Customer Analytics Part II: Putting the Data in Context
Welcome back to our Customer Analytics discussion. We’ve gone over what customer analytics are and where data is sourced. Now let’s take a look at how we assess that data and find usable information.
Focusing on Segments of Data in Context
After cataloguing what feels like an unwieldy amount of information about who our customers are, when and where they find our products online, and how they navigate our webpages, it isn’t always obvious what to take away from the raw numbers. To make them meaningful, we need to think about the relationship between new customer data and other benchmarks, including:
1. Industry data—Look for markers that are significant to your goals and compare your customer data with your competitors’.
⋅ Ask questions such as: Are you converting visitors to customers more or less often than others in the industry? How many visitors do competitor webpages get in comparison to yours? Do you drive visitors from the most common channels or are you serving a niche audience that is funneling from a particular source?
⋅ Use comparison tools—There are a host of tools that let you check in on competitor website traffic, search phrases that lead to your competitors, and online or social media chatter about your competition. Check out this list of comparison tools for some of your options.
⋅ Look at industry reports. For example, Wolfgang Digital’s E-commerce KPI Benchmarks 2017 Report is jam-packed with e-retailer benchmarks to compare against. An important one: e-commerce sites average a 1.6% conversion rate—how does your site compare?
2. Time—Compare your new customer data to your old data.
⋅ Have customer interactions and purchases changed over time? Compare specific data sets over time, e.g. overall purchases or customer lifetime value. Choose a few markers of growth and chart each over time to get a wider view of your company’s overall change.
⋅ Are there are changes in relation to internal and/or external variables? Track changes that occur after shifts within the company, such as a particular ad campaign, but also after macro-level shifts, such as changes in industry regulations.
3. Channel comparisons—Map sales and engagement data from various channels to compare how each performs.
⋅ Use a combination of raw numbers and percentages—I.e. look at both the quantity of purchases from a social media channel and the percentage of viewers who converted. Looking at both will you give you the most accurate understanding of the highest sales and the best ROI.
4. Customer models—Map the steps that are most common to completing a purchase, and also identify the common demographics among your customers.
⋅ Combine your information on these two aspects of customer information to get a more complete picture of how, when and where to reach your customers. Use your data to get a stronger picture of your average customer and your highest value customers.
Still feel like you have a lot of questions about how to really unpack the value of all your customer information? There’s more to our Customer Analytics discussion, so come back to the blog as we continue to dive into Big Data.