation of existing information and trends, and the outcomes that you hypothesize will address customer needs (and grow revenue).

If you’ve ever wondered whether the benefits of a special offer are sufficiently enticing for your customer or why you have so many page hits and so few purchases, then you’ve asked the questions the marketing teams of your competitors are both asking and actively working to answer. Data and experimentation will help you take your website to the next level, better understand your customers’ preferences, and optimize their purchasing journey to drive stronger business outcomes.

So, the question remains: Where do you start? In the case of e-commerce, A/B testing is a great way to use data to test hypotheses and make decisions based on information rather than opinions.

A/B testing helps brands make decisions based on data (Source)

“The idea behind experimentation is that you should be testing things and proving the value of things before seriously investing in them,” says Jonny Longden, head of the conversion division at agency Journey Further. “By experimenting…you only do the things that work and so you’ve already proven [what] will deliver value.”

Knowing and understanding your data foundation is the platform upon which you’ll build your knowledge base and your experimentation roadmap. Read on to discover the key considerations to bear in mind when establishing this foundation.

Five things to consider when building your data foundation

Know what data you’re collecting and why
Knowing what you’re dealing with when it comes to slicing and dicing your data also requires that you understand the basic types and properties of the information to which you have access. Firstly, let’s look at the different types of data:
- First-party data is collected directly from customers, site visitors and followers, making it specific to your products, consumers and operations.
- Second-party data is collected by a secondary party outside of your company or your customers. It’s usually obtained through data-sharing agreements between companies willing to collaborate.
- Third-party data is collected by entirely separate organizations with no consideration for your market or customers; however, it does allow you to draw on increased data points to broaden general understanding.
Data also has different properties or defining characteristics: demographic data tells you who, behavioral data tells you how, transactional data tells you what, and psychographic data tells you why. Want to learn more? Download our e-book, “The Ultimate Personalization Guide”!

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Gathering and collating a mix of this data will then allow you to segment your audience and flesh out a picture of who your customers are and how to meet their needs, joining the dots between customer behavior and preferences, website UX and the buyer journey.

Chad Sanderson, head of product – data platform at Convoy, recommends making metrics your allies to ensure data collection and analysis are synchronized. Knowing what your business leaders care about, and which metrics will move the business forward, will ensure that your data foundation is relevant and set up for success.

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Invest in your data infrastructure
Data is everywhere, in its myriad of forms and gathered from a multitude of sources. Even so, if you’re going to make use of it, you need a robust system for gathering, storing and analyzing it in order to best put it to work. Start by understanding how much first-party data you have the capacity to gather by evaluating your current digital traffic levels. How many people are visiting your site or your app? You can get this information using Google Analytics or a similar platform, and this will help you understand how sophisticated your data-leveraging practices can be and identify gaps where you might need to source supplementary data (second- and third-party).
Next, you’ll need to evaluate your infrastructure. Companies that are further on their data analytics journey will invest in customer data platforms (CDPs) that allow them to collect and analyze data – gathered from a variety of sources and consolidated into a central database – at a more granular level. Stitching together this data via a CDP helps you bring all the pieces together to form a complete picture of your customers and identify any gaps. This is a critical step before you leap into action. Chad Sanderson concurs. “[Start] with the business and what the business needs,” he advises. “Tailoring your… solution to that – whatever that is – is going to be a lot more effective.”‎
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Get consent to build consumer trust
Data security is rightly of foremost concern to consumers. The very users from whom you want to gather that first-party data want to ensure that their private information remains secure. Getting their consent and being transparent about the inherent benefit to them if they agree to your request – be it through giveaways, exclusive offers, additional information or services – will give you the best chance of success. Demonstrating that you adhere to, and take seriously, various data compliance laws (such as GDPR) and good governance will also build trust in your brand and give you the opportunity to make it worth their while through improved UX and personalized experiences.

Build trust in your brand by respecting your users’ private information (Source)

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Collect and discover insights to upgrade your customer strategy
We’ve already covered the fact that data is everywhere. As Chad Sanderson highlighted above, identifying immediate business needs and priorities – as well as focusing on quick wins and low-lift changes that can have a quick and high-level impact – can help you navigate through this minefield. It’s best to think of this section as a four-step process:
ㅤㅤ• Collect data as it flows into your CDP
ㅤㅤ• Transform or calibrate your data so that it can be compared in a
ㅤ ㅤlogical manner
ㅤㅤ• Analyze the data by grouping and categorizing it according to
ㅤ ㅤthe customer segments you’ve identified and benchmarking
ㅤ ㅤagainst business priorities
ㅤㅤ• Activate your insights by pushing the learnings back into
ㅤ ㅤyour platforms and/or your experimentation roadmap and really
ㅤ ㅤput this data to work
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Turn your data into actions
It’s crunch time (no pun about numbers intended)! We’ve examined the different types of data and where to source them, how to be responsible with data collection and how to set up the infrastructure needed to consolidate data and generate insights. We’ve also covered the need to understand business priorities and core strategy to drive data collection, analysis and activation in the same direction. Now we need to put that data and those insights to work.
In the experience economy, where constant evolution is the name of the game, innovation and optimization are the key drivers of experimentation. Taking the data foundation that you’ve built and using it to fuel and nourish your experimentation roadmap will ensure that none of the hard work of your tech, marketing and product teams is in vain. Testing allows you to evaluate alternatives in real time and make data-driven decisions about website UX. It also ensures that business metrics are never far from reach, where conversion and revenue growth take center stage.
Use the data you’ve gathered to fuel your experimentation roadmap (Source)

Invest in a solid data foundation to maximize and scale

At AB Tasty, we apply the Bayesian approach to interpreting data and test results because in A/B testing, this method not only shows whether there is a difference between the tested options but also goes beyond that by calculating a measure of that difference. Being able to identify what that variance is allows you to best understand what you will gain by adopting a permanent change.

Collecting and analyzing data, and then leveraging the insights that you glean, are key to unlocking the next level of experience optimization for your customers and your business. An experimentation roadmap grounded in real-time responsiveness and long-term, server-side improvements will have a solid data foundation approach at its core, where understanding who you want to target and how to act drives success. Furthermore, if you invest in your data foundation – and the five core drivers we’ve explored above – you’ll be equipped to scale your experimentation and allow optimization to become a key business maximizer.

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Apr 21, 2022

8min read

Creating Emotional Connections With Customers Using Data

Mary Kate Cash

Be sure to check out the series introduction, part 1 with Zion & Zion, and part 2 with Cro Metrics if you haven’t read them yet.

For the third blog in our series on a data-driven approach to customer-centric marketing, we talked with our partner Matt Wright, Director of Behavioral Science at Widerfunnel, and Alex Anquetil, Manager of North America Customer Success at AB Tasty, who discuss what emotional connection means in a marketing context, why it’s critical for brands to forge emotional connections with their customers, and how data can be used to both build and measure the efficacy of these connections.

What do we mean when we talk about creating an “emotional connection” in a marketing context?

Simply put, emotions are the driving force behind every purchase. People don’t buy from a given brand because they need a product they could easily find elsewhere, but because they feel an affinity, a sense of trust, well-being, or inclusion with or loyalty to that brand.

In such a crowded market, forging deep emotional connections with customers is essential for marketers to attract and retain customers today. Marketers can’t merely “appeal to emotions,” but need to understand their behaviors and motivations and ensure that their missions and messages align with customers’ emotions and needs.

Matt asks to reframe the question, “What’s the role of emotional decision-making in marketing? People build mental models around their emotions, experiences, and cultural associations. They think of some as ‘good’ or ‘bad’… they tie emotion to them. The key for marketers is to understand which emotions resonate with which group of people. And this is where A/B testing can help you find clues as to what works and what doesn’t. Creating strong emotional connections is paramount, and through experimentation, you can create them all throughout your sales funnel.”

“Our brains have limited bandwidth,” remarks Alex, “so we tend to save our resources for the important things. When we make a simple purchase, we take shortcuts. We grab what’s available from the wheel of our basic emotions – happiness, anger, surprise – to enable us to make quick decisions. If brands can leverage these emotions, whether positive or negative, and align their sales tactics to them, they can create frictionless experiences. The fact that every purchase is emotional is the reason why we don’t have ‘one perfect user interface,’ or ‘one ideal sales funnel:’ every brand, product, and user is different.”

Matt says, “That’s a great analogy. Usability is the foundation, but you need to build upon it. Even if your UI is ugly, in the right circumstances, it will convert. For example, if your website is for a charity, people don’t want you to spend your money on making it look beautiful. They want the money to go to the cause – so they may negatively judge you if you have a digital masterpiece for a website. But if you’re designing for a chic brand, people want it to look and feel exclusive. This is what A/B testing teaches us: it’s not about win or lose, it’s about gathering insights, which I think is often overlooked at a base level of experimentation.”

Why are emotional connections with customers so important for brands?

For Matt, emotion is especially important for positioning. “It’s not something people typically do experimentation around – I wish they did – because the data you can glean from testing things like value propositions or copywriting is extremely valuable for successfully positioning a product. Also, as customers move through their journeys, they’re going to have different emotions at different moments, including doubts, so give them signals to reassure them they’ve made the right decisions. By doing that, you’ll strengthen their loyalty to you.”

