In today’s world, sellers have no choice but to adopt more sophisticated strategies to adapt to increased competition and growing consumer savviness, writes Pini Mandel, CEO and Co-Founder of Quicklizard.
In today’s world, we take it for granted that fixed pricing is a bit of an anomaly. For the majority of human history, people exchanged goods in local marketplaces. In these environments, pricing was extremely dynamic – a give and take between the buyer and seller, impacted by a variety of changing circumstances like production costs, how much the buyer was really willing to pay, and how many competitors were present in the space at the moment. In those days, sellers took factors such as their familiarity with the buyer’s personal situation and finances into account when trying to determine the best possible price for both parties.
Only in the twentieth century, as specialized stores and large retail chains began to dominate the market, did fixed pricing become the norm. Personal and real-time factors ceased to have an impact on the price of a given item. Even fluctuations in production costs like commodities and labor didn’t impact pricing in real time – their impact was only seen over time. However, the fact that it became so ubiquitous doesn’t mean that it made the most sense in all circumstances.
In just two decades, ecommerce and the internet have turned the established retail world on its head. Consumers are now free from their dependence on local sellers, now that they can buy almost anything online.
A plethora of price comparison sites have since been created to give consumers the tools to find the best price for the goods they wanted, whenever they want. This has changed not just how people shop online, but how we shop in physical retail stores. Many consumers use pricing comparison platforms to check out brick-and-mortar stores and how their pricing fares compared to competitors before they even leave their homes.
While price transparency has been empowering for consumers, it hasn’t always been beneficial to retailers. In the new world of ecommerce, they were forced to compete not only with other retailers in their geographic area, but with competitors worldwide. On the other hand, they still had to maintain their profit margins to stay solvent.
Retailers can no longer afford to remain in the world of fixed pricing now that consumers have moved on. They need new tools to succeed in the new world, including tools that leverage data to make the most of real-time opportunities. Retailers need to be in a position to accurately respond to changes in demand, so customers stay happy and they stay in business. They need a simple way to optimize pricing in real time using off-the-shelf algorithms and data models.
There are a lot of elements that impact price—so many that it’s impossible to keep track of them manually or even using standard tools like spreadsheets. These elements include:
Elasticity of demand: This economic term basically refers to how much the demand for a product is influenced by fluctuations in its price. For example, staple foods and fuel are fairly inelastic in terms of pricing – people need to eat and get to work, so they buy those items even when the price is high. Leisure activities and luxury goods are more elastic. When the price goes up, some consumers simply opt out.
Inventory turnover and expiration dates: Food, medicine, and other items have expiration dates, after which point they can’t be sold. While it wouldn’t make sense for a retailer to sell a product with a tiny profit margin when the product has a long shelf life remaining, if the product is about to expire, the equation changes. It’s better to sell it for a small profit than to throw the product out and lose money. Algorithms make the calculation simple with predictive sequential markdowns that help clear out the inventory before it expires.
Seasonal fluctuations: Optimizing pricing for seasonal change is more complex than having an end-of-the-season sale in the entire store. Rather than applying giveaway pricing for wide swathes of inventory, algorithms can cluster items and give the ideal discount on each item to maximize revenue over time. For example, some items may be multi-seasonal and not require a price drop. Optimization can involve coordinating prices with the weather on a specific day, or or in a specific area, or reflect local events or community holidays.
Live tracking of inventory: Off-the-shelf machine learning can help retailers adjust pricing according to what is available in their warehouse at any given time, and their specific sales goals.
Profit optimization doesn’t only benefit the customer, it’s now at the point where both the retailer and the consumer benefit. For a long time, there was a concern that people wouldn’t accept changing prices at retail, but ecommerce giants like Amazon have changed consumer habits. Consumers today are very savvy – they know how to find the best price for the goods they want, and accept the fact that it changes over time.
Sellers have little choice but to adopt more sophisticated pricing strategies. As such, we are seeing price optimization become an increasingly mainstream technology in the retail sector.
We have seen that retailers that adopt optimized pricing typically see about a 30% increase in revenue and an 11% increase in profits. Pricing optimization that considers a wide selection of variables and utilizes sophisticated data analysis has really become the only way to attract a steady customer base, maintain profit margins, and adapt to the challenges of today’s market.
Pini Mandel, CEO and Co-Founder of Quicklizard