IBM: Cognitive pricing to retain and attract customers
Pricing strategy has only become more complex as online retail channels proliferate. Consumers can now compare and check product prices across a range of suppliers, and may hold off purchases for the right deal. For companies a raft of new information about consumer behaviour has become available, which can be used to set pricing strategies. One example of this is the development of cognitive computer technologies that can factor in a wide range of information to automatically update prices to meet customers’ expectations as well as margin objectives.
The internet has in recent years provided consumers with a bevy of new avenues to compare products and their prices. Consumers can now call on a range of independent information about products, as well as engage with companies through different channels. Consumers, especially following falling real wages, have also become better at finding deals or waiting on discounts. The consequence of more empowered consumers on companies is that their brand loyalty has become more difficult to hold onto as price sensitivity increases, while large scale investment has been required to meet consumer digital channel expectations.
Opportunities have also arisen with the adoption of digital technologies and their respective channels. Companies now have better access to the pricing arrangement of their competition, as well as vast troves of information regarding product demand and a host of personal information about consumers. One of the results of the information increase is that companies have been adjusting prices to meet customer demand. Dynamic prising practices reached new heights last year, according to retail analyst firm RSR Research, with 75% of retailers increasing the number of price changes sent to stores and other channels over the last three years.
Creating dynamic pricing strategies is no easy task however, and is, according to a recent IBM white paper titled ‘Attracting and retaining customers with insights-driven dynamic pricing’, developed in broadly three phases:
- The first phase should focus on enabling the organisation to react quickly to changes in market conditions, such as fluctuations in competitor prices or product demand.
- The next phase unleashes the power of data to intelligently inform decisions. According to IBM, next to monitoring the impact on sales volume resulting from a price change, it is also important to consider the price sensitivity with respect to major competitors.
- The last phase, the holy grail of dynamic pricing, is achieved through the application of cognitive computing. This refers to a self-learning environment that ‘understands, reasons, and learns’ from inputs in order to determine the best prices and promotions for customers in context.
The report highlights that getting dynamic prices right is not always merely a function of providing the lowest price in the market. A large variety of factors may need to be considered when setting pricing across products, with prices sometimes customer-dependent based on their loyalty status. Other influences may relate to how companies react to competitor promotions on a key product. Cognitive computing based solutions are able to streamline the decision-making process based on product availability, price sensitivity and customer demand. According to IBM, an 8% pricing change will still enable the retailer to compete and to achieve sales and margin objectives.
