CASE STUDY
Integrated AI Framework Improves CPG Leader’s Forecasting by 41%
A clearer picture enabled leader to identify focus areas, fix leaky buckets, and refocus spends.
Our client, a US-based multi-national CPG leader whose portfolio includes several iconic brands, had run into a difficult situation: targets were being missed, inventory costs were high, and no one knew which drivers to push. To remedy the situation, they needed to improve their consumption forecasting and get a clear picture of the impact to plan marketing actions.
Their forecasting process had to work across countries, include a wealth of data, and be highly accurate. So, they chose NAVIK Marketing AI and the experience of our business experts to build an Integrated Decision Planning Framework (IDPF) to achieve an accurate sales forecast.
Less Bias, More Focus on Forecasts
By capturing multi-source information and making it available in a single location, it was made easier for teams to access high-quality forecasts. In fact, shifting to an Integrated Decision Planning Framework (IDPF) powered by the NAVIK Marketing AI allowed our client to:
Drastically improve supply-side forecast quality (41% in six months)
Reduce forecast bias to 2%.
Identify leaking buckets and focus areas.
Revise their existing strategies to optimize marketing impact and refocus spends.
Seeing the results, the client extended the Integrated Decision Planning Framework (IDPF) from five to ten countries.
Multiple Departments. Multiple Countries. One Framework
An essential part of the IDPF was dealing with the huge amount of data coming from a wide variety of sources: marketing plans, category movements, internal and cross-team data, macro and micro factors, competition data, and marketing strategy in one location.
Our team sorted the data inputs and prepared models to forecast consumption-based sales. We also incorporated ways for the tool to monitor growth and validate monthly and quarterly forecasts to see if any calibration was required.
The tool allowed users to access various types of data inputs and generate consumption forecasts (using causal, time-series, or ensemble forecasting techniques) or demand forecasts (with the option to specify coverage adjustment, inventory adjustment, reconciled demand forecasts, shipment, or volume comparisons) to effectively manage the supply chain. We also developed marketing mix models for each of the client’s brands, allowing them to measure the contribution and effectiveness of marketing activities.
AI Brings Clarity to Strategic Planning
Implementation of the IDPF delivered highly reliable forecasts to the client, enabled them to approach strategy sessions with greater clarity and a stronger idea of how to nurture each brand’s growth.
