keter can set thresholds of value relevant to their understanding of their customers.
For example, RFM analysis can be applied for targeting using email according to how a
customer interacts with an e-commerce site. Values could be assigned to each customer as
Note here boundaries are arbitrary in order to place an equal number into each group
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Chapter 9 Customer relationship management
Frequency:
1 – More than once every 6 months
2 – Every 6 months
3 – Every 3 months
4 – Every 2 months
5 – Monthly
Monetary value:
1 – Less than £10
2 – £ 10– £50
3 – £ 50– £100
4 – £ 100– £200
5 – More than £200
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)
●
Recent oncers
attended <12 months
●
Rusty oncers
attended >12, <36 months
●
Very rusty oncers
attended 36+ months
Twicers:
●
Recent twicer
attended <12 months
●
Rusty twicer
attended >12, <36 months
●
Very rusty twicer
attended in 36+ months
2+ subscribers:
●
Current subscribers
booked 2+ events in current season
●
Recent
booked 2+ last season
●
Very rusty
booked 2+ more than a season ago
Product recommendations and propensity modelling
Propensity modelling
is one name given to the approach of evaluating customer character-
istics and behaviour, in particular previous products or services purchased, and then making
recommendations for the next suitable product. However, it is best known as ‘recommend-
ing the “ next- best product” to existing customers’.
A related acquisition approach is to target potential customers with similar characteristics
through renting direct mail or email lists or advertising online in similar locations.
The following recommendations are based on those in van Duyne et al. (2002):
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’. This 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, i.e. within copy about a product.
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