Customers are the key element that matters the most for running any business. Everything else stands next. Enterprises that handle the Enterprise life cycle effectively succeed longer. The enterprise life cycle would be successful if we know the customer and the customer’s expectations well.
Digital transformation and digital adoption are helping enterprises roll out new features at a rapid pace. But what does that mean to the customer? – 1) value for money, 2) greater service offerings, 3) new experience, 4) convenient choices, and so on.
The customer acquisition strategy will be different from the customer retention strategy. Neither of those things is simpler. However, retention may involve keeping the customers engaged with the product by rolling out new features. In doing so, we should take appropriate measures to verify and validate the features before it would be actually rolled out to the customer. A/B testing comes in handy to help reduce the risk of rolling out a less-liked feature to customers.
Answering the ‘WH’ questions to understand better A/B testing –
1) What? – AB testing means testing multiple variants of the products (Generally 2 variants) with minor changes from each other to understand which one performs better than the other. This is a more powerful technique that helps in promotions, branding, marketing, wise cost spending, etc.
2) Why? – Initially started as a user experience testing mostly targeting UI/UX, AB testing slowly evolved to accommodate different features to understand the customers better. Sometimes AB is rolled out only to friendly users even before rolling out public AB testing.
3) Where? – It’s commonly used in UI/UX to test which assets perform better. But it’s gradually getting into areas like data, machine learning, predictions, and data science to understand the outcome based on different variants. This helps in analyzing the impact in the early stages.
4) How? – Many tools are available to do A/B testing. We have to fix the hypothesis to test, set the goals and KPIs, and determine how the KPIs would transform into actionable items. More than the tools and technology, the complex part of A/B testing is the right thought and idea for the hypothesis that yields meaningful outcomes.
5) When? – Whenever a new feature in a digital product is offered, it is good to do beta testing with A/B testing. It is often used when optimizing marketing campaigns, increasing user engagements, increasing leads and conversions, improving UI/UX experience, and so on.
6) Who? – Predominantly seem like a costly experiment which are most suitable for enterprises, depending on the use cases and the offerings, even bootstrapped startups can do AB testing the digital product to understand the reality vs assumption made while bootstrapping the idea.
Shift left paradigm – A/B testing is another shift left technique where the new features can be experimented with early and only with a limited audience before being rolled out in production for everyone.
Three stages of A/B testing – Detailed the general guidelines and lifecycle involved in the A/B testing phase.
High-level architecture to build A/B testing- It’s true that the majority of times we would end up using A/B testing tools, however, it’s important to understand the high-level architecture of A/B testing. It would help in understanding how the tool intersects with the enterprise data and campaigns.
Modern digital ideas –
- A/B testing and Personalization – While only the majority of A or B is rolled out to production, we should be able to run multiple variants depending on the business proposals at any point in time. This would slowly blur the lines between A/B, personalization, and recommendation engines. Aiming to create a unique user experience by making individualized digital experience possible.
- A/B testing and canary testing – While A/B testing is a way to test the hypothesis using multiple variants, canary testing is used to deliver new features to a certain group of users to reduce risk by validating with a small percentage of the user before rolling the feature to a bigger audience. Depending on audience grouping and sample size, A/B can be used for canary testing without setting up a separate layer for canary testing.
Share your ideas and experience around A/B testing.