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The price is getting higher

29-May-2018

Predictive Pricing

The price is getting higher

Prices that react dynamically to customers’ needs? Data analysis still offers plenty of room for improvement in this area. Companies can not only boost sales and win customers in the short term, the new flexibility will also lead to a more intelligent sales strategy in the long term.

A small discount for the asparagus – and a complimentary apple with the cabbage – weekly market vendors know all the tricks to make their regular customers happy and encourage new customers. Even though buyers and sellers in digital marketplaces do not usually know each other personally, there is a growing wealth of data available to retailers. Those who know how to evaluate it with IT support can play with the prices – and sometimes make them go up. The key word here is price optimization or predictive pricing, in which preferences are weighed, and purchasing decisions and willingness to pay predicted.

An analytical approach to web-based sales not only means higher revenue but also satisfied customers. Many software providers help with the data-supported forecast of which product at what price finds its buyer. While stationary price tags are fixed on store shelves, adjustments can be done much more frequently and faster online in order to make the best use of the willingness to pay in individual cases. Closely connected to this is the general forecast of how strong the demand for certain products will probably be. This is particularly important in e-commerce in order to proactively structure the complex supply chains from the manufacturer to retailer and the customer.

Changing demand, changing prices

The hotel sector and airlines have used dynamic pricing for a long time and charge substantial markups for high-demand times of the day or year. Retailers are also increasingly becoming aware of the opportunities offered by more flexible pricing. In order to benefit from this, comprehensive data sets are necessary. On the one hand, these can come from generally available sources such as which products are sought after on popular search engines. This indicates strong growing demand, which could also be reflected in higher willingness to pay in the near future. What does the weather forecast look like? It might be worthwhile to put together an attractive barbecue package before a sunny weekend.

It becomes more complex on the personal level. What makes the individual user tick? The advantage: Systems and companies keep learning with every website visit and purchase made. With every order, the retailer not only gets money but also a great deal of information. Companies can see what channel someone used to arrive on the website, how long they stayed on individual product pages and at what time they clicked on “Buy.” Such data packages make it possible to always play out incentives. For example, the price can be directly modified, such as with small rebates for customers who have not completed the order even though they have a full shopping cart.

Better prices today and tomorrow

On the other hand, the programs can derive recommendations from all of the orders such as which price strategy fits which product? For some products, absolute low prices may work best even if this results in a lower margin. With other goods, however, a very large number of customers may pick them if the initial price is highly discounted. At certain shopping times, users may be receptive to discounted extras – this increases the revenue per shopping cart that many retailers want.

The software goes through this mass of data in order to recommend appropriate price adjustments or even test them directly. Whether a company strives for the highest possible sales, the highest possible revenue or the highest possible margin – all this is part of the calculations. This is no trivial matter: Besides access to external data sources, the interfaces within the company’s IT also need to function properly, especially the connections to merchandise management systems, ERP programs and marketing clouds.

That is the luxury of the digital world: Just the way that prices can develop dynamically, they can also be tested. A/B tests make it possible to run different price or discount models against each other in real time. You can see in the sales numbers which performs better. In an ideal situation, both buyer and seller are very pleased – like at the weekly market.