1 - Over 12 months

2 - Within last 12 months

3 - Within last 6 months

4 - Within last 3 months

5 - Within last 1 month


1 - More than once every 6 months

2 - Every 6 months

3 - Every 3 months

4 - Every 2 months

5 - Monthly

Monetary value:

Simplified versions of this analysis can be created to make it more manageable, for example a theatre group uses these nine categories for its direct marketing:

Oncers (attended theatre once)

• Very rusty oncer


• Recent twicer

• Very rusty twicer

2+ subscribers:

• Current subscribers

• Very rusty attended <12 months attended >12 <36 months attended in 36+ months attended < 12 months attended >12, < 36 months attended in 36+ months booked 2+ events in current season booked 2+ last season booked 2+ more than a season ago

Another example, with real-world data is shown in Figure 6.13. You can see that plotting customer numbers against recency and frequency in this way for an online company gives a great visual indication of the health of the business and groups that can be targeted to encourage greater repeat purchases.

Propensity modelling

A name given to the approach of evaluating customer characteristics and behaviour and then making recommendations for future products.

Product recommendations and propensity modelling

'Propensity modelling' is one name given to the approach of evaluating customer characteristics and behaviour, in particular previous products or services purchased, and then making recommendations for the next suitable product. However, it is best known as recommending the 'Next Best Product' to existing customers.

A related acquisition approach is to target potential customers with similar characteristics through renting direct mail or e-mail lists or advertising online in similar locations. The following recommendations are based on those in van Duyne et al. (2003).


Recency: Low

Frequency: Low

1 = One purchase

2 = Two purchases

Figure 6.13 Example of RF analysis

Source: Patron (2004)

1 Create automatic product relationships [i.e. Next Best Product]. A low-tech approach to this is, for each product, to group together products, previously purchased together. Then for each product rank product by number of times purchased together to find relationships.

2 Cordon off and minimise the 'real estate' devoted to related products. An area of screen should be reserved for 'Next-best product prompts' for up-selling and cross-selling. However, if these can be made part of the current product they may be more effective.

3 Use familiar 'trigger words'. That is, familiar from using other sites such as Amazon. Such phrases include: 'Related products', 'Your recommendations', 'Similar', 'Customers who bought ...', 'Top 3 related products'.

4 Editorialise about related products. That is, within copy about a product.

5 Allow quick purchase of related products.

6 Sell-related product during checkout. And also on post-transaction pages, i.e. after one item has been added to basket or purchased.

Note that techniques do not necessarily require an expensive recommendations engine except for very large sites.

An example of a site that has simple rules to show related products is UK dot-com Firebox ( shown in Figure 6.14.

An example of an e-retailer that uses many of the techniques described in this section is Debenhams (see Case Study 6).

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