In today's consumer-centric economy, it comes as no surprise that nearly every business process has been disrupted by big data and customer insights. Retailers are getting their hands on larger portions of customer data than ever before. However, many retailers have loads of data, but no idea what to do with it, and no idea how to extract the insights they need to develop the right pricing optimization strategy.
"The key is moving toward a digital transformation of all business processes that creates a digital core and a fully integrated business infrastructure," says Lori Mitchell-Keller, global general manager for consumer insights with business software solutions provider SAP. "With this digital foundation, companies can more easily tap into data from all parts of the business to create a real-time view of the customer. Companies can no longer afford to make business decisions based on old data or partial views. Consumers are making decisions in real time, and businesses need to match this speed of change."
What are the trends and preferences? How much are consumers willing to pay? And via what channel are they willing to shop? These are questions retailers need customer data to answer, Mitchell-Keller says.
"Consumers are making decisions in real time, and businesses need to match this speed of change."
"With the intersection of data and demand, the most effective optimization models incorporate analytics," she explains. "These insights enable retailers to better understand how demand varies at different price levels, and how to maximize sales and profit."
Peter Shapiro, vice president of sales effectiveness practice at market research firm Nielsen, agrees about the importance of adding customer insights to the equation.
"Retailers need to drive loyalty and engage in practices that promote long-term value," he explains. "Short-term dollars and cents [matter] a great deal, but long-term viability suggests that retailers need to have a lens into consumer worth."
EDLP vs. High-Low
Of course, retailers have a number of models from which to choose for pricing optimization. And arguably, they have relied on two of them more than others: everyday low price (EDLP) and high-low. Both models fundamentally are about balancing the investment in regular price and promotional pricing to drive customer response, price perception and loyalty.
EDLP focuses investment in regular price, which offers value to all customers in a simple, consistent manner, says Chris Fladung, vice president, enterprise price and promotion strategy at shopper insights firm 84.51°. However, it does not consider the customer response to promotional pricing or create urgency to make a shopping trip or purchase.
High-low, on the other hand, focuses on deep promotional discounts that do add a sense of urgency and drive trips and purchases, Fladung states. However, it also adds complexity to customer shopping and can hurt price perception if used incorrectly.
Fladung emphasizes the importance that retailers focus on the customer expectation and brand/value perception when selecting a pricing model.
"Customer expectation can be based on how the competitive market is pricing, i.e., deep promotional discounts on clothing, or good everyday pricing on milk and eggs," he says. "Or it can be based on how customers currently view your brand and current pricing tactics, i.e., Walmart EDLP pricing."
The success of each model depends on several factors, Shapiro explains, notably the needs (especially unmet ones) of the marketplace, the competitive retail landscape, the retailer's brand DNA and how it differentiates itself in the marketplace (quality, variety, low cost or no frills, etc.), the strength of the retailer's own brand, and the level of the retailer's operational excellence.
With EDLP, for instance, pricing is harder to pull off because of the growth in marketplace transparency. The advent of omnichannel competition and sophisticated technology can easily expose EDLP "frauds," Shapiro warns. It is, in fact, most effective in situations where consumers value having the best possible price any time they shop in a particular store, but those needs aren't being met. However, such retailers considering EDLP must employ best practices operationally to minimize costs. Such practices could include limiting departments and assortments within categories, or explicitly carrying unique items to drive a new value equation.
Consumers who favor the EDLP model often are those lacking discretionary time, who are accustomed to making one-stop shopping trips. Meanwhile, consumers who favor high-low pricing are those who need the best price on key items and could be willing to "shop hop" for such prices.
"Of course, the goal for retailers is to attract consumers with hot deals and win over their entire trip—not just to be cherry-picked," Shapiro says.
"The goal for retailers is to attract consumers with hot deals and win over their entire trip—not just to be cherry-picked."
However, it's important for retailers to understand that consumer preferences are changing constantly, and loyalty is much harder to maintain, Mitchell-Keller explains. Choosing one model might no longer be the best strategy. Tapping into big data and real-time insights such as social-sentiment analysis allows retailers to identify the optimal strategy.
