Factors to consider when estimating a customer's value across on- and offline channels.
There's a generally accepted view that an organization's multichannel customers are its best customers. The theory is that if a customer buys from an organization through more than one channel, such as in the store, from a catalog, and over the Web, then the customer is more likely to be of higher value than one who makes purchases through one or two channels.
There's a natural inclination to believe that if a customer does business with an organization through more than one channel, it's probable the customer has a higher degree of loyalty and, hence, value. However, analysis mathematics state that multichannel customers are also more likely to be of higher value anyway by the simple virtue of having bought more than once rather than because they bought across different channels. So understanding the value of multichannel strategies requires more careful consideration than simply looking at the average customer value.
There's another dimension to evaluate. In the example, the focus is on the result and the channel in which the transaction occurred. From a customer perspective, that's fine. To fully understand how multichannel strategies are working (or not), you must also understand the dynamics between the channel where the customer was acquired and the channel where the transaction takes place. This is particularly important for understanding the role of the online channel in driving offline transactions. There are two important ingredients to achieving this. First, have tracking mechanisms in place to see multichannel behavior. This can be easier said than done. Second, understand why multichannel behaviors are happening the way they are, then evaluate whether some behaviors are desirable or not.
Determining the appropriate methods for tracking multichannel behavior depends on the organization's industry and the channels it uses to do business. Those could include using a specific telephone number on the Web site for the call center or using source codes or reference numbers to identify customers. Some methods will be more accurate and reliable than others, but businesses must have mechanisms in place to track behaviors before beginning to understand the multichannel puzzle.
Another likely challenge involves integrating the data for different behaviors collected from different channels. Data may need to come from Web analytics systems, call center systems, customer databases, and so on. Data will need to be cleaned, integrated, then analyzed. This may require some different data analysis tools. Plus, the analysis you want to perform depends on the problem you're trying to solve.
Let me give you an example based on 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 center, though the Web channel now makes up a significant proportion of its business. The Web site also allows visitors to download a brochure and shows the number for the call center. Although Web site traffic is growing steadily, the conversion rate wasn't 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 center.
The Web site already had its own special number for the call center, so the number of calls originated online could be tracked. The next stage was to understand how many calls turned into bookings. In this instance, the call center system didn't allow bookings to be tracked against specific inbound numbers. So for a period, call center operators receiving Web calls were asked to track how many 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 Web site. 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. Perhaps not perfect, it seemed to be good enough. From this analysis, we could determine how many people ordered a brochure online and subsequently booked, as well as identify the channel used to make the booking (via the call center or Web site).
This analysis allowed us to do two things. First, we could estimate the total value being delivered to the organization. This was not just the value of the online bookings but also the value of the bookings that came through to the call center on the special Web site number and even those who had ordered a brochure from the Web site and had subsequently booked via the normal call center number. In this case a significant proportion of the Internet channel's total value to the organization came from its delivery of business into the offline channels. The analysis also highlighted that the organization had been historically underestimating the true return on investment.
Second, the analysis allowed us to explore the dynamics of the interaction between the online and offline channels and to understand why some of these behaviors were happening. I'll go into that in more detail next time. Till then...
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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