Alex thinks that first impressions matter, and if you don’t connect on the first day, you may not get another shot. “People look for meaning in what they buy, even when it’s something as banal as a pack of batteries. Utilitarian products can have ‘the right’ signals attached to them (think of the Energizer bunny, and the tradition and reliability attached to it). No one wants to buy products that have negative connotations. When it comes to clothing or luxury items, these are 100% emotional, and it’s essential for marketers to confer the correct image and status by selling to the right groups (because, of course, there are in-groups and out-groups by the brand’s standards) and by attaching the right emotions and motivators specific to each brand and product.

Should brands create different types of emotional connections for different audiences?

Again, Matt has a preliminary question to reposition how we approach the subject: “Is it worth it to build multiple experiences? The best way to decide is to start small then go deeper, and keep testing until the data leads you to a value proposition. If the data shows you it’s worth it, then build different approaches, yes.”

But Alex, who’s familiar with both the French and US markets, says yes right away. “When looking at short term and long term outcomes, I think there have to be different types of emotional connections for different cultural or geographical audiences. The question is, do you want the emotions to serve sales or marketing at all costs? In other words, do you want your value proposition to associate your brand with specific emotions? When brands expand to new markets, they may require different approaches. For example, certain French luxury brands sell product collections only in France and entirely different ones in the US. With perfume, US customers tend to buy larger bottles, while the French buy smaller ones, due to different cultural priorities and motivators.

Examples of motivators and leverage:

Source: HBR.org, “THE NEW SCIENCE OF CUSTOMER EMOTIONS,” NOVEMBER 2015, SCOTT MAGIDS, ALAN ZORFAS, AND DANIEL LEEMON

“You can analyze your own market data to find out what your highest-value group is and what their motivators are, then push that to the market and take everyone on your journey, or you can do it the other way around, and make sales your ultimate objective.”

Matt thinks the brand will usually lead and cites the example of Netflix. “There’s a debate going on right now to decide whether, in order to keep growing, Netflix should sell ads. Now, they can probably run an A/B test and find out they’ll make more money if they do sell ads, obviously. But how will that affect their brand image in the short, medium, and long term? They might not lose money, but on an emotional level, they might lose a lot of their historical appeal.

“When dabbling with emotions, it’s not as simple as just an A/B test. When making strategic decisions, experimentation can certainly help incrementally optimize things, but it can do bigger things, including help you make key decisions, better understand your customers, innovate, take risks… Not enough people realize the power of advanced testing. Companies that use them see exponential improvements.”

Talking about experimentation tools, Matt explains: “Early on in the industry, we talked about A/B testing in pretty much only an optimization win-or-lose mindset. And it’s so much more than that. When you make this investment, it’s going to help you make decisions, not just find tiny, incremental bits of revenue for your company. There’s a resourcing problem: conversion rate improvement isn’t the only thing you can do, there’s a huge range of other things you can achieve, and teams need more than a CRO manager to effect the full capabilities. It’s a key competitive differentiator.”

How can data be used to create emotional connections in marketing?

It’s a lot harder to target audiences today due to cookie policy changes and new regulations. But as Matt says (and everyone else agrees), “First-party data will lead to strong positioning and really good ads that connect with users. Because it’s owned by brands, it’s going to be the best quality data for testing hypotheses and segmenting data so brands can offer personalized, exclusive experiences.”

Alex puts it this way: “At the end of the day, you’re still going to be tracking conversions and clicks, so you need to do the groundwork in marketing. It’s more advanced than usability testing. To test for emotions, you have to do some groundwork and some guesswork. You need to know your brand; you need to work with market research. And when you find an emotion aligned with what you want your brand to represent, you need to identify a segment of high-potential customers. Then you find the motivators you associate with that segment, thanks to qualitative research and feedback; then you need to quantify all of that to see if you’re correct. Then you push motivators, measure results, see what boosts efficiency, retention, loyalty, customer lifetime value… and discover whether you’ve got a winning proposition.”

Matt grins: “I wouldn’t call any part of that approach ‘guesswork’. You’re simply combining qualitative with quantitative to come up with better hypotheses for testing. It’s the heart of good experimentation.”

The next installment in our Customer-Centric Data Series will be out in two weeks. Don’t miss it!

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Apr 20, 2022

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The Impact of Experimentation on Cumulative Layout Shift (CLS)

John Hughes

We teamed up with our friends at Creative CX to take a look at the impact of experimentation on Core Web Vitals. Read our guest blog from Creative CX’s CTO Nelson Sousa giving you insights into how CLS can affect your Google ranking, the pros and cons of experiments server and client side, as well as organisational and technical considerations to improve your site experience through testing, personalisation and experimentation.

What are Core Web Vitals?

Core Web Vitals (CWV) are a set of three primary metrics that affect your Google search ranking. According to StarCounter, the behemoth search engine accounts for 92% of the global market share. This change has the potential to reshape the way we look at optimising our websites. As more and more competing businesses seek to outdo one another for the top spots in search results.

One notable difference with CWV is that changes are focused on the user experience. Google wants to ensure that users receive relevant content and are directed to optimised applications. The change aims to minimise items jumping around the screen or moving from their initial position. The ability to quickly and successfully interact with an interface and ensure that the largest painted element appears on the screen in a reasonable amount of time.

Core Web Vitals

What is CLS?

Let’s imagine the following scenario:

You are navigated to a website. Click on an element. It immediately moves away from its position on the page. This is a common frustration. It means you click elsewhere on a page, or on a link, which navigates you somewhere else again! Forcing you to go back and attempt to click your desired element again.

You have experienced what is known as Cumulative Layout Shift, or for short, CLS; a metric used to determine visual stability during the entire lifespan of a webpage. It is measured by score, and according to Core Web Vitals, webpages should not exceed a CLS score of 0.1

CLS within Experimentation

When working with client-side experimentation, a large percentage of A/B testing focuses on making experimentation changes on the client side (in the browser). This is a common pattern, which normally involves placing a HTML tag in your website, so that the browser can make a request to the experimentation tool’s server. Such experimentation tools have become increasingly important as Tech teams are no longer the sole entities making changes to a website.

For many, this is a great breakthrough.

It means marketing and other less technical teams access friendly user interfaces to manipulate websites without the need of a developer.It also frees up time for programmers to concentrate on other more technical aspects.

One drawback for client-side, is certain elements can be displayed to the user before the experimentation tool has had a chance to perform its changes. Once the tool finally executes and completes its changes, it may insert new elements in the same position where other elements already exist. Pushing those other elements further down the page. This downward push is an example of CLS in action.

Bear in mind that this only affects experiments above the fold. Elements initially visible on the page without the need of scrolling.

So when should you check for CLS and its impact upon the application? The answer is up for debate. Some companies begin to consider it during the design phase, while others during the User Acceptance Testing phase. No matter what your approach is, however, it should always be considered before publishing an experiment live to your customer base.

Common CLS culprits

According to Google’s article on optimising CLS, the most common causes of CLS are:

Images without dimensions
Ads, embeds, and iframes without dimensions
Dynamically injected content
Web Fonts causing FOIT/FOUT
Actions waiting for a network response before updating DOM

Overall CLS Considerations

Team awareness and communication

Each variation change creates a unique CLS score. This score is a primary point in your prioritisation mechanism. It shapes the way you approach an idea. It also helps to determine whether or not a specific experiment will be carried out.

Including analysis from performance testing tools during your ideation and design phases can help you understand how your experiment will affect your CLS score. At Creative CX, we encourage weekly communication with our clients, and discuss CLS impact on a per-experiment basis.

Should we run experiments despite possible CLS impact?

Although in an ideal world you would look to keep the CLS score to 0, this isn’t always the case. Some experiment ideas may go over the threshold, but that doesn’t mean you cannot run the experiment.

If you have data-backed reasons to expect the experiment to generate an uplift in revenue or other metrics, the CLS impact can be ignored for the lifetime of the experiment. Don’t let the CLS score to deter you from generating ideas and making them come to life.

Constant monitoring of your web pages

Even after an experiment is live, it is vital to use performance testing tools and continuously monitor your pages to see if your experiments or changes cause unprecedented harmful effects. These tools will help you analyse your CLS impact and other key metrics such as First Contentful Paint and Time to Interactivity

Be aware of everyone’s role and impact

For the impact of experimentation on Web Core Vitals, you should be aware of two main things:

What is the impact of your provider?
What is the impact of modifications you make through this platform?

Experimentation platforms mainly impact two Web Vitals: Total Blocking Time and Speed Index. The way you use your platform, on the other hand, could potentially impact CLS and LCP (Largest Contentful Paint).

Vendors should do their best to minimize their technical footprint on TBT and Speed Index. There are best practices you should follow to keep your CLS and LCP values, without the vendor being held liable.

Here, we’ll cover both aspects:

Be aware of what’s downloaded when adding a tag to your site (TBT and Speed Index)

When you insert any snippet from an experimentation vendor onto your pages, you are basically making a network request to download a JavaScript file that will then execute a set of modifications on your page. This file is, by its nature, a moving piece: based on your usage – due to the number and nature of your experimentations, its size evolves.

The bigger the file, the more impact it can have on loading time. So, it’s important to always keep an eye on it. Especially as more stakeholders in your company will embrace experimentation and will want to run tests.

To limit the impact of experimenting on metrics such as Total Blocking Time and Speed Index, you should download strictly the minimum to run your experiment. Providers like AB Tasty make this possible using a modular approach.

Dynamic Imports

Using dynamic imports, the user only downloads what is necessary. For instance, if a user is visiting the website from a desktop, the file won’t include modules required for tests that affect mobile. If you have a campaign that targets only logged in users to your site, modifications won’t be included in the JavaScript file downloaded by anonymous users.

