Leveraging an AI ‘nervous system’ to reimagine retail pricing

To leverage the full transformational potential of this emerging technology, retailers need to invest in building their own AI. Not something generic or cookie cutter, but AI that is specific to their unique business, made up of composable blocks that can be re-configured as needed. This will allow AI to ingest data from all systems and push decisions back to all systems.  Putting AI at the heart of the business in this way provides flexibility and creates opportunities to infuse AI across the entire retail value chain.

For any retailer looking to start building their own AI “nervous system”, pricing is a great place to start. Because pricing is by nature a numerical pursuit, it lends itself well to the data-driven and probabilistic approach of AI. Building AI for pricing can also help lay the foundation for additional AI use cases in a retail business. Some important trends around using AI for pricing include:

1. Setting the right price, the first time

Whether we’re talking about homewares, footwear or denim, setting the right initial price must be done using a range of data inputs. Whilst a traditional cost-plus technique applies a crude margin uplift to COGS, modern approaches take into consideration a wide range of datasets. For example, competitor pricing, customer expectations, demand signals, availability across the market and brand metrics can all be used to help determine the optimal initial selling price. Another consideration is the role of distribution partners and the possibility that your initial prices may actually only be a recommendation (i.e. MSRP). All of these factors can be considered in the round by machine learning models in order to determine the optimal price.

2. Breaking out independent effects of price elasticity

The age-old adage that high prices scare away buyers is of course still true but determining the effect of price on sales independent of other variables remains elusive without the help of some sophisticated data science. For example, stock availability and competitor activity can mask the actual price elasticity of your product. If you plot your prices and promotions over time against sales history for the same period, the trends you observe may not tell the whole story. It’s important to set promotional prices with the full knowledge of how price impacts demand in your market and category.

3. Pricing in cohorts and categories

In traditional retail workflows, pricing is usually set at an individual product level but there can be great benefits from optimizing price in cohorts. Data clustering can help determine the ideal cohorts to use, be that category, sub-category, price band, demand or some other combination of factors. You can then move to optimize the pricing and promotion of products so that the impact of changing the price of one product is considered in the context of the full cohort. This ensures individual price changes do not happen in a silo.

4. Managing the proliferation of selling channels and competition

The multitude of ways for consumers to shop means pricing is more competitive than ever. In the future retail pricing will become increasingly contextualised, real-time, and programmatic. Retailers must be equipped for more frequent price changes in order to keep up with competition in digital channels, requiring both a change in mindset and adoption of new technology. Retailers will need to monitor prices and demand across the market, adjust prices, and operationalize those price changes both in store and online. In the fullness of time, a more data-driven and algorithmic approach to buying, merchandise planning and customer segmentation can also help build competitive advantage.

In closing, the opportunities for AI to modernize pricing in retail are truly transformational. Now is the time to invest in an AI platform and AI pricing applications that can build competitive advantage.