Optimize assortment plans with systematic testing of localized assortment strategies

Case Study

Optimize assortment plans with systematic testing of localized assortment strategies

Challenge

As more and more retailers make the transition (or try to) from a product centric assortment strategy to a more customer centric strategy, they are still faced with many issues like determining the optimal product mix and inventory size. These issues become strategically important to solve as customers are always looking for a personalized shopping experience (both online and offline); the right assortment will drive more foot falls, repeat purchases, increase sales and profitability

Striking the perfect balance with your assortments requires data and use of advanced analytics. Specifically, you need to analyze historical data and market intelligence in order to forecast trends, launch products or promotions at the right time and prevent out of stocks. All of this needs to be done with ever changing customer preferences and how the assortment relates to local factors and seasonal factors.

Approach

Convergytics deployed an end to end merchandising solution starting with category trends & forecasting to help merchant planners understand product trends, product affinities by various customer & geographical dimensions. 

 

With this as the basis, we created a framework of A/B testing to test new localized assortment strategies based on SKU rationalization to ensure maximum coverage of assortment with always customer preferences as the pivot. 

 

We have been able to create a data driven continuous improvement (test & learn cycle) framework so merchant planners can now use intelligent design to measure impact and fine tune assortment strategies.

Result

Assortment strategies recommendations have resulted in an increase in traffic (online by 18% and stores by 12%) resulting in basket size by 15% and reduction in inventory carrying costs by 10%

Increase in traffic (online and stores) by

18% & 12%

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Run centralized pricing strategies with real time offer optimization

Case Study

Run centralized pricing strategies with real time offer optimization

Challenge

Retailers can feel overwhelmed as the market place is abundant with pricing products with all of them claiming to be built on AI framework. The reality is most of these solutions miss the mark and cannot mirror how customers, prices & brands interact in the market. All this leads to an inaccurate demand estimation, inaccurate price elasticity measures eventually leading to a weak pricing solution.

In the example below, we helped our client implement a centralized base price optimization (feature-based pricing for SKUs) and using that as foundation to setup store level offer & mark-down optimizations. Our client had gone through two failed implementations from other vendors and 

Approach

For a premium watch retailer, Convergytics deployed a pricing solution based on choice-based conjoint framework and surface optimization models. The custom solution developed for our client get a deep understanding of demand patterns, elasticity measures, product-price interaction effects across categories. These insights were the basis of an optimal base price and helped in defining various pricing / promotional strategies 

Result

As a result of the engagement, the retailer saw an increase in profitability by 17%. The solution simplified pricing analysis with a web-based interface that allowed users to analyze key pricing metrics and their impact in real time. Today, pricing analysts using the solution to test several “What If Scenarios” and chose the winning pricing strategy

Increase in profitability by

17%

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