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Building Predictive Models for FMCG Innovation Success

Building Predictive Models for FMCG Innovation Success

CHALLENGE

How do you effectively retain competitive advantage through innovation in a well-established category? This was the challenge facing one of our FMCG clients as they battled a triple threat. Budget cuts limited their innovation and R&D resources while they faced accelerating innovation cycles and intense competitive pressure to retain market share. They needed to rethink their approach to managing their innovation pipeline. Specifically, they needed to streamline the process across 20+ brands in 50+ markets

APPROACH

The client was focused on redefining its innovation strategy. To do that, we started with the underlying need: defining what a successful innovation means for them. Redefining success meant changing the framework for defining innovation types. Rather than the client’s historical approach of categorization by product enhancement, we proposed a framework around business needs. This ensured that the framework used to define innovation success reflected the right business goals. Additionally, this allowed us to help the client better evaluate long-term strategic bets versus quicker tactical wins that were being driven by their prior innovation strategy.

There no time to waste. We needed to base our recommendations on data that was already available and tracked by the client. Adding additional KPIs would make the process too cumbersome to implement across the 500+ innovations that the client launched globally, every year. Once the basic datasets were analyzed and ROI calculations aligned, identifying successful NPI post-facto was easy. However, the real challenge remained – how to predict the likelihood of an innovation’s success? And more importantly, how to stage-gate investments for support of the new innovation’s launch in different markets?

This is where our number-crunchers came to the rescue. We looked at the past performance of not just the client’s new launches, but also those of their competitors over the past 3 years. Analyzing these for different variables like sales values, volumes, shares, and brand equity impact, we were able to map trends at a market and competitor level for different innovation typologies. This allowed us to build a predictive model that let the client review scenarios and assess the ideal stage-gates for innovation support decisions.

RESULTS

The new innovation assessment and stage-gate support framework allowed the client to streamline the new product launch process. It helped them reduce the number of new launches they undertook by over 10% while continuing to build on competitive advantage through new products. They implemented a standard minimum new launch support program duration, which meant new launches were not rushed into or out of the markets too early. This approach also helped the client improve the success rate of its new innovations by ~10%.

Talk to us about building an innovation framework that sets up new products for success. Reach out at hello@grailinsights.com

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