Every import also uses a caching policy based on its purpose. For instance, consent management or analytics modules can be cached for a long time. While campaign modules (the ones that hold your modifications) have a much shorter lifespan because you want updates you’re making to be reflected as soon as possible. Some modules can also be loaded asynchronously which has no impact on performance. For example, analytics modules used for tracking purposes.

To make it easy to monitor the impact on performance, AB Tasty also includes a tool, named “Performance Center”. The benefit of this is that you get a real time preview of your file size. It also provides on-going recommendations based on your account and campaign setup:

to stop campaigns that have been running for too long and that add unnecessarily weight to the file,
to update features on running campaigns, that have benefited from performance updates since their introduction (ex: widgets).

How are you loading your experimentation tool?

A common way to load an A/B testing platform is by inserting a script tag directly into your codebase, usually in the head tag of the HTML. This would normally require the help of a developer; therefore, some teams choose the route of using a tag manager as it is accessible by non-technical staff members.

This is certainly against best practice. Tag managers cannot guarantee when a specific tag will fire. Considering the tool will be making changes to your website, it is ideal for it to execute as soon as possible.

Normally it’s placed as high up the head tag of the HTML as possible. Right after any meta tags (as these provide metadata to the entire document), and before external libraries that deal with asynchronous tasks (e.g. tracking vendors such as ad networks). Even if some vendors provide asynchronous snippets to not block rendering, it’s better to load synchronously to avoid flickering issues, also called FOOC (Flash of Original Content).

Best Practice for flickering issues

Other best practice to solve this flickering issue include:

Make sure your solution uses vanilla JavaScript to render modifications. Some solutions still rely on the jQuery library for DOM manipulation, adding one additional network request. If you are already using jQuery on your site, make sure that your provider relies on your version rather than downloading a second version.
Optimize your code. For a solution to modify an element on your site, it must first select it. You could simplify this targeting process by adding unique ids or classes to the element. This avoids unnecessary processing to spot the right DOM element to update. For instance, rather than having to resolve “body > header > div > ul > li:first-child > a > span”, a quicker way would be to just resolve “span.first-link-in-header”.
Optimize the code auto generated by your provider.When playing around with any WYSIWYG editors, you may add several unnecessary JavaScript instructions. Quickly analyse the generated code and optimize it by rearranging it or removing needless parts.
Rely as much as possible on stylesheets. Adding a stylesheet class to apply a specific treatment is generally faster than adding the same treatment using a set of JavaScript instructions.
Ensure that your solution provides a cache mechanism for the script and relies on as many points of presence as possible (CDN)so the script can be loaded as quickly as possible, wherever your user is located.
Be aware of how you insert the script from your vendor. As performance optimization is getting more advanced, it’s easy to mess around with concepts such as async or defer, if you don’t fully understand them and their consequences.

Be wary of imported fonts

Unless you are using a Web Safe font, which many businesses can’t due to their branding, the browser needs to fetch a copy of the font so that it can be applied to the text on the website. This new font may be larger or smaller than the original font, causing a reflow of the elements. Using the CSS font-display property, alongside preloading your primary webfonts, can increase the change of a font meeting the first paint, and help specify how a font is displayed, potentially eliminating a layout shift.

Think carefully about the variation changes

When adding new HTML to the page, consider if you can replace an existing element with an element of similar size, thus minimising the layout shifts. Likewise, if you are inserting a brand-new element, do preliminary testing, to ensure that the shift is not exceeding the CLS threshold.

Technical CLS considerations

Always use size attributes for the width and height of your images, videos and other embedded items, such as advertisements, and iframes. We suggest using CSS aspect ratio properties for images specifically. Unlike older responsive practices, it will determine the size of the image before it is downloaded by the browser. The more common aspect ratios out there in the present day are 4:3 and 16:9. In other words, for every 4 units across, the screen is 3 units deep, and every 16 units across, the screen is 9 units deep, respectively.

screen size Knowing one dimension makes it possible to calculate the other. If you have an element with 1000px width, your height would be 750px. This calculation is made as follows:

height = 1000 x (3 / 4)

When rendering elements to the browser, the initial layout often determines the width of a HTML element. With the aspect ratio provided, the corresponding height can be calculated and reserved. Handy tools such as Calculate Aspect Ratio can be used to do the heavy lifting math for you.

Use CSS transform property

The CSS transform property is a CSS trigger which will not trigger any geometry changes or painting. This will allow changing the element’s size without triggering any layout shifts. Animations and transitions, when done correctly with the user’s experience in mind, are a great way to guide the user from one state to another.

Move experiment to the server-side

Experimenting with many changes at once is considered against best practice. The weight of the tags used can affect the speed of the site. It may be worth moving these changes to the server-side, so that they are brought in upon initial page load. We have seen a shift in many sectors, where security in optimal, such as banking, to experiment server-side to avoid the use of tags altogether. This way, once a testing tool completes the changes, layout shift is minimised.

Working hand in hand with developers is the key to running server-side tests such as this. It requires good synchronisation between all stockholders, from marketing to product to engineering teams. Some level of experience is necessary. Moving to server-side experiments just for the sake of performance must be properly evaluated.

Server-side testing shouldn’t be confused with Tag Management server-side implementation. Some websites that implement a client-side experimentation platform through tag managers (which is a bad idea, as described previously), may be under the impressions that they can move their experimentation tag on the server-side as well and get some of tag management server-side benefits, namely reducing the number of networks request to 3rd party vendors. If this is applicable for some tracking vendors (Goggle Analytics, Facebook conversions API…), this won’t work with experiment tags that need to apply updates on DOM elements.

Summary

The above solutions are there to give you an overview of real life scenarios. Prioritise the work to be done in your tech stack. This is the key factor in improving the site experience in general. This could include moving requests to the server, using a front-end or server-side library that better meets your needs. All the way to rethinking your CDN provider and where that are available versus where most of your users are located.

One way to start is by using a free web tool such as Lighthouse and get reports about your website. This would give you the insight to begin testing elements and features that are directly or indirectly causing low scores.

For example, if you have a large banner image that is the cause of your Largest Contentful Paint appearing long after your page begins loading, you could experiment with different background images and test different designs against one another to understand which one loads the most efficiently. Repeat this process for all the CWV metrics, and if you’re feeling brave, dive into other metrics available in the Lighthouse tools.

While much thought has gone into the exact CWV levels to strive for, it does not mean Google will take you out of their search ranking as they will still prioritise overall information and relevant content over page experience. Not all companies will be able to hit these metrics, but it certainly sets standards to aim for.

Written by Nelson Sousa, Chief Technology Officer, Creative CX

Nelson is an expert in the field of experimentation and website development with over 15 years’ experience, specialising in UX driven web design, development, and optimisation.

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Apr 12, 2022

9min read

The Rise of the Experience Economy and How It’s Shaping the Future of E-Commerce

AB Tasty

What is the experience economy?

Let’s say you have an online shop and in that online shop you have a product. Your product is designer eyewear and prescription glasses. A customer visits your online shop to learn about your product. That customer needs to determine which frames will suit their face and what size to order. A similar shop that sells similar products to yours offers free shipping and free returns of up to 3 pairs at no charge, or the use of a virtual reality assistant, via their mobile app, to help their customers make purchasing decisions without needing to visit a store. Your shop, though well-intentioned and bug-free, does not. The customer’s experience researching and selecting their product is what ultimately drives their decision-making process, and they purchase from the other shop. And the next time they need glasses, they purchase from that other shop again. That’s the experience economy.

What is the experience economy

In the experience economy, finding a differentiating edge is crucial for brands (Source)

Expressed in more academic terms, the experience economy is the packaging of goods and services into a bundle such that the experience of acquiring or consuming is the key selling point – it’s the reason the customer came into your shop in the first place.

In 1998, two Harvard researchers published an article detailing the concept of the experience economy for the first time, using a birthday cake analogy to eventually draw out the definition we see above. These days, the concept is more important than ever, as the rapidly evolving digital transformation of the way we consume information and goods creates a never-ending, multi-channel interaction between brands and consumers. And it’s key to your overall business success.

How e-commerce brands can succeed in the experience economy

In the age of digitalization, not only do all brands have websites, incorporating an e-commerce platform for online sales, but they also have Facebook, Instagram, TikTok and Snapchat accounts, more than likely a YouTube channel, a web browser adapted to mobile devices and an app to sit alongside it. In short, multiple channels and touchpoints for their customers to interact and engage with them, and multiple opportunities to create experiences to acquire new customers and drive sales. This all makes for a non-linear shopping experience, and requires careful examination of what customers expect on which channel and at which time.

How the experience economy is shaping the future

A customer-first mindset is crucial for businesses that are looking to win the digital CX game (Source)

How can brands adapt to shifting consumer preferences

At AB Tasty, we’re convinced that the brands opting for a “business as usual” approach will quickly be left in the dust. Customers expect better servicing, more meaningful interactions and suggest that they’ll spend more when brands deliver. This means having a strategy that considers multiple channels, across physical, digital and social touch points, and adapts to the preferences of each individual so that interactions remain authentic and personal. If you’re engaging with customers without being able to have in-person contact, experience matters even more, because consumers still want to be seen as individuals with their own unique needs. Ultimately, their experience will influence their buying decisions and according to Salesforce, 66% of consumers expect companies to understand their unique needs and preferences.

