Scientific techniques for effective promotions
Great promotions should drive value for stakeholders
Every year, retailers and consumer packaged goods (CPG) companies spend billions on promotions and promotional planning. However, the economic performance of promotions varies significantly. Margin giveaway and sales dilution are commonplace with the upshot being that large retailers continue to invest heavily in promotions. So how can retailers drive promotions that not only excite customers but also make more economic sense?
Great promotions should drive value for vendors, retailers, and customers. But the imperfect data leveraged to execute the critical task of promotional planning has made the triangulation of this impossible. With data availability and accuracy improving every day, retailers and CPG players can leverage scientific techniques to drive more optimal plans. There are five principles that will help build stronger promotional capability and consistently deliver effective store promotions (Figure 1).
Principle 1: Define clear strategic goals
Create category-specific objectives aligned with business strategies
Many retailers fail to define clear goals for their promotions and focus on lapping activity. While repeating last year’s promotions is a well-trodden path and a safe approach, it fails to adapt to the strategic requirements of the retailer today or in the future and can limit performance.
Often, promotional plans simply chase an undetermined uplift across a plethora of metrics. Purely targeting sales (or sometimes incremental sales) when planning promotions limits its effectiveness. Setting category-specific objectives and ensuring alignment with the overall business strategy is critical for making a strategic impact. The objectives, for example, could be to drive footfall, enhance value perception, drive margin, or create brand awareness.
Promotional planning and analysis for mass in-store promotions have historically focused on sales and margin; and it’s logical to start here. Adding a customer overlay by leveraging data will allow retailers to target specific customer groups that are either strategically important to them or are most promotionally sensitive. For example, are promotions appealing to all customers equally? What customer groups should store promotions appeal to? The reality is that many products only appeal to specific segments of customers. The promotion on a branded 250 ml olive oil may appeal only to upmarket singles. The spicy branded BBQ sauce promotion may not appeal to families. Understanding this appeal profile will help drive strong price perception to the right customer groups.
With AI-driven intelligence, a customer-first mindset, and strategic clarity, retailers can realize a step-change in promotional outcomes.
Principle 2: Leverage AI, data science for decision-making
Drive automation to focus more on strategic planning
Planning trade promotions for stores is fraught with several challenges, that getting it right seems difficult. Most planners and promotional decision-makers are up against unrealistic timelines, fluid processes, vendor challenges, and changing trading conditions. Added to this, many are working with imperfect data, which can exacerbate the difficulties they face. Leveraging data science, in particular AI, can automate hundreds of inter-connected promotional micro-decisions, giving promotion planners more time to focus on strategic planning. A promotional planner may be thinking of many lines across both own label and branded goods and many promotional periods, often simultaneously. The complexity can easily multiply as each promotional SKU (or set of SKUs) comes with its own set of micro-decisions. By pivoting to data-driven decision-making, promotion planners can seek answers to strategic questions such as:
What are the best products to promote to maximize response?
What products appeal to which customers?
What is the right promotional discount?
What is the right promotional mechanic—price cut, percentage discounts, buy one, get one free (BOGOF), buy one, get three—to drive the desired customer behaviors?
How can I optimize the promotion of this SKU-mechanic combination in store?
Will I get the sales I need?
Will the promotions cannibalize my full-price sales and margin too much?
Getting these promotional micro-decisions correct for every SKU, for every promotion in each promotional period, is near-enough mission impossible. And that’s before you begin to understand the cross-relationships between related products and the implications for sales and margin. Rather than working with best guesses or limited rear-view analysis, promotion optimization systems can drive the right micro-decisions to help retailers hit their desired sales targets, promotional participation rates, and objectives. The rewards are substantial.
Much of the promotional insights often focus on historical analysis. But forecasting and predicting promotional performance and category-level incremental performance ahead of plans going live can be gold dust. The key to having accurate forecasts is a science-led approach that learns to adapt to consumer behavior as it changes. This ensures accuracy, relevance, and credibility. For the models and forecasts to reflect the latest consumer behavior, it is critical to feed the AI models with regular transactional data coupled with model parameterization (such as refreshing the factors used in the AI model). Self-learning approaches not only ensure robust, relevant outputs based on the latest consumer behaviors but also ensure the credibility and longevity of the approach.
Principle 3: Deploy an incrementality-based evaluation framework
Embed frameworks in promotional planning to make right decisions
Best-in-class promotional evaluation frameworks deliver simplicity and clarity to the end-user while leveraging advanced science to understand incrementality. They leverage margin, sales, and customer KPIs to understand which promotion has or will perform well. Understanding what incremental sales and/or margins are due to promotions is critical. It goes beyond evaluating sales spikes at a product level and gives deeper insights into what is driving category and customer performance.
Promotional frameworks should be embedded in the promotional planning process and used consistently throughout the ‘plan-do-review’ cycle to make the right decisions. Embedding it within the technology or software can systemize its use. When integrated with forecasting science, the framework can predict possible poor performance even before the promotion goes live. Leveraging customer data to understand behavioral patterns, customer appeal, and customer KPIs can further support the understanding of broader promotional objectives such as trials, footfalls, or strategic segment-based KPIs and should be leveraged wherever possible.
Principle 4: Ensure good data inputs
Cost accuracy is critical to drive better outcomes
Although promotional planning and optimization often focus on sales outcomes, aspects such as uplift, margin performance, and incrementality can give a deeper understanding of both forecasted performance and historical review. For a detailed understanding of promotion margin performance at the lowest levels, cost accuracy and transparency are critical elements. Promotional costs and the associated funding can take many shapes and present various complexities. Once this detailed understanding of cost is in place and data is regularly available, retailers can take huge steps forward to control and manage margin effectiveness and drive better outcomes.
Principle 5: Work closely with suppliers
Closer collaboration is key for win-win outcomes
Promotional planning is typically plagued by problems associated with managing excel spreadsheets, data inconsistencies, inefficiencies, and lots of manual work. Additionally, from a retailer perspective, a portfolio of suppliers can be inputting and adding to multiple promotional plans over time or all at once. The complexity this causes, alongside the requirement for accurate cost data inputs, accurate product data, volume data, and legal agreements, means that sharing data is a necessity. Closer collaboration, sharing of planning data and promo funding data, and a shared evaluation framework can facilitate better promotional planning and drive a set of shared objectives that both parties can win with.
Get more value from promotions
Customer-first approach will maximize promotion outcomes
Store or online promotions have experienced highly varied economic performance for many years. This has been primarily due to imperfect data, poor predictive intelligence, and unclear strategic objectives. With AI-driven intelligence and optimization, a customer-first mindset, and strategic clarity, retailers can stop economic underperformance. They can further realize a step-change in promotional outcomes, leading to more value for all promotional stakeholders, higher margin outcomes, and lower sales dilution.