case study
Customer segmentation for a pharmacy chain
The aim of the project was to increase the frequency of purchase and total spend of the pharmacy's existing customers by using advanced segmentation and targeted marketing
Project summary
Our client is a large pharmacy chain with an annual turnover of over USD 1 billion.
The aim of the project was to increase the frequency of purchase and total spend of the pharmacy's existing customers by using advanced segmentation and targeted marketing.
During the project, we identified customers with the highest estimated response rates (segmentation stage) and developed tailored offers to improve monetization of these customers (monetization stage):
Our client is a large pharmacy chain with an annual turnover of over USD 1 billion.
The aim of the project was to increase the frequency of purchase and total spend of the pharmacy's existing customers by using advanced segmentation and targeted marketing.
During the project, we identified customers with the highest estimated response rates (segmentation stage) and developed tailored offers to improve monetization of these customers (monetization stage):
Stage 1. Customer segmentation
We used two levels of customer segmentation: RFM and product-based segmentation to identify the groups of customers with highest estimated response rate:
We used two levels of customer segmentation: RFM and product-based segmentation to identify the groups of customers with highest estimated response rate:
At the RFM stage, we segmented the audience by spend level, frequency of purchase and recency of last purchase:
The top 20 to 50% of customers by spend, with the last purchase less than three months ago, with no more than one purchase per month, tend to perform well as part of a targeted marketing effort and are therefore selected as the result of the first stage of segmentation:
Having identified the preliminary segment for target marketing efforts (~16% of the customer base), we dug deeper to understand the product preferences of these customers.
By identifying customers with a bias towards a particular product category, we can tailor the marketing communications and further increase the response rate.
If a particular product category represents more than 20% of a customer's spend, we conclude that there is a bias towards that product category for that customer:
By identifying customers with a bias towards a particular product category, we can tailor the marketing communications and further increase the response rate.
If a particular product category represents more than 20% of a customer's spend, we conclude that there is a bias towards that product category for that customer:
We can get better response rates by offering a temporary promo code on the lung medicine to customer #3 and a promo code on the eye drops to customer #5.
We applied two filters in sequence: first an RFM filter and then an additional filter on customers active in certain product categories. This gave us the segments that were used for the targeted marketing campaigns (stage 2).
Stage 2. Customer monetization
We’ve launched target marketing campaigns for 16 microsegments, which were derived at the previous stage. The test campaigns were tailored to the specifics of each segment to maximize the response rate and keep the return on marketing investments at a desirable level.
We applied two filters in sequence: first an RFM filter and then an additional filter on customers active in certain product categories. This gave us the segments that were used for the targeted marketing campaigns (stage 2).
Stage 2. Customer monetization
We’ve launched target marketing campaigns for 16 microsegments, which were derived at the previous stage. The test campaigns were tailored to the specifics of each segment to maximize the response rate and keep the return on marketing investments at a desirable level.
Our algorithms analyze the results of the test campaigns to generate new ones: some of them to scale and others for further testing. The Yalladata engine uses an iterative test & learn process to provide the client with the best parameters for CRM marketing campaigns.
Our team of consultants are ready to help your business monetise your existing customer base, using a data-driven approach to increase LTV, frequency of purchase, average bill and other key customer metrics.
Our team of consultants are ready to help your business monetise your existing customer base, using a data-driven approach to increase LTV, frequency of purchase, average bill and other key customer metrics.
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