Fortunately or not, staring into the crystal ball won’t help retailers predict the future, but there are some techniques able to forecast the behavior of the customers and their reaction to price changes or discounts. What are those techniques?
According to the eMarketer survey, “The Most Important Technologies for Achieving Value from Future Data Use,” 49.3% of those who were questioned, stated that “predictive analytics and modeling” is the best technology for making profit from using data in the long run.
If you make the right assumptions about pricing, then you could always stay on top of your competition.Get Demo
Predictive pricing may be expressed in one simple equation, resolving how price influences the demand.
Some retailers use a simple Excel spreadsheet to solve the equation, though it gets impossible for 5-10 thousand SKU.
Additionally, in modern eCommerce, it’s impossible to express the dependence between price and sales volume in just one figure. We need to take into account the psychological pricing, competitor activities and strategies and many other factors.
To resolve these issues, machine learning and cloud computing are of great use for producing customer insights.
Price elasticity is the relation between unit price and the number of sold items, which affects the total revenue.
Predictive pricing is first of all the question of demand. If you don’t predict demand, you may buy a too large amount of product and lose the value of it with time increasing inventory cost. Or, you may buy not enough product and not satisfy your customers losing the opportunity to gain more.
First, due to non-linear demand, the price increase may not influence demand at all, while price decrease strongly affects it. That happens in the case of law brand awareness.
Don’t forget about psychological pricing: changing price from $7 to $9 may have a completely different effect than changing it from $9 to $11.
Another difficulty is evoked by your competitors, who may also affect your sales: it’s not only the price in that counts. So the equation gets extremely difficult and to resolve it special programs and tools are developed.
Basing on historical data predictive pricing platform counts a coefficient helping precisely predict demand at a very large scale taking into account the price changes, sales history, and other data.
The predictive analytics tool provides a retailer with a demand model, able to forecast demand volume changes to determine the right pricing in the future. With the right prediction, you will increase sales and revenue, having notifications every time a price increase is possible without any harm to demand.
There are some ways in which predictive pricing can benefit a retailer. Let’s take a look at a few of the major reasons:
Predictive pricing software is beneficial for large businesses since it helps them respond to the market fast through the notification system. That’s how it happens: when the program estimates that price increase won’t affect demand, you get an alert and go to the predictive pricing calculator to see what happens if you increase the price by 5%. You may see that demand goes up in this case and optimize the price just in time.
The method uses machine learning, so you can get ahead of your competition due to the correct and relevant data that is collected and then analyzed to make solid predictions.
With the help of a predictive analytics platform such as Competera, you can get the most relevant information in order to make as accurate predictions as possible. As a result, there will be no market that you won’t be able to conquer.
Competera predictive pricing platform can help you set the best price for an item based on accurate predictions in fast-paced markets and accurate competitive data.
Alerts, reports, and the results sent via API, an advanced library of pricing rules
Tailored pricing programs, Guidance hub, testing
Price elasticity calculation, predictive pricing, prices per segment
using predictive pricing platform with demand modeling and price simulation from CompeteraGet Pilot