Identify high-value customers who are loyal and buy from you often.
Run an RFM analysis using your CRM’s built-in functionality or a third-party app or plugin.
Many CRM platforms have a standard RFM analysis tool available. If you don’t have access to one, check out third party apps and plugins like Barilliance, Clevertap, Optimove, or Growmatik for WooCommerce.
If you are doing an RFM analysis manually, export your ecommerce platform’s customer data. Export each field as a separate column: Customer name, First order date, Last order date, Total number of orders, and Total customer sales.
Establish an internal RFM score for recency, frequency, and monetary value, using a scale of 1-5 or 1-10.
A high number indicates recent activity, repeat visits, and lots of revenue.
If your annual revenue is influenced by seasonality, you need to take this into account when applying customer segmentation.
Apply the RFM score to your customer data, ranking customers for each of the three columns. Add recency, frequency, and monetary value scores to get a total score for each customer.
Use the RFM scores to create segments that identify each customer’s trends, such as loyal customers who score high in all areas, potential loyalists who have a high spend but not a lot of history, and brand-new users who may need nurturing.
Create high-touch marketing and customer service strategies for highly loyal customers who deserve more personal attention and superior customer support.
Create personalized reactivation marketing campaigns for at-risk customers who scored high on sales and frequency, but scored low on recency.
Nurture new customers who scored low on frequency but high on overall RFM, using marketing techniques like personalized onboarding or special discounts.
Upsell loyal but low-spending customers who scored high on frequency and recency, but low on monetary value, with incentives like free shipping if they order a certain amount.
Create additional incentives and targeted messaging on any additional RFM segments you make, getting as granular as you need to with your RFM segments.
For 3, I see how the segmentation is made is not explained. Strategies might vary based on seasonality, but for a large database, this brekdown is no simple task. So I want to exemplify how I split them based on seasonality for Beauty segment - I take one year (ex: 12 months from last year 1st of April 2021- 1st of April 2022); for recency, I choose the last 30 days (March) and score it 5, then the previous months (Jan+ Feb- I score them 4), then I check for seasonality months and peaks in sales and try to include those months- Nov+ Dec (Black friday and Christmas when most sales are made) in one single category (recency 3 )- here you have one time clients who buy of a higher value only in these months and usually you need to check if they bought previously, if not, treat them as new customers only activated by discounts; then Aug+ Sep +Oct is 2 and 1 is April-July.
@hesh_fekry we can leave it here in the comments, if someone needs it; It’s a step that has to be taken, everyone will segment somehow, yet I wanted to highlight the detail. So I suggest we keep the example in the comment and write in Playbook at 3 only “If your annual revenue is influenced by seasonality, you need to take this into account when applying customer segmentation.” Thank you.