How do consumer decision trees support your pricing and promotion decisions?

Consumer decision trees are invaluable in retail for understanding the purchasing journey. Consider a consumer buying running shoes: Need Recognition: They realize they need new shoes due to wear or new fitness goals. Information Search: They browse reviews, visit brand websites, or ask friends for recommendations. Evaluation of Alternatives: They compare options based on price, brand reputation, features, and design. Purchase Decision: The consumer selects a product. Retailers can influence this with promotions, easy checkout processes, and product availability. Post-Purchase Behavior: They evaluate their satisfaction. Retailers can enhance this with customer service, warranties, and feedback opportunities.

Consumer Decision Trees aren't static entities. They evolve with time, trends, personal experiences, and sometimes even with fleeting moods and sentiments in the market. Understanding and influencing these mental pathways is the holy grail for marketers and product managers. It's about more than just knowing what customers want; it's about predicting what they might want next, and why.

The consumer or shopper decision tree is a fundamental piece of information that can help brands to increase the experience at point of sales, generating higher chances to increase sales and stand out versus the competition. The decision tree is a visualization of all process, criterias and sequence that consumers use when selecting a product ti buy. Its based on market dynamics from every category or industry but most of all, its based on behaviors and thoughts we all have as humans. Variables such as price, brand, flavor, or even color can be attributes used by consumers in the valuable moment of potentially buy your product or the competitor.

Well, I never had a tree but I did observe something that might have given me an eye as to how consumers are influenced by certain products. They looked at some ads and basically made their decision based on not just the look of the products but our pricing and seeing them live.

Entender melhor com dados e analises, com esta a experiencia de consumo no PDV, outra coisa e entender se o PDV esta "P.M" maior ou menor do que deveria ser o plano, assim por final avaliar o mercado.

I have experienced that in case of online marketplaces, data is abundant and therefore designing of accuracte consumer decision trees are smoother. For example, access to customer's purchase history, product reviews, search queries, cart abandonment rates, time of purchase and customer demographics are goldmines for building consumer decision trees. Tracking user behavior on the platform, such as product comparisons and time spent on product pages, provides even greater insights into the decision pattern of customer segments. Integrating data from external sources like social media and market research can offer a more comprehensive view of consumer behavior.

Here's a simpler way to create a consumer decision tree (CDT): Pick a product category (headphones). Start with the need (listen to music). Branch out key choices (wired vs. wireless, over-ear vs. on-ear). Keep branching till final options (brand & model). Focus on in-store factors (placement, branding, info). Keep it simple: Start basic, focus on main factors.Clarity is key: Use easy-to-understand words.Get feedback: See if it reflects real buying behavior.

Generally from my point of view, consumer decision trees has to be a study, with a methodology behind that can support it and make the results strong enough to be used along the time. The study can be conducted simply by asking potential consumers to segregate products from a specific category the same way as they would go for shopping. Also, new technologies has been used such as eye tracking, on-time research, etc that can help to reduce cost and get faster results.

Creating effective consumer decision trees begins with rigorous market research. It’s essential to gather and analyze data that reflects real consumer behavior, whether through surveys, purchase history, or other analytics. Collaboration across functions—particularly with marketing, sales, and data science teams—ensures that the decision trees are built on a foundation of comprehensive insights. Validating these trees through consumer feedback further sharpens our understanding and guides us in making more informed decisions.

Here's how to use consumer decision trees (CDTs) for pricing: Identify Price Sensitivity: By analyzing the CDT, see where price becomes a major decision point. This might be at the brand or feature level. Segment by Price: The CDT can help identify customer segments who prioritize price vs. other factors like brand or features. This helps with targeted pricing strategies. Price Competitors Strategically: See where your product sits within the CDT compared to competitors. This helps you decide on premium pricing, value pricing, or matching competitor prices. Test Price Changes: Use the CDT to predict how consumers might react to price adjustments at different decision points.

Consumer decision tree can be critical to understand price elasticity and on what level price become critical as part of the process to select a product. For example, As you know that brand is the number one factor, then flavor and size are the following attributes you can use this information to them understand the price elasticity of your product against your competitors.

In my experience, Consumer Decision Trees (CDTs) are essential for identifying cross-elasticities and setting optimal price gaps between similar products. Understanding how consumers perceive product relationships allows retailers to segment their offerings effectively and avoid pricing mismatches. Two key elements are crucial: - Robust and normalized product master data – Without clean, structured data, it’s impossible to map meaningful product relationships. - A tool that enables you to easily define and adjust price relationships between similar products.

Decision trees provide predictive capabilities that help us anticipate how promotional offers and pricing adjustments will impact consumer behavior. This predictive power allows us to optimize our promotional campaigns, ensuring they resonate with customers and drive desired outcomes. By leveraging consumer decision trees, we not only enhance the precision of our pricing decisions but also align our promotions more closely with consumer expectations. This strategic alignment not only boosts sales but also enhances overall customer satisfaction by delivering value that resonates deeply with our target audience.

When it comes to pricing, consumer decision trees serve as invaluable tools for identifying price sensitivity at various decision points. By understanding where price plays a crucial role in the decision-making process, we can develop pricing strategies that align with consumer priorities and optimize revenue. This approach allows us to structure our pricing tiers more effectively, focusing on the attributes that matter most to our target audience.

For small retail formats, grasping customer decision trees is crucial. This knowledge enables businesses to maximize consumer demand coverage with a limited assortment or effectively manage it during promotional periods. However, many retailers today struggle to measure the impact of cannibalized sales within their respective categories.

In my experience, consumer decision trees are just one tool that can be used but it's more often that not over analyzed when deciding promotional ability. Often times CMs can get lost in the idea of building promotion calendars, analyzing Nielsen data or focusing heavily on loyalty card or T-Log information and forget about the core basics of promoting. Seasonality, Competition, New Item Initiatives or simple go to market or promotion strategy are key factors that affect consumer decisions. For example, If you promote an item 22 times a year, it will fall high on the consumer decision tree. If you don't promote an item often but you can build a basket with it, it should be considered for promotions. Don't let paralysis by analysis cost you.

Promotion strategies also benefit significantly from consumer decision trees. These trees enable us to identify which product attributes are most likely to influence consumer choices during promotional campaigns. By targeting promotions at the right decision nodes—such as offering discounts on popular brands or sizes—we can drive higher engagement and conversion rates. The ability to adjust promotional strategies based on real-time consumer behavior further enhances the effectiveness of our marketing efforts.

Updating consumer decision trees is critical to maintaining their relevance in a dynamic market environment. Regularly refreshing these trees with the latest data allows to stay aligned with evolving consumer preferences and market trends. Leveraging advanced analytics and real-time data enables to quickly adapt to changes and continuously refine strategic approach.

For this one I would say this one that they would just take the items and display them with the prices so customers know how much to spend on the items that are displayed and make their decisions accordingly.