The Challenges of Integrated Forecasting

‍

• By far the most common problem brands face when it comes to forecasting is the lack of an organized and comprehensive forecasting process. Not only do they try to manually create and manage their forecasts in spreadsheets, which leads to increased errors, they forget key steps, such as forgetting to add in new distribution or a new promotion.
• This is why it’s key for brands who are serious about forecasting to invest in a trade promotion management system that includes the ability to forecast. Not only does this give brands the benefits of a unified platform (no more juggling multiple spreadsheets!) it also allows them to link their trade promotions, deductions, and forecasts, ensuring that they’re always getting the most accurate and up-to-date picture of how they’re performing.

‍

‍

• CPG data ecosystems span POS trends, shipments, inventory flows, pricing changes, and marketing calendars, often fragmented across incompatible sources and formats. Stitching together unified visibility is difficult.
• Many brands lack access to robust syndicated data sources to understand true demand signals, competitive context, and shopper behavior necessary to ground forecasts in facts.

‍

• Existing tools like spreadsheets and ERP modules lack the ease of use, indirect to first receiver roll-up, connection to promotions, and use of different data sources to enable real-time collaboration across teams.
• Running holistic what-if scenarios factoring all the cross-functional variables becomes nearly impossible at the speed of retail execution required.

‍

• Aligning volume forecasts to constantly shifting promotion calendars with various overlapping offer mechanics, creative tactics, and activation windows is tedious.
• Tracking all your trade promotions in one system becomes even more essential as your brand grows. Trying to manually keep track of, and calculate, essential factors like precise incremental promotional lift, inventory, actual vs. expected performance, and deductions becomes increasingly difficult and often negatively impacts cashflow.

‍

• Even when high-quality data is accessible, many CPG brands lack the statistical sophistication to leverage machine learning approaches that dramatically improve forecast precision.
• Attracting and retaining specialized talent versed in applying advanced analytics to commercial decision-making is costly and challenging, especially for smaller brands.