Many companies scramble to make successful changes to trade planning that affect business performance without the necessary insight and flexibility provided by predictive analytics. However, by simplifying the trade planning process using analytical insight and predictive modeling, companies can make more accurate, objective-focused decisions on their #2 line item cost.
For example, one of the greatest obstacles to improving sales planning is the accuracy and usefulness of a company’s data. In a recent Food Business News article, “PepsiCo preparing for change and redefining growth,” Keith Nunes quotes Pepsi-Co CEO, Indra Nooyi, as stating “I think you’re beginning to see the limitations of syndicated data.”
Nunes also notes that a Pepsi-Co analyst reports that the “company’s information regarding its North America Beverages business diverged from syndicated scanner data by approximately 450 basis points.”
"Advanced predictive analytics capabilities can give companies the power they need to make intelligent changes, achieve growth and contribute a significant annuitized return on the trade spend investment."
Statements about “dirty data” such as this highlight a challenge surrounding strategic planning in which a lack of accuracy creates doubt and complicates the process, resulting in disappointing ROI.
So how can companies redefine strategy, get a better handle on the health of their trade investment and bring simplicity and flexibility to trade planning? Advanced predictive analytics capabilities can give companies the power they need to make intelligent changes, achieve growth and contribute a significant annuitized return on the trade spend investment.
As it is, many companies have their data siloed among departments, which leads to redundancy, inaccurate strategic assessment and underperformance against corporate objectives. But when that data is cleansed and harmonized, it presents one version of the truth for all departments so that they can have the most accurate picture of the business, putting analysis (including quantified ROI) at their fingertips.
Specifically, analytics can both simplify the trade planning process and allow for pivotal flexibility by allowing companies to manage data during post-event analysis to see accurate, timely and quantifiable results.
"When data is cleansed and harmonized, it presents one version of the truth."
Furthermore, this accurate insight is what drives predictive models to align planning with company goals. With this, plans become data-driven and pragmatic practices drive user adoption and, ultimately, company growth.
Looking at examples set by companies that are struggling to grow and maintain results, facing existing obstacles with the help of predictive analytics eliminates limitations and provides the framework for weaving intelligent decision making into current practices. To get there, companies will need to be ready to adopt analytics-driven planning approaches that take a proactive approach to growth. In doing so, organizations arm trade and sales teams with simplified and flexible predictive planning processes that yield the quantifiable results CPG companies need to remain viable in today’s dynamically changing environment.
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