The world of retail trade promotions is changing for both retailers and consumer goods manufacturers. Advanced technologies, particularly Artificial Intelligence (AI) and machine learning, are now being utilized to optimize promotional strategies, deliver deeper insights, and ultimately drive a significant improvement in the performance and return on investment (ROI) of trade promotions.
Common Types of Trade Promotions
Traditionally, trade promotions are special schemes or discounts offered by manufacturers to retailers with the aim of increasing the sales of their products. These promotions come in various forms:
Off-Invoice Promotions: These are straightforward price reductions on the invoice, making the product cheaper for the retailer.
Bill-Backs: Here, the retailer pays the regular price but gets a certain amount back once they prove they’ve sold the items.
Display Allowances: This is a fund provided by the manufacturer to the retailer to create in-store displays for the product.
Free Goods: The retailer gets some quantity of the product free of charge, encouraging them to buy more.
Scan-Backs: These are deals where the manufacturer gives the retailer money back for every unit they sell.
Buy-Backs: This is where the manufacturer agrees to buy back unsold goods after a specified period.
Traditional Approaches: Calculating Lift from IRI Nielsen Scan Data and All Commodity Volumes (ACVs)
In the past, the success of trade promotions was typically measured using techniques such as calculating ‘lift’ from IRI Nielsen scan data and ACVs. ‘Lift’ is the increase in sales due to a promotional event, calculated by comparing promotional sales to a baseline of what the sales would have been in the absence of the promotion.
The ACV represents the total volume of all products sold within a specific retail market. By measuring the ACV distribution of a particular product, manufacturers and retailers can gain a sense of how widely the product is sold and use that information to strategize their promotions.
In both cases, this process has been heavily reliant on looking backward at past promotions performance to make estimates about future performance.
Emergence of Predictive AI and Machine Learning
However, while traditional methods provide useful insights, they often fail to account for several crucial factors that impact sales, like cannibalization, halo effects, and competitive takeout.
Cannibalization refers to a situation where a promotional item eats into the sales of other non-promoted items from the same brand. Halo effects are the opposite, where the promotion of one product boosts the sales of other products from the same brand. Competitive takeout, on the other hand, is where a promotion attracts customers from competing brands.
Today, Predictive AI and machine learning are being increasingly used to analyze these complexities and predict promotional outcomes with much greater accuracy. These technologies can analyze vast amounts of historical and real-time data, identify patterns, and make accurate predictions about the future.
Predictive models can forecast the potential lift of a promotion, considering factors like brand loyalty, competitor promotions, seasonality, and store traffic. They can also quantify the impact of cannibalization and halo effects, and predict competitive takeout, allowing for more precise promotional planning and execution.
Improved Measurement of Promotion Performance and ROI
By leveraging Predictive AI and machine learning, companies can significantly improve their measurement of promotion performance and ROI. These technologies provide a much more granular and nuanced understanding of how promotions influence sales and customer behavior. They enable companies to identify which promotions work best for which products, in which locations, and at what times.
Additionally, AI-powered retail merchandise planning software can also provide insights into the optimal allocation of trade funds, ensuring that promotional investments are directed towards the most impactful activities. This leads to more effective promotions, higher sales, and improved ROI.
Generative AI and The Future of Trade Promotions
As we look towards the future, Generative AI promises to further revolutionize the world of retail trade promotions. Generative AI models are capable of creating new content – in this context, new promotions and related visual merchandising.
By leveraging massive amounts of data on past promotions and their performance, Generative AI can ‘dream up’ new promotional ideas that are predicted to be highly effective. It can also create appealing visual merchandising for both in-store and online retail environments, optimizing for factors such as shopper attention and product discoverability.
In conclusion, the use of Predictive and Generative AI solutions like OmniThink.AI in retail trade promotions represents a powerful tool for companies seeking to gain a competitive edge. By enabling a more nuanced understanding of promotional impacts and facilitating more effective promotional planning, these technologies hold the promise of significantly boosting promotional performance and ROI. As such, they represent an investment that forward-thinking retail companies cannot afford to ignore.
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