Customer Decision Tree

🎓 Definition

Often called CDT (short for Customer Decision Tree), the Customer Decision Tree is the visual translation, in products groups and segments, of the successive logical questions a shopper is asking herself when buying a product in a category.

For the Retailer, the CDT helps define the Category and Segments as well as the product grouping and their adjacencies.

It is not only a tool for understanding shopper buying motives but also a strategical tool that serves as a guiding line for:

• Visual representations of shelf space – planograms, and assigning product positioning on shelves
• Design of efficient store layouts
• Assortment decisions and pricing and promotion strategies.

For the Customers, the CDT helps them to easily read the shelves, measure the choice offered and easily find the products they want.

🧪 Example of Customer Decision Tree

One possible captured customer decision path for the Baby Diaper Shopper from the top down goes like this:

“Diaper for Baby” → Type: Pants → Size: Small → Brand: Mamypoko  → Final Product: X 40 Pcs.

The basic elements of the CDT are simple. We have:

1. A Shopper Needs (“Diaper for Baby”)
2. An appropriate Offer that meets the expectations (“Newborn”, “Small”, “Medium”, “Large”)
3. A Place in the physical store where Retailers provide the solution (“shelf space”).

❓Key benefits of the Customer Decision Tree

Effective Merchandising:  CDT supports Retailers in creating visually appealing and customer-friendly displays. Aligning product displays with customer decision-making patterns can optimize visual merchandising, making it easier for shoppers to navigate the store and discover new products.

Price Optimization: Utilizing CDT, Retailers can determine the ideal pricing for products by gauging the sensitivity of Shoppers to price fluctuations and understanding their comparison metrics against competitors.

For instance, if Shoppers prioritize brand reputation over cost, Retailers can justify a higher price for certain products, setting them apart from more affordable options. Conversely, if Shopper prioritizes cost over brand reputation, introducing discounts or bundled offers might be more appealing.

More effective marketing messages: It can also help design effective promotions for a product by understanding how Shoppers respond to different types of incentives and messages.

For example, if Shoppers are loyal to a specific brand, Retailers can reward them with loyalty programs, coupons, or free samples.

Improved Shelf Layout:  It guides Retailers to place products in a manner that ensures that top-selling or complementary items are conveniently located together, improving overall customer satisfaction.

Market Responsiveness: As shopping behavior evolves, Retailers can use the CDT framework to identify emerging trends and make informed decisions about product selection and placement.

How is the Customer Decision Tree developed and what methodologies are used to gather the data necessary to construct it?

The Customer Decision Tree (CDT) is typically developed through a combination of market research, consumer behavior analysis, and data analytics. Retailers often employ various methodologies such as surveys, focus groups, and observational studies to gather insights into shopper preferences, buying motives, and decision-making patterns. Advanced analytics and data mining techniques may also be used to analyze purchase histories, loyalty card data, and other relevant datasets to identify trends and patterns. The collected data is then organized and visualized into a hierarchical structure that represents the successive logical questions a shopper asks when buying a product in a specific category.

Are there any specific industries or retail segments where the Customer Decision Tree is more effective or commonly used?

While the Customer Decision Tree can be applied across various industries and retail segments, it is particularly effective in industries with a wide range of product choices and complex decision-making processes, such as consumer electronics, apparel, and grocery. It is commonly used in retail environments where visual merchandising and shelf space optimization play a crucial role in influencing consumer purchasing decisions. However, the applicability and effectiveness of the CDT may vary depending on the specific characteristics of the industry, the target audience, and the complexity of the product offerings.

How often should Retailers update their Customer Decision Tree to reflect changes in customer behavior and market trends?

Retailers should aim to update their Customer Decision Tree regularly to stay aligned with evolving customer behavior, market trends, and competitive landscape. While there is no one-size-fits-all timeframe for updates, it is generally recommended to review and revise the CDT at least annually or whenever there is a significant shift in consumer preferences, buying habits, or market dynamics. This ensures that the CDT remains relevant and effective in guiding merchandising, pricing, and promotional strategies to meet the changing needs and expectations of shoppers.