I call people involved with creating pricing strategies margin magicians. It sounds so much better than pricing strategist and it’s a lot closer to the truth. The magic is a balancing act between margins and volume, supply and demand, competition and competitive advantage. Data already plays a big role in determining price and has for a very long time. When I talk to teams about data enabled pricing they come to the conversation saying, “We already do that.”
Where Small Data Can Take Pricing Strategy
Your businessprobably does too. The business has talked to customers and found that there is a spread of prices they’re willing to pay as well as a spread of prices being charged. Senior leaders have asked the question, why is that? Why are some customers willing to spend more and some willing to spend less? You probably have data on that too. Brand, quality, features and other common themes rise to the top. The experiments you’ve run have revealed behavioral trends too. Things like categorical thinking come up and influence how people perceive price.
Let’s ask the deeper question about why customers pay different prices. It’s a question about how we make buying decisions. Let’s take a customer at random. They’re looking to buy a product in a competitive market so they have options. Many pricing strategies hold that, all things being equal about the products, the lowest price wins. If products are differentiated from each other, then the one which is the best fit for the customer’s need at the lowest price wins.
That’s because most pricing strategies assume customers to be rational decision makers which could not be farther from the truth. Rational decision making only happens when the customer knows all possible options (decision outcome pairs in the decision space) and has enough information to be certain about how much value (probability and loss or utility) they’ll get from each option. Does that describe many/any of your customers?
Customers make decisions under uncertainty. As a result two customers with identical product needs can have two completely different prices that they’re willing to pay. There’s a solution for that called price discrimination. We offer the same good at different prices to different customer groups. Since customers talk to each other we’ve also had to come up with clever ways to justify price discrimination. A plane ticket usually costs more closer to the day of the flight than it does two weeks in advance. Clothes go on sale at the end of the season or during holidays. Buy smaller quantities and you’ll pay more than someone buying in bulk. Better negotiators get a better price.
Big Data Starts To Add Value with 4 Basic Insights
If you look at enough datasets and experiments about customer buying behaviors in relationship to price you’ll discover just how deep the irrational decision making runs. To make a long presentation short, customers pay whatever a company can convince them to up to a budgetary maximum. That’s a big data insight about pricing and intuitively you’ve always known that. The data demonstrates that pricing doesn’t operate alone in customer decision making.
To get the most out of your data you have to ask the right questions. With respect to pricing strategy the right question is how do I use price to maximize the value I get from each customer? Without big data many pricing strategies look at this question from the perspective of a single sale.
The better metric is Customer Lifetime Value (CLV) or the total value of each customer over their entire relationship with the business. Before you think I’ve brought you a ridiculously difficult problem to solve check out this free CLV calculator from the folks at Harvard. All you need is some basic info about your customer buying habits and retention rates.
Thinking in terms of CLV is leading to some very innovative and lucrative pricing strategies. If you look at how Google and Amazon price, you’re looking at some of the most sophisticated pricing strategies out there. They are driven by large datasets and are aimed at increasing CLV. That leads to the second big data insight about pricing. Companies can use pricing in tandem with product, brand and marketing strategies to increase CLV. Again, intuitively you probably already knew that.
The key next step is gaining a deeper understanding of the customer. Using analytics to learn then predict how likely the customer is to be loyal, how many products they’re likely to buy and how much a business can do to drive both behaviors are all critical parts of a data driven pricing strategy. Companies like Sephora drive 80% of all sales through their customer loyalty system and have amassed a significant dataset on customer buying behaviors. Casinos do much the same thing with some casinos logging 90% of all play through their loyalty system.Grocery stores have a high percentage of spend through their rewards cards. CRM is also big in the B2B space, providing the same data for analysis. The result is a picture of CLV that allows businesses to tailor pricing to drive loyalty/repeat spending and maximize margins on infrequent or one time customers.
The third big data insight comes from a fairly sophisticated layering of loyalty, pricing and marketing data from these datasets. Brand engagement is a significantly higher driver of customer loyalty than price. Loyalty systems that build a connection to customers through personalized engagement and experiences, like Sephora’s, have much higher CLVs and retention rates than those using discounts. To make a long data presentation short (if you want the long version email me), discounting frequently leads to the opposite of the desired behavior.
The only thing companies do by indiscriminately lowering prices is train customers to game the system for lower prices. All a competitor has to do to lure those customers away is offer a lower price and the lost margins meant to drive loyalty have resulting in exactly the opposite behavior. Retail has learned this lesson the hard way and is now working its way back to a more profitable business model.
