Recency, Frequency Value scoring

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)
  • Gender

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:

RECENCY:

  • 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

FREQUENCY:

  • 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.

RFV0

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?
RFV1

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 mc@dmcounsel.co.uk.

©Michael Collins 2007-2018

 

 

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Travel companies are lucky….

Travel businesses are luckier than many other sectors. They have data on their customers and their customers’ enquiries and purchases and so can derive tremendous benefits from analysing these vast amounts of data, uncovering hidden nuggets of opportunity among their data, once they know how to go through it systematically and leverage the effect on profitability. Analytics software is useful, but some concept of what should be analysed is also of value.

We will often create a RFV model for tour operators or travel agents, combining Recency (of last booking), Frequency (regularity of bookings) and Value (either total spend or average booking value, for example) to create a score for each customer. We have found that adding additional elements expands the score from the 3-letter acronym to a multi-letter acronym such as RFVCIA where you add on the instances for example of Complaints, Introductions to friends or relations and Abandoned on-line baskets committed by the customer.

This score then becomes a comparator between customers, a selection criterion for campaign or proposition and changes in the individual’s score over time become a valid measure of success of the relationship.

Applying demographic, lifestyle and psychographic profiling provides an even more granular segmentation and the ability to apply the concept to prospects, with the benefit of making relevant offers to convert them to customers.

This leads us no to the second most frequent model we create: the market basket model. Knowing what destinations or types of travel different customers prefer is fundamental in programme planning, product development and in driving cross-selling or up-selling opportunities. Being able to compose and analyse the combination of travel products that customers buy will enable the travel marketer not only to determine how best to develop the business with those customers, but, by extrapolating this information, estimate the buying potential amongst the remainder of the customer base.

The often-cited (and some say apocryphal) “beer and nappies” story is an illustration of what can be achieved. Those that don’t know the story can read it at www.dmcounsel.co.uk.

This type of analysis is certainly not the exclusive domain of supermarkets or Amazon.com (we’ve all encountered their ‘people who have read this book have also bought this other book’).

If you can identify a customer as having purchased a product then market basket analysis such as was applied here can deliver cross-sale opportunities by making the right proposition in communications, positioning complementary products together on the shop floor or on the website or catalogue page or driving the pitch made by a telephone sales agent.

Such models can be created without investment in analytical software and can be applied in rules-based workflow and business process, with the dynamics regularly reviewed and the model(s) enhanced.

The extended 3-letter acronym to drive retail direct sales

Analytics software is useful, but some concept of what should be analysed is also of value. Retailers will often create a RFV model, combining Recency (of last purchase), Frequency (regularity of purchase) and Value (either total spend or average order value, for example) to create a score for each customer.

We have found that adding additional elements expands the score from the 3-letter acronym to a multi-letter acronym such as RFVRCA where you add on the instances of Returns, Complaints and Abandoned on-line baskets committed by the customer.

This score then becomes a comparitor between customers, a selection criterion for campaign or proposition and changes in the individual’s score over time become a valid measure of success of the relationship.

Applying demographic, lifestyle and psychographic profiling provides an even more granular segmentation and the ability to apply the concept to prospects, with the benefit of making relevant offers to convert them to customers.

Such models can be created without investment in analytical software and can be applied in rules-based workflow and business process, with the dynamics regularly reviewed and the model(s) enhanced.

The Power Of an MBA (Market Basket Analysis)

Businesses can find tremendous benefits from analysing the vast amounts of data they collect finding hidden nuggets of information among their data, once they know how to go through it systematically and leverage the effect on profitability.

In practice, all too often marketers are concerned with using data to drive promotion, but true insight into customers has impact on all the elements of the marketing mix.

The knowledge to be derived out of customer data can be used outside the selling or marketing communications environment. Knowing what products different types of customers prefer is fundamental in planning range, in stock control and in driving cross-selling or up-selling opportunities. Being able to compose and analyse the combination of products that customers buy, either at one visit or over time, will enable the marketer not only to determine how best to develop the business with those customers, but, by extrapolating this information, estimate the buying potential amongst the remainder of the customer base.

The often-cited (and some say apocryphal) “beer and nappies” story is an illustration of what can be achieved. Those that don’t know the story can read it at www.dmcounsel.co.uk. A notable example of this concept took place in a Spanish airport duty free shop. Analysis of the EPOS data showed a significant trend of purchases that comprised solely either brandy and cigars or whisky and cigarettes. When the airport data was matched by flight number (remember, each duty free sale has the passenger’s flight number on the transaction record) it became apparent that the first transaction type related to passengers en route to Germany and the second type related to passengers with the UK as their destination. What was also noted was that these purchases were all made within 10-15 minutes of the scheduled departure time for the respective flights. This meant that people were passing through the shop quickly at the last moment, just picking up the two most important items on their shopping list.

The management therefore put sales points for the respective combinations of products actually in the gate area for the German and UK flights, thus generating incremental sales amongst the passengers who really felt they had no time to make a purchase at the shop.

This type of analysis is certainly not the exclusive domain of duty-free shops, supermarkets or Amazon.com (we’ve all encountered their ‘people who have read this book have also bought this other book’).

If a company can identify a customer as having purchased a product then market basket analysis such as was applied here can deliver cross-sale opportunities by making the right proposition in communications, positioning complementary products together on the shop floor or on the website or catalogue page or driving the pitch made by a telephone sales agent.

You can use such techniques to determine the real cost of being out of stock of key items and the implications on supply chain. But, by combining market basket analysis with customer profiling, you unleash powerful techniques that can generate significant increases in sales.