Our Dynamic Pricing Optimizer (DPO) helped
an Indonesian CPG leader realize a 10% increase
in ROI in their Promotional Strategy

INDUSTRY OVERVIEW

With a growing middle class population, Emerging Markets are massive growth drivers for CPG Clients. Although households are seeing increasing wallet sizes, they are still sensitive to price points. CPG clients often resort to promotions as a key tactic to improve their top line revenue and garner customer loyalty.

The process relies heavily on competitor intelligence and legacy business knowledge. However, prevailing market dynamics and lack of scientifically validated assumptions on consumer behaviour get in the way of realising the maximum potential of these promotions.

THE PROBLEM

The client is the market leader in domestic insecticides, air fresheners and baby care products in Indonesia. With a customer base of over 16 million households, almost 80% of the revenue is realized through Modern Trade stores and a vast network of Mini Markets (MM). In fact, General Trade retailers rely on Modern Trade stores to stock up their inventory. Customer affinity towards MM is driven by deeper discounts, practicality and proximity of these stores to buyer's home.

Promotion Planning in such a complex distribution network is difficult, and business teams often struggle in articulating true effects of their promotional efforts. The client wanted to instil a data-driven approach to this process that would let them assess multiple scenarios upfront through an automated decisioning cockpit. This would help them in allocating their spends effectively, and thereby maximise the return.

OUR ROLE

We helped our client in designing a promotion recommendation solution for the Trade Marketing team to help plan for promotions effectively, foresee the expected lift in sales, simulate competitive scenarios to improve the overall planning exercise.

Methodology

Ganit addressed the problem by designing 3 well-defined solutions:

• Pre-Event Analytics:
  A hypothesis-driven approach on various brands and product lines were formulated and tested to statistically validate broadly held assumptions by the business teams. The results were used as guidelines by the team to plan promotions.
  
  We also developed Decision Boards capturing operational KPIs to analyze top and bottom line effects of previously executed promotions.
• Promo Optimiser Tool:
  We collaborated closely with the business teams to design an Dynamic Promo Optimizer(DPO) tool. The tool allows teams to specify constraints on spend and expected competiton intensity. These inputs feed into the advanced forecasting system embedded into the tool which helps predict the expected baseline and lift in sales for a given promotion period and promotion rate. The optimiser also lets the team plan multiple scenarios by identifying optimal price points to maximise overall profit.
• Post-Event Analytics :
  This includes exploratory analyses of monthly promotions to provide deeper insights into Competitor Intensity, improvements in Operational KPIs, and reflections on actual performance versus what was predicted.
  
  This lets the team analyze whether the promotions have achieved expected outcomes, enabling them to leverage better promotional strategies in the future.

How did we enable consumption?

• First Phase
  Dynamic Promo Optimizer(DPO) was tested in sync with "as-is" processes to understand the effectiveness, efficiency and improvisations needed. This process also helped the teams get accustomed to the tool before completely integrating it across the board.
• Second Phase
  The Dynamic Promo Optimizer (DPO) tool was deployed to test out multiple scenarios for localized campaigns across various product lines. The plans were finalised and sent for in-store execution.

Our impact

Established an automated trade planning workflow, ensuring an increase in returns by up to 10%. The tool's ability to determine optimal price points for any promotional objective also helped the team save man-hours otherwise spent on estimating potential returns. We achieved almost 90% accuracy in forecasting baseline and lift in sales across all products, resulting in precise evaluation of operational KPIs.

Decision Boards capturing effects of cannibalization, markdown, pull forward were also rolled out to help the business teams take quicker decisions on any on-going campaign.