Customer Centric data transformation: Improving size availability at stores leads to 25% additional sales.

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For the last 16 months, I have been working on an accelerated transformation for two fashion retailers. It was not an easy task, I started like any consultant would do, but failed to add measurable impact due to two main reasons:

  • I struggled to identify use cases and depended too much on departments to develop the right use case.
  • Accepted that the current way people are working is the best way, and these teams know best what they are doing:

This quickly pushed us towards a cost center like any IT team. I learned the hard way how to be a value driver and be measured on the value we are going to add.

Through this experience, I have learned the following principle that i am using now to have a real impact.

Principles for Value-Driven and Customer-Centric Transformation:

  1. Start with the Customer and Work Backwards: Identify pain points like size unavailability through direct observation and focus on resolving them.
  2. Focus on Controllable Input Metrics: Shift from blaming uncontrollable factors to optimizing metrics like size availability that directly impact customer satisfaction.
  3. Develop Better Measures: Move beyond outdated metrics by creating new, actionable ones tailored to customer behavior (e.g., focusing on major size availability).
  4. Optimize at the Store Level: Track size availability at individual stores, ensuring localized improvements instead of relying on aggregate averages.

I will explain here how each of these principles is applied by walking you through a case study.

1. Start with the Customer and Work Backwards A successful data transformation program starts with customer pain points, not internal assumptions. In this case study, our data and insights team spent 3 days in stores to deeply understand what was frustrating customers.

One of the key findings we chose to focus on was: product size unavailability — a critical factor causing customers to leave without making a purchase. Every unsold product due to size unavailability is a missed opportunity and a direct hit on your bottom line.

Business Impact:

Approximately 20%-30% of customers abandon their purchases due to size unavailability, addressing this issue could recover an estimated 25% in sales annually. For a retailer with $100 million in annual revenue, this equates to $25 million in recoverable revenue, providing a clear and compelling ROI for focusing on size availability as a core metric.

Observe we did not start a priority of a siloed department process if is that we would have innovated in a siloed environment.

Lesson for Retail Executives: If you want real transformation, go to the source — your customers. Observe their journey, listen to their pain points, and work backward to fix what matters.

2. Move from Output Metrics to Controllable Input Metrics another lesson is we did not start directly with sales, that is an output metric that cannot be controlled. Customer sales in our priority, but what causes an increase or drop in sales that we can control, this was product size availability, you might ask how.

  • Sales is an outcome that is a lagging indicator, it has already happened, and nothing can be done about it now. but what drives it? Conversion.
  • Conversion is an input, for sales, but still not directly controllable, it depends on product availability and customer service. Product availability also sometimes means size availability.
  • Size Availability is a controllable input metric and a leading indicator of sales. When the right sizes are consistently in stock, conversion rates improve, directly boosting sales. Unlike uncontrollable factors such as weather or competitor activity, size availability can be actively measured and optimized. This makes it a critical focus for aligning inventory strategies with customer needs and driving better outcomes.
Lesson: Identify and measure the controllable input metrics and optimize that

3. Challenge Existing Metrics: Are They Customer-Centric?

The traditional way of measuring size availability, such as using broken assortments (e.g., fewer than 7 SKUs), does not align with the customer experience.

The current process:

We have two types of shoe assortments: a 15-SKU assortment and a 12-SKU assortment.

  • An assortment is classified as "broken" when there are 7 or fewer SKUs left in the 15-SKU assortment or 6 or fewer SKUs left in the 12-SKU assortment. ​
  • However, this metric only considers the total number of SKUs and fails to capture the availability of the most in-demand sizes that drive the majority of sales. For example, a 15-SKU assortment might still meet the threshold but miss key sizes like 38 or 39, which could make it effectively unsellable from the customer’s perspective. This oversight results in lost sales and customer frustration, as availability thresholds do not align with actual buying behavior.
  • This method assumes overall availability equates to customer satisfaction, but it fails to account for the importance of specific sizes that customers seek.

4. A Better Way: Customer-Centric Size Availability

Learn from Industry leaders: Example from Zara

Zara’s approach to broken size calculation is an excellent example of a customer-centric metric system. They classify sizes into 'major sizes' (e.g., Medium, which contributes 40% of sales for a shirt) and 'minor sizes.' As soon as a product runs out of its major size in-store, it is labeled as a 'cut-sized' product and moved out of the store. This ensures that shelves remain stocked with products that meet customer expectations, directly addressing the friction of unavailable popular sizes.

The new approach solves these gaps by aligning metrics to the customer's reality.

Inspired by Zara, we tailored our approach with measures to suit our specific retail context, We considered two approaches for defining 'broken size':

  1. Top 2 Sizes Unavailable: A product is considered cut-sized if either of the top two major sizes is unavailable. For instance, sizes 38 and 39 are unavailable, the product is flagged.
    • Reasoning: The top two sizes often account for the 45-50% of sales in key product categories. Ensuring these sizes are available minimizes the risk of losing high-value customers.
  2. Top 3 Sizes Unavailable: A broader definition where the unavailability of any of the top three sizes (e.g., 38, 39, and 40) qualifies the product as cut-sized.
    • Reasoning: Including three sizes adds a buffer for slight variations in customer preferences across different locations. This ensures a more comprehensive approach to availability and reduces the risk of missed opportunities.

Ultimately, we selected the Top 2 Sizes Unavailable approach for its balance of precision and action-ability in addressing the most critical gaps that impact customer satisfaction and conversion. By focusing on these high-demand sizes, we directly target the critical gaps that impact customer satisfaction and conversion.

5. Store-Level Optimization is the Key to Action ability: We observed that our supply and planning was looking at overall size availability almost every day, but at the store level, they will sometimes wait a month.  Aggregated size availability hides critical gaps that frustrate customers at the store level. Why? Because customers shop at specific stores, not across averages.

  • A product might appear to have good availability overall, but if key sizes are missing at multiple stores, customers still leave frustrated.
  • To drive results, size availability must be tracked at the store level to identify and resolve cut-size issues where they impact customers most.

Hence our 1st version of the insights dashboard was designed in a way that would show not only at the total level but also under concerned stores, product styles and product categories. 

Roadmap:

  • Once these new measures are accepted and fully understood by the team, we will connect this insight to action, action in this case is Inter store transfer.

Conclusion: A Value-Driven Data Transformation Program as this example illustrates must:

  1. Start with the customer and work backward.
  2. Focus on controllable input metrics like size availability.
  3. Align metrics to customer experience rather than internal thresholds.
  4. Enable store-level optimization to make insights actionable.
  5. You should not only provide Insights but also help with subsequent action. The real value lies in doing the action.