The digital world is changing — but many organizations are still measuring customer behavior with static metrics like pageviews and bounce rates. For some, these metrics are completely sufficient; you’re getting basic results that help you take the business forward. But others are itching for more and are already moving toward more comprehensive insights. How do you know where your customer insights stack up against your peers? How do you take your analytics to the next level? Welcome to the Customer Data Maturity Curve.
What is the Customer Data Maturity Curve?
The Customer Data Maturity Curve helps organizations understand the sophistication of their user behavior analytics in relation to what’s possible (Figure 1). With this curve, you can identify where your business is in this progression. You can also learn how to understand when you’ve reached the next step in your analytics program. In the rest of this blog, I’ll explain each stage in detail.
Figure 1. The Customer Data Maturity Curve illustrates the progression of behavioral analytics.
Stage 1: Measure Traffic
The first stage along the curve is to simply understand how many people are accessing various parts of your website. Typically organizations achieve this level of insights by using Google Analytics Free or other similar platforms. This stage enables you to gain basic data, but you’re limited to a static aggregate view. In other words, you can’t see how specific cohorts are using your site. For example, at this stage, you may be able to see that your features page has a longer time on page. But what’s missing is the drill-down: Do customers interact with the page differently than prospects? There’s no way to gain the information needed to make true data-driven decisions.
Stage 2: Optimize Acquisition Channels
As organizations gain understanding of what users do when traffic lands on their site, they can begin to separate out high-value traffic. They use these groupings to optimize what traffic drivers are best for the business. (For example, you can discover whether Facebook generates a large number of users to your site.) With this data, you can determine where and how to invest efforts to gain a larger audience. What’s missing: how these users engage with your site and what content is most likely to convert them.
Stage 3: Drive User Engagement and Conversion Rates
Organizations that reach this step gain solid insights on naturally converting traffic. They also also start getting data on how specific groups of visitors interact with the site and where they drop off in the conversion funnel. This enables you to begin to experiment on ways to achieve higher conversion rates. This stage typically marks the point where organizations abandon the “page view model” of analytics. Instead they trade up to a sophisticated user/event model, which provides insights on granular customer behavior instead.
Stage 4. Understand the Customer Digital Journey
This stage is when organizations realize their user event schema can provide valuable on-page interaction data. And because the customer’s journey rarely begins or ends with just website interactions, you’re beginning to integrate and combine datasets from various parts of the customer journey. The result? A more complete view of what aspects across channels you can optimize.
Stage 5. Unsilo Data and Create a Unified Customer View
Stage 5 is the pinnacle of the curve — and for good reason. Organizations that reach this advanced level combine disparate datasets with ETL pipelines. They also clean and organize large amounts of downstream data to expand the time and resources available to analyze customer insights and react accordingly. To reach this level, organizations must find a durable, automated way to combine customer data in a clean schema downstream.
It can sometimes be challenging to move up the Analytics Maturity Curve, but the results are always worth it. To learn more about this curve and how Heap can help, check out The Death of Web Analytics (as you Knew It).
And feel free to comment with your analytics experience. How did you expand on your insights? Do you agree with the progression of the Analytics Maturity Curve?