Point of View

The three Cs of trade promotions in the new normal: Continuous, connected, cognitive

  • Facebook
  • Twitter
  • Linkedin
  • Email
Explore

Covid-19 has left CPG companies' trade promotions plans in tatters as consumers have altered their purchasing patterns across SKUs, categories, channels, and brands. CPG leaders are struggling to predict how long these trends will last in the new normal. How much of the shift to online shopping will be sustained? Will the growth in home cooking continue once restaurants reopen? How long will the focus on hygiene products remain? New trade promotion plans must navigate these shifts to eliminate wasted promotional spend while protecting and growing future revenues.

Trade promotion challenges

Trade promotion and contract management leaders are prioritizing these main challenges as they navigate the pandemic:

  • Historical demand and promotional data have become an unreliable basis for forecasting without major interventions
  • Swift actions are needed to handle disruption to promotion and contract terms and conditions agreed only months or weeks earlier
  • Revenue leakage has spiked across all areas of promotional pricing, from ineffective pricing to enforcement of terms
  • Current systems are rigid and cumbersome, requiring high levels of investment to use and support
  • Reacting and adapting to rapid market shifts is difficult

Traditional methods to grow and protect revenue through promotional pricing are under threat.

Promotional modeling

Traditional methods and models for projecting or forecasting promotional performance rely on years of historical data. They account for trends, seasonality, holidays, and how pricing and promotional tactics performed in the past. These methods can be very effective in an environment in which consumer behavior is relatively static and an organization isn't rolling out new products, pricing, or tactics.

But what happens when business-as-usual is disrupted? These backward-looking, static models force a decision to be made (often arbitrarily) on what to do with the historical data. Do we make assumptions about what the historical data would have been under the new circumstances? Do we count the disruptions as a series of outliers and have the model ignore them? Or, do we just override the output and replace it with a best guess?

Promotional tactics

Promotional tactics (temporary price reductions, ads, displays, coupons) and their various flavors (mailer versus store flyers, lobby versus end cap displays) all yield different ROI in terms of promotional lifts and costs and are forecastable in a relatively stable environment.

However, when this stability is disrupted, promotions can yield unpredictable results. Prior to the pandemic, the CPG world was already moving to be more contactless, with home deliveries, drive-up pickups, and in-store pickups already being rolled out by big box players. Covid-19 has accelerated these trends as retailers introduce measures to protect their employees and customers. This dramatic reduction of foot traffic in stores has reduced the number of customers being converted from shoppers into consumers, reducing the effectiveness of promotions.

Shoppers:

  • Browse 
  • React to ads and displays 
  • Can be upsold to 
  • Purchase more items 
  • Switch brands based on price 

Consumers: 

  • Get in and out of stores quickly 
  • Purchase only what they need 
  • Stick to their usual brands 

How can organizations adapt to this shift to contactless ordering and still get reliable and actionable data to model and predict the effectiveness of future promotions?

Unlock business resilience

Learn more

Promotional timelines

Traditionally, promotional funding is budgeted and allocated on an annual basis and promotional plans are planned at least a quarter ahead. Usually these are labor-intensive processes that require hundreds or thousands of hours per individual on an annual basis and require multiple layers of approvals. Once these plans are in place, changes can be costly in terms of both labor and lost opportunities.

Covid-19's disruption to global supply chains and soaring demand for some products has led to product shortages and the rationing of some products at the retail level. Promotions have been cancelled due to lack of availability or plans changed to meet ration limits. SKU rationalization efforts have also played havoc with promotional plans.

Continuous, connected, cognitive promotional pricing

To solve these problems, leading CPG companies are adopting the three Cs as foundational elements in their promotional pricing process.

Continuous

Promotional pricing plans should be agile enough to shift both strategically and tactically, while still meeting regulatory and financial requirements. Promotions planners can accomplish this by creating and curating dynamic planning processes that focus on exceptions and opportunities in real-time. This approach involves changing where, when, and how decisions are made across trade promotions teams:

  • Instead of relying on monthly or quarterly planning cycles, data and plans should be managed in real-time or near real-time via daily or weekly cycles
  • Management roles should change from deciding and enforcing to coaching and supporting so managers can set dynamic guidelines for enforcement and data trigger points to identify bigger picture opportunities
  • Team member roles should change from a workflow and data translation orientation to a problem solving and communications orientation. This way team members have the authority to quickly solve problems and take advantage of opportunities knowing their solutions meet regulatory and financial requirements

These changes build on a foundation of RPA and AI technologies to optimize promotional pricing plans. For example, a retailer may be forced to ration a specific product, thus failing to meet the terms of a promotion agreed to months earlier to never be met, but in a dynamic, continuous world a decision can be made in near real-time to change the promotion terms to meet the new ration limit.

Connected

In a nutshell, this means no more silos of information or delays in communication. The overarching goal is a single source of truth for trade promotions, with up-to-date, accurate data the organization can rely on. This can be supported by:

  • Using social media to test the effectiveness of different promotional pricing strategies
  • Leveraging robotic process automation (RPA) and AI to optimize promotional tactics and pricing that meet current guidelines
  • Building on a foundation of data hubs to collect and harmonize data across products, customers, promotions, and pricing

Cognitive

Promotion and pricing processes should be human-curated, not human-driven. This means using RPA for routine tasks like data entry and approvals and AI for jobs like promotional calendar creation and individual promotion optimization.

  • AI-reinforced learnings will build resilience by quickly adapting to changes in buyer behavior and making new promotional recommendations
  • RPA can quickly execute these changes and automate routine tasks that rely on simple rule-based decision trees
  • Any processes that require human input should be combined with AI to suggest decision options and solutions. AI will learn from these decisions and continuously refine its suggestions through reinforced learning

Eventually, converting human and AI-supported processes to RPA plus AI processes will free up time for the team to focus on growth opportunities.

The pivot from run to grow

What each organization's new normal will look like is still unclear because future lockdowns, trade restrictions, and health concerns will all impact customer behavior. But what is clear is that trade promotion and pricing strategies will need to become more dynamic so they can adapt at speed as the situation unfolds.

By embracing this philosophy of continuous, connected, and cognitive promotional pricing, organizations will have the tools to optimize future revenue and pivot from running promotions to growing the business.

Visit our consumer goods page

Learn more