How do you measure the impact of a new feature on an E-Commerce site?

Whatever you add to your shop - test. Even though it seems like a clear go-to, test. Working on hundreds of stores through all different kinds of industries, what I've learned is that there's no one-size fits all. Set goals and go towards them. Allow yourself to be wrong. Learn from it. What I see as the biggest mistake in setting goals and metrics is not enough effort to improve them. Even some of our 8-9 figure stores clients make this mistake. They test just for sake. It's OK to lose the test. It's not OK to have goals and metrics wrong. Abraham Lincoln was quoted as saying “ Give me six hours to chop down a tree and I will spend the first four sharpening the ax.” That's the whole point.

Defining the right goals and metrics is definitely the right first step. But there's one thing I always like to layer on top of it: The hypothesis. Your hypothesis should be the very reason you're running the test to begin with. A great hypothesis is specific, measurable (goal & metric), Relevant, and Testable. One place I often see stores get hung up on is not thinking about testing the impact of features or changes as a constant, iterative process. It's not a one-and-done process. Example hypothesis: 1. Having live chat added to our site will reduce support emails by at least 10%. 2. Having videos about a product directly on a product listing page will increase conversion rate by at least 5%.

Test everything you add to your shop, even if it seems like a clear choice. In my experience with diverse industries, there's no one-size-fits-all solution. Set goals, embrace the possibility of being wrong, and learn from it. The key mistake in goal-setting is insufficient effort in improvement. Even high-performing stores make this error. Testing for the sake of it is fine, but it's not acceptable to have inaccurate goals.

To effectively measure the impact of a new feature on an E-Commerce site, it's crucial to set clear goals and metrics before. For instance, if the goal is to enhance customer satisfaction and retention by adding a live chat feature, metrics such as the number of chat sessions, average chat duration, satisfaction rating, and repeat purchase rate should be considered. Establishing a baseline (current state) and a target (desired state with the new feature) provides a benchmark for assessment. It's essential to approach feature implementation with a strategic mindset, focusing on specific outcomes rather than just introducing features for the sake of it.

When setting goals and metrics for implementing a new feature, it is crucial to define your desired outcomes and how you will measure them. For instance, if your goal is to enhance customer satisfaction and retention by incorporating a live chat feature on your website, you can establish metrics such as the number of chat sessions, average chat duration, chat satisfaction rating, and repeat purchase rate. Additionally, it is important to determine your baseline, which reflects the current state of your website prior to implementing the new feature. This baseline will serve as a benchmark for evaluating the impact of the new feature. Furthermore, you should establish a target, representing the desired state with the new feature in place.

Multivariate testing (MVT) that was suggested in this intro is a HUGE no-go for me. Testing is really important, not because winning or losing, but because of learning. MVT limits that. You have so many variables and even if 1 variation is winning on the main goal, it can lose on others hard. And you end up confused. For example, on PDP you test different CTAs on ATC button and copy that leads to it. Your main goal is ATC. You have a copy that is motivational and CTA "I want this" and it's winning in ATCs. But copy that is educational and CTAs like "Add to cart" are winning in revenue. What do you do? Why? IMO, only ABs are valid, and multi page ABs when you're testing the funnel. Keep it simple for you to learn from it.

Choosing a testing method is crucial for evaluating a new website feature. A/B testing compares two groups – one with the feature and one without – to determine performance. Alternatively, multivariate testing assesses multiple variations simultaneously. A/B testing is efficient for isolating the impact of a single feature, while multivariate testing provides a comprehensive understanding of interactions between elements. The choice depends on specific goals and complexity. In my view, A/B testing offers clearer insights for singular features, while multivariate testing is valuable for complex changes. The decision should align with the specific goals of the feature implementation.

Here's 2 pitfalls I've learned to avoid when it comes to choosing how to test a hypothesis on an eCommerce store. 1. A/B testing performs better over the long term with more data and underperforms in the short-term with less data. If you're wanting to see a more immediate benefit without hitting the "statistical relevance" necessary for good A/B testing, multi-armed-bandit ends up being a great method that optimizes for shorter term tests. 2. Remember to have your control group Your control group is what you're doing today, or what doing nothing looks like. Without the control group, you don't really have a good view.

