Maximizing ROI Through Customer-Level Discriminatory Pricing

While campaigns are a must for companies in B2C sectors seeking to drive up revenues, they also have an adverse affect on the bottom line, in that they partially cannibalize already guaranteed revenues (thus driving down profit margins). Price Sensitivity Analysis (PSA) is a sure-fire way to minimalize this loss…

At the very heart of B2C sector company marketing efforts are customer campaigns. A world without campaigns is at this point unimaginable, as a majority of companies are running one or several at the same time. Walk into a mall, check out a bank’s website, or stop by a mobile operator shop and take a look at the walls and windows; there are sure to be dozens of posters advertising a “latest and greatest” limited time campaign, with some sort of benefit being propositioned to potential clients to capture their business.

Campaigns have at this point become a de-facto standard in B2C, such that consumers constantly expect some type of benefit for giving their business to a company. Some facts from a variety of recent surveys:

  • 74% of consumers believe they are entitled to a discount when buying consumer items, regardless of price.
  • 20% of consumers refuse to pay full price for anything.
  • 65% of women and 41% of men admit to searching for a discount offer before making a purchase.
  • 69% of shoppers are disappointed and frustrated at having to pay full price.
  • 22% will always choose a restaurant where they know there is an offer.
  • 95% of consumers expect the number of discounts on offer to grow substantially in the next two years.

The impact on margins of campaigns can be significant, such that the bottom line can be negatively impacted. Without an x% increase in sales, an x% campaign discount on a given product or service cannot be offset. An illustration from an electronics store to illustrate this:

Campaign Impact

Despite a solid 25% increase in the number of laptops sold as well as around a 12% in revenues generated from the sale of laptops, the bottom line of this electronics store is negatively impacted, with around  a 40% decrease in profits vs. the period prior to the campaign.

Companies need to be very careful when conducting campaigns. We recommend that they conduct pilots prior to ramping them up if and when possible, as well as cancel those that appear to be headed in the wrong direction (this of course can be done only when there is an exit opportunity, a mass announced campaign can be near impossible to cancel before it runs for the stated duration). We also recommend that they conduct a customer level campaign price sensitivity analysis (PSA), so as to maximize the return on their investments.

The core objective of PSA is to prevent the cannibalization of revenues during campaigns, such that those customers who normally have and intend to pay full price for a given product or service do so. How? By not conducting any type of communications with them in regards to the campaign, as well as by keeping campaigns out of sales points that are disproportionately frequented by them.

Think about it – if a company has a specific group of satisfied customers who are willing to come back again and again and consistently pay full price for a given product or service, would it make sense for that company to offer discounts on those products or services to these customers? Not really. The objective of campaigns should be around moving an unsellable product or service, winning new customers, or winning back old customers who have drifted away, not cannibalizing guaranteed revenues. Just as a company that has a limited quantity on hand of a given product or service but is able to sell all of it at full price would never conduct a campaign, so too should companies avoid conducting campaigns in and around customers willing to pay full price.

PSA is an approach that will help companies identify these customers, with actions that can then be taken to ensure they continue to pay full price. To conduct and establish ongoing PSA efforts, the following six steps need to be conducted:

1. Normal Price Sensitivity Analysis: The first step is to go back historically (one to two years should suffice) and identify all of the products or services that customers have purchased at full price. For each customer, a score should be generated around their willingness to pay full price, both in terms of quantity as well as frequency. The idea here is to find those customers who give full share-of-wallet to the company at full price; that is, customers who show no decrease in their spending pattern, consistently pay full price during normal pricing periods, and purchase an amount of products or services that equate to the ideal expected amount a given customer would purchase over a period of time. For a gas station, this would be a customer who comes in at least once a week to fill his or her gas tank for a year. For a supermarket, this would be a single adult who comes in at least once a week and purchases at least $100 USD or so per week for a full year. For a mobile operator, this would be a subscriber who has an ARPU each month well above his peers for a full year.

2. Campaign Price Sensitivity Analysis: With the above completed, the next step is to identify those customers that are price sensitive; that is, customers who show an increase in their normal shopping patterns during campaigns, that are seeking out campaigns to ramp up their spend, that seem to give full share-of-wallet only when campaigns are running, when products or services can be procured for a discounted price. Just as in the above step, a score should be generated for each customer around their behavior during campaigns.

3. Gap Analysis: Bringing the above two together, the next step will be to identify “full-priced traditional loyalists (FPTLs),” those individuals who:

  • Are full share-of-wallet shoppers when products and services are at full price
  • Do not exhibit a disproportionate increase in spending when campaigns are running.
  • Do not exhibit a tendency to purchase products or services they do not traditionally purchase just because a discount is being offered

4. Location Analysis: With a customer analysis complete, the next step will be to identify which sales points (or channels) are disproportionately utilized by FPTLs. By examining sales performance during and outside campaigns on a point-by-point basis, as well as the frequency of full-priced traditional loyalists to use one point or another, a score can be assigned to each sales point, as well as to each sales channel, indicating the overall willingness of the specific location’s customer base to pay full price, to not be swayed by campaigns.

5. Strategy Development: The next step is to bring it all together in designing a strategy around ensuring revenues are not needlessly cannibalized, by avoiding conducting and / or promoting campaigns to FPTLs. The company should examine each of the ways in which it can achieve this mission; some straightforward as well as more complex and creative strategies that can be considered here (relevant only to some companies in some sectors, not across-the-board):

  • Not communicating campaigns on a one-to-one level, be it via e-mail, SMS or mailer, with FPTLs.
  • Not putting up billboards regarding campaigns in areas where FPTLs live
  • Not running commercials around campaigns on television channels or radio stations they listen to (this may require some market research be conducted with FPTLs)
  • Not conducting campaigns at specific sales points that are overwhelmingly utilized by FPTLs.
  • Not communicating campaigns during channel visits by FPTLS (i.e. no campaign communication when an FPTL logs in to their supermarket online delivery account, no campaign communication when an FPTL uses an ATM).

6. Piloting: The last but most important step is to conduct piloting around the above strategies before conducting a full-scale rollout. Companies have to be very careful to avoid being perceived as intentionally avoiding giving benefits to their best customers. The tactics undertaken have to be carefully tested, to assess the impact not only on the bottom line, but on the sensitivity of customers to being opted out of campaigns. By conducting pilots across a few locations and with a handful of customers, companies should be able to understand which strategies to deploy on a full-scale, which to scrap.

The above steps, once completed, should be conducted again and again on an ongoing basis, to determine if modifications need to be made to the designed strategies, to understand if specific customers are no FPTLs, or if specific locations are no longer disproportionately utilized by FPTLs.

Tags : analytics, business intelligence, campaign, consulting, customer analytics, data mining, experiment, fact-based, intelligence, marketing campaigns, price sensitivity, pricing, pricing sensitivity, retail, sales, strategy