Effective Pricing for Customer Delight

Consumers daily purchase decisions are influenced by a number of factors. One of the primary factors is price. From social media to digital ad buying, faster mediums are driving the way consumers are reacting to price. Finding the right price is an age-old discipline in retail, and has evolved over time to incorporate the changing needs of business, as well as dynamism in demand, competition and customer perception of value.

The globalization and e-commerce explosion has enabled customers to compare product pricing in an instant. The role of price on consumer behavior is complex and influential. While historical and past-looking data on demand, sales volume, seasonality trends, etc., have been integral in guiding pricing strategies, recently retailers have started looking at how they can leverage customer shopping behavior in defining price strategies.

For instance, consumers will readily visit a low-priced gas station to save some money. They'll also be thrilled if McDonald's started selling a new low-priced burger. However, consumers would not have the  same reaction if they're offered a vehicle for $1,000. Psychological pricing strategies in retail are increasingly being adopted of late, including tiered pricing, anchor pricing and dynamic pricing. Therefore, no matter how educated, experienced or talented marketers may be, understanding consumer behavior has become paramount.

Approaching Pricing Dynamically

Dynamic pricing has garnered a lot of attention lately and is increasingly being evaluated by almost both online-only or omnichannel retailers. However, the term "dynamic pricing" has a negative connotation to many consumers, as it translates to price discrimination by customer or customer segment. Although this isn't necessarily true.

Dynamic/differentiated pricing that's done based on the day, time of day, seasonality or time period (and not always differentiated by customer segment) are still valid, powerful forms of dynamic pricing and have proven to be effective in increasing inventory turns and ultimately increasing profits.

The Tough Road to Achieving Price Optimization

There are myriad challenges on the road to pliant or dynamic pricing and price optimization as a discipline, including the need for strategic decisions combined with on-the-ground data and analytics initiatives.

1. Data Accuracy

The primary challenge for dynamic pricing initiatives is the nonavailability of data-driven process and technology pipeline to effect price changes in a near real-time manner. Price changes need to be realized at the desired time of day, across the desired markets, customer segments and channels for dynamic pricing by a few cents up or down to take effect as desired.

2. Algorithmic Mishaps Due to Product Range

Another big hurdle for most traditional retail enterprises with huge product assortments running in tens of thousands of SKUs is the lack of clean base price data to start with. When base prices do not follow the pattern of customer perception, any effect of discounting or dynamic pricing becomes adverse.

Programming algorithms therefore need to ensure typical rules are followed even when introducing time-sensitive pricing changes. And there's a need for a larger rule-driven approach.

For example, there's a need to codify and persist basic rules that mandate that the in-house brand must be priced lower than a national brand across all variants OR that the effective unit price of a higher-quantity variant is lower than effective unit price of lower-quantity variant, and so on.

3. Customer Behavior is Unpredictable

Customer perception of price is a tricky thing to factor into strategy. If customers come to believe that the product is unworthy of the price or the price isn't reflective of the brand value associated with a product, then a retailer ends up overselling or underselling the product. It's not unfathomable that some consumers would decide to hold off on a purchase or move away from the brand/retailer. But with the availability of rich product information, which customers can access for research via digital channels, it becomes technically possible to correlate what product information does to price from a customer point of view.

For example, when customers rate a camera and provide a review on a retailer’s website or social page, it’s possible to mine the data and correlate the product attributes the customer perceived as valuable in relation to the price of the camera. Similarly, when customers filter a product category, then by style and by brand, it becomes possible to mine the clickstream logs and derive correlations on what features of a category of products are perceived to be valuable.

Data Analytics for Gleaning Customer Perceptions With Pliant Pricing

It's evident the key to winning the retail pricing game is feeding back customer perceptions and behavior (both online and offline) into day-to-day price management. And hence there's a need to establish a pipeline which effectively captures customer insights and automates price management. Consider the below example:

• Clickstream analytics provide behavior metrics on customer preferences for product features, conversions, abandonment and so on for digital channels. Similarly, point-of-sale transactions along with possible paths to purchase tracking provide customer insights on dwell times, pass throughs and conversions in-store.
• Transforming these insights to decipher product-feature affinity in correlation to price is the next step. This correlation of product attribute or feature affinity to price becomes foundational as the key price levers for a category could now become a focused set of four or five significant features of the product as perceived by the customer. Such correlation insights based on key price levers also paves the path to demystify the associations that exist between product variants internal to the retailer’s assortment, as well as the ability to benchmark the associations of price with competition for continuous monitoring.
• The next step in the pipeline would then be an intelligent optimization engine which not only takes past sales, seasonality, competition factors into price optimization, but also this important input on what are the primary features driving product price perception from a customer point of view.
• Finally, optimizing price a few cents up or down integrated to digital channels becomes much more doable to affect dynamic pricing. However, the last mile of the price pipeline to affect dynamic price changes in-store can only happen with the adoption of technology like electronic shelf labels — which is yet another very interesting space that's fast evolving with myriad vendors and solutions.

Now, with such a robust pipeline that provides continuous integration of customer behavior and feedback into day-to-day pricing and price management, retailers can aspire to be much better positioned on optimizing on pricing to improve top line and profitability.

Having the right data analytics takes the guesswork out of pricing strategies. Dynamic pricing is the ideal solution to face competition, and if used correctly can also become a tool to gain and keep customer loyalty. With sustained implementation, dynamic pricing can garner customer confidence and  increase revenues and profits for any retailer.

Anitha Rajagopalan is a principal consultant with Happiest Minds Technologies, a digital transformation IT consulting and services company.