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ANALYSIS STEPS
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Understand the profiles and behaviour of your customers
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This requires an examination of your
customers by looking at the information you hold about each individual. You can
also profile at a household or business level by rolling up the data from the
individuals within each. Variables to examine should include lifetime value,
spend patterns, product purchase patterns and behaviour, and attributes.
Personal attributes could include: age, gender, lifestyle, leisure interests,
occupation, income, accommodation type, geo-demographics... Company attributes
could include: number of employees, turnover, type of business, job position,
job responsibility...
These variables need to be understood
both individually and in terms of how they interact - affect and impact - upon
one another. External data can be matched in to further increase your customer
understanding, validate the information you hold and add additional dimensions
to aid your targeting and help you really visualise who buys what, when, why,
how and how often. |
Profile to add clarity and sharpen
customer understanding! |
Segmentation
Once you understand your customer
profiles you can use this and other database information to statistically
segment individuals sharing similar characteristics. There are two main
approaches to segmentation. The first uses techniques such as cluster analysis
to create natural groups by measuring how closely attributes correlate. You can
then examine the attributes for each group and describe them to paint a picture
of your customer segment - average age 50, minimum age 45, maximum age 55, male,
spend average £500 per year, etc.
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The
second approach to segmentation uses predictive modelling (such as regression
analysis or neural networks) or response modelling (such as decision tree
analysis including CHAID). These
techniques can be used to identify which of your customers are most likely to
respond to your offers, which generate higher levels of sales, who are more
likely to result in bad debt, etc. The different types of customer can be
related to recency, frequency and value of purchases, products or service
purchased, payment method, offer, etc., plus their profile data. Clearly the
actual variables used will be specific to your industry sector.
Usually modelling results in scores
being derived - likelihood to buy, likelihood to respond, likelihood to lapse,
likelihood to be your champion, etc. - and these stored against each customer
record on your database. The scores are then used to group and rank individuals
within segments and can also include other predictive factors such as future
profitability, in total or by product. |
Segment customers by many factors
including lifetime value, profitability, recency, frequency and value |
Segmentation, predictive and response
modelling will enable you to make informed marketing decisions to get more from
your promotional spend and help prevent waste - in terms of both money and time
- by not contacting people who are unlikely to respond to your offer. And
you will be able to invest appropriately in marketing to the individuals who are
more likely to make repeat purchases.
Click here for behaviour analysis
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