Create a personalized, relevant shopping experience for each customer (Source)

Figuring out what your customers want doesn’t just need to be a guessing game, experimentation is standard practice for the experience economy. In B2C environments, marketing teams test website performance using a range of experiments that examine layout, colors, purchase journeys, product information and visual features to ensure no stone is left unturned in maximizing transactions and revenue. And adopting an experimentation mindset really is a win-win. On the one hand, you’re identifying the best way to interact with your customers – identifying what they respond to and what they want – and on the other, you’re maximizing every opportunity to drive purchases and serve your bottom line.

Why prioritizing customer experiences matters

That’s all very well and good, you might say, but what difference does it really make? Plenty, in fact. Relevant and personalized consumer experiences are key to keeping your brand ahead of its competitors. Let’s explore some of the reasons for this.

Loyalty is hard-earned and easily lost
PWC’s 2021 Global Consumer Insights Survey found that 84% of shoppers trust brands that provide exceptional customer service, but one in three will walk away after just one negative shopping experience. In a similar vein, Qualtrics’ 2022 Global Consumer Trends survey reported that 60% of consumers would buy more if businesses treated them better, and also determined that 9.5% of your overall revenue is at risk from negative shopping experiences. These statistics still haven’t convinced you? Read on!
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Seamlessness is synonymous with success
You can design any number of gimmicks to attract attention, but it’s the seamless ones that stick. Take the Clarins Singles Day Wheel of Fortune promotion, where any customer landing on the brand’s desktop or mobile site in EMEA saw a pop-up to spin the wheel. They were then rewarded with one of six special offers, which was automatically added to their basket via a promo code at the checkout. This automatic add proved crucial: Results were strong across all key territories, with Ireland particularly notable, seeing a 495% increase in orders and a 585% increase in revenue. Clarins uncovered a clever, engaging offer and coupled it with a seamless UX process for their shoppers, delivering simply stunning results.Clarins delivered a customer experience on par with their clients’ expectations (Source)
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Stagnate and you’ll be left behind
To innovate or not to innovate, is it even a question? If you’re thinking about it, then your competitors almost certainly are too. And if you’re not trying something new, you almost certainly risk falling behind. While bug-free websites and a smooth journey through the purchase funnel is great, it’s also the bare minimum that you should be doing. Salesforce found that 91% of customers are more likely to make a repeat purchase from a company after a positive customer experience. Delivering a seamless, multichannel experience across all business interactions is integral to staying ahead and it’s clear there is still scope for brands to optimize.

4 examples of brands that are excelling in the experience economy

As we’ve seen in the above section, brands that embrace the experience economy are best-positioned to see increased loyalty, repeat business, and convert their customers into advocates for their products. Pushing beyond experiences into memorable interactions for their consumers has allowed some of the best-known brands in the world to gain further ground on their direct competitors, all while staying true to their core values. Let’s take a look at the best-in-class trends and examples of the experience economy model.

Nike

Nike is driven by delivering innovative products, services and experiences to inspire athletes. One such experience is their Nike Fit solution: an AI-driven app that allows you to virtually measure and fit your foot to ensure you choose the right pair of Nike shoes, no matter the style nor the shape of your foot, and without having to leave your living room.

Nike Fit

Nike introduces innovative solutions to their clients’ biggest point of friction (Source)

Sephora

In 2019, Sephora pioneered their intelligent digital mirror in the brand’s Madrid flagship, using the power of AI to deliver hyper-personalized experiences and product recommendations to shoppers. The mirror not only allows consumers to “test” products by displaying how they’ll render when applied, it also provides personalized product recommendations and suggestions based on an analysis of the customer’s features.

Sephora

Sephora develops new ways to offer their customers personalized recommendations (Source)

Starbucks

Starbucks has revolutionized their physical footprint by opening pickup-only stores in key, high-traffic locations where rental space is at a premium and busy lives mean in-and-out transactions are the order of the day. This store concept allows coffee lovers to order and pay ahead of time, via the Starbucks mobile app, and nominate the pickup location, for a speedy service that saves tedious, peak-hour queues. Not to mention a boost to sales per square foot, a key metric in the brick-and-mortar retail space.

Starbucks

Starbucks identifies their customers’ needs and delivers an optimal shopping experience (Source)

Asos

This online fashion retailer was founded in London in 2000, and now sells over 850 brands around the world. In identifying one of the key barriers to online shopping for clothes – choosing the correct size – Asos developed their Fit Assistant tool to ensure customers could navigate the online shopping experience hassle-free. Available on both desktop and mobile, Fit Assistant delivers personalized recommendations according to shoppers’ individual shapes and sizes.

Fit Assistant Technology - Recommend

Asos optimizes their customers’ online shopping experience (Source)

Why the experience economy is here to stay

Through a combination of rapid digital transformation, technological innovation of smart devices (phones, tablets, watches and more), and the increasing pace of our daily lives, the manner in which we consume products has evolved beyond mere acquisition. How we consume the product matters. How we feel about how we consume the product matters. How the brand ensures we enjoy our consumption of the product matters. And if your brand is not up for the challenge and staying ahead of the game, consumers will find one that is. It’s as simple as that. Evolve, innovate, and deliver seamless brand experiences, and you’ll lead the competition, win market share and generate growth.

If you’re looking for some guidance on how to deliver impactful brand experiences that will “wow” your customers, draw inspiration from the first-ever digital customer journey that maps out how to drive optimization and innovation to take your customer experience to the next level.

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Alpha vs. Beta Testing: What are the Differences?

Rowan Haddad

Prior to the launch of a product, a number of tests are usually run to ensure that a software is ready for release and provides a good user experience. The purpose of these tests would be to validate the software before going ahead with a final release to your end-users.

These sorts of tests are essential to make sure that the software you’re releasing is free of bugs and meets the quality and requirements expected by your customers.

Among such tests are alpha and beta tests. These tests are conducted towards the end of the software development life cycle (SDLC) to test releases outside the development team to help uncover any issues that would otherwise not show up in previous tests that are run in more controlled environments.

What is alpha testing?

Alpha testing is typically run on internal users by the QA team (Quality Assurance) to make sure that the software meets all expectations and is working as it should. Thus, it represents an opportunity to evaluate the performance and functionality of a product release as well as obtain feedback from technical users.

In other words, the main purpose of this test is to uncover any bugs or issues to resolve them before the final product is released to users. It helps ensure bug-free functionality by carrying out tasks that a typical user may perform.

This test is usually performed when your product is nearly complete towards the end of the software development cycle in a staging environment, which attempts to mimic an actual production environment as closely as possible, but before beta testing, which we’ll get to later.

It seeks to answer the question whether your product actually works.

Alpha testing involves two main phases:

The first phase is run by software developers using debugging tools or software to catch bugs quickly.
The second phase is performed by the QA team and may involve ‘white box’ and ‘black box’ testing. A white box test will test the software system’s design and internal structure allowing QA testers to ‘look inside’ the product. A black box test, meanwhile, will test the system’s input and output functionality.

The advantages of this type of testing are clear.

It allows teams to locate bugs and issues that managed to escape previous tests so that they may be fixed before they reach your end-users.

Up until that point, tests were focused on testing specific parts of the software but alpha testing, on the other hand, looks to see if the software as a whole functions correctly.

In other words, it enables teams to validate the quality and functionality of their releases before it is released to customers. Put simply, Alpha testing opens up the application to receive initial feedback.

This results in improved software quality as the software is tested in an environment that is a very close replica of the environment it will eventually be used in, hence creating realistic testing conditions. This also allows the QA team to understand how the software will behave when it is later released to end-users.

To sum up, alpha testing provides an opportunity to put your product in real user environments but with technical users who are more adept at identifying and discovering bugs before conducting beta tests with actual real-world users.

However, conducting alpha testing may prolong the test execution cycle thereby delaying the release of the product to your end-users. Also, keep in mind that since the software is still in the development stage, alpha testing doesn’t provide in-depth testing of the functionality of the software.

Now, we will move on to the next testing phase, beta testing.

What is beta testing?

Beta testing involves releasing the software to a limited number of real users external to the organization. As a result, this type of testing is done in a production environment.

These users will then be asked to provide their feedback on the release, also named “beta version”. Beta testing, then, is an opportunity that allows users to test out the product to uncover any bugs or issues related to user experience (UX) before it is rolled out to the rest of your users.

In other words, it represents the final stage of testing before releasing the product to a wider audience.

It also enables teams to run security and reliability tests as those tests cannot be conducted in a staging or lab environment.

There are many ways to implement beta testing. For example, often, companies will ask a select number of users to willingly op-in to get early access to the software. The advantage of this is that these users will be aware that the beta version may not be very stable and so they are more forgiving of any potential bugs and are happy to provide the feedback you need to optimize your product.

To be more specific, you may go for a closed or open beta test. In an open test, anyone can use the product but users are given a clear indication that the product is a beta version so they know that it’s still a work in progress.

Meanwhile, in a closed test, as in the example given above, the testing is limited to a specific set of users, which would be by invite only. These users would be composed of early adopters, current customers or even paid beta testers.

Such exclusivity is one way to build close relationships with specific users as you are demonstrating that you value their opinion in particular before doing a wider release.

The advantage of this testing is clear. It is the first chance to test how the software will behave in real-world settings and observe how your end-users interact with it and what the user experience looks like.

Product managers, in particular, can make use of the feedback received to collect ideas and suggestions when planning future releases.