Beyond EDLP, High-Low
Aside from EDLP and high-low, retailers can rely on several models for price optimization. Cost-based pricing examines the margins and ideal price needed to make a profit. These costs set the price floor and determine how much a retailer can charge, Mitchell-Keller says. Although a number of retailers implement the model to provide a fair rate of return and lower prices, there is a push to increase the number of sales while accepting lower margins.
"The cost-based method requires significant cooperation along the supply chain and requires a seamless marketing plan," she offers.
Value-based pricing—called the "gold standard" for many retailers by Shapiro—also is a popular strategy, Mitchell-Keller says. It focuses on utilizing the most accurate customer modeling to achieve ideal optimization. The model takes advantage of a customers' sense of value to assign the ideal price. If a retailer is equipped with the latest data and customer information, value-based pricing offers the chance to match pricing to demand for high-value products.
"Ideally, the retailer has dynamic, real-time price-modeling tools based on real-time customer data," Mitchell-Keller points out. "New strategies such as creating a digital core are enabling this type of real-time modeling."
However, Shapiro warns that the value-based pricing model can strike consumers as "inherently unfair."
Another model Shapiro suggests is portfolio pricing, which balances strategic and tactical needs. The model is advantageous to retailers because it incorporates many objectives products have. Although it can be complex, the potential rewards for successfully executing it are high.
And a model gaining more traction with many savvy retailers and leading CPGs is price pack architecture, says Rob Wilson, managing director at L.E.K. Consulting. For this method, it is key for retailers to understand consumers' willingness to pay for features and benefits versus the cost to deliver, which can be accomplished through a conjoint survey and simulation.
"For example, one- and two-person households are the largest and fastest-growing segments of the U.S. population," he explains. "These consumers are often willing to pay a much higher price per ounce for smaller-portion packages—think less is more—or resealable packaging. Working with CPGs to optimize their offerings to consumers for the experience or occasion of the category and planogramming appropriately during category resets can drive profitable growth over the long term."
"Only by predicting customer response and tailoring our pricing to customer response can we put the customer at the center of our pricing strategy."
Price Optimization without Analytics?
It's rare to find a price optimization method that doesn't rely on predictive analytics, as the practice works only in an environment that is not highly competitive, and few such places exist today in the CPG retail marketplace. Still, such optimization methods do exist. For instance, on occasion, retailers may replace predictive analytics with broad strategic measures, Shapiro says.
"For example, taking the top 100 products and pricing them a certain way—e.g., half the margin versus the average product—to drive the perception of low price could be one such practice," he states. "We would suggest that even this scenario would benefit from predictive analytics."
And some strategies can get downright creative. For instance, Wilson notes that he has seen retailers collaborate with their CPG partners to measure event success at the store level, going so far as to offer rewards such as a fishing trip to store managers that drive the most lift from promotional events.
"This creates a fun atmosphere and incentive to maximize execution efficiency at the local level," he explains.
However, Fladung notes that methods that don't rely on predictive analytics, which tend to use margin rules or price differentials as the basis for pricing, do not take customer response into account. And this can be harmful.
"Only by predicting customer response and tailoring our pricing to customer response can we put the customer at the center of our pricing strategy," he states. "The future of price optimization puts the customer at the center more than ever before. This may at first be accomplished by taking into account responses on a segmented basis, e.g., ensuring that our best customers are engaged in a category promotion, and will soon progress to more personalized pricing. The next generation of price optimization systems must pave the way for this paradigm shift."
But in the end, whatever the method, price optimization has to be seen as a long-term investment. Although there is constant short-term financial pressure on rate to optimize pricing and drive profit to the bottom line, this ultimately can lead to price increases, which, in turn, could erode price perception and customer loyalty.
"Optimizing for the long term typically involves investing profit and decreasing rate in the short term in order to improve customer price perception and loyalty," Fladung states. "This long-term strategy drives basket and profit-dollar growth. Our goal is to utilize price and promotion to delight shoppers and build customer loyalty, which will, in turn, drive baskets and total store performance."