Analyzing Multichannel Dynamics

Once you understand the measurement aspects of multichannel dynamics, it’s time to analyze those measurements. Having put in place the mechanisms to track cross-channel behavior, you must explore the observed dynamics of the interaction between the online and offline channels, understand why some of these behaviors are happening, and whether they’re desirable.

To begin, try to understand why a customer who starts a transaction online ends up completing it offline. Many organizations prefer online transactions, as they’re cheaper to process. This channel shift may occur because of the organization’s business rules, consumer behaviors, or the product’s complexities.

Organize Your Services

We worked with a bank a while back to map out the channel dynamics and to measure the channel shift from online to offline. The project was complicated because the bank had installed Internet terminals in its branches to allow prospective customers to fill in applications for some of its simpler products online.

The idea was that it would reduce the need for customers to wait until branch personnel were available and that one branch person could help many customers at the same time. Branch personnel would also be freed up to sell more complex, higher-value products.

The bank found, however, that branch personnel would often lure people away from the branch terminals to do the transaction on their own systems. Why? Because branch personnel didn’t get commissioned on sales made on branch terminals.

To get the desired behavior, the bank captured the branch terminals’ IP addresses, linked them to the sales made on the terminals, then allocated those sales back to the branches. That made the branch personnel much happier about allowing people to self-serve in the branch.

Look at Demographics and Behavior

Take, for example, a travel company that serves an older target market. Having set up the measurement tracking capability to look at cross-channel behavior, we analyzed why channel shift happened. We compared the bookings that had been made on the Web site against bookings that had been started online and completed in the call center.

We found the person’s gender was the biggest factor. Men were more likely to do their research and book online. Even if they ordered a brochure, they were more likely to go back online to make the actual booking rather than call the call center. Women were far more likely to use the site for research and to order the brochure before contacting the call center to make the booking. Focus groups confirmed that this was the preferred approach for women.

Check Out Site Issues

In some cases, channel shift might come down to site issues. We conducted a similar piece of analysis for an insurance company looking at channel dynamics on its car insurance products. Once again, we compared online booking data with data for bookings started online and completed in the call center.

We looked at many different characteristics, including the type of insurance cover, the car being insured, and policy-holder demographics. Given the breadth of the data in this instance, we used CHAID analysis (define) to identify the most important characteristics for predicting channel shift.

The results were somewhat surprising. The most influential factor was whether people had bought a particular optional extra on the policy. If they had, they were far more likely to have completed the transaction in the call center.

Armed with this information, the company reviewed the site processes for buying this particular optional extra on the policy. It found the process could be improved to reduce the need for people to contact the call center.

When channel shift is down to organizational or site issues, you can address them. Other factors may be more ingrained in the way customers want to do business. In these cases, channel shifting should be embraced — as long as it’s recognized accordingly.

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