The use of predictive analytics is indispensable in the course of digitalization. It serves to discover and analyze the traces of digital customers in big data across all contact points and channels in order to make predictions about future customer behavior and customer characteristics.
Due to the rapidly growing amount of data sets, predictive analytics methods are able to recognize and use patterns in the available data. Not using existing data could be a waste of resources for the organization. For this reason, the methods are used to make the use of resources more efficient and to fulfill the essential functions described in this article.
A recommendation is only efficient for a customer if it addresses the individual interests and needs of the customer. Predictive Analytics understands the consumer’s buying behavior by combining information from past purchases or current searches to derive the customer’s future actions.
The “one price fits all” times are long gone. This is particularly noticeable when, for example, two people open the same online hotel booking portal at the same time and compare prices. Prices will vary because they are based on the history and search queries of the respective user.
At this point, the magic word for pricing policy is Dynamic Pricing. Online providers can sell their inventories at the best price and significantly increase their profits with increasing product demand by exploiting customers’ willingness to pay.
If there is insufficient knowledge about the prices of the competition in the market, it can happen that the online shop offers the identical product at more favourable conditions, although the higher prices are also demanded by the competition. This in turn would indicate a strong loss of consumer surplus. With the help of intelligent software support, the pricing policy can be optimized in comparison to the competition.
Vertical value chain integration
Predictive analytics is able to generate added value in various business areas (e.g. warehousing and procurement). By automating orders and returns, the entire process can be made more efficient and cost-effective in the long term.
Business Intelligence for quick decisions
An extremely valuable argument for predictive analytics applications is the ability to anticipate customer expectations and market trends in the future. They can be the driver for more conversions and sales, which is made possible by tailor-made prices and product selection tailored to the customer profile. Understanding a customer’s motivation can lead to a significant increase in sales and to better product placement in the long term.
Improved Customer Experience
The majority of buyers in e-commerce are digital natives. The digital customers of this generation want to see the products they have an affinity for when they open the online shop. The use of predictive analytics is therefore very helpful for potential customers who can spend a higher budget to address them directly.
Algorithms also for smaller companies
In the future, more and more companies will be able to use intelligent algorithms because costs will become lower and lower. The fact that algorithms are learning faster than ever before is extremely positive and indispensable. For this reason, it is important to use the power of forecasts as traders to your own advantage.