Beta testing is a way these managers can observe usage behavior and analytics to confirm that users are interacting with the product as expected. They may also run experiments and A/B tests of features to decide which one to choose for a general release.

This, in turn, allows developers to uncover any bugs in real production and less controlled environments so that they may be fixed before a full launch.

Many bugs may have been discovered during alpha testing by your internal users but nothing can truly simulate real world users, which is why beta testing is necessary after alpha testing.

However, as we’ve seen, beta testing is conducted in real environments as opposed to controlled environments during alpha testing and so the former is more difficult to control.

Feature flags and beta testing: safer testing in production

During beta testing, you are essentially testing in production, which doesn’t come without its risks but luckily there is a way to mitigate those risks: by using feature flags.

A feature flag is a software development tool that helps decouple deployment from release, giving you full control over the release process. With feature flags, you can perform beta tests by enabling features for certain users and turning them off for everyone else.

Feature flags also act as a kill switch so that you can gradually roll out features to users to test performance and if something goes wrong, you can just as easily roll it back or turn off the buggy feature.

Feature flags are a great way for all teams within an organization to carry out beta testing as using feature flags for beta testing means even non-technical users such as product and marketing teams can turn on features for specific users, which means they’re not so reliant on development teams anymore.

Alpha vs beta testing

The major advantage of such types of testing is that it helps the development team to identify issues in advance before it goes to launch, allowing them to fix these issues early on before going for a full release.

However, as already alluded to in above sections, there are still major differences between these two types of testing, some of which are summarized in the table below.

	Alpha α	Beta β
Testers	Internal employees	End-users or customers not part of the organization
Environment	Requires a specific environment for testing	Does not require a testing environment
What’s tested	Functionality and usability are tested while security and reliability tests are not carried out in depth	Reliability, security and stability tests are key aspects of a beta test
Testing technique	Uses both white and black box testing techniques	Focuses mainly on black box testing
When	Run before the product officially launches to the market	Run after the product is launched
Purpose	Test that the product is working as it should to evaluate product quality	Understand how real users interact with the product and to evaluate customer satisfaction
Duration	Long execution cycle	Short process usually only lasting a few weeks
Post-test actions	Any bugs or issued discovered will be immediately rectified	Most issues identified and feedback received will be implemented as improvements future versions of the product

Conclusion

Clearly, testing is important to ensure the delivery of high quality, bug-free releases. There are a number of tests carried out throughout a software’s life cycle, each of which serves a unique purpose.

Here we looked at two important ones that occur towards the end of a software’s life cycle: alpha and beta testing.

Both alpha and beta tests are an important part of the testing process as they provide a valuable means to highlight crucial issues with your releases and provide user feedback, both internally and externally.

Alpha testing helps validate the quality of your software while beta testing allows you to obtain real-world feedback to ensure you’re building products that your customers actually like.

Therefore, in the testing lifecycle, both alpha and beta testing are essential.

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Using Experimentation Data to Uncover Customer Needs

Mary Kate Cash

For the second blog in our series on a data-driven approach to customer-centric marketing, we talked with our partner Ryan Lucht, Director of Strategy at Cro Metrics, and AB Tasty data expert Hubert Wassner, who explore the evolution of customer needs, why understanding them is so important for brands today, and the role experimentation plays in meeting those needs. Be sure to check out the series introduction and part 1 if you missed those.

What do we mean when we say “customer needs”?

Today’s savvy customers expect a lot from brands: connected journeys, personalization, innovation, data protection. They’re used to seamless online interactions. They want to find products easily. They want a frictionless experience and flexible payment methods. And if a brand doesn’t deliver, they’ll switch to one that does.“It’s a good thing, it’s forced us to become a lot more customer-centric and make our websites easier to use,” says Ryan.

And it pays to meet customer needs and deliver great customer experiences. In a 2020 CX poll, 91% of respondents said they were more likely to make a repeat purchase after a positive experience, and 71% said they’d made a purchase decision based on experience quality.

Why is it important for businesses and brands to understand customer needs?

Ryan believes it’s vital to offer customers the right information at the right time. “Customer needs look different at different stages in the customer journey. When you’re running an experimentation program, the order in which information is presented matters. If you dive into the details too soon, you risk overloading or boring your visitors, but if you wait too long to introduce core information like a refund policy, or a piece of your value proposition, you’ll lose business.

“One of our clients, a leading chain of gyms in the United States, is a good example. A crucial part of their strategy is reassuring customers they’ll feel comfortable in their gyms – that their gyms are inclusive, body-positive, and everyone fits in. It’s an important customer need. But on their homepage, the first thing people wanted to find was ‘Where’s the closest gym to me?’ So they shifted to putting location-centric information first, and establishing their brand afterward: It increased subscriptions immediately. It’s bottom-line revenue impact: if you’re not solving for the right needs at the right time, you’re leaving money on the table.”

Hubert adds, “In online business, your competitors are just a click away, so if users get bored on your site, or it takes them too long to find what they need, they’ll simply go elsewhere. The experience you offer needs to be frustration-free. There are growth results to be found at every stage of the purchase journey, so always think competitively, someone else is surely doing it better somewhere.”

Ryan agrees: “When it comes to understanding your customers’ needs, the more you leverage experimentation and advanced forms of data science, the better your competitive advantage. The reason is that customer needs are unique from one brand to another. An established brand can sell itself differently than a niche brand or a newcomer brand.”

Where can the most valuable data about customer needs be accessed?

There are tools that automate data access. “At AB Tasty,” says Hubert, “we’d begin by installing a tag to gather transaction information for developers to see, for example, when users visit sites and when they leave, to measure bounce rate. Agencies can also gather information about products and purchases, as well as metrics like conversion rate, access to the cart page, dates of sale, and how much conversion rates change within engagement levels (see chart). Once you have enough data, you can begin to test different metrics to see which ones increase the engagement levels of different audience segments.”

Does your site adapt to frequent users or does it treat them as new each time?

Ryan likes to ask executives, “Do you think someone coming to your website for the very first time has the same needs as someone coming for the fifth time? And of course, the answer is ‘no.’ But think ‘new’ vs ‘returning’, or traffic sources – people come to the website from different places: email, advertisements, Facebook, TikTok, Google. All these people have very different contexts, and this gives us a hundred different test ideas. When he asks that same group whether or not they treat a new visitor differently than a returning visitor, unfortunately, the answer is still usually ‘no.’ This is just one of the countless opportunities for brands to use even the most basic data points to start to differentiate experiences.

“It can get very complex very quickly. If you’re going to do an AI or an ML model, it’s important to understand which of the variables we’re asking the model to look at are actually correlative and which are just noise.”

How can data from disparate sources be organized/prioritized for testing?

To combine data in order to act on it in a meaningful way, Hubert explains that the most common tools are CRM plug-ins. “If your user is identified, you can automatically gather more first-party information to save along with the customer journey, which can help you to segment for experimentation a posteriori.”

For Ryan, using disparate data sources isn’t just about prioritizing test ideas, it’s about better understanding test outcomes. “I’ve never met anybody who can use data to predict which test ideas will win. If they could, everyone would have staggering win rates, but even when you look at Microsoft, Google and Netflix, only one in ten ideas pans out. The more impactful side of using disparate data sources occurs after you run an experiment, because experiment results are really only one specific learning; it’s how you tie those results back to other sources of customer data or analytics to piece together a narrative you think might be true and how you use those results to inform your next test idea.”

How can experimentation help you gain insight into customer needs?

For Ryan, insight can be gained on two levels. “An individual experiment can tell you whether you’re getting closer to or further away from fulfilling customer needs, based on if they’re doing the thing you hoped they’d do, but there’s also a meta-analysis level, where once you’ve run a lot of experiments, you start to recognize the patterns. For example, every time we emphasize pricing and make it clearer, we get better outcomes, so perhaps one customer need is better price understanding. Individual experiments help us, but we really learn the broader themes once we’ve run a lot of experiments.”

Hubert adds, “In my mind, when you build the test, you have to answer a question, not focus on making an improvement, because maybe you won’t improve anything, but you will learn something, you’ll increase your knowledge, and that’s the real goal.”

How can insights about customer needs contribute to a better customer experience?

“The better and more granularly you understand your customers and their needs, the more it will seem to them that you’re able to read their minds; you’ll please them and win their loyalty and win in the marketplace. The businesses that are doing this – doing a lot of experiments and doing them well – are the businesses that win. It’s as simple as that”, states Ryan.

“When testing, we shouldn’t be asking business questions, but user questions and only then adding business KPIs,” says Hubert. “It’s vital to ask customer-centric questions first, not business-centric ones. This way, you can boost your business while better satisfying your customers.”

Ryan agrees: “It’s a very positive way to frame it. This is how we win with our customers because if we’re better at meeting their needs, everyone wins.”

Want more coverage of data topics? Be sure to come back in two weeks for the third installment in our Customer-Centric Data Series!

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Maximizing the Value of Customer Data Through Experimentation

Mary Kate Cash

Check out the introduction to the Customer-Centric Data Series here.

For the first blog in our series on the different ways you can utilize data to help brands build a more customer-centric vision, our partner Aimee Bos, VP of Analytics and Data Strategy at Zion & Zion, and AB Tasty’s Chief Data Scientist, Hubert Wassner, delve into how experimentation data can help you better understand your customers. They explore the who, what, and when of testing, discuss key customer insight metrics, the importance of audience sizes, where your best ideas for testing are lurking, and more.

Why is experimentation important for understanding customers?

