In this period of high inflation, retailers will be challenged with razor-thin margins – which only continue to shrink.
The shift to online channels and increasing competition with industries that are recovering their share of customers post-pandemic mean retailers will continue to face challenges to their profitability.
AI, which simulates millions of items, pricing, demand, space, and assortment decisions, is helping leading retailers solve pandemic-related challenges and increase margins. One of the ways AI can make a difference is by supporting pricing decisions – identifying the optimal everyday, regular, and promotional prices for a store’s entire assortment.
AI technology will be a boon for retailers who get started today – providing a competitive edge and ensuring survival in this unstable inflationary period.
How AI ‘prices for profit’
In retail, there are a number of factors impacting consumer behavior, and hence sales, that retailers intuitively understand. For example, customers buy solutions, not items. As such, discounting a product like ground beef will lead to increased sales of products that are part of its solution – i.e., pasta and tomato sauce for a pasta dinner. In addition to product affinities, other factors include cannibalization, pantry loading, price elasticity, promotional elasticity, and seasonality. Leveraging these factors when making pricing decisions is critical to price for profit.
Though these factors are intuitive, it is simply beyond human ability to consider all the factors that influence pricing for all products across a retailer’s assortment. To effectively optimize pricing, AI technology is required.
The data used to calculate these factors can be sourced from a variety of places, including:
- transactional or TLOG data
- product master data
- manufacturer cost and funding data
- past promotions
- store assortment and planogram
- competitive price data
- marketing data
Using this data, the AI system will compute the aforementioned factors and simulate pricing scenarios – determining which combination of regular and promotional prices will lead to maximum profit, sales, traffic, or other business outcomes. Simulation will determine the optimal set of prices and the system will make decisions and deliver specific and actionable recommendations to retailers - providing recommended prices for each item at each store every week.
AI vs. traditional pricing
In addition to taking over difficult pricing decisions, AI systems provide distinct advantages that traditional pricing tools do not offer. For example, retailers often have products in their assortment with only a handful of historical data points.
To make pricing decisions for products like these, retailers would need to spend time collecting new data. However, AI-based simulations provide retailers with the opportunity to create the data, allowing for learning to happen faster than the pace of time. This is because simulations can compute the effects of years of new price points as fast as computing power allows.
Additionally, AI technology is far more flexible. The systems are capable of operating in dynamic environments, like in the increasingly prevalent e-commerce channels where price changes happen multiple times per day - making traditional data scientist-led methods of price optimization obsolete, as human-led modelling cannot deliver these daily changes.
The item mix – Getting it right
When evaluating and recommending prices for merchants, many pricing technologies overlook the item mix – a component that is critical to drive successful pricing.
Customers’ item needs are a priority when it comes to buying, with pricing only providing additional incentive to buy. As such, optimizing the item mix is necessary to price for profit – a task that AI is well suited for.
Primarily, AI will help retailers extend the reach of their promotions, determining the combination of items that will meet the item needs of customers across numerous customer segments. AI will also deliver a combination of items that maximizes the relationships between products and the total store impact of those relationships. The system will account for factors like cannibalization, pantry loading, and seasonality – driving bigger baskets and traffic, and ultimately growing total store sales and margins.
Optimizing the item mix puts retailers in a much better position to price for profit. When a high-impact item is discounted (i.e., ground beef), customer traffic will increase and the sales of its related items (i.e., pasta and tomato sauce) will also increase.
By keeping the price of the related items at their regular price, or even at a slightly increased price, retailers can recoup the margin loss on the high-impact item as well as drive higher profits due to the significant increase in sales – a direct result of AI-powered assortment optimization and strategic pricing.
Increased competition and inflation mean retailers have more work than ever before. What’s more, retailers are being challenged with increasingly thin margins that are threatening their profitability. Leveraging AI has become necessary for retailers who, not only want to secure their survival, but want to lead the retail space.