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Integrating AI for competitive advantage in CPG

Managing the ever-increasing competition pressures for Consumer Packaged Goods (CPG) companies is complex. With stores spanning multiple countries, realizing the differences between geographies becomes a prerequisite to business success. At the same time, an eye needs to be kept on evolving consumer demands. This requires a mountain of data and human resources to make sense of.

Categorizing stores, figuring out the potential sales of various products, and planning routes and distribution add up to an incredible amount of effort. Incorporating an AI-based approach can help ease the load and help foster more efficient route-to-market strategies.


Overcoming obstacles in retail management

Our client, a CPG company, fell short of fully capitalizing on the potential of their retail locations due to outdated practices. They relied on a uniform must-stock list (MSL) for all stores, regardless of regional differences or other dynamic factors. Manual and time-consuming processes hampered their route planning. Moreover, simplistic store segmentation prevented them from maximizing their overall potential.

Customizing retail strategies for dynamic markets

The generic MSL approach overlooked each store’s distinctive characteristics and requirements, resulting in missed opportunities for product assortment and sales. The client struggled to tailor inventory effectively to local demand without considering regional preferences or store dynamics.

Streamlining distribution with AI-powered route optimization

Manual route planning for product delivery was time-consuming and inefficient. This led to higher costs and missed opportunities for optimization. This outdated method hindered the client’s ability to adapt to changing market conditions or to capitalize on the most efficient distribution strategies.

Enhancing store segmentation with advanced analytics

The simplistic segmentation of stores into unidimensional categories did not allow a nuanced understanding of each location’s performance or needs. This lack of detailed insight made it challenging to implement targeted improvements or accurately measure the impact of various interventions.

The need was clear: a more sophisticated approach to product assortment, placement, and sales strategies, coupled with an enhanced understanding of store performance. To achieve this, implementing AI-driven algorithms was essential for refining customer segmentation, optimizing delivery routes, and tailoring stock-keeping unit recommendations. This would be achieved by analyzing internal and external data sets.


Building the blueprint for AI-driven retail success

We began by identifying the issues within their system. Then, we brought in data sets and helped build external data partnerships to gain essential insights. The next step included an exploratory data analysis to test our hypotheses. Lastly, we moved on to store segmentation, route planning, and stock recommendations.

We conducted a clustering exercise within store segmentation to understand store cohorts and each cluster’s attributes and differentiation points. We then used the information on sales performance and market potential to create a 2×2 matrix for segmentation. These outputs were paired with the front-end operation.

What we provided:

Precision retail route planning and store optimization

The matrix we created allowed us to streamline the route-planning process using deep learning models, building a custom algorithm for store scheduling and routing. Store potential was then optimized with MSL rankings and AI-enabled recommendations. Outputs from the models were tested with the client before going live, and the impact of our solution was measured for six months post-launch.

Our solution was divided into three areas to create a self-driven intelligent enterprise.

Advance discovery Packaged AI Actionable insights

Management of internal data sets and building external data partnerships to gain insights. We investigated whether AI/predictive analytics can improve store segmentation and help align sales reps’ coverage.

Re-segmented stores based on characteristics, performance, location, and category competitive intelligence.

Realigning sales reps’ activity to focus on stores with higher potential increased store visits with reduced cost and improved store coverage.


AI-Enabled strategies for retail success

The immediate impact:

Our solution immediately enhanced the productivity of the sales representatives and facilitated the AI in performing order-taking intelligence.

The long-term benefits:

Our Route-To-Market solution leverages a digital-first approach to help the sales and marketing teams re-imagine their go-to-market strategy. They can expect to see a 4% increase in net revenue, in addition to:


Increase in-store visits


Improvement in-store coverage


Reduction in cost-to-serve

Access the PDF version of this case study for convenient reference and easy sharing.

Download PDF The art of realizing in-store potential

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