Put simply, experimentation enables brands to “perfect” their products. By improving upon the value that’s already been developed, the customer experience is improved. And each time a new feature or option is added to a product, consistent A/B testing ensures consistent customer reactions. Experimentation operates in a feedback loop with customers, moving beyond conversions or acquisition, improving adoption and retention, eventually making your product indispensable to your customers.

Which key metrics deliver the best insights about customers?

Hubert says, “Basically, the metrics that deliver reliable customer insights are conversion rate and average cart value, segmented on meaningful criteria such as geolocation, or CRM data. But there are others that are interesting, such as revenue per visitor (RPV). It’s a low-value metric but important to monitor.

“And average order value (AOV) is another. This metric will vary enormously over time so it shouldn’t be taken as fact. Seasonality (think Christmas or Black Friday, for example), or even one huge buyer can skew the statistics. It needs to be viewed in multiple contexts to get a better understanding of progress – not just Year over Year but Month over Month and even Week over Week to be effectively computed.

“AOV and RPV are important because their omission can lead to data bias. People often forget to analyze metrics about non-converting visitors. Of course, AOV only gives you data about those who actually make it fully through the purchase cycle.”

And Aimee agrees, “Well, win rate, of course. For e-commerce it’s conversions, value, RPV, how they’re moving the needle, are they increasing the value of the average order? We want as much data as possible at the most granular level possible for lead generation, gated content, and micro-conversions… These smaller tests can be tied to more customer-centric metrics, as opposed to larger business-level metrics such as revenue, growth, number of customers, ROI, etc.”

Where are the best sources for experimentation ideas?

Aimee has her own process. “I start by asking myself what my business objectives are (micro/macro). Then I check Google Analytics and ask myself ‘Where are conversions not happening?’ For experimentation ideas, I check tools like HotJar, voice-of-customer data (VoC), Qualtrics data, see actual customer feedback, user panelists: give them choices, ask what they prefer. Always hypothesize friction points, these will give you your best ideas for testing!”

Hubert likes to get his ideas from NPS scores. “Net promoter score (NPS) has useful information and comments and can be a good starting point for fact-based rather than random hypotheses, which are a waste of time. NPS can add some real focus to well-designed tests. It’s based on a single question: On a scale of 0 to 10, how likely are you to recommend this company’s product or service to a friend or a colleague? NPS is a good way to identify areas that need improvement, but as a signifier of a company’s CX score, it needs to be paired with qualitative insights to understand the context behind the score.”

How do I pull everything together? What do I need to carry out my tests?

Obviously, you need a tool to run your AB tests and collect the data necessary to make good hypotheses, but a good way to add a big boost to your testing program – and help drive more ROI – is with tools like Contentsquare or Fullstory which offer more data on customer behavior and experience to focus your testing data. Designed to bridge the gap between the digital experiences companies think they’re offering their customers and what customers are actually getting, analytics platforms can provide real opportunities for useful testing hypotheses by offering more educated guesses about variables for testing to improve CX.

Aimee has an important note about initial data collection, too. “You also need three months of data before you begin testing if you want reliable results, and you need to be sure it’s accurate. Most people rely on Google Analytics (GA). That’s a lot of data to handle and organize. A Customer Data Platform (CDP) represents a significant investment, but centralizing your data in one is extremely useful for customer segmentation and detailed analysis. The sooner you can invest in a tool like a CDP, the better for a sustainable data architecture strategy.

I’m ready to test, but I have several hypotheses. How to begin?

According to Aimee, “when that happens, we break large problems into smaller ones. We have a customer that wants to triple their business and also wants a CDP this year among other goals. It’s a lot! To help them, we build out a customer journey roadmap to see what influences the client’s goals. We select five or six high-level goals (landing page, navigation measured against click-through rate, for example), then test various aspects of each of these goals.”

Hubert notes, “it’s possible to test more than one hypothesis at once if your sample size is big enough. But first, you need to know what the statistical power of your experiment is. Small sample sizes can only detect big effects: it’s important to know the order of magnitude in order to carry out meaningful experiments. It’s always best to test your variables on a large audience, with varied behaviors and needs, in order to get the most reliable and informed results.”

Is there value in running data-gathering experiments (as opposed to improving conversion / driving a specific metric)?

Hubert is a full believer in testing no matter what you think may happen. “Testing is always useful because a good test teaches you something, you learn something, win or lose. As long as you have a hypothesis. For instance, measuring the effect of a (supposed) selling feature (like an ad or sale) is useful. You know how much an ad or a sale costs, but without experimenting you don’t know how much it pays.

“Or say you have a 100% win rate. That means you’re not learning anymore. So you test to gain new information in other areas, you don’t just stand still. You minimize losses to maximize wins.”

Enjoy what you read? Be sure to read part 2 of the Customer-Centric Data Series here.

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Flutter for Mobile App Development: Overview and Benefits

Rowan Haddad

As mobile usage has increased and spread widely all over the world, mobile apps, as a result, have also risen in popularity. This means it’s important for app developers to stay current on the latest development tools in order to build high quality apps.

There are now many tools out there and if you’ve been keeping up with the mobile development world, you’ve probably already come across Flutter, which has generated a lot of buzz since its release in 2017.

What is Flutter?

Flutter is a free and open-source UI software development kit created by Google. It is used to develop natively compiled, multi-platform applications for mobile, web, and desktop from a single codebase and programming language.

This has greatly contributed to Flutter’s appeal and why it’s become so popular today.

It has been designed to support mobile apps that run on both Android and iOS allowing you to build apps on different platforms from the same codebase. This means, for example, that the code you used to publish your Android app can also be deployed on iOS, the web or desktop with little change.

Moreover, Flutter’s support for desktop devices means you can scale your app to Windows, Linux and Mac and hence reach more users on these platforms’ app stores.

Put simply, Flutter essentially provides a widget-based UI library, including UI elements such as text boxes, buttons and image blocks that can be used to create and personalize your app.

To give a better visual of the coding process when writing your first app in Flutter, the below image gives a glimpse of a code for a basic Flutter app-everything in the code is a widget:

We will come back to Flutter widgets in a bit but for a deeper understanding of Flutter, it is worth taking a look at its architecture first to understand the concepts that form its design.

Flutter architecture

Flutter consists of two major parts- a framework with a UI library based on widgets, which contains various reusable UI elements, such as sliders, buttons, text inputs, and others, and a software development kit (SDK)- the tools that help develop apps i.e., to compile code into native machine code for the use of iOS and Android.

The following image shows the major components of Flutter, which are organized around layers. This layered, extensible system exists as a series of independent libraries that each depend on the underlying layer.

The three main architectural layers of Flutter consist of:

Framework: Developers interact with Flutter primarily through the Flutter framework, a modern reactive framework written in the Dart language. This framework provides a rich set of libraries that are divided into layers: basic foundational classes, rendering layer, widgets layer and Material and Cupertino libraries.

Engine: In the diagram above, we can see that the next level of Flutter is an Engine written in C++ and which provides low-level implementation of Flutter’s core APIs.

Embedder: This platform-specific embedder provides an entry point and coordinates with the underlying operating system to access services such as accessibility, rendering surfaces, and input. The embedder is written in a platform-specific language such as Java and C++ for Android, Objective-C/Objective-C++ for iOS and macOS, and C++ for Windows and Linux.

Flutter uses Dart, also created by Google, for writing code. Dart is a client-optimized language for developing fast apps on any platform with the goal of offering the most productive programming language for multi-platform development.

You can look at Flutter as the front-end UI that you use to develop the app and Dart as the backend code that is automatically generated.

Dart compilers and runtimes support the combination of both just-in-time (JIT) compilation, which provides a faster development cycle and allows for hot reloads of the Flutter app, as well as ahead-of-time (AOT) compilation. AOT compiled programs usually run quicker as they are compiled before execution while JIT programs are executed at or just before runtime.

Therefore, Dart combines the advantages of both JIT and AOT compilation: fast development cycles, and fast execution and startup times.

Everything on Flutter is a widget

In Flutter, basically everything is a widget, which is the core concept of the framework. Widgets are the building blocks of a Flutter’s app user interface and represent an immutable declaration of part of the user interface. In other words, they declare how a UI should look.

Thus, to build any app in Flutter, you start with a widget. The main underlying idea is that you build your UI out of widgets. Widgets describe what their view should look like given their current configuration and state. It includes a text widget, row widget, column widget, container widget, and many more.

To be more precise, Flutter draws its own widgets and uses the Skia library for rendering UI instead of relying on platform-specific components, which reduces communication between the Flutter apps and the mobile platforms. In other words, as Flutter uses its own rendering engine, UI built in Flutter can be launched on virtually any platform. This ensures consistency of the application interface across all platforms.

As far as their layout, the widgets within a Flutter app form a hierarchy where a widget is a composition of other widgets and each widget inherits properties from its parent. Widgets, then, are arranged into a tree and nested inside of each other to parent and child widget.

The image below illustrates how the UI is built using widgets:

Flutter has a rich library of pre-built widgets, which speeds up and simplifies the process of creating a UI/UX. It’s also possible to create custom widgets.

There are primarily two types of widgets in Flutter: stateless, whose properties don’t change over time such as Icon, IconButton, and Text, and stateful, which are dynamic in nature such as Checkbox, Radio, Slider, InkWell and Form.

Furthermore, Flutter has specific widgets specific to a particular platform – Material Design for Android or Cupertino style for iOS.

