By Tina Wung, Global VP of Marketing at Nisum
Dynamic, or personalised, pricing walks a fine line between attracting and retaining customers and driving them away. Just over one-fifth of US consumers say they won’t shop at businesses that use dynamic pricing, compared with 25% who are okay with it if it works to their advantage.
Getting the price-value ratio right and communicating transparently about dynamic pricing is critical. Unfortunately, many companies and industries do not do this well. For example, there has been long standing backlash against the highly concentrated ticketing industry, led by behemoth Ticketmaster. There are widespread complaints that many resellers, such as this UK-based company, are pushing “vastly inflated prices excluding ordinary fans [from gigs].” In physical stores, digital price labels discourage shoppers from trusting what items cost – shoppers are concerned that the practice merely attempts to pad companies’ bottom lines.
Regardless of negative consumer perceptions, a 2024 study of over 250 UK marketers found over half say they are likely to adopt some form of surge pricing. Customers already know that if they book an Uber during rush hour or in heavy rain, costs soar. The ride-hailing app offers transparent upfront figures, along with real-time personalised options such as upselling customers to “priority” if they need a ride quicker during peak times.
So, where is the line between providing customers with desired value and building trust? The answer lies in knowing your customer and providing a holistic, personalised experience, with dynamic AI-powered pricing as one piece of that equation.
Generative AI-powered personalised pricing
E-commerce has made it particularly easy for shoppers to determine how much retailers charge for similar products, making dynamic pricing software a valuable competitive tool. The market is projected to reach $7.3 billion by 2027.
Personalised pricing strategies work best when marketers consider a wide range of data, such as individual customer shopping and purchasing behaviour, social media accounts, competitor pricing, environmental and geographical factors, basic supply and demand factors, and other market dynamics. Price intelligence engines leverage unified data platforms to ingest data from a variety of different sources, run pricing scenarios and recommendations, and integrate with marketing mix models to validate the price.
Generative AI can be a major game changer compared to previous rules-based pricing tools, as the engine can now continuously learn from the new data inputs in real-time to improve its models and optimise the prices.
Transparent communications build trust
Consumer loyalty is increasingly volatile, so retailers must carefully weigh market conditions against price points to avoid losing customers through misjudgement. Moreover, shoppers need to feel confident they’re not being taken advantage of. Transparent pricing policies can help ease customer anxiety. Openly explaining price fluctuations, why the customer has received a specific price, and what that price goes toward can improve customer satisfaction and avoid them feeling like they’re being overcharged. Brands can build on this trust by clearly articulating the factors influencing price changes, such as market conditions, inventory levels, or seasonal demand. This demonstration ensures customers understand that the pricing model isn’t arbitrary but responsive to real-world factors.
Framing dynamic pricing in a positive light is equally important. Businesses can emphasise the customer-centric benefits – such as the potential for lower prices during off-peak periods – positioning it as a win-win approach. Providing tools like real-time price updates or alerts also empowers consumers, reinforcing the idea that dynamic pricing is designed with their best interests in mind. Ultimately, clear communication and a focus on fairness are key to ensuring customers embrace dynamic pricing as an opportunity rather than an obstacle.
Dynamic pricing in practice
When developing a pricing strategy – or any plan – it helps to examine what works already.
Urban Outfitters stays ahead of the competition by making decisions based on real data, not instinct. Its smart systems help them with everything from figuring out what’s hot in the market to testing out dynamic marketing and pricing strategies.
While Ross Stores doesn’t explicitly use pricing AI in the same way, its pricing strategy is dynamic in the sense that it adapts to the availability of discounted goods and market conditions. Its approach is short and sweet: sell brand-name merchandise at 20-60% off department store prices. This value proposition attracts a wide range of customers, from budget-conscious shoppers to bargain hunters looking for quality products at discounted prices.
Zumiez, an apparel, footwear, and accessories retailer strategically focused on customer-centricity, maintains profitability by aligning its inventory management and pricing. In a bid to increase profitability, the company is prioritising full-price selling in Europe, mirroring a successful strategy employed in international markets. This approach preserves a perception of high quality and exclusivity.
The bottom line for retailers looking to introduce dynamic pricing strategies is knowing the unique behaviours and factors of individual customers, and leveraging that data to deliver the right price with the right message in the right place at the right time. To achieve this, retailers cannot rely solely on existing rules-based price engine software. They must leverage new adaptive technology such as Generative AI, integrate those engines with 360 holistic data (frequent competitor pricing analysis, geographical and environmental factors, supply and demand conditions, and more), and use marketing mix tools to ensure the prices delivered are fair, and continuously learn and refine their pricing insights and strategy. In doing so, retailers will drive customer satisfaction, long term value, and profitability, growing their revenue for sustained success.
