How to Determine What Data to Collect for Customer Analytics
The art and science of customer analytics means turning metrics into insights. But you need metrics to begin with. Metrics can be found all across an organization and a customer’s journey. First it’s a good idea to get a better understanding of problems and opportunities for your customers.
You need to collect data from each of the following four customer analytics data types.
Descriptive: The descriptive data becomes the template for whom you measure. It includes demographic data like gender, age, geography, and income. It also includes self-described attitudes and preferences toward products, categories, and technology. From this data, you can create meaningful segments (for example, early adopters or value-seekers) and personas.
You can collect this data from purchases, registrations, surveys, interviews, and contextual inquiries.
Behavioral: The behavioral data becomes the framework for testing experiences. It is the general pattern customers exhibit when using your products and services. It includes making purchases, registering, browsing, and using different devices.
For example, customers of certain product categories, like consumer electronics or home furniture, tend to browse products on their tablet at night and make purchases on their desktop during the day.
Interaction: The interaction data becomes the task scenarios that you simulate and measure during a usability test. It includes the clicks, navigation paths, and browsing activities found on websites and software.
The classic usability test typically focuses on this level of granularity by simulating real interactions. You can use real-time data from A/B testing, Google Analytics, and lab-based or unmoderated testing to collect data for this grouping.
Attitudinal: Preference data, opinions, desirability, branding, and sentiments are usually captured in surveys, focus groups, and usability tests. This is where questionnaires like the SUPR-Q, System Usability Scale (SUS), or the Net Promoter Score quantifies how interactions and behaviors affect attitudes. These attitudes will then affect some self-described descriptive attributes quantified in the descriptive grouping.
Improvements you make that affect attitudinal data, like increased trust and loyalty, drive further buying behavior.