Scaling-up analytics: what major CPGs are doing right

How do successful analytic teams at consumer goods companies scale impact?

Instead of being stuck in pilot purgatory and eking out small wins, learn how AI analytics at leading CPGs are making an enterprise-wide difference.

The gap between analytic aspirations and ability is widening across the industry.

The economic shock created by the pandemic and its recovery has already highlighted the competitive advantage of effective deployment of 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.

Challenges unique to analytics adoption in CPG

Embedding data-driven culture

Incumbents are exploring how to match the needs of the digital age with their core ways of working. To underscore the size of the challenge: annual sales growth from the world’s 50 largest consumer brands (including Unilever, Nestlé, PepsiCo) plummeted from 7.7% in 2006-11 to just 0.7% in 2012-16. In that same time span, brands like BrewDog, Chobani, and the Honest Company captured meaningful market share while becoming digital-first companies. This shows that M&A alone is clearly not enough; incumbents can’t buy a data-driven DNA.

Decentralized infrastructure

The sluggish progress in implementing large-scale analytics or technology transformation programs in CPGs stems in part from their decentralized and matrixed nature. While this infrastructure has led to marketing and product development prowess, it has hindered the ability to strategically invest in data and analytics platforms or to build the agile ways of working necessary to scale them.

Winner’s playbook

CPG companies that are winning in AI analytics have focused on executing in critical areas, including three that are particularly challenging:

  • Developing the right talent base and operating model
  • Building the right data and digital platforms
  • Actioning learnings and implementing outputs into operations.

Let’s take a closer look at how to bridge the gap between analytic aspirations and ability.

Developing in-house talent

Many companies have a team of analysts who are well-placed for driving business insight (BI). Yet in order to ensure the success of an analytics project, data science expertise are required.

Driven by the data science skills shortage, new solutions are beginning to emerge that accelerate an analyst’s workflow by automating key activities such as driver discovery and model building.  Automating driver discovery saves analysts from the painstaking process of searching for correlations that prove or disprove an individual hypothesis by allowing them to screen millions of hypotheses at once.

This also reduces the potential for bias as it is no longer incumbent on data scientists to determine which hypotheses to test; instead, they can concentrate on selecting the most relevant to use as insights or building blocks for a machine learning model.

These solutions also lower the technical barrier to entry for machine learning, enabling business analysts to take on more of a lead role – and bringing us a step closer to the democratization of AI.

Building the right data platform

CPGs need to reflect the diversity of the markets they operate within, but this poses a challenge: how do you build scalable AI solutions that support a decentralized business model?

For all AI use cases, data scientists rely upon a suite of tools and processes to ingest and transform data and insert it into a storage solution or app.  Before cloud-based technologies reached maturity, building this infrastructure was both costly and time-consuming – local differences in the data stack had a knock-on effect on how data needed to be ingested, processed, and stored.

Enterprise-grade cloud solutions cut through these difficulties by enabling companies to spin up storage solutions as and when needed and make use of an ecosystem of third-party solutions to cope with diverse data ingestion and transformation needs.  For large CPGs like ABInBev, which has been migrating its physical data centers to Microsoft’s Azure Platform, these technologies pave the way for centralized analytics centers of excellence that remain in step with operational realities on the ground.

Further downstream, CPGs are embracing automated driver discovery platforms to enable insights generation at scale without sacrificing local market understanding.  Previously, analysts would search for correlations manually – a process that is slow, relies heavily on local market understanding and is difficult to refresh.  By relying on a machine to surface potential drivers, AI solutions can be scaled quickly across geographies but still capture the individual dynamics of each local market.

Actioning insights from the frontlines

Early adopters of AI in consumer goods are already unlocking the benefits of operations-focused solutions. Earlier this year, 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.

AI-driven operations solutions have enormous potential to transform decision-making across frontline commercial teams, especially field teams. Teams that have adopted AI analytics are using these solutions to identify stores with high potential for revenue growth and to propose targeted actions to increase brand share within individual stores.

There are a few key reasons why the field is embracing operations solutions.

The first is that 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 it’s 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 field solutions 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.

AI Analytics: powering solutions when the stakes are high

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.

There are parallels in manufacturing, where the Kaizen philosophy of empowering the workforce to deliver continuous improvement propelled Japan to its status as an industrial powerhouse towards the end of the 20th century and is now mainstream among Western companies.  AI promises to bring an equivalent wave of transformation to commercial operations and companies that embrace this change will see a marked advantage over their competitors — large or small.


No items found.
No items found.

It was easier in this project since we used this outpout

Business Insights

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Predictive Models

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis


Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Features For
External Models

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Business Automation

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis


Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Join our event about this topic today.

Learn all about the SparkBeyond mission and product vision.