Five steps to build in-house, best-in-class Promotion Optimization capabilities to drive your Operating Profit.

If you are a ~$1B Manufacturer or Distributor that spends ~ 15% of its Gross Revenues on Promotions & Rebates, every 50bps improvement in your Promo ROIs will drive +$0.75MM to your EBITDA. Let me describe the steps to take to enable these capabilities for your organization.

If I asked you how much Incremental Gross Profit $ you're getting for each $1 invested in Price Promotions, you probably wouldn't know the answer.

Outside of mid-to large-scale Consumer Products companies, most Manufacturers (Consumer Durables, Medical Devices, Auto Parts, etc.) don't have robust (or any) Promotion Effectiveness & Optimization solutions in place.

Most CFOs, Heads of Sales, and Pricing Leaders of companies that spend 10%+ of their Gross Revenues on Price Promotions can't quantify the ROI of their Promotional Investments. Their teams are relegated to looking at basic sales and pricing reports to judge historical effectiveness and plan for the next period's promotional calendar.

A common misconception among executives is that building Promotion Effectiveness & Optimization capabilities is an expensive investment with 6-12 month development times, only to see it fail ~ 60-80% of the time due to implementation flaws or lack of adoption.

Indeed, most turnkey solutions are expensive, difficult to use, lack customization, and are often ineffective.

Fortunately, most manufacturers have the proper data assets to build actionable Promotion Effectiveness & Optimization solutions in-house, using methods and technologies they are already familiar with.

Here are the 5 steps to build an in-house Pricing & Promotion Analytics solution in 90-120 days to deliver high-value realization. How? Because you will develop it collaboratively with a Core Team of IT, Finance, Marketing, Merchandising, and Sales leaders, along with a select group of Power Users brought along the journey at each step.

Step 1: Identify the Promo Effectiveness Metrics and Scenario Analytics capabilities you want to build.

Step 2: Collaborate with business and IT to deploy a purpose-built Pricing & Promotional Data Warehouse (e.g., Azure, GCP, AWS) by bringing in Promotional Spend & Offer Details, Internal Sales Transactions, Distributor Sellout, Market / Competitive Data and Advertising data.

Step 3: Build Unit Demand Models that enable you to quantify Price Elasticities (used for scenario analyses) and delineate Base vs. Incremental Unit sales. Here, I recommend you move beyond Linear Regression and try a more accurate ML approach.

Step 4: Aggregate and harmonize your internal, external, and modeled data (base, incremental, price & promo elasticity coefficients) into a purpose-built Promotion Analytics data set.

Step 5: Connect a popular self-serve BI tool (e.g., Tableau, Power BI) for a highly customized, dynamic, and visual Pricing & Promotion Analytics platform with what-if scenario analysis capabilities.