Understanding multi-channel dynamics – Part 1

There is a generally accepted view that an organisation’s multi-channel customers are its best customers. The theory is that if a customer buys from an organisation over more than one channel, for example in the store, from a catalogue and over the web, then they are more likely to be of higher value than if they just purchase through one or two channels. I can see there is a natural inclination to believe that if a customer does business with an organisation over more than one channel that it is probable that the customer has a higher degree of loyalty and hence value. However, the mathematics of the analysis state that a multi-channel customer is also more likely to be of higher value anyway by the simple virtue of having bought more than once rather than necessarily because they bought across different channels. So understanding the value of multi-channel strategies requires a bit more careful consideration than simply looking at the average customer value.

There is another dimension I think as well to evaluating the impact of multi-channels strategies. In the example above the focus is on the result and the channel in which the transaction occurred. From a customer perspective that is fine but to fully understand how multi-channel strategies are working (or not) it’s also important to understand the dynamics between the channel that the customer was acquired in and the channel in which the transaction takes place. This is particularly important for understanding the role of the online channel in driving offline transactions and there are two important ingredients to achieving this. The first important thing is to have the tracking mechanisms in place to be able see multi-channel behaviour. I admit this can be easier said than done. The second important thing is to understand why multi-channel behaviours are happening the way that they are and then to evaluate whether some of these behaviours are desirable or not.

The type of industry an organisation is in and the type of channels it uses to do business will determine the appropriate methods it can use to track multi-channel behaviour. For example, the use of a specific telephone number on the website for the call centre or using source codes or reference numbers to identify customers. Some of these methods will be more accurate and reliable than others but the initial solution to understanding the multi-channel puzzle is to have at least some mechanisms in place to track behaviours.

The next issue is then to understand the behaviours that are being tracked. It’s likely that first challenge will be to integrate the data from the different channels. Data may need to come from web analytics systems, call centre systems, customer databases and so on. Data will need to be cleaned, integrated and then analysed. This may require some different data analysis tools. The type of analysis you need to do will depend on the type of problem you are trying to solve. Let me give you an example based upon work we have done in the travel industry.

A company sells holidays to an older target market. The main channel historically has been telephone sales through a call centre though the web channel now makes up a significant proportion of their business. The website also allows visitors to download a brochure and it also gives the number for the call centre. Although web site traffic is growing steadily, the conversion rate was not increasing. Increased sales were a function of increased traffic.

The company wanted to increase the conversion rate to get more bookings transacted online as opposed to through the more costly call centre.

The website already had its own special number for the call centre so the number of calls that originated online could be tracked. The next stage was to understand how many of these calls turned into bookings. In this instance the call centre system didn’t allow bookings to be tracked against specific inbound numbers, so for a period of time call centre operatives receiving “web calls” were asked to track how many of them resulted in a sale. In this way a conversion rate could be calculated.

The other aspect was to understand what happened when people ordered a brochure from the website. The approach here was to match the names and addresses of people who had ordered the brochure online and to cross-reference them against bookings received in subsequent months and to look at what channel they had booked through. Although perhaps not perfect it seemed to be good enough. From this analysis we could determine how many of those people who had ordered a brochure online had subsequently booked and which channel they had used to make the booking (via the call centre or via the website).

This analysis allowed us to do two things. First of all we were able to estimate the total value being delivered to the organisation. This was not just the value of the online bookings but also the value of the bookings that came through to the call centre on the special website number and even those who had ordered a brochure from the website and had subsequently booked via the normal call centre number. In this case a significant proportion of the internet channel’s total value to the organisation came from its delivery of business into the offline channels and highlighted that the way that the organisation had been historically measuring the value had been underestimating the true Return on Investment.

The second thing that the analysis allowed us to do was to explore the dynamics of the interaction between the online and offline channels and to understand why some of these behaviours were happening. I’ll go into that in more detail next time. Till then…

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