The Challenge
A manufacturer client wanted to better understand purchase influences for their target shoppers in the hard-to-track convenience store channel, as well as intensify their focus on thought leadership.
The Method
The manufacturer had recently secured the Category Captaincy. They wanted to maintain their reputation as a thought leader by gaining deeper insights into the core drivers impacting decision-making at shelf for a group of products shelved together as a “category” in the convenience channel. They decided to partner with InContext Solutions to utilize our virtual Shopper Decision Tree (SDT) solution which could then help narrow down specific concepts to further test.
This client chose to use a virtual Shopper Decision Tree in order to save time and money while allowing them to learn both the attitudinal and behavioral motivations behind the decisions of their target shoppers, at the point of purchase. They were able to learn product substitutability insights and walk rate data for a convenience channel that is traditionally hard to track, while determining what influenced shopper purchases and why they made the choice they did.
Key Questions:
- What category structure makes sense from the shopper’s point of view?
- How and what tradeoffs do consumers make when shopping within a specific category or location of the store?
- What opportunities exist to make the category easier to shop?
- What potential changes to assortment should be explored?
Respondents were giving pre- and post-shopping questionnaires, with a virtual shopping exercise in-between. During the shopping exercise, respondents were asked to make purchase decisions based on their first choice, and what they would buy instead if that product was not available, and finally when they would walk away without making a purchase.
The Results
The manufacturer was able to learn what factors differentiated products from one another, what defined “category” and why, as well as what types of SKUs were most important to have on shelf. Utilizing virtual decision tree solutions allowed them to extract these deep insights from a traditionally hard-to-track channel otherwise not possible with traditional decision tree methods. They ultimately were able to distinguish opportunities to test expanded assortment and expand SKU offerings, saving them time and money on testing down the road.
