Dynamic Pricing: Win-win Approach for Customers and Retailers

Dynamic Pricing (DP) is a well-established practice leveraged with precision by organizations such as Amazon, Uber, and Walmart. Now augmented by AI, Dynamic Pricing is a key enabling pillar for businesses in the competitive retail segment. Amazon, for instance, leverages ML algorithms to dynamically shift product prices every 10 minutes, realizing a 25% rise in profit.¹ Across verticals, businesses are successfully nudging product/service buying decisions with optimal prices, interacting dynamically with market force by integrating latest technologies such as AI to augment their efforts.

For sellers, Dynamic Pricing has become a go-to strategy to compete and improve the bottom line. But in the age where customer loyalty is a significant value creator, to what extent are they willing to dynamically price their offerings, and risk adverse customer reaction? With great power over pricing comes an even greater responsibility for retailers to strike a balance between profits and a loyal customer base.

As we explore an evolved dynamic pricing landscape up close, there’s a win-win approach on the horizon for retailers to bank on.

Dynamic Pricing in the Age of Data & AI:

Simply put, dynamic pricing helps companies automate price adjustments on demand, market conditions, and other considerations to undercut the competition and drive revenue and profit.

From the pre-internet rule-based pricing to leveraging AI-led dynamic pricing algorithms for optimized pricing strategies, the retail sector has come a long way. Dynamic pricing is increasingly fluid and data-driven, and involves real-time analysis of transactions, external data, demand, and purchase patterns to enable businesses to benefit through rapid responses to market events.

While apparel, footwear, fashion, and similar retail segments are resorting to dynamic pricing and offering discounts for a competitive edge, local community-centric segments like QSR or last-mile delivery segments are also pricing their offerings based on seasonality, holidays, events in the community, perception of volume, and demand. With an increasing number of retailers and CPG players are investing in recommendation engines, success hinges on how quickly they assess events and how fine-grained they can make their pricing response.

Key Elements of Dynamic Pricing:

To price it right retailers bank on three key pillars. Forecasting, Data, and Technology.

Forecasting

The central pillar of Dynamic Pricing is forecasting. Retailers who can predict the near future and its impact on demand and supply will be able to leverage dynamic pricing the most efficiently. But it’s inefficient if retailers only consider forecasting for demand. Having an accurate model to estimate demand is a key to successful dynamic pricing. But retailers should also consider external market drivers, including those of the competitors. Successful pricing rests on getting both demand and competitor forecasting right.

Data

If forecasting is the central pillar for dynamic pricing, data is its foundation. AI and machine learning driven real-time intelligent pricing calls for robust availability and accessibility of data from multiple internal and external drivers - from competitor actions to customer behavior to economic fluctuations. When setting prices, retailers look at how customers value their products. They try to influence and increase the value customers perceive, using data about customer behavior as well as information about competitors' prices and products.

Technology

Technology is the third and final crucial element for dynamic pricing. From defining pricing levels to automated real-time price adjustments, what was earlier manual is now automated with sophisticated AI and ML algorithms as the core of smarter and more efficient digital pricing tools.

Let's say Walmart needs to determine where Amazon’s pricing patterns are moving. With the sheer volume of data from online buyers and in-store customers, the ML models of its pricing intelligence solution can scrape data, identify patterns for each segment, and discern the optimal price. Over time, this engine will learn the context of how Amazon changes its pricing in a micro-segment or for a unit and will respond to that.

Organizations that have a better AI-driven engine and pricing intelligence application will benefit in the ongoing price wars.

Who Truly Benefits - Retailers or Customers?

With consumers being privy to online prices, the landscape is more competitive and incentivizes retailers to match discounts offered by competitors to some degree, preventing price gouging.

Retailers can alert loyal customers to imminent price increases, allowing them to benefit from purchases at the current lower price point.
Discounts during a limited window before prices rise can be a reward for brand loyalty.
With effective forecasting, retailers can mark down prices early for an "early mover" promotional pricing edge.

Offering discounts when product/service costs are low, or matching competitor promotions will be crucial for retailers to foster consumer loyalty through providing value during pricing fluctuations.

Now that we have spoken about the fundamentals of Dynamic pricing, the next article will discuss the impact and how forward-looking enterprises can leverage technology to reimagine and amplify their dynamic pricing initiatives.