How to increase sales with a hyper-personalized pricing strategy
04/17/2023 - Pricing strategy
Hyper-personalized pricing exclusively targets customers based on their online behaviour, purchases, and tastes. Hyper personalization works more effectively than other pricing strategies as it is based on key user features, such as their willingness to pay — the maximum price users are willing to pay for a product. For an eCommerce to implement hyper-personalized prices, it must have the necessary technology and structures to collect and analyze a significant volume of data. This data forms the foundation of this — or any other — effective pricing strategy. We explain how you can implement hyper-personalized pricing in your company.
The key to hyper-personalized pricing: Data analysis and artificial intelligence
Before you can find out what your customers need, what they want, and what prices attract them most, you must collect a vast amount of data about their demographics and online activities. Processing this data using software with artificial intelligence algorithms and machine learning capabilities, will allow you to identify significant patterns in customers’ consumption habits. This will lead you to draw valuable conclusions and speed up decision-making. These algorithms are simply rules that make it possible to link each type of user with the best price for them at all times.
Applying Big Data to eCommerce works with hyper-personalized pricing and when optimizing other pricing strategies, such as dynamic pricing. The ultimate goal is to leverage the competitive advantage of data to perfect prices as much as possible, thereby boosting sales. You will also be able to increase the volume of loyal customers, as consumers believe that the brand or retailer understands them and adapts to them.
Volume and type of data
For data analysis to be effective, the sample needs to be large enough for it to be possible to find statistical correlations. A representative sample for a population survey gives a good idea of size. The number must be sufficient to extrapolate conclusions about the citizens of a given region.
Equally, the data sample must be varied — from different populations, age groups, countries, etc. It also needs to align with the key indicators most consistent with your growth goals, such as willingness to pay. This can vary from one minute to the next, depending on consumers’ situation and the market.
Speed is key in helping you gain the most benefit when applying Big Data to pricing and personalized prices. Data collection and analysis must be as fast as possible, almost a real-time study, if we are to react to market fluctuations before competitors. Conducting a quantitative study at a single point in time, as can happen with a market study, is insufficient. The process must be carried out continuously over time.
Implementing advanced pricing software
The most agile and simple way to achieve all this is with pricing software like Reactev. These software platforms collect millions of data and then process it based on advanced pricing rules moulded by your specific goals. One of Reactev’s main features is to recommend price changes for each moment in time, target market, and product. Repricing is automated and improves as the software learns from the information it collects. This technology directly impacts consumer satisfaction and sales.
Category: Pricing strategy
