While conversing with a CPG Director at a POI event in Budapest, he mentioned how some 28 years ago, an article in the Harvard Business Review offered an assessment of the difficulties facing manufacturers in the fast-moving consumer goods sector. He explained how eerily prescient this assessment was of what was to come down the road.

鈥淭he Costly Bargain of Trade Promotion鈥� (March-April 1990) took note of the shifting power from manufacturing to trade, and the attendant rising cost to manufacturers of trade promotion activities. This has all come to pass: trade spend consistently grows faster than top line revenue and unit sales volume and is on average the second largest expense category on FMCG firms鈥� P&L statements after cost of goods sold.

Even now, so many years after the warning signs started flashing red, many competitors in the sector still lack a solid financial framework from which to build effective trade promotion management (TPM) activities. Without the right financial processes around key decision points 鈥� including budgeting methodologies, funding alternatives and agreed-upon metrics for key performance indicators (KPIs) 鈥� trade and marketing managers will not have an informed understanding of the many variables that influence returns on alternative trade investment choices.

The Silo Legacy

One problem common to many players in the FMCG space is the legacy of decision-making silos. Traditionally, decisions about how to allocate a firm鈥檚 annual marketing budget came out of a top-down process in the finance department, segmented into discrete marketing functions like promotions, assortment mix, category management and advertising. Most of the subsequent decisions within each silo were handled by managers with little cross-collaboration and relatively little in the way of quantitative methods working from a common data platform.

The problem actually got worse as the use of technology applications increased in marketing departments. Rather than helping managers of different marketing activities work off of a common view of demand, technology fractured along those siloed departmental lines. This resulted in suboptimal communications as important bottom-up insights from sales personnel in the field got lost in the silos, thereby preventing smooth integration between top-down budgeting processes and bottom-up demand inputs.

Road to Integration

So how do you get to the right financial framework for optimizing trade promotion decisions? For an organization of medium to large size, there are many granular issues to be addressed regarding existing processes, organizational structure, your legacy technology environment and so forth. Before wading into the weeds of implementation, though, you need to step back and define the overarching objective.

Here is a succinct big-picture formulation: The key objective should be to integrate financial planning with trade promotion decisions around a unified picture of demand. The basic idea here is that financial information needs to be an integral part of the operational decision-making that goes on at the level of promotions, assortment mix and other key marketing levers. It鈥檚 not enough, in other words, to simply articulate a financial plan, set a budget, define rates and agree on a metric (such as ROI) to track progress. You need the right tools 鈥� processes and technology platforms & applications alike 鈥� to facilitate this integration.

Where You Are, Where You Want to Be

Having defined the overall strategic goal, the next step is to figure out where your current capabilities are in relation to industry best practices. This kind of gap analysis will help you make a practical, realistic assessment about what needs to be done in order to successfully integrate financial planning with trade planning. An example of this is taking stock of your key performance indicators (KPIs).

Return on investment (ROI) is widely used as a benchmark for trade spend effectiveness. However, other KPIs can augment the picture given by ROI. Sometimes ROI does not factor retail execution data. How many displays were placed in store vs. JBP agreement? Did all the stores have POS displayed at the entrance?

Other considerations involve the current technology environment and its adaptability to new processes outside current functions. How easy is it to incorporate business intelligence inputs from the field, and to update those as demand patterns evolve? Are demand-facing and supply-facing applications able to communicate with each other in support of a unified view?

These issues are just the tip of the iceberg. The economics of trade spend should make them a top priority. When that Harvard Business Review article came out in 1990, there was no social media, no online shopping channels, no mobile marketing. The world for FMCG competitors today is far more complex than it was years ago. The need for effective integration of trade promotion with financial planning is all the more urgent. Are you ready to embrace this change?

Promax TPO has predictive planning capability 鈥� based on machine learning concepts, it provides users the ability to quickly and efficiently conduct 鈥榳hat 鈥� if鈥� analysis to model different promotion scenarios, and review systematically generated business outcomes to determine the best combination of promotional inputs. Our Data Science service enables a powerful planning and analysis ecosystem that can be tailored to the needs of each business stakeholder. Our services teams are led by experienced domain experts to help you create predictive models to give your business that competitive advantage through best practices in trade promotion optimization.

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