Introduction to the RFV Model Recency – Frequency – Value

So you’ve put together your first email newsletter, perfected the message content, and triple-checked your subject line. You hit Send and the email goes off to the entire subscriber base – it’s definitely going to lead to a 95% open rate, right?

If you’re like most marketers, you’ll quickly realise that the average open rate for most email newsletters is usually less than 25% and that’s okay. The reality is, whether you’re sending an email newsletter, or making phone calls, or posting a link on Facebook, only a small subset of your audience will engage at any given point.

The lesson that many of us learn is not to tailor our marketing around reaching 100% of a hypothetical potential audience, but to target the individuals who actually engage with your business. It’s delivering the right message, at the right time, to the right person.

But how do you identify these individuals who interact with your business? Is there a way to quantitatively measure this, beyond just remembering the names of your favourite customers?

One key strategy is the use of RFV in analysing data and sending more focused communications.

Recency
When the customer last placed an order

Frequency
How many orders the customer placed over a period of time

Value
How much the customer spent

These three variables can tell us a great deal about the customer and their buying habits. It relies on the fundamental premise that someone who recently bought something, who shops often and who spent a lot is more likely to respond to your next campaign than someone who bought something a long time ago, shopped infrequently and spent next to nothing.

Coupled with customer lifetime value analysis, RFV enables companies to significantly increase response rates by sending offers to focused subsets that are more likely to purchase.

Email marketing

In email marketing, we haven’t been as focused as we should because of two common misconceptions:

1: Email is cheap, so there’s no downside to blasting the message to everyone.

There’s actually a pretty big downside: list fatigue. Over time, as recipients receive multiple messages they don’t find relevant, they become less and less likely to respond, and more and more likely to report your messages as spam.

Lists do have costs associated for both storage and sending; which is driven by the size of the list. This in turn, lowers your sender-reputation score, the most important factor ISPs use in determining whether to filter your message to the junk folder instead of the inbox. The end result? A damaged brand (“Just what I need, another email from that company”) and deliverability issues that can prevent engaged recipients from receiving your messages.

2: You get a bigger response rate if you send to a bigger list.

It seems logical, but it is just not true. Sending to an additional number of warm bodies does not necessarily generate a higher response rate. In fact, it can actually produce higher spam complaints. You are much more likely to increase response rates when you send relevant, targeted messages to smaller subsets of your lists.

How to Apply RFV Segmentation to Email Lists

Email marketing allows you to measure customer interaction and engagement in many more ways than direct mailing could ever do. Depending on your email database and marketing strategy, other metrics can be used in place of the traditional RFV parameters, giving you many different ways to perform RFV analysis:

Once you’ve decided which metrics make the most sense for your business, you’ll need to tie your email database to the system that contains purchase or conversion history, such as your CRM or Web-analytics tool.

Now you are ready to perform RFV segmentation

Because RFV has been a direct-marketing staple for so many years, many popular data-mining and statistical analysis tools generate ready-made RFV-classification reports. However, if you don’t use statistical analysis tools, fear not. Unlike predictive-modelling techniques, RFV is based on past customer results and does not require heavy data analysis.

One way to perform RFV segmentation is to simply sort your list for recency in order of highest to lowest. You then divide the list into five equal segments, giving the top 20 percent a recency score of 5, the next 20 percent a score of 4 and so on. Each recency segment is then sorted for frequency and divided into five equal segments, resulting in 25 recency plus frequency segments. Each of these segments is then sorted for value and divided into five equal segments, leaving you with 125 segments that have RFV scores ranging from 555 to 111. This is your RFV index.

Of course, depending on the size of your database, you could divide it into deciles or other n-tiles, instead of quintiles. Or if you are very familiar with your database, you could simply use intuitive groupings, such as “purchased in last month, last three months, last six months or greater than six months,” as the basis for your RFV classifications. As you can see, RFV is very adaptable, and with some experimentation, you will be able to obtain dramatic lift gains.

Sending Different Types of Email Campaigns to Different RFV Segments

Once you’ve assigned all of the records in your database a specific RFV classification, you run a test campaign, typically on 10 percent of your list, to determine which RFV groups to mail to.

In traditional direct mail, you perform a break-even analysis to determine which mailing recipients are profitable. You look at the test-group response rate for each RFV cell, and then stop mailing to cells whose response rates are less than the rate required to break even on mailing costs.

In email, however, the goal is not to simply stop mailing to your weakest segments, it’s to find the right tactics that resonate with and re-engage lower-scoring recipients, too. So you can test different types of messages to see which RFV segments respond best to which types of campaigns, and stop sending those particular campaign types to segments that fail the breakeven test.

Or, instead of using the breakeven metric, you could simply compare the conversion rates of different RFV segments and send future campaigns only to the groups who convert the best.

Here are some ideas for the types of campaigns that may work best with different RFV segments:

• High Recency / High Frequency / High Value:

Reward your most loyal customers and prospects with exclusive email privileges that make them feel special. For example, some retailers automatically offer free shipping and other perks to their best online customers.

• High Recency / Low Frequency / Low Value:

This segment includes your newest customers or subscribers. Give them a good first impression of your company with welcome offers, product-usage tips or other information that newbies would find helpful.

• Low Recency / Low Frequency / Low Value:

As in direct marketing, your least-engaged recipients simply may not be worth mailing to. But in email marketing, they may be great candidates for a nurture campaign, developing them to become your next wave of loyal and valuable customers.

Use RFV to Send Better Messages to Your Best Email Recipients

RFV segmentation is a relatively simple way to break down your email list based on recipients’ past behaviours. Use this strategic approach to segmentation to increase response rates and convert more prospects into happy customers.

To find out more about segmenting your data or communication method once your data has been analysed, please feel free to contact our team at WeClick Media:

Call on 01202 612430 or email us at hello@weclickmedia.com

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