Customer-Centric Assortment: Driving Loyalty via Data

Are traditional approaches to assortment management even worth the effort anymore? Hypercompetition and almost instantaneous trend changes have profoundly impacted how things are done. Change is hard but adapting to fluid conditions is the only solution, and one of the ways to do this is to implement agile customer-centric assortment optimization. Such a strategy allows you to flexibly optimize your assortment in order to stop merely reacting to trends but anticipate them and even stay ahead of them. Market leaders like Amazon have already jumped on the bandwagon, so why haven't you?

Key Takeaways

Customer-focused assortment strategies maximize sales and adapt to market changes.

• Agile assortment reduces slow movers. 

• Customer data drives smarter SKUs. 

• Helps align inventory with local demand. 

• Boosts profitability by meeting preferences. 

• Continuous optimization is needed.

90% of customers will stay loyal once you offer them premium services. This includes personalized assortment formation. This approach is one of the boldest among assortment strategies and is aimed at customer satisfaction. It combines flexibility in decision-making with customer needs. This is how the optimal assortment is ensured, which balances the correspondence to the preferences of the audience and the business goals of the retailer. The main principles include:

• quickly adjusting the assortment based on data on trends, customer behavior, and sales;
• using the principles of A/B testing and pilot launches of new products;
• studying consumer preferences by analyzing behavioral patterns and traditional methods (e.g., surveys);
• using artificial intelligence-based tools to personalize the assortment in different market segments;
• excluding low-margin products from the assortment that only take up valuable space in the warehouse and on the shelf;
• assortment planning based on the elasticity of demand, consumer preferences, and competitors' actions.

For example, an analysis of sales data and customer behavior shows that demand for vegan products has increased in some of the chain's stores. Instead of conducting a long-term analysis and planning changes that would take several weeks or months, the retailer quickly tests the new assortment in several branches, analyzes the data, and—if the solution is successful—scales it up. In essence, this is proactive demand generation.

Leveraging data analytics and other approaches helps to form an effective assortment to increase profits and satisfy customer needs.

The traditional approach to assortment development is dominated by the analysis of historical sales data. This is no longer enough, as this data does not provide insight into customer behavior. What is needed is a deep assortment strategy - in particular, predicting customer actions based on behavioral models and sophisticated algorithms.

The process:

1. The retailer analyzes the frequency of purchases of certain products. For example, coffee and milk are a natural pair, and that means that these products should be placed next to each other and considered interdependent when forming the assortment.
2. Next, you need to identify correlations between products: for example, if a retailer removes almond milk from the assortment and coffee sales fall, this signals the need to reconsider.
3. The following step is to assess the reasons for customer rejection. Prices, competitors' promotions, or the availability of analogs all need to be analyzed.

Let's say you're testing out removing the cheapest wine from your assortment and expecting customers to simply choose a more expensive alternative. However, data analysis shows that most customers simply go to buy the excluded drink from competitors. So, the decision is wrong and you might lose market share.

High-profile market trends do not always mean that “hype” products are profitable for the business. It just so happens that huge sales bring minimal profit due to low margins. Meanwhile, sales of niche products bring in much more. That is why retailers need to:

1. Analyze the product assortment in terms of demand-profitability for each product.
2. Determine which products give almost no real profit.
3. Check the effects of substitution: what will people buy if certain products are removed?

Let's say you have two types of chocolate bars in your assortment, one of them has become popular on TikTok and is bought in large numbers. However, it's cheap and makes minimal profit. The other is an established chocolate from a premium brand. Excluding the cheap bar from the assortment will not see the sales of the premium one increase. The decision is wrong because it leads to the loss of customers.

Consumer demand is constantly changing: it depends on price increases or decreases, product availability, popularity, and other factors. It is worth understanding these interdependencies to make informed decisions. So you need to do the following.

1. Determine how many substitutes each product has.
2. Analyze what customers are willing to pay more for a certain product.
3. Investigate changes in revenue after optimizing the assortment.

Let's imagine that a pharmacy chain withdraws one of its vitamin complexes because its full-fledged counterpart from another brand is selling better. It turns out that customers continue to buy this alternative, and the total revenue has not changed. The decision was right and a truly redundant product was removed.

For effective assortment optimization, it is important to consider logistics and warehouse capabilities. Some products—even if they are very profitable—are not worth selling if their supply is difficult. That's why you need to:

1. Analyze the delivery times and reliability of suppliers.
2. Consider the level of working capital required to maintain inventory.
3. Estimate the ratio between warehousing costs and potential profit.

For example, a supermarket chain sells exotic fruits that are in steady demand. However, the fruit has a short shelf life and the supply is unstable. They are often out of stock, and because of that, customer interest gradually declines. The retailer should test the complete exclusion of fruit from the assortment or try seasonal sales.

Now Amazon conducts in-depth research and uses algorithms to optimize the assortment, reduce the share of unsold goods, and increase conversion. Other giants like Walmart adapt the assortment based on the location of a particular outlet and local customer preferences.

Group stores by similar consumer habits. For example, if classic suits sell better in some regions, while sports attire sells better in others, adjust the assortment to meet the needs of the audience.

To implement actionable insights for an effective deep assortment, you need to study your customers closer than just sales data. Implement AI and machine learning tools to analyze data. They can reveal interesting trends. For example, find out that many customers buy peanut butter with oatmeal bread. This is a trend that would have been impossible to spot using traditional research methods.

Offer different assortment options to different groups of customers. For example, sell popular branded items in one outlet and lesser-known but cheaper alternatives in another. Analyzing the results will show which model generates more income.

The implementation of an assortment strategy relies on the use of several scientific methods.

The three pillars are sales data, analysis of consumer behavior, and information about competitors. For example, if your closest competitor has lowered the price of a product group, evaluate how this has affected your sales. It may be worth changing the price or assortment.

Don't change your assortment right away, but test the available options. Try removing certain products from the shelves, offer analogs, and create sets. If sales continue without loss of profit (or profit increases), your model is correct.

Use algorithmic systems to predict demand. They are much more effective than traditional methods because they are not about probabilities and assumptions but about mathematically correct forecasts.

Check KPIs, including margins, revenue, and customer satisfaction. Make timely decisions. For example, if you changed the assortment, sales increase but profits fell, then review your pricing strategies and offer structure.

To achieve demand-driven growth, you need powerful digital solutions. LEAFIO AI Assortment Planning Software provides the necessary tools for demand planning and will be the main driver in creating a customer-centric assortment strategy. It provides:

• A deep understanding of demand through the analysis of sales data, seasonality, and buying patterns. The system recommends the most effective products.
• Optimization of the assortment by balancing basic, trend, and seasonal products to meet the needs of different customer groups. At the same time, it is possible to cover categories without excessive stocks of those who move slowly.
• Customization and expansion of the product range depending on the type and format of the store: from a village shop to a hypermarket. Enable hyperlocalization based on real-time demand signals.
• Improving cooperation with suppliers through timely implementation and phasing out of products.
• Alignment with merchandising and shelf space by integrating with planogram software for better shelf execution.
• Category matching and optimization of the sales area. The system helps retailers test and improve assortments through virtual simulations.