At first glance, Dynamic Pricing Systems and Recommendation Engines may not have too much in common. Dynamic Pricing is meant for targeted price optimization so a retailer can sustainably grow revenue and profit. Recommendation Engines, on the other hand, ensure that customers receive excellent online service, even though there is no sales associate at their side while they shop.
While Dynamic Pricing Systems and Recommendation Engines operate in different worlds, they have one very important thing in common: they generate added value from data and focus on the retailer’s most important asset: customers. After all, customers are the ones who ultimately decide on the success or failure of a retailer. In this context Dynamic Pricing Systems and Recommendation Engines have even one more thing in common: The most successful retailers (especially Amazon) have been using both technologies for years!
With a Recommendation Engine, you as a retailer can control the customer experience of every single shopper. You are able to specify at which customer touchpoints what products or product groups are to be offered. The AI then selects the products that best match the customer’s interests. From there, you can decide whether the AI should only show products that are directly geared to the customer’s interests – offering the best possible service – or whether it should also recommend additional products that offer your business high margin, turnover or sales potential.
In most cases, the combination of both approaches is worthwhile as it achieves customer satisfaction and serves your business targets profitably. Additionally, I would like to emphasize a great benefit that you generate here quite incidentally: You gain a deeper understanding of your customers’ interactions with products, such as:
- Which items are very high in demand.
- Which items they buy frequently or notably rarely.
- And – maybe most important – you will see, which items often end up in shopping baskets or on watch lists but are ultimately not bought.
With all this information, you’re able to optimize inventory levels and also make price decisions dependent on the most important indicator of all: the customers. With dynamic price optimization, you can supplement your pricinge processes with a system that makes statistically valid and profitable decisions – for your entire product range. More importantly, the AI uses your existing data (e.g. those from the Recommendation Engine) and converts them into additional income because it runs processes that a pricing or category manager cannot perform:
- The AI calculates the daily demand for each individual item in your assortment.
- The AI automatically considers a range of influencing factors, such as stock level, target sales date or crucial competitive information.
- The AI aggregates this information and derives a valid sales forecast for each individual item.
Based on this, a daily updated price is calculated for each product within the framework of the pricing management specifications. Retailers still have full control over the strategic direction of their pricing; The AI only controls the processes that cannot be handled manually – neither qualitatively (mathematical accuracy and inclusion of ALL relevant pricing factors) nor quantitatively (for all items in the assortment).
To close the circle, Dynamic Pricing Systems and Recommendation Engines can support each other and maximize the common added value. Recommendations can ensure that customers are made aware of very specific products – for example, those that you as a retailer would like to sell as soon as possible. If these products are also given an attractive price by the AI, you can boost your sales in a very targeted manner and avoid disadvantageous discounts.
Would you like to learn more about the effects of both systems in place? Just a few keywords about that: Bundle pricing and couponing! You will find out more in my next blog post – or, in the meantime, sign up to be the first to get this information. :-)
