Escalent took data we already had and produced something incredibly valuable from it. They analyzed our performance data and gave us a predictor for future product successes. This immediately saved us money and helped us focus our efforts. A great experience.
A Fortune 500 CPG company was facing increased competition and tighter margins. They felt as if they were placing roulette bets rather than making strategically informed investment decisions. They needed a more predictive product development model. So we proposed a robust analytic framework to support decisions regarding where to continue or end support for product innovation.
We started by looking at the company’s successful and unsuccessful product innovation launches, providing a fresh view of the key influence parameters. We then built a model that included impact measurement for variables such as post-launch support variability, competitor strategy, and end-market specifics.
The client estimates they saved $150MM in underperforming product launches. Our model was a new lens for evaluating and predicting product innovation success. It offered specific recommendations on how long products should be supported and when they should be discontinued. The model took specific inputs and provided predictive performance outputs. We also provided benchmarks and metrics to track competitors’ new launch strategies. The result was a tangible, actionable framework that allowed the client to cut 22% of unnecessary product development costs from their budget and focus more on those products with better-predicted performance.