Pricing, Profitability, and Customers
The customer is the king. And the key to unlocking the mystery of customer happiness lies in not just experience and intangibles. But something very fundamental and tangible – pricing. Every customer makes decisions in their daily purchases, influenced by some factors, Product Pricing being a critical one out of them. And rightly so, finding the right price is an age-old discipline in retail that has been a significant focus for most retailers.
Today this has evolved to incorporate the changing needs of business, dynamism in customer demand, competition and customer perception of value.
The impact of globalization and the e-commerce explosion gave an edge and flexibility to the customers to compare product and service prices in an instant, therefore making the role of price on consumer behavior more critical. A customer can now compare prices of different products on various portals along with the offers and discounts attached with it. The solution to this new challenge is psychological pricing strategies, which are increasingly being adopted by retailers – for instance, dynamic pricing, tiered pricing, anchor pricing. Decoding consumer behavior has, therefore, become the vital factor, no matter how accomplished, experienced or capable marketers may be.
Why Dynamic Pricing?
Be it online, in particular, or the omnichannel retailers, dynamic pricing is progressively being implemented by almost all formats of retailers today. The term ‘dynamic pricing,’ however, has a lot of negative connotation associated from a customer point of view since it translates to price discrimination by customer segment. But is it true?
Dynamic or, differentiated pricing and price optimization that is done based on the day, time of day, seasonality or time period, and not always differentiated by customer segment are still effective, powerful forms of dynamic pricing and have demonstrated to be instrumental in growing inventory turns and eventually increasing profits.
Path to Profitability with Price Optimization and Dynamic Pricing
The path to price optimization involves innumerable challenges. The need for strategic decisions combined with on the ground data and analytics initiatives makes it pertinent to have a set of best practices employed while implementing price optimization.
Data accuracy: The pressing challenge for dynamic pricing/optimization initiatives is the lack of data-driven procedure and technology pipeline to effect price changes in a near real-time manner. The price changes need to be recognized and implemented at a preferred time of day, across the preferred markets, preferred customer segments and channels for dynamic pricing and optimization of prices by few cents up or down to take effect as desired.
Algorithmic slip ups due to large product lines/ assortments
Huge product catalogue running in thousands of SKUs and changing price structure leads to the unavailability of clean base price data to start with. If the base prices do not follow the pattern of customer perception, any outcome of discounting or dynamic pricing becomes adverse.
Programming algorithms, therefore, need to ensure distinctive rules are in place while introducing time sensitive pricing changes. Additionally, there is a need for a larger rule-driven approach to figure price inconsistencies in the base data and address them.
Unpredictable Customer Behavior
Most importantly, customer perception of price is a tricky thing to factor into price strategy. A customer might perceive price as the brand value associated with a product, and if the price is not in alignment to the customer’s perception, the retailer ends up overselling or underselling the product.
For instance, when customers rate a refrigerator and provide the review on retailer’s website or on social media, it’s possible to mine the data and co-relate the product attributes the customer perceived as of value relative to the price point of the refrigerator.
It is, therefore, evident that key to winning the retail pricing game is feeding back the customer perceptions and behavior on digital and offline channels into the day to day price management process. And hence there is a need to establish a pipeline which effectively captures customer insights and automates price management.
With such a robust pipeline that provides an uninterrupted amalgamation of customer behavior and feedback into the day to day pricing and price management, retailers can aim to be much better positioned on optimizing on the critical lever called pricing to improve top line as well as profitability.
The right data analytics helps in eliminating anomalies, and the speculation out of pricing strategies and dynamic pricing can act as the right solution to face competition, improve profitability and to gain and retain customer loyalty.