How frontline CPG teams use data-led recommendations

The COVID-19 pandemic reshaped consumer and retail landscapes, pushing CPG companies to become largely reactive as a result. 

Several CPG companies responded to unprecedented changes in consumer behavior and market dynamics by focusing on short-term survival over sustainable growth.

But as the pandemic recedes and inflationary pressures increase, CPG leaders are now renewing their focus on advanced analytics to spot future pockets of growth and upgrade the core commercial capability areas that helps them to out-execute the competition: 

• portfolio and innovation strategy
• data-driven marketing
• revenue growth management
• holistic omnichannel sales strategy
• and in-market execution.

Much of the above is fueled by optimizing the decision-making across frontline commercial teams. These teams can use advanced analytics to identify stores with high potential for revenue growth and to propose targeted actions to increase brand share within individual stores.

CPG leaders recognize that driving consistent top-line growth rests with these field teams. 

For years, CPG companies have used conventional analytics to capture historic trends, leaving frontline commercial teams to infer which stores to target, and how best to promote their products. 

Frontline teams don’t have the time nor the expertise to mine analytics reports. Rather, they need data-led recommendations on which they can act in the required moment.

Overloaded by KPIs and weighed down by restrictive top-down processes designed to compensate for inadequate bottom-up decision-making, large consumer brands are struggling to compete. The agile challengers, on the other hand, are empowering their teams to identify and act quickly upon everyday opportunities.

This can be seen in action when observing field sales teams who are responsible for a large territory of convenience stores.

First, they must map out which stores to visit and how often. But therein lies the problem:

• There isn’t time to visit all the stores 
• The potential for sales growth varies significantly store-to-store

This generates a number of challenges: 

Which stores should the field team prioritize? 

What’s the optimal number of visits? 

When during the day or during the week is the best time to visit? 

What’s the right sales target to manage the team against for each store?

Next, the team must consider what actions to propose to store managers in order to drive sales.

This is no easy task when you take into account:

• The diverse, ever-changing landscape of store-sales drivers – given that no two stores are the same, and their needs change from week to week.

• The myriad different below-the-line levers that influence commercial performance, from assortment to promotions, shopper marketing and shelf execution.

Getting these decisions right – and winning over store managers – is crucial. On their own, each has little impact, but taken together they can be transformational.

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The convenience sector is a key proving-ground for effective, data-driven operational decision making. Early adopters of advanced AI analytics in the convenience sector are already unlocking the benefits of operations-focused solutions. For example, a leading global snacks brand achieved a 1.5% uplift in convenience store sales in a mature Latin American market just by equipping its field team with store-level assortment recommendations.

Empowering frontline teams to act independently and strive for continuous improvement isn’t a new concept – it forms the backbone of modern manufacturing. The Kaizen philosophy – centered around the compounding effect of small, incremental improvements – propelled Japan to its status as an industrial powerhouse towards the end of the 20th century.

AI promises to bring an equivalent wave of transformation to commercial operations; companies that embrace this change will see a marked advantage over their competitors – large or small.

For all field solutions, there is a common set of data sources to consider as part of the data stack: 

Core data: Retail account and syndicated EPoS data

External effects: Census data, points of interest, footfall data, holidays & events and above-the-line media

Direct action levers: Store visit data, local media e.g. outdoor advertising, targeted digital marketing

Retailer action levers:  Field feedback, in-store cameras / sensors, retailer data on assortment, price, promotions and shopper marketing activity

Based on the above data stack, a complete field solution should address a complementary mix of “Where to play?” and “How to play?” use cases and possess the following key attributes:

• Propose stores to visit and actions tailored to individual stores
• Vary recommendations based on commercial need, i.e., if churn is a risk, actions should centre around this, not maximizing sales or profit
• Be refreshed on a regular basis to capture changing retail dynamics
• Display the drivers behind a recommendation to help the field influence the retailer
• Possess space for the field to comment on the store’s characteristics

The size of the opportunity is significant, particularly in the convenience channel where growth has consistently out-performed big box retailing but store execution varies greatly.

To underscore this point, a large consumer goods company recently discovered that its share of convenience store category sales in a key European market rose by an average of 14 percentage points following a visit from a member of its field team.

Second, the data landscape for explaining store performance is evolving rapidly, with valuable new datasets from sources such as public transport APIs and mobile GPS data becoming available.

Building a field solution is a continuous process, so the team tasked with delivering the solution should sit as close to the business as possible to capture user feedback and support iterative cycles of development. Typically, this is led by a sales analytics team sited within the commercial business unit.

The build program for a field solution should also have a dedicated data strategy that ensures the data stack continues to evolve over time as new sources of data emerge. This should allow for fast test-and-learn cycles to evaluate new providers and establish whether their data surfaces new, actionable sales drivers.

As AI for commercial operations on consumer goods companies unlocks effective bottom-up decision making across both business and sustainability dimensions, frontline teams – and particularly those who interface with retailers – are likely to find themselves entrusted with even greater decision-making authority and influence, and an increasing share of investment.

The economic shock created by the pandemic and its recovery has already highlighted the competitive advantage of effective deployment of advanced AI analytics. No longer does physical scale translate to margins through procurement and operations. Instead, forward-leaning global CPGs are leveraging their data to drive margin and share, nurturing it as a strategic asset and applying it in ways that have a concrete impact on their business.