Data Driven Marketing–
how will you measure your clients’ ‘value’?
Does your business, company or organization have customers?
The answer is probably yes, so this article can be of help to you.
One of the techniques for measuring customer value is called RFM.
It’s simple to do and can help you increase sales and profits, understand which
customers are more ‘worthy’, and address existing customers according to categories
of ‘worthy’ the value.
Eyal Marcus, CEO of Splash – Smart Digital Marketing
Every business has customers. Whether it’s an online store that carries out thousands or
millions of transactions every day or month, whether it’s a huge Fuel company that customers
buy gas from stations and snacks from the affiliate stores, a small grocery store, or a consultant
that accompanies other companies – they all have customers.
One way to measure customer value is called RFM – which enables you to segment your customers by what the last purchase date was, how many times did they purchase from you in a time frame you specify (say, one year back) and how much money did they spend on purchases from you.
In the world of Data-Driven Marketing – this model allows every Marketing Manager to conduct a simple and cost-effective analysis of customer worth.
By the way, the model is true not only for measuring monetary value, but can be applied in a variety of areas. Visits to your website, contributions to your foundation, use of your App and more.
If we use the model in order to measure visits to tour website, for instance, we’ll check the last entry of the client, how many times did they enter, how much time did the spend in the website in total – where our goal would be to increase the frequency of website entries and time spent in the website.
R - Recency
How long it has been since the last customer interaction.
How much time has passed since last purchase or visit to the website or App.
Naturally, the shorter the time between visits is – the more likely the
interaction with the customer is to bear fruit.
F - Frequency
How often do customers interact with you within a specified time frame = how many times have
they made a purchase in the past year, how many donors have contributed in the past decade,
how many times have users entered the App in the past month, and so on.
Naturally – the more frequent the interaction is, the more the customer will be loyal.
M - Monetary
This measurement measures how much money in total the customer spent in a specified time
frame. It is also important to measure the average transaction – how much money the customer
has spent in total, the frequency (that is, Monetary divided by Frequency) so that you can
measure the worth by average amounts and not just total amounts.
And now- How can you do your own RFM analysis, even if you don’t have any
Collect all relevant customer information. Quantity of purchases, spending amounts, spending
times. You can consolidate all the information in Excel for example.
For each customer – calculate the time since the last purchase (Recency). The calculation can be
in hours, days, weeks or months. Also, calculate the frequency of purchases in the specified
time frame (Frequency – it is customary to do the calculation for the last year, but this depends
on the business). And, of course, add a column of total amount spent in the specified time
frame, and average amount spent (Monetary).
Group the customers according to the variable values in the table:
Now each customer can give a score according to their respective group, according to the
business rules you have defined, for example: A customer who purchased two months ago will
receive “1” for the Recency. A customer who has purchased 17 times in the past year will receive
a “2” value in Frequency. And a customer who spent 5500 dollars in the past year will receive a
“2” value under Monetary.
The score the customer will receive will be – “1-2-2”. That is, a value that is comprised of all
three parts (between 1-1-1 to 4-4-4)
Or we can add all the “scores” and get a score of between 3 (1 + 1 + 1) and 12 (4 + 4 + 4).
Or we might decide that the value of the time of last purchase time is worth more than the rest –
and so we will multiply it by 3.
There are many variations in determining the value and worth of customers, and the numbers
are only supposed to express the division of existing customers into different categories – from
most worthwhile to least worthwhile.
We did the division, so what did we end up with? We now have all our customers divided by
important parameters and. in fact, we have created a customer segmentation. And now there
are many options:
We can dedicate different messages to different customers. You’ve purchased 17 times in the
last year? You’re a loyal customer, we will prepare a dedicated message for you. We might also
ask you to recommend us to you friends, as you are certainly true to the brand. You haven’t
purchased in two years – maybe we’ll create a special message and try to get you back with
a big discount.
You’ve spent a lot of money on purchase this past year? We’ll write a thank you message and
offer you a free bottle of wine.
We can create various mailing lists – according to customer segmentation.
We can also offer various discounts – for different customers, according to loyalty rankings,
amounts spent and amount of purchases.
We can create different campaigns on Facebook, Google or LinkedIn – with targeted messages
according to customer group.
We can also establish different customer clubs – according to the level of engagement
and expenditure of the customer.
The purpose of the whole model is to give a value to each customer, and to divide all our
customers into different groups. Different groups that are divided by worth for us,
will enable us a greater ROI from each group.
A note in conclusion – as with the application of other models in the world if Data Driven
Marketing, here too, it is important to constantly check whether the business rules that you’ve
defined are suitable. A / B testing of the messages according to the model and testing the
improvement in expenditure will help you understand what works more and what works less.
It is important to update and change the constancy if it isn’t proving itself.