Price Optimization: How Physical Retailers Can Match The Agility of Online Retailers
Brick-and-mortar retailers often encounter the problem of optimal pricing. They want to know when, and by how much should they increase or decrease their prices so as to maximize revenue. Companies with seasonal and perishable goods have the additional headache of selling off existing inventory without compromising on profitability.
Despite intense competition and increased business pressures, most legacy retailers still use relatively simple strategies to determine prices. They try to compete on low prices and more often than not end up threatening their own margins. Online retail behemoths such as Amazon and Walmart, on the other hand, apply variable pricing strategies to drive up their conversion rates. It was found that a Samsung Galaxy S7 phone on Amazon could be bought for as much as £510.29 or as less as £439 on a single day – a price fluctuation of 14% during a period of 24 hours.
Ecommerce retailers analyze a whole bunch of data to continually estimate optimal prices for their products. Depending on their business and the product category, they change prices as often as once a week or many times in a single hour. To accelerate the sale of limited inventory, hotels and airlines also routinely use intelligent pricing strategies. Setting the right price tag at the right time can offer a strategic advantage. Done well, a mere 1% price increase can deliver an 11% impact on profit.
In a world where comparison shopping is the norm, physical retailers need to match the agility of online retailers and use every piece of data available for price optimization. This article will discuss how retailers can maximize revenue by using dynamic and personalized pricing strategies.
What is Dynamic Pricing
In dynamic pricing, prices respond to changing market dynamics in real-time. It takes into account demand and supply pressures, inventory, time frames, seasonality and competitors’ prices. What it doesn’t take into account are – individual customers, their loyalty or their behavioral traits. Every customer who finds himself in a store at a certain time will be subject to the same dynamic prices irrespective of their past purchasing history or loyalty.
What is Personalized Pricing
Personalized pricing on the other hand responds to who the customer is, what is his behavior profile and what he is willing to pay. By studying personal data points, a seller can charge different prices to different people for the same product. Auto dealerships are known to typically offer personalized pricing. Depending on how a particular customer is dressed, where he lives, what he does and what he currently drives, a car salesman tries to determine the highest price a customer is willing to pay.
How In-store Analytics can Help in Maximizing Revenue
In order to make strategic pricing decisions, retailers can no longer rely on speculation. it’s essential that they go beyond their gut feeling and have a data driven and a proactive understanding of their store and their entire customer base. However, given the scale of most retail operations, it’s practically impossible to manually monitor store activities and customer behavior. This is where it pays off to invest in technological capabilities. They bring the insight, agility and speed needed in a volatile retail environment.
Let’s take the case of an apparel retailer and see how analytics can help him make better pricing decisions. The apparel retailer gets fresh merchandise every winter, which is then sold at marked prices until mid-January, after which they run the end of season sales. The prices are progressively marked down – starting from a mere 10% and going all the way up to 80% in some cases. Now, instead of running the risk of being stuck with unsold inventory despite marking down prices, the apparel retailer can adopt a demand based dynamic pricing strategy.
An in-store analytics solution can track in real-time the apparel categories which are moving fast and slow. If a new line of sweaters is doing reasonably well, one could achieve a premium by marking up its prices. Consecutively, the retailer can slash prices of a slow moving line in order to spur it along. Depending on demand variation, prices can be changed on a weekly or daily basis. The idea is to be able to respond to changing dynamics in real time or near real time. By obtaining real-time insights, the retailer can dynamically adjust prices and strategically offset any losses that may accrue due to unsold inventory later.
How to Personalize Prices for Individual Customers
According to PwC’s Global Pricing Survey, more than 60% companies set prices in relation to costs, or matching competitors. They don’t really factor in customer behavior while setting prices. In a hyper-competitive industry, every piece of data counts. Ignoring two key data pieces (location data and behavioral data) while determining prices can have a major impact on the bottom line.
To understand how customer behavior insights can positively affect sales and enable personalized pricing, consider this example – a customer enters a department store on a Monday evening. The in-store analytics solution identifies him as a ‘frequent weekday grocery shopper.’ Based on his shopper profile, the store manager decides to send him a targeted engagement for the day’s leftover bakery goods. Given that it’s a Monday evening, sales are anyway low. So, the store owner sends him a discount coupon for bakery items as he moves closer to that aisle. By setting a price point likely to inspire a purchase and delivering it within a context relevant to the customer, the store manager has increased the chances of conversion.
How to Measure Customer Response to Price Changes
Variable pricing strategies almost always raises the question of whether or not customers will accept it. Analytics can once again help you answer this perplexing question. To monitor and understand the results a price reduction/increase may yield, an in-store analytics system can collect the following data points across multiple store locations, both before and after the price adjustment.
- Total number of customers who spend time at a specific product zone
- Average dwell time of a customer in specific product zones
- Number of visits it takes before a customer makes a purchase
- How many products are sold in each category
- Total revenue per category
- Time periods when the products are purchased most and least (weekdays, weekends, holidays, mornings, evenings or nights)
- The demographic which usually makes these product purchases (age, gender, ethnicity, income, education levels)
The above data can reveal which locations have the best latitude of price acceptance and which locations are not very responsive to price adjustments. One can also identify the items or product categories which are least or most sensitive to prices. Based off of these insights, retailers can make strategic pricing decisions. They can choose to either mark up prices in categories that are least sensitive to prices and in locations which are more accepting of price fluctuations or cut down prices at locations registering a sluggish growth.
De-risk Your Pricing Decisions by Eliminating All Unknowns
Price is a key lever for both businesses and customers because price adjustments not only influence which products customers buy, but also when they buy them. Both dynamic and personalized pricing strategies offer a feasible and attractive path to increase revenues and profit margins but they require a certain degree of analytical sophistication. If retailers don’t invest in developing these analytical capabilities and implement them in a timely manner, they could find themselves competing on low prices – a strategy that is likely to yield far lesser returns than expected.
To gain a better perspective on how using data more effectively can benefit your retail operations, download our whitepaper, The Physical Store of the Future.