Most companies today comfortably fall into the second stage of competency when it comes to their customer data. They don’t have a good handle on what they have or how to use it, but do fully realize the deficit it creates in their experiences.
Great customer understanding and the data that allows that understanding are the backbone of an amazing experience, but brands often spend very little time and energy harvesting and nurturing their own data. They spend money on third-party data and expensive targeting technologies, but let their own customer data sit in silos across their organization.
Your data is way more than campaign performance, site analytics, profile information or shopping history – although those are a great start. Your data view should include any imprint a customer can make when they interact with your brand: customer service logs, surveys, traffic patterns (both physical and digital), brand mentions, rankings and search queries. Explore and monitor every touchpoint where a trace of what a customer thinks, says or does can be collected. This is your data. These are the places where patterns can be found and where someday you might use machine learning or other techniques to analyze and find your next great opportunity.
Even as the dialogue around data mining becomes more volatile, customers are increasingly eager to connect more devices and share more of their personal information. This information exchange happens in the hopes of a smarter, better experience – for their homes, their shopping, their health and so much more. Chances are your industry is connecting too, so find those places for data exchange and build around them.
The most reliable data you will ever have access to is the data you collect across your own experiences and through your channels and products. The cost of great data science and AI integrations are still too expensive for many organizations today. But you can start collecting and building data libraries even before you can realize the power they might have on your future customer experiences. Many of the largest data miners today were listening, clustering and patterning way before they were able use the information to power new offerings or experiences.