Benefits

Hot reload

Flutter enables faster app development using its hot reload which allows developers to view code changes as soon as they are saved. The changed code can then be reloaded immediately while the app is running and without losing the current application state, which means that developers can make quick changes and see the outcome immediately.

It also makes it easier to test out new ideas and experiment with new features helping boost creativity.

Reload in action

Enhanced performance

Unlike other cross-platform frameworks, there isn’t any kind of bridge converting Flutter widgets into the native components, which would slow things down and cause performance issues. Instead, Flutter’s core graphic machine, Skia (a 2D graphics rendering library), compiles the UI to native code without the need for any additional software as the user interacts with the app.

React Native, for example, uses JavaScript as a programming language to build an application and it uses a bridge to communicate with native components, which would affect the overall performance of the app.

As Flutter doesn’t need this bridge, apps built using Flutter run faster than apps developed with React Native.

Easy to learn and use

Compared to React Native or Java, Flutter is much easier to learn and use.

As discussed previously, Flutter uses widgets which makes the UI easy to use providing you with all the features you need to build your app.

The language Flutter uses, Dart, is also easy to learn especially for those who have already worked with other popular languages such as Java and C#.

Faster development and release time to market

One of the most prominent features of Flutter is its hot reload, as mentioned above. Hot reload allows developers to see changes to their codes almost instantly. In other words, with the Flutter framework, there is no need to recompile the code every time you make a change. This is what largely makes Flutter app development time faster than others.

Furthermore, since developers are working with one code base for both iOS and Android apps, this means they don’t have to write a platform-specific code. Any 2D-based UI can be implemented in Flutter without interacting with a native application counterpart.

Therefore, using a single code base, you can develop one UI design for both iOS and Android, which helps save time and resources.

Open to everyone

Flutter is open source and free to use and it also provides extensive documentation and community support in case any issues arise. Because of its rising popularity, Flutter has a fast-growing community of developers who are always ready to share their knowledge and experience so there’s a lot of support out there to help you get set up with the tool.

Flutter vs other app development frameworks

As we’ve seen, Flutter offers many advantages that arguably make it a superior choice to other hybrid development frameworks such as React Native.

There are many things that sets Flutter apart from React Native- things we’ve already mentioned such as the fact that the latter’s programming language, Javascript, requires a bridge to communicate with different native components while in Flutter no bridge is needed and so this ensures faster app development.

Also, the great thing about Flutter that also sets it apart is that it has UI widgets packaged within itself without relying on any third-party tools like React Native, which could affect app performance. In other words, within the Flutter framework, you have everything you need for developing mobile apps. Not to mention that Flutter provides a unified UI experience across all platforms.

Flutter is also ideal if you’re looking to build a minimum viable product (MVP) if you’re short on time using its expansive widget collection.

Many major companies today have used Flutter to develop their apps including:

Google Ads, Alibaba, Hamilton Musical as well as Reflectly (who provide their own take on why they decided to switch from React Native to Flutter).

To sum it all up…

Flutter is a great choice for mobile app development projects of any size. Because it’s easy to learn and comes with clear and detailed documentation, developers can become acquainted with the Flutter framework quite quickly.

You can even integrate a feature flag solution inside your Flutter app, which will give you more control over your app releases by targeting certain user groups as well as allowing you to easily roll back releases in case anything goes wrong with minimal impact.

Feature flags will allow you to bypass app store validation as with remote config implemented through feature flags, any changes can be made instantly and remotely and then released to a small subset of users to get feedback before releasing it to everyone else. Therefore, you can upgrade your app continuously in real-time based on feedback from your users without waiting on app store approval.

With AB Tasty, you can easily get your Flutter app up and running on our feature flagging platform using our client library with preconfigured methods to implement the Decision API.

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A Data-Driven Approach to Customer-Centric Marketing

Mary Kate Cash

At AB Tasty, we think, breathe, eat, drink, and sleep experimentation – all in the name of improving digital experiences. Over the coming months, we’re going to peel back a layer to take a closer look at what’s under the hood of experimentation: the data. Data drives the experimentation cycle – all of the ideation, hypotheses, statistical management, and test-result analysis. It’s no secret that today’s world runs on data, and the development of your digital experience should be no different.

Customers today – whether speaking of a business buyer or an everyday consumer – prioritize experience over other aspects of a brand. Over the coming months, we’ll be talking with some of our partners and data experts at AB Tasty to explore how brands can use data to better profile customers, understand their needs, and forge valuable emotional connections with them, as well as to measure overall digital impact and build a data-based, customer-centric vision.

Before we jump right in, let’s take a moment to center our discussions of data within a privacy-conscious scope.

Every marketer knows that nothing is more precious than customer data, but acquiring it has become increasingly thorny. Europe’s far-reaching General Data Protection Regulation (GDPR), enforced in 2018, was a game-changer, requiring companies to get consent before collecting personal data. The California Consumer Privacy Act (CCPA) soon followed, giving consumers the right, among other things, to opt-out from the sale of their data.

Even if you think your business isn’t subject to such regulations, you might need to consider compliance anyway. E-commerce has erased national borders, allowing goods and services to be purchased with little regard for their origin. The globalization of brands means that an influencer in Pennsylvania who posts about your products could drive Parisian customers to your site, and suddenly you’re collecting data subject to GDPR guidelines – which require customer consent for use.

Leveraging the right customer data

Understanding your customers and their needs and changing behaviors is key to delivering timely, relevant messages that boost loyalty and drive revenue. Whether your company sells yoga mats, yams, or yacht insurance, you need data to enhance their experience with you and strengthen your relationship with them.

But how can you leverage the data you need while ensuring your customers continue to trust you? In recent years, consumers have grown skeptical of handing over their personal data. According to a 2021 survey by KMPG, 86% of consumers questioned said they feel a growing concern about data privacy. And they should be: the same survey showed that 62% of business leaders felt that their companies should do more to protect customer data.

Thanks to the well-deserved death of third-party cookies, marketers are now seeking the data they need by forging consent-driven first-party relationships with their audiences. While this is a step in the right direction, data privacy needs to go further.

Enhancing brand value through consent- and privacy-oriented processes

Consumers are more likely to buy from companies with transparent privacy practices that clearly explain how personal data is collected, used, and stored. Giving or withholding consent for the use of their data should be effortless, and if requested, customers should know that brands will not only delete all the data they’ve stored, but also remove any access privileges they may have granted to partners or third parties.

By making consent and preferences easily manageable, a multitude of data can be shared at every customer touchpoint, revealing customer behaviors, preferences, attitudes, and values. To deal with this omnichannel data, a Consent Management Platform (CMP) can help you collect and handle personal information in a privacy-first way. A CMP enables you to maintain consent logs across all customer-facing channels, ensuring that the personal data processing is always in line with the data subject’s preferences, adding an ethical dimension to the customer experience.

Ethical handling of customer data is mission-critical if brands are to succeed today. From big tech to retail, companies of every stripe are taking an ethical and privacy-centered approach to data, because, as an article in the Harvard Business Review aptly put it, “Privacy is to the digital age what product safety was to the Industrial Age.”

Customer data can help you deliver relevant, personalized, and innovative experiences.

It can build your brand by generating new leads, predicting sales and marketing trends, and enabling you to create the personalized messages that customers love. But unless your data is protected and unbreachable, your customer base is at risk.

At AB Tasty, we’re actively committed to ensuring compliance with all relevant privacy regulations and to being entirely transparent with our users with regard to the consensual first-party and impersonal statistical data we collect when they visit our site. We strive to ensure that our partner agencies and SMEs take accountability and responsibility for the use of their customers’ personal data and respond rapidly should customers want to opt-out or be forgotten.

In this series of articles, we’ll be looking at using data to get value from anonymous visitors, using experimentation to discover customer needs, creating emotional connections to customers with data, and using data to measure your digital impact – all of this featuring data experts from the industry to guide us on our journey. See you soon!

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What Are the Best Git Branching Strategies

Rowan Haddad

In modern software development, speed and agility are crucial when it comes to developing and releasing software. However, when you have a large team of developers working simultaneously, branching and merging code can become messy fast.

Therefore, teams need to have a process in place to implement multiple changes at once. This is where having an efficient branching strategy becomes a priority for these teams.

What is a branching strategy?

Branches are primarily used as a means for teams to develop features giving them a separate workspace for their code. These branches are usually merged back to a master branch upon completion of work. In this way, features (and any bug and bug fixes) are kept apart from each other allowing you to fix mistakes more easily.

This means that branches protect the mainline of code and any changes made to any given branch don’t affect other developers.

A branching strategy, therefore, is the strategy that software development teams adopt when writing, merging and deploying code when using a version control system.

It is essentially a set of rules that developers can follow to stipulate how they interact with a shared codebase.

Such a strategy is necessary as it helps keep repositories organized to avoid errors in the application and the dreaded merge hell when multiple developers are working simultaneously and are all adding their changes at the same time.

Such merge conflicts would eventually deter shipping code quickly and thus hindering from creating and maintaining an efficient DevOps process as the whole purpose of DevOps is creating a fast workflow that would allow for the release of small batches of code.

Thus, adhering to a branching strategy will help solve this issue so that developers can work together without stepping on each other’s toes. In other words, it enables teams to work in parallel to achieve faster releases and fewer conflicts by creating a clear process when making changes to source control.

When we talk about branches, we are referring to independent lines of code that branch off the master branch, allowing developers to work independently before merging their changes back to the code base.