If you are running A/B tests make sure you are working with enough data to get statistical significance As a rule of thumb a minimum of 20,000 sessions should be exposed to an A/B test before concluding the test If you google "Statistical significance" calculator you will find tools which help you deem whether a test you have run is statistically significant

When testing a new feature on your site, you can use A/B testing or multivariate testing. A/B testing involves splitting your traffic into two groups and comparing their performance with or without the new feature. Multivariate testing tests multiple variations or features simultaneously to find the best combination for improved performance. The choice depends on the feature's complexity, available resources, timeline, and risk tolerance.

After selecting a testing method, collecting and analyzing data is vital. Use tools like Google Analytics, consider factors like sample size and test duration, and be mindful of external influences. Analyzing patterns and insights helps understand how the new feature affects site performance and user behavior. In my view, thorough data analysis is crucial for informed decision-making, ensuring not only the effectiveness of the feature but also insights for ongoing improvements.

When you collect and analyze data for the impact of your new features, it's essential to isolate and remove the novelty effect in your analysis. Whenever you launch something new and innovative, a segment of your customers is likelier to engage with new features because they're early adopters. This might lead to a short-term bump in your metrics, which is often statistically significant. However, the moment you launch something new, or your users go wary and stop engaging with your feature, your metrics would revert to baseline. Hence it's important to run long-term holdout experiments to keep assessing the value of your current features instead of just stacking them on top of each other.

When evaluating a new feature, go beyond surface metrics. Delve into the qualitative data like user feedback and session recordings to understand the 'why' behind the numbers. This holistic approach reveals not just the what and how of user interactions, but also the emotional and contextual factors driving them. It's about piecing together a complete story. Consider the subtleties in user journeys—perhaps the feature works well for new visitors but confuses loyal customers. By marrying quantitative data with qualitative insights, you ensure decisions are both informed and empathetic, leading to a more engaging and intuitive site.

To measure the impact of a new feature on an e-commerce site, first analyze sales metrics, observing changes in revenue, order values and conversion rates post-implementation. Track user engagement through page views, click-through rates, and feature usage to gauge interaction. Assess customer acquisition and retention rates, noting any shifts in new sign-ups or repeat purchases. Gather customer feedback and monitor satisfaction scores to understand perceived value. Conduct A/B testing to compare performance between users exposed to the feature and a control group, isolating its direct effects. Finally, perform a cost analysis of implementation versus the feature's financial return and benchmark against competitors to gauge market standing.

Post-implementation, meticulous data collection and analysis are crucial. Utilize tools like Google Analytics, Optimizely, or Hotjar to track and measure site performance. Factor in sample size, test duration, and significance, considering external influences like seasonality or marketing campaigns. Seek patterns and trends in the data, extracting insights that illuminate the impact of the new feature on site performance and user behavior. This analytical approach empowers informed decision-making, facilitating continuous refinement for optimal results.

After analyzing the data, make a decision on whether to keep, modify, or discard the new feature. Communicate findings to stakeholders, gather feedback, and, if keeping the feature, monitor and make adjustments. Learn from mistakes if modifying or discarding, and always aim to test, measure, and improve your site's performance with new features. In my opinion, a continuous improvement mindset is crucial in the ever-evolving digital landscape. Embracing feedback and being open to adjustments ensures ongoing success in enhancing user experience and overall site performance.

You should always be running A/B tests to discern the real impact of new features. These tests are not just about choosing the 'better' option, but understanding why one outperforms another. Dive deep into user behavior, segmenting your data to uncover how different groups respond. Maybe the feature benefits one demographic greatly while hindering another. By continuously iterating based on these nuanced insights, not only do you refine the feature, but you also enhance your understanding of your user base

Making informed decisions and iterating based on data analysis is a crucial step in E-Commerce feature implementation. Companies like Spotify excel in this area, frequently making decisions on new features based on extensive user data and feedback. After analyzing the impact, they communicate their findings to stakeholders and users, ensuring transparency and building trust. Decisions to keep, modify, or discard features are then made, always with an eye on continuous improvement. This approach of regular monitoring and adaptation ensures that the E-Commerce platform remains dynamic and user-centric. #Spotify #ecommerce #ContinuousImprovement

Exactly! Once you have analyzed the data and have made a decision about the new feature, it is important to communicate your findings to stakeholders and users. Their feedback and suggestions can provide valuable insights and help inform your decision-making process. If you choose to keep the new feature, it is essential to monitor its performance over time and make adjustments as necessary to ensure continued success. If modifications or discarding is required, it is important to learn from the mistakes and iterate on the approach. The key is to always test, measure, and improve your site's performance with new features.