We are not all the same and neither are our customers! So it should come as no great surprise that customer segmentation is key to ensuring you market successfully to your customers; right message at the right time.
At the heart of direct marketing is data and using this valuable resource to ensure you send appropriate propositions to the right customers segment.
Many organisations think that they need to buy in external lifestyle or business demographics in order to better understand their customers. Whilst there may be benefits from these data resources, the value of in-house data should not be overlooked.
The most common areas around which to segment customers are:
- Geography: Region, City or Postcode
- Product: Category or even specific product
- Response: (which campaigns have they responded to)
- Source: (how their details were acquired)
These do not provide much insight into what makes a customer different when compared with any other customer in for instance a similar geography. But when combined with a measure of customer performance they become more revealing.
The classic method for measuring customer performance is to score each customer based on:
- How recent was the last order placed by this customer – Recency
- How many times has a customer ordered on average within a predetermined period – Frequency
- How much they spend or what margin they contribute when they order –Value
How to score customers
For each customer run enquiries on your database to identify:
- Date of first and last order
- Number of orders
- Spend value (this can be gross margin, order value, whatever appropriate measure of value of customer expenditure)
From these calculate:
- RECENCY: Time since last order is current date minus date of last order (Days)
- FREQUENCY: Duration of custom: current date minus date of first order (Days) divided by Number of orders
- VALUE: In some cases you may wish to calculate a total value (i.e. sum the value measures) in others an average is adequate, calculated by dividing the total value measure by the number of orders
These give you absolute values for each customer. The next stage is to categorise these into segments by determining relevant groups of values and then apply a corresponding score to each customer.
Taking each element in turn, the objective is to achieve categories that have broadly speaking similar numbers of customers in each.
The number of categories will vary depending on your business. For example a mail order clothing company:
- 1 to 30 days – score 5
- 31 to 60 days – score 4
- 61 to 90 days – score 3
- 91 to 180 days – score 2
- 181 to 365 days – score 1
- Greater than 365 days – score 0
- At least once per month – score 5
- At least once in three months – score 4
- At least once in six months – score 3
- At least once in greater than six months – score 1
- Once time only purchase – score 0
VALUE: Clearly this depends on the business in question. If Gross margin is not readily identifiable, then the sales value could be used, but consideration should be given to the distribution of margin across the products your organisation sells.
Assuming similar ranges of scores are applied for Value then your best customers would be those with scores of 555 (highest worth) down to 000 (lowest worth).
Remember this is not a discrete value of say five hundred and fifty five (highest worth), but an identifier composed of 5 elements: 5-5-5.
This therefore provides an identifier for each customer which can be used as selection criteria for campaigns e.g. You want to communicate with all reasonably recent high spenders who are not the most frequent customers with the objective of increasing the number of purchases they make in a period; so you might select customers with an RFV quotient of 414, 425, 414 and 525.
An on-going scoring process enables you to track purchasing and the effect of campaigns on customer behaviours. So in our example, how many has our campaign converted into 525s and 555s?
This comparison will also provide direction for future campaigns to address churn, unprofitable customers and up-selling.
Other attributes can be score in a similar way: how long the customer has been a customer (longevity), number of complaints, cancellations or returns, referrals to friends and relations, abandoned baskets, quotations not taken up, etc., all adding to the behavioural profile of the customers.
Marketing systems can accommodate automated workflows for communications, reporting or alerts when customers significantly change score, to drive relationship marketing strategies.
For more guidance on RFV modelling or help with building your own models contact Michael Collins at email@example.com.
©Michael Collins 2007-2018