Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
Solution
TCS Optumera™
TCS Optumera™: AI-Powered Strategic Intelligence Platform for Retail
Highlights
- Retailers and consumer packaged goods (CPG) companies have been spending billions on promotions with highly varied performance.
- Successful promotions should drive value for vendors, retailers, and customers.
- There are five principles that can help build stronger promotional capability and consistently deliver profitable promotions.
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Data-driven store promotions is key
Great promotions should drive value for all stakeholders.
Every year, retailers and consumer packaged goods (CPG) companies spend billions on pricing and promotions strategies. However, the economic performance of promotions varies significantly. Margin giveaway and sales dilution are commonplace with the upshot being that large retailers are continuing to throw away millions. So how can retailers drive store promotions that not only excite customers but are 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.
Five principles for profitable store promotions
Figure 1: Five principles for profitable store promotions
Five principles for profitable store promotions
Principle 1: Define clear strategic goals
Create category-specific objectives aligned with business strategies.
Typically, while crafting retail promotions strategies, most businesses fail to define clear goals 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, retail promotions strategies 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.
Retail promotions strategies and analysis for mass 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 of a branded 250ml (about 8.45 oz) 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 pricing strategies and a customer-first mindset, retailers can realize a step-change in store promotions outcomes.
Principle 2: Leverage AI, data science for decision-making
Drive automation to focus more on pricing and promotions strategies.
Planning trade promotions for stores is fraught with several challenges and 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 retail promotions strategies. 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 their own set of micro-decisions. By pivoting to AI-powered retail promotions strategies, 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 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?
In retail promotion strategies, getting these 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-relationship between related products, and the implications for sales and margin. Rather than working with best guesses or limited rear-view analysis, AI-powered pricing and promotion platforms 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 to leverage AI-powered pricing and promotion platforms that learns to adapt to consumer behavior as it changes. This ensures accuracy, relevance, and credibility. Feeding the AI models regular transactional data, coupled with regular model parameterization, i.e, refreshing the factors used in the model, is critical to ensure models and forecasts reflect latest consumer behavior. Self-learning approaches not only ensure robust, relevant outputs based on latest consumer behaviors but also ensure credibility and longevity of the approach.
Principle 3: Deploy an incrementality-based evaluation framework
Embed frameworks in pricing and promotions strategies 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.
Frameworks should be a part of the retail pricing and promotions strategies; for instance, promotional frameworks should be embedded in the promotional planning process and used consistently throughout the ‘plan-do-review’ cycle to get 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 inputted and added 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
A 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. By pivoting to AI-driven pricing and promotions platforms, adopting a customer-first mindset, and strategic clarity, retailers can stop economic under-performance. 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.
