Customer journey in the digital world is no longer about understanding and managing the various customer touch points. There is a need to understand and observe customers across the journey on various channels.

This is important because customer behaviour patterns vary between channels. Other than that data gets generated at the different touch points notwithstanding the channels.

A simple example: A young woman visits the website of a fashion retailer. She goes through various options about the attire of her choice, narrows down on a few options. She then visits a few other websites to check out the prices of similar attire. After making a decision, she visits the store to try out the dress, check colour options and then buys a few accessories to improve the looks of the attire. Even while doing so she browses her mobile to check prices of accessories. She again checks with a fashion review magazine to understand design and designer pedigree.

Look at the amount of data she has encountered or generated in the purchase process.

The data therefore becomes diverse and hence the marketing team in the fashion house feel the need for a single source of truth across channels which drives the need for data integration. A single source of truth about customers results from this integration. Insights derived from this single source of customer truth is important to help cross functional teams to unify while providing customer response irrespective of channels of engagement.

Here’s where the concept of predictive analytics can provide a deeper understanding of customer behaviour patterns across channels. However, insights are not useful if they are not packaged well. This means it is important going beyond standard metrics – like bounce rates, conversion rates. Also in addition to demographic data, applying behavioural statistics and ethnographic design thinking can make the insights derived even more powerful while getting a 360 degree view of the customer. As techniques advance, possibilities like machine learning for customer analytics for automated decision making becomes real. This can enhance learning from customer activity on which advanced analytics techniques have been applied.

Advanced analytics for customer journey understanding has infinite possibilities.