What is Assortment Optimization?
Assortment optimization involves selecting the ideal mix of products to stock on your shelves. It necessitates a deep understanding of the retailer's processes and objectives and the available data to craft a compelling, data-driven assortment strategy and planograms.
However, aligning assortment merchandising strategies with consumer purchasing behavior and adapting to changes promptly has been the holy grail of the category management industry for years. Traditional merchandising solutions have remained relatively static since the 1980s, adding pressure to existing systems and human decision-making processes. Current "First Generation" (FirstGen) solutions rely on outdated industry norms and human assumptions about what "can" and "cannot" be done. They also struggle to manage ever-expanding datasets and merchandising rules, making it exceedingly difficult to translate external category trends and insights into actionable, value-added business strategies. As a result, assortment trend analysis, category assessment, assortment optimization, planogram development, and enhancing the shopper experience at retail stores become nearly impossible tasks. This is why most retailers and CPG (Consumer Packaged Goods) companies focus on an "averages-of-averages" approach, crafting assortment plans at the cluster level.
In contrast, "Next Generation" (NextGen) merchandising solutions aim to enhance category management by leveraging vast datasets and sophisticated machine learning algorithms. These solutions integrate tasks like assortment analysis, category assessment, assortment optimization, and planogram development, allowing category managers to focus on their strengths: strategic, rapid, and transparent decision-making in assortment planning with retailers.
These NextGen solutions augment assortment strategies and market actions with unprecedented transparency. The industry is transitioning from an era focused on "planning" and "automation" to one centered on the "augmentation" of human decision-making, offering a new level of competitive advantage. This is where the industry, HIVERY, and HIVERY Curate specifically, are headed. We are pioneering this new "solution space" for the next generation of category management professionals, essentially offering the ability to maximize assortment profitability at the push of a button. It is about:
Hyper-local Insights: NextGen solutions like HIVERY Curate use Artificial Intelligence to offer hyper-local, retail-specific store-level insights. This allows for identifying growth opportunities that were previously difficult or impossible to discern.
Simulation with Real-world Constraints: Considering real-world limitations, it can simultaneously simulate and qualify dynamic strategies. This ensures the strategies align with the brand's portfolio and the retailer's objectives.
Efficient Category Plans: NextGen aids in driving locally relevant category plans, effectively merchandised and operationally efficient.
Scenario Planning for Growth: NextGen employs machine learning models and store-level data to evaluate strategies based on real-world constraints. This offers qualified predictions regarding the potential impact of specific strategies, aiding decision-making.
Answering Critical Questions: With AI Curate, businesses can obtain answers to pivotal questions such as:
- How to maximize the productivity of existing space?
- Strategies to rationalize SKUs at each store while limiting churn to a specified percentage.
- Understanding the impact of demand transfer across the product portfolio.
- Opportunities for re-optimizing cluster strategies and planogram numbers.
- Where will the new items perform best? HIVERY Curate can simulate and qualify different product innovation strategies by analyzing historical store-level data and considering real-world constraints. This helps you identify the optimal stores or locations for launching your new items, maximizing their performance and potential.
- What impact will adding or removing items have on sales, profit, and units? HIVERY Curate's predictive analysis capabilities enable you to understand the potential impact of adding or removing new items from your assortment. This helps you make informed decisions that balance profitability with growth and minimize churn.
- How can you minimize churn while maximizing the growth of new products?
HIVERY Curate is a robust tool that utilizes AI and machine learning to offer valuable insights. It empowers businesses with informed decision-making capabilities in the retail sector. As a NextGen solution, HIVERY Curate will serve as an AI co-pilot to assist you in making complex decisions related to assortment and space. This tool will be instrumental in helping you make better decisions and improve your business outcomes.
Adopting these NextGen merchandising solutions enables retailers and CPG companies to transition from fixed seasonal merchandising plans to more dynamic planning that adapts to consumer interests and needs. Not all consumer trends are predictable, but modern merchandising augmentation systems empower retailers to respond to short-term fluctuations in demand for specific products. With these tools, retailers assume greater responsibility for category management.
What are the Benefits of Getting Assortment Optimization Right?
Mastering assortment optimization is an ongoing, time-consuming process that can seem daunting with traditional methods, given the sheer volume of data, rules, and constraints. However, when executed correctly, the benefits are substantial. You can identify and replace lower-profit products with higher-profit alternatives without sacrificing volume. You can also group complementary products, increasing the likelihood of additional sales. The result is increased sales, reduced costs, and a significant boost to your bottom line.
How Do You Achieve This?
Analyzing historical data alongside complex operational rules and business constraints is crucial to excel in assortment optimization. You must ensure that any product swap under consideration will neither reduce sales volume nor violate important rules and constraints. The more data you scrutinize, the more accurate your analysis will be. For instance, a currently well-performing brand may show signs of decline when you extend your data horizon to include historical data from the past few years. This could indicate either a lack of investment from the brand or a general decline in the category. The more data you have and the more complex your rules and constraints, the more you'll likely need to invest in specialized software to guide your decisions. The better you understand your sales trends, the more effectively you anticipate and preempt changes.
Related resources you might be interested in:
- Edge Assortment Strategy: Is It the Retail Revolution You Need?
- Discovering 'Sleeper Stores': How AI is Redefining Retail Insights and Shaping Buying Behaviors
- Interactive Assortment Planning: Using AI-Driven Visual Insights to Make Difficult Decisions
- Can you find individual stores that are worth going to store-specific assortments? AI can