Case Study

How We Improved Loyalty And Increased Sales For A Beauty And Skin Care Retailer

Brand Background

Our client is a beauty and skin care retailer that offers a line of bath, body and home-ambience products in their more than 90 stores across Canada. Inspired by Italian tradition, their products are formulated using the finest natural ingredients that appeal to both the male and female market.

The Challenge

The company had just recently launched a new loyalty program – a card based points system.

While the new program gave them access to new customer data, no analysis was being completed to gain customer insights or to improve sales. By leveraging the program more effectively, we could better understand their customers and then identify ways to increase engagement and sales.

The Solution

With our expertise in loyalty and data science, we helped the retailer noticeably gain more from their program. We accomplished this through:

● RFM segmentation to identify the most valuable members.

● Top Product Index Analysis to identify the most popular products among the best members.

● Basket Analysis to identify the products most likely to be added to the shopping cart.

Our methodology demonstrated how success could be achieved by providing both marketing and merchandising teams with relevant metrics that were more customer-centric.

Because of our analyses, the team no longer made decisions based on aggregate purchase history and instead made decisions based on customer patterns that drove sales.

The how

The RFM segmentation consisted of scoring every single member of their loyalty program using 3 criteria:

● Recency (period of time since the last transaction)

● Frequency (total number of transactions over the last 18 months)

● Monetary (total spend over the last 18 months). The main objective of the segmentation was to identify who their best members were.

Typically, a very small group of customers generates a significant percentage of sales. By understanding who these best customers are and how they interact with the brand, we could identify where the efforts should be focused. More resources and preferential treatment were allocated to those bringing the most to their business.

Top Product Index Analysis

Once these best members were identified, we leveraged transactional data to further dig into their shopping habits. We found that members across the various RFM segments don’t value the same products.

A top product index was created to identify what products were most valued by members across the different segments and especially best members. By analyzing their patterns, the retailer could encourage these highly valued members to continue interacting with the brand.

This allowed the marketing team to prioritize the relevant product categories that are pertinent to each segment when communicating to customers. For example, feature deals on Face Masks and Skin Care were offered to the best members because of their relevance. It was a success with an increased response rate.

For merchandising, the analysis provided insights to which products were most valued to the best members. The team then ensured those products were always in-stock, visible and easily accessible in-store and online. For loyalty management, the insights determined where to distribute bonus points. And in this case, it went towards products that are important to high value members.

Basket Analysis

A final basket analysis was also completed to help the merchandising team understand what products were mostly purchased together. By analyzing the contents of members’ baskets, we could identify (by percentage probability) which base products and which associated products are likely to be bought together.

This analysis helped the merchandising team identify exciting bundling opportunities.

Results and Insights

The biggest success was a clear understanding of the customers and their purchasing habits. Key insights were uncovered and identified which helped both the marketing and merchandising teams to:

● Enhance product offering in-store and online.

● Understand what products to feature and offers to display and increase relevancy and response.

● Better allocate loyalty budget and resources towards the right customers.

● And better allocate right bonus incentives to encourage more products to be added to the members’ baskets

Additional resources and preferential treatment were allocated to high-value members, those bringing the most to the business.

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