Recently, I attended a steering committee meeting at one of our clients, the US division of a large consumer products company. In the preceding months, we helped design and implement new processes and systems for this company. It was heartening to hear several sales & marketing managers across the organization cite specific examples of how their people can now make faster, smarter decisions as a team.
What were the key issues this consumer products company was facing? And what are the specific intelligence capabilities that enabled this company to improve its sales planning and execution?
Two Key Problems
Management asked us to address two issues they were concerned about to support its goals of driving significant year-over-year growth in market share and margins.
1) Margin Erosion Due to “Sell-In” Focus
This company primarily sold its products through large national retailers and a set of distributors serving smaller regional retailers. A dedicated account sales team was responsible for sales to large retailers. When orders from and shipments to the large retailers—their “sell-in”—were not meeting the annual sales plans, account sales teams responded primarily by making price moves and offering more incentives to retailers, all in an attempt to drive sell-in.
These tactics drove down margins. Even worse, they did not always result in more end consumer sales, i.e., “sell-outs.” The company needed to deal with the problems causing low sell-outs; otherwise, the team’s attempts to improve sell-in would be in vain while potentially damaging the brand’s premium image through excessive price discounting. They needed to change the organization’s modus operandi from focusing on sell-in to concentrating on the market and consumer.
2) Effectiveness of Field Sales Resources and Initiatives
Besides the account teams that served the large national accounts, this company also had a field sales organization arranged by regional territories. This field sales organization performed store visits, set up in-store fixtures, conducted in-store demos, and offered various other activities designed to improve sell-out to end consumers.
However, management could not ensure that the initiatives were deployed to the right stores, and they could not assess the effectiveness of initiatives intended to improve sell-out. They needed a way to leverage data and analytics to make better decisions regarding field resource allocation and initiatives to drive more sell-out volume for the same investment.
Sell-In versus Sell-Out
The Solution
The transformation solution we created for this company has three pillars: a Market Knowledge Graph, a Decision Analytics Team, and Intelligent Systems of Engagement.
1) Market Knowledge Graph: From Scattered Data to Searchable, Connected Knowledge & Insights
As we engaged with the company, it was clear that the organization had plenty of market intelligence data. You could say they were drowning in data. They had Point-of-Sale (POS) data from all the top national retailers, purchased syndicated market intelligence data from various market intelligence data providers, and the marketing teams collected consumer research data. Additionally, different roles would pull in retailer, competitor, and consumer data available in the public domain on an ad-hoc basis.
All this data was spread across various databases and was not being compiled in one place to connect the dots. Without a comprehensive picture of the marketplace at an aggregate and granular level, the company’s strategic and operational decision-making was severely disadvantaged.
To address this issue, we built a “Market Knowledge Graph”—an intelligent, big data graph that connected all market-related data the company was already receiving and new data from public sources. This gave the company instant visibility and insight into the following:
- Local markets (down to zip codes);
- Consumer demographics by various attributes for each local market;
- Target consumer segments for each product category with specified attributes and how those attributes map to attributes of publicly available demographic data;
- Retailers and the specific stores that serve each local market;
- Top competitors and their products;
- POS data by store;
- Syndicated market size, share, and competitor data;
- Size of opportunity versus the company’s share by a local market to identify underserved opportunities; and,
- Continuously updated views on web interfaces of their competitive positions relative to product assortment, pricing, in-store promotions, and availability.
2) Decision Science Team: Enhancing Quality of Insights
Getting data connected into a complete market view was the first challenge, but the company did not have the necessary analytics resources or skills to process the constantly changing data. As part of the service we delivered, we set up a “Decision Analytics” team, staffed with experts who used a variety of advanced modeling and analytics techniques (like clustering, correlation analysis, and decision trees) to draw insights from the market knowledge graph and support both strategic and operational decisions.
Strategic Insights – Pinpointing highest-value consumers, markets, stores, and initiatives.
Our decision analysts built models that connected consumer segment attributes for a given product category, consumer demographic data by each zip code, retail stores by zip code, POS data from stores by zip code, and other relevant market data. By employing advanced clustering and decision tree analytics on these models, they helped management get rich insights that answered the following questions:
- What are the highest opportunity local markets (i.e., zip codes) for each product category, given the target consumer segments?
- What are the underserved local markets by each product category?
- What are the retailer’s high-value stores that serve each of those local markets?
- What stores should a particular product be assorted in?
- Are field sales resources targeted toward the right stores?
By leveraging experimental pilots of different in-store initiatives and studying associated POS data to understand volume lifts, the team evaluated various initiatives’ effectiveness and return on investment (ROI) and developed an initiative playbook for each product category and store type.
Operational Insights – Executing the strategy and making rapid course corrections
After developing strategies, we worked with sales leadership to create a set of daily, weekly, and monthly processes for sharing operational insights with account, field, and product marketing teams. With the data and strategies already in place, they could keep track of the key performance indicators in real-time and answer questions like:
- Are the products assorted per our plan across the target high-value stores?
- Are promotion and in-store initiatives driving desired sell-out volume?
- Are we having stock-out issues at any of the high-value stores?
- Does the channel inventory need to be rebalanced across the stores?
- What are the sell-out forecasts likely to be?
The decision analysts leveraged visibility into marketing, in-store promotions, and initiative calendars and correlated that with POS data, competitive pricing, and product assortment information collected from various public sources. They put together operational insights specific to each account, category, and field team through this process.
3) Intelligent Systems of Engagement: Empowering Sales & Marketing Organizations to convert Insights into Actions
One of the challenges driving the change was that the sales organization had a poor track record of adopting systems. Despite spending money on traditional ERP-centric planning systems and various visualization and reporting systems, the sales organization relied on Excel, PowerPoint, and email to collaborate and make decisions. Without plans, however, it is impossible to make process changes stick.
The system needed to be extremely easy to use and intuitive for the company to implement the solution. Instead of forcing users to learn new systems, we employed a “System of Engagement” strategy using our game-changing mPower platform, which enabled each sale & marketing employee to get insights specific to their role through familiar interfaces like email, Excel and mobile devices, all of which connected to the underlying knowledge graph. It enabled them to collaborate, plan and execute better as a team.
After implementing the engagement system, the field teams’ productivity and effectiveness improved tremendously. Now, from either email or mobile applications, field managers can access insights such as:
- What happened last week?
- Which stores should be focused on for next week,
- What issues should be investigated?
- What initiatives should be prioritized?
Account managers, visiting retailers to negotiate product assortments and distribution support for the upcoming season, are armed by the system—right on their smartphones or tablets—with specific insights and recommendations.
Sales management receives insights into account and field team performance and forecasts relative to plan at both aggregate and granular levels—and with all of the context to reveal the causes of any deviations between the plan and reality. This allows them to learn from one market and apply those insights to others.
The figure below provides a high-level blueprint for this transformational solution.
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Author’s Note: The case study is an amalgamation of real issues observed across many consumer product companies. We took some creative liberties with the narrative to make it enjoyable reading.
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