Dynamic Pricing De-Mystified

Retailers are being hit by the awareness that always-on customers, who are price loyal rather than retailer or brand loyal, do price comparisons 24/7. This has prompted a surge of interest from retailers on the topic of dynamic pricing. With Amazon adjusting thousands of prices daily, retailers can no longer allow long lead times or take a set-it-and-forget-it approach to pricing.

Dynamic pricing is one of the hottest, but also one of the most controversial and misunderstood, topics among retailers. There are plenty of misconceptions about what "it" is, which means setting the record straight is a good starting point for an effective evaluation. For example, some common misconceptions associated with dynamic pricing include:

  • Changing the price of a single item several times a day.
  • Only raising prices or, conversely, reflexively changing prices to match or come in below a certain competitor's price.
  • An automated price bot used in a silo — that ensures a mindless race to the bottom.
  • Only being applicable to online retailers. Some brick and mortar retailers do not realize dynamic pricing is a viable option.
  • Being theoretical, optional or a nice-to-have.
  • Believing shoppers will view more frequent price changes negatively.

The reality of dynamic pricing is quite different. For starters, one of the biggest myths is that it is theoretical. It isn't. It is here and growing rapidly in adoption. Amazon has been using dynamic pricing for years and more and more retailers are adopting the approach. When dynamic pricing is combined with price optimization built on artificial intelligence and machine learning science, it can monitor the market shifts that influence shoppers' price sensitivity levels. That ensures prices are maintained that will be considered fair and be in line with what shoppers are willing to pay. At the same time, the outcome is one that respects a retailer's pricing policies to achieve strategic and financial objectives.

Understanding Elasticity

However, because shoppers' price elasticities can vary considerably an even greater challenge is placed on the retailer. The beauty of dynamic price optimization is that it can determine a shopper's price elasticities at an item and store level across the retailer's entire assortment. At the same time, it understands each category's strategy, such as margin enhancing, basket driving or revenue generating to balance price increases and decreases across the assortment to deliver results that are optimal to both the shopper and the retailer.

Interestingly, and counter to what many assume, shoppers have a strong sense of fairness and they are comfortable with the idea of dynamic price optimization and the use of data science to change prices. A March 2017 consumer study conducted by Forrester Consulting on behalf of Revionics revealed two key findings. First, 78 percent of shoppers felt it moderately fair to very fair for retailers to use data science to increase or decrease prices as long as those prices were fair and aligned with something they were willing to pay. Second, only 6 percent felt it wasn't fair at all to leverage data science to set prices.

This widespread acceptance by shoppers of dynamic pricing in retail can be attributed to the pervasive usage of dynamic pricing in industries such airlines, hotels, sports and rideshare services. These industries have groomed shoppers to become accustomed to high-frequency price changes just as they have reshaped shoppers' other expectations of retailers when it comes to store experience, ease of payment and customer service.

However, in the context of the retail industry it is important to understand what dynamic pricing truly is: a hands-off approach that systematically determines what the "right" price changes are and rapidly executes them. It is intended to run within specific user-defined thresholds and will not exceed them. In the event an optimal price recommendation wants to exceed these predefined thresholds, dynamic pricing employs alerting and exception management capabilities. This triggers human intervention to review and either reject or approve the recommendation.

It is equally important to understand that dynamic pricing is not equivalent to changing the price of an item several times a day. It is changing the price of an item only when it is necessary to keep it in alignment with the shopper's price sensitivity and perception of a retailer's price image, while also achieving the retailer's defined strategic and financial objectives. Even Amazon only changes their price on any given item one or two times a day. It's the fact that Amazon changes prices on so many items within its massive assortment that contributes to the volume of price changes attributed to it.

What it's not

Dynamic pricing is not simply adjusting prices to match or come in lower than a price offered by a competitor. In fact, the recent Forrester shopper study reveals that relatively few shoppers are enticed by price-matching policies, with only 17 percent saying they buy products based solely on the cheapest price and the same percentage saying they would demand price-matching on products they wish to purchase.

Another misperception is that dynamic pricing is confused with price bots. It is important to know that these are separate capabilities. Price bots are merely a data collection method, using intelligent algorithms to match competitive assortments, collect and bring back the competitive pricing and assortment data. Dynamic pricing consumes this data, along with numerous other critical data sources such as shopper behavior data, market basket affinities, seasonality, transaction data, weather, and many other data sources to recommend and execute intelligent price changes.

Another myth that needs to be toppled is the assumption that dynamic pricing is just for online commerce. Unlike many retailers, who still struggle with implementing a truly integrated omnichannel strategy, shoppers do not see channels. Shoppers instead see a single path to purchase, seamlessly switching channels as they do their research, comparison-shop, assess social media input and ultimately purchase an item.

Interestingly, and again contrary to popular belief, shoppers do not expect consistency between online and in-store. The Forrester study revealed that generally shoppers expect lower prices online versus in-store for major retail categories. A notable exception is grocery, where most respondents expected prices to be the same or lower in-store as opposed to on-line.

It is true that brick-and-mortar retailers have far more constraints when it comes to executing frequent price changes. Unlike the online world where price updates are handled systematically, in-store is a different beast that traditionally requires expensive and costly in-store labor to execute price changes. Increasingly, however, dynamic pricing is being adopted in brick-and-mortar stores where electronic shelf labels (ESLs) are present. It is worth noting, though, that even the most automated ESL-using retailers typically change prices at most nightly. They are not executing price changes while shoppers roam the store. Not even the most tech-savvy shopper wants to experience a different price at checkout than was promised when the item was selected at the shelf.

The Way Forward

Retailers have learned the hard way that shoppers are highly price sensitive and loyalty to any retailer, brand, or product can be fleeting. Shoppers have complete price transparency and expect complete flexibility on where they purchase and where the item is fulfilled, which has created a competitive landscape that has never been more hostile. Some retailers are struggling to remain relevant and responsive to their shoppers and others are failing. Still others don't help themselves by engaging in never-ending price wars and excessive in ineffective promotions, causing margin erosion and declining market share.

The time has never been more right for retailers to seek a sustainable competitive advantage. Retailers have no choice but to become more responsive to the fast-changing behaviors of today's millennial and tomorrow's generation Z shoppers. Those who combine the power of price optimization with the automated, machine-learning approach of dynamic pricing continue to grow more responsive to shoppers through the delivery of fair prices and appropriate price changes at just the right time.

Cheryl Sullivan is Chief Marketing and Strategy Officer for Revionics, a provider of profit optimization software that enables retailers to leverage predictive analytics and demand-based science to build shopper-centric, responsive merchandising strategies.