At Brooks Bell, we build insight-driven organizations. We help teams uncover insights from their activities and share them across their organizations to be applied on a larger scale. This feedback loop of activity>insights>repeat is critical to success in today’s fast-moving environment. It enables insights from one team to be used to fuel activities from another in perpetuity. The positive impact on efficiency and alignment can’t be denied. But where should teams focus their time and attention to ensure they make the biggest impact?
A primary driver of customer insights is experimentation, so let’s start there. With a history steeped in A/B and multivariate testing, Brooks Bell knows the impact test insights can have when applied across an organization. As experts in analytics, we know the importance of focusing experimentation efforts in the areas that count. In order to help unlock the most powerful insights that we can have the most confidence in, we look to the most impactful customer journeys with the biggest room for improvement. To help us deliver consistent results, we created our proprietary Opportunity Model to help us prioritize when and where to allocate resources. While I can’t divulge our secrets, I’d like to introduce you to this general concept.
The Opportunity Model is based on two types of data: quantitative and qualitative. The quantitative data tells us where the most eyes are and when the critical activities are (or aren’t) happening, among many other things. It’s important that we know about traffic and conversion because those are important components to achieving statistically significant results in any test. The qualitative tells us the “why.” It is important to know the “why” to evaluate our ability to improve the experience. Our Analytics team is heavily involved in quantitative data storytelling, while our Research Insights team leads the charge on the qualitative side. When these two teams and data sources unite, you’re given the “what” and “why” data you need to make informed decisions.
Let’s first dig into some details of the quantitive data used to fuel the Opportunity Model. We’re looking for the data to tell us the 1) impact and 2) potential of the pages/areas along the customer journey. When considering the impact, we measure how much a page/area impacts the conversion funnel. For example, the percentage of people adding to their cart, starting to fill out a form, or completing a purchase. Then, we determine the potential impact if we were to make an improvement. This allows us to see which pages/areas have the largest revenue potential, alongside what type of lift would be required in order for that to happen. We also consider industry benchmarks, historical benchmarks, and diminishing returns in our calculations to determine how much-untapped potential there may be. We get this data from Brooks Bell’s proprietary dataset.
Regarding the qualitative component of the Opportunity Model, we’re looking for data to direct us toward the possible improvements we could make. For example, how optimized is the user interface (UI)? Is it clear and simple for the customer to complete what they came to do? Is there friction in the way of them completing their task and possibly reducing conversion? Challenges that can be resolved in the UI likely have a higher potential for improvement, while things more difficult to influence (motivation, brand, timing, etc) would have less potential.
By combining these two types of data, we can rank the pages/areas according to their Opportunity Score, which indicates how much value they can generate through testing. The higher the Opportunity Score, the more likely a page/area is to produce winning tests that will significantly impact the bottom line alongside the customer insights we’ll glean. We use the Opportunity Scores to inform the testing roadmap the team will execute, ensuring a scalable approach that acknowledges potential from any angle.
Sounds good conceptually, but let’s check out some real examples!
For a financial services client, we identified a landing page as one of the highest opportunity areas on their website. This page greatly impacted the conversion funnel, as it was the first point of contact for most visitors and influenced their decision to explore the products and services offered. It also had a high potential for improvement, as it had a low conversion rate compared to industry standards and had several elements that could be optimized, such as headlines, images, copy, and calls to action. By applying the opportunity model, we prioritized this page for testing and generated several hypotheses based on our data. We then ran multiple tests on this page. We implemented several winning variations that collectively increased its conversion rate by over 20% and generated an estimated $1.5 million in incremental annualized revenue for the client.
For a retail client, we identified their Product Detail Page (PDP) as one of the highest opportunity areas on their website. This page had a high impact on the funnel, as it was the last step before adding a product to the cart and influenced the purchase decision of the visitors. It also had a high potential for improvement, as it had a low average order value compared to industry standards and had several elements that could be optimized, such as product images, descriptions, reviews, and cross-sell recommendations. By applying the opportunity model, we prioritized this page for testing and generated several hypotheses based on our data. We then ran multiple tests on this page. We implemented several winning variations that collectively increased its revenue per visit by over 12% and generated an estimated $3 million in incremental annualized revenue for the client.
We’ve used this approach for years with our clients. As you saw in the examples above, the ROI potential is huge. But let me share some of the other benefits we’ve observed:
- Save time and resources by focusing on the most valuable pages or areas for testing
- Increase testing velocity and throughput by running more quality tests that yield more insights
- Enhance testing quality and validity by using data-driven hypotheses and prioritization
- Boost testing culture and maturity by adopting a systematic and strategic approach
Feel free to reach out to learn more about our Opportunity Model or share your approach. I am happy to brainstorm or connect you with one of the experts on our team to help you maximize the impact of your program!