The lockdown is over, slowly the shops reopen and activities restart believing that e-commerce will remain a much more used sales channel than in the past. Don’t leave anyone behind. Find out how your customers have behaved online during the lockdown, plan ad hoc marketing campaigns for them and help your business get going faster.

We have already seen the benefits you can get from combining ecommerce and marketing automation. Try to think about the level of customization that your journeys could reach if you had available for each customer the RFM index, which is the result of an algorithm able to group your customers in different clusters according to their purchasing behavior.

RFM and ecommerce

It is widely believed that customers to focus on are the loyal ones, who have bought more recently or spent more. With few resources you keep them on board in a profitable way. Acquiring a new customer costs at least 5 times more than retaining an existing one (source: Harvard Business Review).

However, it might be worth getting a better understanding of the purchasing dynamics of the last few weeks, getting to know new customers or perhaps discovering the value of those that seemed less interesting in the past.

What they have in common, however, is that for each of them your brand should have a different but conscious engagement strategy. And so RFM measurement becomes a valuable support.

It becomes intuitive as talking about RFM that the ecommerce should be looked at with a double valence, because:

• It provides the data for further processing resulting in the synthetic index.
• It uses the result of this processing to segment and create a better customer experience, retain more customers and increase their value.

Why a RFM analysis?

The RFM analysis, in addition to calculating a precise data useful for direct customer management, for example during a phone call with your Customer Care, proves to be a valid segmentation technique of profiles into categories starting from the history of the transactions in a given period of time.

From the combination  of the three variables:

• Recency: when they bought last time.
• Frequency: how often they bought.
• Monetary: how much they spent,

you can identify homogeneous groups of customers and design relevant content that will drive them to an action or purchase.

Or you may decide that under a certain value or for some clusters it is no longer convenient to continue to promote engagement activities for example for customers who have not purchased recently, frequently and whose spending has been low or negligible.

The model is applicable for all brands, however, it is necessary to consider the characteristics of the sector, products, seasonality, etc.

RFM audience segmentation is the key to achieve the level of personalization your customers expect, targeted campaigns instead of initiatives aimed at your entire audience.

You can use the calculated information, for example, to:

• Discover valuable but at risk of churn customers who usually buy frequently and who have not shown up in the last 2 months. Reconnect them with your brand through customer retention initiatives. Send them targeted proposals, special discounts, recommendations and valuable content.
• Identify who bought a lot in 2020 before the lockdown, but not lately, and carry out remarketing campaigns on social media – for example Facebook – or up-selling.
• Get to know the Top Customers who mantained the same status even during the health emergency and differentiate communications to increase loyalty with cross-selling proposals.
• Select new customers who have spent little in quarantine and promote nurturing initiatives to encourage other purchases with discounts and promotions making them more profitable and pushing, why not, even the visit to the store now that it is feasible.
• And so on…

It is immediately apparent that this clustering model is useful in many ways.

If, on one hand, it identifies your customers’ buying habits, on the other hand it defines which of them is more sensitive to your brand and responsive. You can then use the model to increase the value of individuals by switching them from one cluster to another and:

• Understand which types of activities are most appreciated.
• Find out which customers respond best to each of these.

In this way it also becomes a useful decision-making tool in the marketing planning phase because it allows you to direct your resources and invest in targeted initiatives that can ensure a quick return by improving conversion rates and helping to achieve business objectives.

The RFM algorithm of Contactlab

The platform gives you the opportunity to create your targets intuitively and immediately using the RFM index as a condition – and more generally all the standard or customized insights that our Business Intelligence team can build with you quickly and easily. Thanks to our experts you can apply the RFM algorithm to your customer base and define 11 behavioral patterns.

After choosing a scale of votes (1-5) and assigning one vote to each of the three variables Recency, Frequency and Monetary, from the 125 combinations you can identify 11 clusters to associate a set of actions for customer engagement and marketing activities really 1-to-1.

Let’s see some examples together.

CHAMPIONS

They have bought recently, frequently and their level of spending is very high. These are typically loyal customers who must be rewarded and you have to keep the three variables at high levels. You could even rely on them as early adopters of new products or services and certainly rely on them as a channel to promote your offer.

POTENTIAL LOYALISTS

They may be the new lockdown customers: they are recent but have spent a lot several times. Offer them membership cards or loyalty programs and push cross-selling activities with recommendation of other products or services related to what they have already bought.

PROMISING

They’ve been customers for the past few weeks, but they haven’t spent much. Was it really their intention, or did they not have the time or were they not properly solicited? Try to interest them, insist perhaps with free trial programs, check their value to the end and then decide if it’s worth continuing to follow them.

CUSTOMER NEEDING ATTENTION

All values are above average: recency, frequency and even monetary but have not purchased so recently. That it’s because of the medical emergency? Will it just be a temporary condition or is there something else? You could bid on them in time by doing campaigns based on past purchases. You have to do everything you can to reactivate them.

AT RISK

They spent a lot and bought frequently, but… a long time ago. Even before the lockdown, they’d been absent for some time. Their value has always been high and you could count on them, you have to reconnect them. You’ve got to do something, plan personalized emails to the extreme, don’t let them go to the competition: if they don’t buy from you, they’re doing it or they will do it elsewhere.

RECENT CUSTOMERS

During the  health emergencies, consumers have had more time to browse online and look for alternatives to their usual habits. These contacts have purchased recently, so maybe right during lockdown but not frequently. Maybe did they take advantage of the special offer you designed a couple of months ago to new customers for their first purchase? Contact them, follow them in the on-boarding process and gradually build a new relationship that will soon become valuable for both of you.

Now, what have we discovered more from the Contactlab model?

The RFM calculation has to help you develop customized campaigns for each phase of the life cycle of all individual customers – clusters are made from identifiable and actionable profiles – but understanding the transaction data deeply, you can predict the (re)purchase process that becomes the true engine of the future of your brand.

Concluding

RFM shows you a realistic snapshot of how your ecommerce customers behaved during lockdown. If you want to further enhance the analysis, you can also merge the data offline. From your customers’ online and offline purchases, you know habits and preferences and measure their current value. You can recognize buying behavior patterns and answer questions like ‘What are my top customers and which ones I can consider lost?’. It is one of the most powerful segmentation approaches in terms of targeting messages and offers.

Contact us and our team of experts will show you how to make the best use of the segmentation and automation features of the Contactlab Marketing Cloud platform, combine them with your ecommerce and other systems, collect as much information about your contacts as possible, process it and get new insights like the RFM index for 1-to-1 marketing strategies.