Customer data is one of your most valuable assets, and it’s richer and more readily available today than ever before, thanks to big data and business intelligence (BI) technologies. These technologies have enabled companies to break down data silos and move from a two-dimensional to a three-dimensional customer view, gaining valuable insights into customer motivations, preferences, and habits and allowing them to steer their business accordingly.
The greater the quantity and variety of customer data you collect, centralise and analyse, the more complete your customer picture will become. I’ll focus on three types of customer data:
All companies will have CRM, sales and customer support systems, containing basic customer data such as contact details, marketing data and sales data. The challenge with each of these is that they are often in silos and not utilised to full potential.
Customer Journey Data
Customers interact with your brand across a wide range of touch points and each interaction is a step within a longer customer journey, telling the full story only when viewed in its entirety.
Connecting the dots to reveal the full picture requires dedicated effort. Start by collecting:
For each interaction point, make an inventory of:
Then centralise the data. For example, if a customer phones customer support after reviewing a bill and then viewing your cancellation policy, connect those events
When I consult for clients with a significant online presence, I typically help them jump start a big data approach to analysing their clickstream data.
But why is clickstream data so useful?
Right from the start, we get an understanding of
In addition, we can dive into the impact of A/B testing in ways that are not possible without big data.
This application has been one of the biggest drivers of big data efforts within online companies over the past few years.
More than simply collecting the data, you’ll want to use modern data science techniques to better understand your customers. You’ll use the data you have collected to start classifying customers into personas such as ‘value shoppers’, ‘quality shoppers’, ‘trend setters’, ‘family shoppers’, or ‘young professions’.
You may also use your customer data for recommender systems, perhaps leveraging a graph linking customers based on static and dynamic data. You can estimate how likely each customer is to purchase each product or perform an important action, such as posting a product review or registering for a trial subscription.
Collecting, connecting, and analysing customer data requires some dedicated effort, but those who ignore the possibilities run the risk of alienating their customers and becoming irrelevant.
Modern data tools have made it easier today than ever before to use data, and many companies have paved the way by demonstrating valuable use cases. The challenge remains to apply these tools and methods for the benefits of your own business and customers.
David Stephenson PhD is an internationally recognised expert in the data science and big data analytics. He is the author of new book Big Data Demystified: How to use big data, data science and AI to make better business decisions and gain competitive advantage
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