Latency is related to Frequency - it is the average time between customer events in the customer lifecycle. Examples include the average time between web site visits, second and third purchase and e-mail clickthroughs. Online applications of latency include putting in place triggers that alert companies to customer behaviour outside the norm, for example increased interest or disinterest, and then to manage this behaviour using e-communications or traditional communications. For example, if a B2B or B2C organisation with a long interval between purchases would find that the average latency increased for a particular customer, then they may be investigating an additional purchase (their recency and frequency would likely increase also). E-mails, phone calls or direct mail could then be used to target this person with relevant offers according to what they were searching for.

Hurdle rate

According to Novo (2003), 'hurdle rate' refers to the percentage of customers in a group (such as in a segment or on a list) who have completed an action. It is a useful concept, although the terminology doesn't really describe its application. Its value is that it can be used to compare the engagement of different groups or to set targets to increase engagement with online channels as the examples below show:

• 20% of customers have visited in past 6 months

• 5% of customers have made 3 or more purchases in year

• 60% of registrants have logged on to system in year

• 30% have clicked through on e-mail in year.

Grouping customers into different RFM categories

In the examples above, each division for Recency, Frequency and Monetary value is placed in an arbitrary position to place a roughly equal number of customers in each group. This approach is also useful since the marketer can set thresholds of value relevant to their understanding of their customers.

RFM analysis involves two techniques for grouping customers

1 Statistical RFM analysis

This involves placing an equal number of customers in each RFM category using quintiles of 20% (10 deciles can also be used for larger databases) as shown in Figure 6.12. The figure also shows one application of RFM with a view to using communications channels more effectively. Lower-cost e-communications can be used to correspond with customers who use only services more frequently since they prefer these channels while more expensive offline communications can be used for customers who seem to prefer traditional channels.


Recency Frequency Monetary

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