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 quin-
tiles of 20 per cent (10 deciles can also be used for larger databases) 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.
2 Arbitrary divisions of customer database
This approach is also useful since the marketer 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 follows:
Recency:
1 – Over 12 months
2 – Within last 12 months
3 – Within last 6 months
4 – Within last 3 months
5 – Within last 1 month
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 oncer attended 612 months
● Rusty oncer attended 712 but 636 months
● Very rusty oncer attended in 36+ months
Twicers:
● Recent twicer attended 612 months
● Rusty twicer attended 712 but 636 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 charac-
teristics 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 character-
istics 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. (2003):
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 the basket or purchased.
Applying virtual communities and social networks for CRM
We discussed some of the psychological reasons for the popularity of social networks in
Chapter 2 in the section on consumer buyer behaviour and in Chapter 9 we will review
some of the related Web 2.0 marketing techniques that can be used for customer acquisi-
tion. But in this section, we consider why social networks have developed and how they
can be used to develop customer understanding and for relationship building.
The reasons for the popularity of virtual communities today such as the social networks
Facebook, Google+ and LinkedIn can be traced back to the nineteenth century. The
German sociologist Ferdinand Tonnies (1855–1936) made the distinction between public
society and private community (Loomis, 1957). Tonnies employed the terms Gemeinschaft,
meaning ‘community’ (informal, organic or instinctive ties typified by the family or neigh-
bourhood), and Gesellschaft, meaning ‘society’ (formal, impersonal, instrumental, goal-
orientated relations typified by big cities, the state and large organisations). Membership
of Gemeinschaft is self-fulfilling (intrinsic motivation), whereas being a member of a
Gesellschaft is a means to further individual goals (extrinsic motivation).
Marshall McLuhan (1964) posited that ‘cool’ (meaning on-going and shared) and inclu-
sive ‘electric media’ (meaning telephone and television, rather than books) would ‘retribalise’
human society into clusters of affiliation. Nicholas Negroponte (1995) predicted that in the
near future ‘we will socialise in digital neighbourhoods’. Manuel Castells (1996) has devel-
oped the concept of ‘networked individualism’, in which individuals build their networks
online and offline on the basis of values, interests and projects, and believes that ‘our socie-
ties are increasingly structured around the bipolar opposition of the Net and the Self.