Maximizing Incrementality and Minimizing Cannibalization: How AI Builds Confidence and Trust in Category Strategies
In the ever-evolving world of retail, making informed decisions regarding SKU rationalization, new product innovation, and assortment strategies is crucial. One key aspect that continually arises is understanding the impact of cannibalization and the incrementality associated with various assortment strategies. As a category development professional, your ability to confidently and transparently present strategies with low cannibalization rates and high incrementality becomes paramount. Fortunately, with a simple user interface (UI) and powerful AI models, CPG suppliers and retail buyers can now interact with AI in previously impossible ways. These advanced AI-driven retail analytics offer game-changing solutions, empowering professionals to make data-driven decisions easily.
Before we go further, if you want to learn more about the data science of demand transference at HIVERY, download this whitepaper: Unlocking the Secrets of Latent Shoppers: HIVERY's Demand Transfer Model Explained.
Understanding Incrementality and Cannibalization
Incrementality and cannibalization are two vital concepts regarding category performance and assortment strategies. Incrementality refers to the extent to which a new product or assortment strategy drives additional sales and attracts new shoppers without cannibalizing existing sales. On the other hand, cannibalization occurs when a new product or assortment strategy diminishes the sales of existing products within the same category.
Presenting Compelling Selling Stories
To convince retail buyers or merchants and retail partners of the effectiveness of your assortment strategy, it is essential to emphasize low cannibalization rates and high incrementality. A low cannibalization rate demonstrates that your assortment strategy won't jeopardize existing product sales. Simultaneously, highlighting high incrementality indicates that your strategy brings in new customers and generates additional sales.
Leveraging Store-Level Data to Prove Incrementality
Data becomes your greatest ally in supporting your claims. You can delve into store-level data and extract valuable insights with AI and advanced retail analytics. Analyzing shopper behavior, purchase patterns, and spending habits enables you to quantify the incrementality of your assortment strategy. If you can demonstrate that your strategy appeals to high-value shoppers or aligns with the aspirations of the retail partner, you have a compelling selling story that is hard to resist.
The Power of AI-Driven Machine Learning Models
AI is a co-pilot in your strategic decision-making processes, offering unprecedented capabilities. By leveraging AI-driven machine learning models, you can predict the impact of introducing new SKUs or rationalizing existing ones. These models analyze vast amounts of data, uncover hidden correlations, and generate actionable recommendations. With the power of AI at your disposal, you gain the confidence to present strategies that strike the perfect balance between low cannibalization and high incrementality.
Demand transfer in HIVERY Curate
HIVERY Curate's demand transfer feature is powered by a custom machine learning model that assists CPG brands and retailers in comprehending how modifications in their store's product assortment can affect their sales. In simple terms, this Curate uses store-level data analysis techniques and Customer Decision Tree (CDT) frameworks to identify patterns in shoppers' behavior and categorize products based on their competitiveness for sales.
The model predicts the likelihood of a shopper buying a particular product in a store based on the popularity of that product in the category and the shoppers' preferences. It also considers the possibility that a shopper might not buy anything and predicts that some sales may be lost entirely and not transferred to other products.
By understanding the mix of product categories that influence shoppers' behavior at each store, the model helps retailers optimize their product assortments to maximize sales. For example, if a popular product is removed from a store, the model can predict how much demand will transfer to other products in the same category. This allows retailers to make informed decisions about their product assortment and ensure they meet their shoppers' needs and preferences.
As a category development professional, your success lies in your ability to present assortment strategies that build trust and generate results confidently. By harnessing the potential of HIVERY Curate, you can unlock store-level insights and make data-driven decisions that maximize incrementality while minimizing cannibalization. Embrace the power of AI as your co-pilot, and embark on a journey towards propelling your category's growth and success. With compelling selling stories backed by data and the ability to demonstrate the impact of your strategies, you can instill confidence and trust in your category strategies, ensuring sustained success in the dynamic world of retail.