I worked with a manufacturer who had started running discounts at the end of each quarter to drive additional volume. It was successful so the company continued the practice for two years before they realized a problem. Customers became trained to hold their orders until the end of quarter. Margins dropped significantly so the discounts were stopped. Competitors continued to offer their discount programs and customers were lost. I was told by a Macy’s store manager in the early 2000’s that they’d trained their customers to wait for the sale and they didn’t know how to reverse that trend without losing customers. It’s a problem that spans across markets.
This goes back to the first big data insight. Customers pay whatever a company can convince them to up to a budgetary maximum. With discounting a business is convincing a customer to pay a lower price. Tell a customer that a $39.99 product is on sale for $24.99 enough times and they believe the product is only worth $24.99.That leads to the fourth big data insight. Only discount when it adds to the customer’s engagement with the brand.
Starbucks is a good example of this. Through their loyalty program customers buy a certain number of drinks and then get one free. Rather than sending the message of this drink is worth $0, it says, “Thanks for your business. Here’s how much we appreciated it.” Apple is another good example with the iPhone. When a new model comes out, the old model is discounted. That opens the older model to a new market, driving volume but it tells customers with the old phone something too. Your phone is not as valuable as the new one. That perceived loss of prestige drives upgrades in a couple of their customer segments.
We’ve come full circle, returning to the deep dive into customer decision making. If we were rational decision makers, utility would reign supreme over our decision making process. That would put price at the forefront of the process. However we’re not rational and our perceptions, beliefs and biases play heavily into our decisions. A strong brand connection plays more into the equation than pricing.
Big Data Can Do a Lot More For Pricing
Big data is most effective when insights are layered and they begin to reveal patterns that were previously unknown to the business. Once it’s been revealed, pricing strategy’s role in maximizing CLV and enabling brand engagement is better understood. The pitfalls of discounting are easier to avoid. The focus can shift from fairly obvious insights to discovering new patterns in customer segments. This is where an algorithmic approach breaks down and heuristics become a lot more successful.
Heuristics allow new patterns to be recognized by the machine. The first four insights sound like common sense because intuitively we’re able to come to the conclusions ourselves through experience and anecdotes. Those types of insights are what algorithms are able to reveal. Sift through enough datasets and some basic patterns become obvious. There are other patterns in the data that aren’t so obvious. Detecting those require heuristic methods that are able to detect subtle patterns in very large datasets.
The goal moves from categorizing customers after a few interactions to categorizing the customer during their first; from broad categories to increasingly granular ones. These categorizations when combined with our four basic insights about pricing allow for real time pricing strategies that are effective across multiple channels. Point strategies maximize margins while also enabling loyalty and repeat purchases. Heuristics allow these pricing strategies to be personalized by customer category with increasing granularity.
Longer term strategies can also be created. Customers change over time. Businesses grow or shrink and people become more/less affluent or sophisticated. From a CLV perspective it’s inefficient to allow customers to leave a brand because of these changes. Tiered pricing and product lines are one method big data reveals is effective in preventing these types of departures. Toyota doesn’t want to lose high end customers because they don’t have high end cars so they created the Lexus line. Nissan has Infinity and Honda has Acura with the same goal. Mercedes wants to expand its reach to younger buyers and has introduced new car lines to accomplish this goal. Those buyers now begin the loyalty cycle earlier and CLVs grow. The combination of brand loyalty and tiered product/pricing strategies also becomes more granular, again allowing for greater personalization.
This increase in personalization goes a long way towards creating that connection between customer and brand that I discussed earlier. The goal of big data enabled pricing is increasing levels of personalization that drive increasing levels of brand loyalty and higher CLVs. As the business’s proficiency with big data and advanced analytics grows, the categorizations become faster, more accurate and more granular.
The Bottom Line: Why Use Big Data For Pricing Strategy?
There are two drivers for big data and heuristic enabled pricing: customer preferences and competitive pressures. As customer loyalty systems become more prevalent and increasingly sophisticated, customers are beginning to expect higher levels of personalization. They’re expecting pricing to match their levels of loyalty. Progressive recognized this trend a few years ago. Their loyalty system gives pricing and perks to new customers based on their loyalty to their last insurance company. The message is clear, “If your last company didn’t appreciate your loyalty come to us and we will from day 1.”
This is the second driver for data enabled pricing. Competitors are luring customers away with data enabled pricing strategies. This will drive even the staunchest holdouts to adopt the methodology. Those businesses that don’t will find it difficult to compete in the next two to three years. The bottom line is big data enabled pricing is a matter of business differentiation in the short term and business survival in the long run.
Author Bio: Vineet Vashishta is the founder of V-Squared Consulting, a leading edge data science services company. He has spent the last 20 years in retail/eComm, gaming, hospitality, and finance building the teams,infrastructure and capabilities behind some of the most advanced analytics companies in the US.
You can follow him on Twitter: @V_Vashishta.