In this post, we will outline some of the branching strategies that teams use in order to organize their workflow where we will look at their pros and cons and which strategy you should choose based on your needs, objectives and your team’s capabilities.

Why you need a branching strategy

As mentioned above, having a branching strategy is necessary to avoid conflicts when merging and to allow for the easier integration of changes into the master trunk.

A branching strategy aims to:

Enhance productivity by ensuring proper coordination among developers
Enable parallel development
Help organize a series of planned, structured releases
Map a clear path when making changes to software through to production
Maintain a bug-free code where developers can quickly fix issues and get these changes back to production without disrupting the development workflow

Git branching

Branches are not just exclusive to Git. However, in this article we focus on Git due to the many advantages this model of branching offers.

Consequently, before we delve into the various branching strategies out there, including Git branching strategies, we will take a look at how Git actually handles branches and why it’s a standout among other VCS tools.

Put simply, Git and other version control tools allow developers to track, manage and organize their code.

Git branches allow developers to diverge from the main branch by creating separate branches to isolate code changes. The default branch in Git is the master branch.

The biggest advantage of a Git branch is that it’s ‘lightweight’, meaning that data consists of a series of snapshots so with every commit you make, Git takes a picture of what your files look like at that moment and stores a reference to that snapshot. This means that these branches aren’t just copies of the file system but simply a pointer to the latest commit.

Meanwhile, other VCS tools store information as a list of file-based changes which may slow things down and use up significant space.

In Git, a branch is essentially a reference or a pointer to the latest commit in a given context; it’s not a container for commits. As you create new commits in the new branch, Git creates new pointers to track the changes. Git branches, then, can be seen as a pointer to a snapshot of your changes.

The images below illustrate this concept, where the top image shows the master branch and a pointer pointing to the last commit and the image right below it shows what happens when you create a new branch called ‘dev’- a new pointer now points to the latest commit.

To sum up, the Git branching model is lightweight compared to other version control systems; this is why it’s so easy and cheap to create branches in Git, as the whole code doesn’t need to be copied to the branch creating a large amount of duplicate files, as opposed to other VCS tools.

What are some common Git branching strategies?

GitFlow

Considered to be a bit complicated and advanced for many of today’s projects, GitFlow enables parallel development where developers can work separately from the master branch on features where a feature branch is created from the master branch.

Afterwards, when changes are complete, the developer merges these changes back to the master branch for release.

This branching strategy consists of the following branches:

Master
Develop
Feature- to develop new features that branches off the develop branch
Release- help prepare a new production release; usually branched from the develop branch and must be merged back to both develop and master
Hotfix- also helps prepare for a release but unlike release branches, hotfix branches arise from a bug that has been discovered and must be resolved; it enables developers to keep working on their own changes on the develop branch while the bug is being fixed.

The main and develop branches are considered to be the main branches, with an infinite lifetime, while the rest are supporting branches that are meant to aid parallel development among developers, usually short-lived.

Author: Vincent Driessen
source
License: Creative Commons BY-SA

GitFlow pros and cons

Perhaps the most obvious benefit of this model is that it allows for parallel development to protect the production code so the main branch remains stable for release while developers work on separate branches.

Moreover, the various types of branches make it easier for developers to organize their work. This strategy contains separate and straightforward branches for specific purposes though for that reason it may become complicated for many use cases.

It is also ideal when handling multiple versions of the production code.

However, as more branches are added, they may become difficult to manage as developers merge their changes from the development branch to the main. Developers will first need to create the release branch then make sure any final work is also merged back into the development branch and then that release branch will need to be merged into the main branch.

In the event that changes are tested and the test fails, it would become increasingly difficult to figure out where the issue is exactly as developers are lost in a sea of commits.

Indeed, due to GitFlow’s complexity, it could slow down the development process and release cycle. In that sense, GitFlow is not an efficient approach for teams wanting to implement continuous integration and continuous delivery.

Thus, in that case a much simpler workflow such as GitHub Flow is recommended.

GitHub Flow

GitHub Flow is a simpler alternative to GitFlow ideal for smaller teams as they don’t need to manage multiple versions.

Unlike GitFlow, this model doesn’t have release branches. You start off with the main branch then developers create branches, feature branches that stem directly from the master, to isolate their work which are then merged back into main. The feature branch is then deleted.

The main idea behind this model is keeping the master code in a constant deployable state and hence can support continuous integration and continuous delivery processes.

GitHub Flow pros and cons

Github Flow focuses on Agile principles and so it is a fast and streamlined branching strategy with short production cycles and frequent releases.

This strategy also allows for fast feedback loops so that teams can quickly identify issues and resolve them.

Since there is no development branch as you are testing and automating changes to one branch which allows for quick and continuous deployment.

This strategy is particularly suited for small teams and web applications and it is ideal when you need to maintain a single production version.

Thus, this strategy is not suitable for handling multiple versions of the code.

Furthermore, the lack of development branches makes this strategy more susceptible to bugs and so can lead to an unstable production code if branches are not properly tested before merging with the master-release preparation and bug fixes happen in this branch. The master branch, as a result, can become cluttered more easily as it serves as both a production and development branch.

A further disadvantage is as this model is more suited to small teams and hence, as teams grow merge conflicts can occur as everyone is merging to the same branch and there is a lack of transparency meaning developers cannot see what other developers are working on.

GitLab Flow

GitLab Flow is a simpler alternative to GitFlow that combines feature-driven development and feature branching with issue tracking.

With GitFlow, developers create a develop branch and make that the default while GitLab Flow works with the main branch right away.

GitLab Flow is great when you want to maintain multiple environments and when you prefer to have a staging environment separate from the production environment. Then, whenever the main branch is ready to be deployed, you can merge back into the production branch and release it.

Thus, this strategy offers propers isolation between environments allowing developers to maintain several versions of software in different environments.

While GitHub Flow assumes that you can deploy into production whenever you merge a feature branch into the master, GitLab Flow seeks to resolve that issue by allowing the code to pass through internal environments before it reaches production, as seen in the image below.

Therefore, this method is suited for situations where you don’t control the timing of the release, such as an iOS app that needs to go through the App store validation first or when you have specific deployment windows.

Trunk-based development

Trunk-based development is a branching strategy that in fact requires no branches but instead, developers integrate their changes into a shared trunk at least once a day. This shared trunk should be ready for release anytime.

The main idea behind this strategy is that developers make smaller changes more frequently and thus the goal is to limit long-lasting branches and avoid merge conflicts as all developers work on the same branch. In other words, developers commit directly into the trunk without the use of branches.

Consequently, trunk-based development is a key enabler of continuous integration (CI) and continuous delivery (CD) since changes are done more frequently to the trunk, often multiple times a day (CI) which allows features to be released much faster (CD).

This strategy is often combined with feature flags. As the trunk is always kept ready for release, feature flags help decouple deployment from release so any changes that are not ready can be wrapped in a feature flag and kept hidden while features that are complete can be released to end-users without delay.

Trunk-based development pros and cons

As we’ve seen, trunk-based development paves the way for continuous integration as the trunk is kept constantly updated.

It also enhances collaboration as developers have better visibility over what changes other developers are making as commits are made directly into the trunk without the need for branches. This is unlike other branching methods where each developer works independently in their own branch and any changes that occur in that branch can only be seen after merging into the main branch.

Because trunk-based development does not require branches, this eliminates the stress of long-lived branches and hence, merge conflicts or the so-called ‘merge hell’ as developers are pushing small changes much more often. This also makes it easier to resolve any conflicts that may arise.

Finally, this strategy allows for quicker releases as the shared trunk is kept in a constant releasable state with a continuous stream of work being integrated into the trunk which results in a more stable release.

However, this strategy is suited to more senior developers as this strategy offers a great amount of autonomy which non-experienced developers might find daunting as they are interacting directly with the shared trunk. Thus, for a more junior team whose work you may need to monitor closely, you may opt for a Git branching strategy.

How to choose the best branching strategy for your team

When first starting out, it’s best to keep things simple and so initially GitHub Flow or Trunk-based development may work best. They are also ideal for smaller teams requiring only a single version of a release to be maintained.

GitFlow is great for open-source projects that require strict access control to changes. This is especially important as open-source projects allow anyone to contribute and so with Git Flow, you can check what is being introduced into the source code.

However, GitFlow, as previously mentioned, is not suitable when wanting to implement a DevOps environment. In this case, the other strategies discussed are a better fit for an Agile DevOps process and to support your CI and CD pipeline.

The following table summarizes the strategies discussed in this article and which strategy is appropriate in which context:

Product type and its release method	Team size	Collaboration maturity	Applicable mainstream branch mode
All	Small team	High	Trunk-Based Development (TBD)
Products that support continuous deployment and release, such as SaaS products	Middle	Moderate	GitHub-Flow and TBD
Products with a definite release window and a periodic version release cadence, such as iOS apps	Middle	Moderate	Git-Flow and GitLab-Flow with release branch
Products that are demanding for product quality and support continuous deployment and release, such as basic platform products	Middle	Moderate	GitLab-Flow
Products that are demanding for product quality and have a long maintenance cycle for released versions, such as 2B basic platform products	Large	Moderate	Git-Flow

To sum up, there is no such thing as the perfect strategy. The strategy you choose will depend on your team and the nature and complexity of your project and so this should be evaluated on a case-by-case basis.

It’s also fine to start off with one strategy and adapt it over time according to your needs. Needless to say, whatever strategy you end up choosing should aim to increase your team’s productivity by giving them a clear and consistent strategy to organize their work.