A basket decision tree, also known as BDT, is a shopper basket analytical tool used by Retailers to discover relationships and patterns among their Loyal & Non-Loyal Shoppers.
This analytical technique sheds light on sales variations driven by the buying behavior of customers, helping Retailers understand the reasons for sales variations, whether they are influenced by spending or foot traffic.
🧪 Case studies and example of Basket Decision Tree
For example, let's consider the above basket decision tree analysis. We can observe the following trends:
- Sales value is growing at a rate of +4.28%.
- Loyalty card sales have decreased by -3.03%, while Non-loyalty card sales have increased by +11.79%.
- The increase in Non-Loyalty carded Shoppers' sales is primarily due to a rise in total visits or foot traffic, which has increased by +22.25%. This increase in traffic compensates for a decrease in Basket Value by -8.56%.
- Conversely, Loyalty card Shoppers have experienced slow growth in foot traffic, with an increase of only +4.69%. This, combined with a decrease in basket value by -8.98%, has led to a decline in sales for this shopper group.
- The main root cause of the decline in both shopper groups can be attributed to a decrease in units per basket, indicating that shoppers are purchasing lesser quantities.
In conclusion, we observe a decrease in quantities purchased by both shopper groups. However, the higher foot traffic generated by Non-Loyalty carded Shoppers' supports total sales growth.
Based on these insights, Retailers can optimize their commercial offerings and tailor their marketing strategies accordingly. They can choose to focus on generating foot traffic from Loyalty card Shoppers or aim for a global increase in quantities purchased by all shoppers.
Please note that this is a generalized example, and the specific insights and recommendations would depend on the actual data and context of the analysis.
❓What is used for
Once the Basket decision tree is constructed, Retailers can utilize it for various purposes.
- Retailers can gain insight into the factors driving sales variations, such as spending or traffic between their Loyal & Non-Loyal Shoppers.
- The analysis enables Retailers to focus on specific areas to address sales decline or capitalize on sales growth opportunities.
- Understanding the global trends of the shoppers enables retailers to address the key factors and segment their loyal shoppers based on frequency, basket tier, and ABC segments to discover which segments of shoppers to address.
- By analyzing the behaviors and preferences of loyal Shoppers. Retailers can identify opportunities to strengthen relationships and improve customer satisfaction. They can develop Loyalty Programs, personalized offers, and other initiatives to incentivize repeat purchases and foster long-term loyalty.
- Sharing the insights gained from BDT analysis, Manufacturers can work closely with Retailers to align on assortments, promotions, and pricing strategies. As BDT analysis also provides information regarding Penetration, Avg Price per Unit and flexibility of period selection.
🖥️ Make it happen in Ulys
How to access Basket Decision Tree in Ulys
Step 1: Select the Basket Decision Tree under the Behaviors Menu.
Step 2: Select the Option in the Filter Menu and select the period you want to measure.
And here it is. The information can be viewed in two perspective. First is the global summary and the analysis between Loyal vs Non-Loyal Shopper.