Marketing is a risky business. Anyone who's ever pulled the trigger on a major marketing campaign with no real guarantee of a return can certainly relate.
Despite all the efforts at market and product research, customers are hard to read, and they don't always respond to offers the way marketers expect them to. It's probably no coincidence that CMOs have the shortest average job tenure among all C-level executives, according to executive search firm Spencer Stuart. Risky business, indeed.
A Marketer Lifeline
But it doesn't have to be that way. By borrowing risk analysis techniques from the investment and risk management world, it is possible for marketers to gain control.
Risk analysis gives marketers the opportunity to simulate and forecast the effects of various marketing efforts on consumer behavior and translate those effects into tangible returns such as revenue, corporate growth, and profits. It gives marketing decision makers the opportunity to know the likely impact of their initiatives ahead of time. That way, they can determine the best course of action before opening the wallet.
Sound complicated? Not at all. The process centers on building financial models that connect customer behaviors to financial outcomes: i.e., customer acquisition, growth, and retention. For example, let's start with a standard question: What is the overall risk and return on marketing investment (ROMI) reward of a direct mail piece for "campaign A?"
In this case, the financial model is used to determine the likelihood that a customer will engage in the desired behaviors (opening the piece of mail, responding, making a purchase, etc.), and how these behaviors translate into financial outcomes (incremental sales). Each step of the way comes with risk (wrong address, uninterested prospect, etc.). By combining the uncertainty of each risk factor, we can estimate the overall risk and just how successful the direct mail effort for campaign A is likely to be.
A National Retailer Turns Insight Into ROMI
To see risk analysis in action, consider the case of a large national retailer. Its marketing department was facing a common yet thorny problem. Being primarily a "cash business," the company was not able to identify and track its repeat customers. Internal research projected huge potential in revenue lift if only the marketing department could identify, understand, target, and grow its "cash" customers. But facing flat or declining revenues, the retailer grew increasingly frustrated with the ineffectiveness of its marketing efforts.
To tackle the problem, the retailer decided to launch a consumer loyalty program. The strategy made sense for several reasons. First, it would increase opportunities to identify and learn more about the value, needs, preferences, and behaviors of customers. Second, it would generate incremental sales via promotions tied to the program. Finally, a consumer loyalty program would help the company compete with rivals that were also getting into the loyalty game.
Making Smarter Spend Decisions
The retailer set aside $7 million for its new program, but the plan quickly hit a snag: How and where to spend the money. A flood of questions followed: Among all of the different program designs, which type of consumer loyalty program would give them the best ROMI? Could the retailer get the same ROMI by spending only $6 million rather than $7 million? Would a small uptick in the overall spend—say a jump to $7.2 million—result in a much higher ROMI?
Carlson Marketing used risk analysis to solve the retailer's marketing investment problem. The analysis boiled down to three key issues:
- Which treatment had the likeliest probability of success
- How much to invest
- The most critical success factors.
Before any banner ads went up or mailers went out the door, the first step was to come up with potential designs for the program. To guide the selection process, the retailer had three criteria. Each program had to identify and track customers, act as a platform to treat customers differently, and enable the retailer to acquire, grow, and retain customers.
Three loyalty program designs met the initial criteria:
- A traditional points-based loyalty program
- An exclusive club program offering members special access to discounts, events, products, and services
- A cause-based program in which members agree to receive promotions in return for a portion of their purchases going to charity
Building the Model
With three options on the table, the next question became: Which program would provide the biggest payoff? Using risk analysis, financial models for each design were created to estimate each program's ability to drive the desired consumer behaviors, then translate those behaviors into monetary value.
The process began with building assumptions, or inputs. Models often include a number of assumptions. Keep in mind that the results of a model are only as good as what is put in, so it's critical to be as rigorous as possible regarding assumptions. For instance, one assumption for the retailer's loyalty program was estimating the "year-one enrollment rate," or the number of consumers who would sign up for the program during its first year.
The simplest approach to building such an assumption is to make a point estimate—essentially, pick a number based on expert opinion, experience, or a best guess. History shows, however, that the actual program enrollment rate is likely to be larger or smaller than the estimates marketers typically come up with. In this case, rather than "ballpark" an assumption, Carlson Marketing used a triangular distribution approach to achieve a more accurate estimate of enrollment rate.
Once all of the assumptions were baked in, three financial models were built—one for each loyalty program design under consideration. Each model yielded an ROMI estimate as well as the probability of achieving that estimated outcome. By comparing the results of the different models, the retailer could "test drive" the different loyalty programs, select the best program for the job, and invest in a full-scale program rollout.
Which program stood out? By comparing the results of the models it became clear that the points-based program offered the highest ROMI and the best chance of exceeding the minimum 20% ROMI hurdle rate that the retailer required.
The points-based program was predicted to produce just over a 41% ROMI. It also came with an 80% chance that the 5-year ROMI would exceed the 20% hurdle rate (see chart "Comparing Results").
Numbers with Direction
Numbers aren't the only outcome of a marketing risk analysis. By engaging in the modeling process, marketers also uncover the critical success factors for making the program pay off, an activity that Carlson Marketing refers to as "sensitivity analysis."
In this case, a sensitivity analysis on the retailer's points-based loyalty program revealed three important success factors: (1) Gather additional data, (2) Interview more subject matter experts, and (3) Engage in continuous improvement of success factors 1 and 2.
Backed by the ROMI analysis, these success factors gave the marketing department the insight it needed to spend with confidence. It knew which type of loyalty program would strengthen customer relationships and drive the highest ROMI for its $7 million spend—all at an acceptable level of risk.
Why Guess when You Can Know?
Marketers cannot eliminate risk entirely, but they can manage it. At one level, unforeseen market shifts will always appear, new competitors will enter the arena, and customers will have a mind of their own. But that doesn't mean a marketer's only option is to run a few tests, pull the trigger on a campaign, and hope for the best. By using straightforward risk analysis and developing financial models, marketers can keep risk in check and know ahead of time where to focus their resources and how their programs will perform.
How Much Does a Marketing Decision Cost?
Everyone knows that looks can be deceiving, but many marketers still go on hunch rather than due diligence when it comes to launching campaigns.
Not long ago, an enterprising marketer at a leading tire manufacturer hatched the idea for a simple campaign to steal market share: Offer customers a $50 gift certificate for every set of four tires purchased. At first glance, it looked like a winner. The message was clear and the offer compelling. There was just one problem: The campaign never had a chance of being successful.
A simple up-front risk analysis could have saved the manufacturer a lot of trouble—and a lot of money. Here's how: The manufacturer offered tires in three price ranges. For the sake of illustration, let's label the ranges low, medium and high. If the average profit for the manufacturer on the sale of a set of low-end tires is $30, mid-range tires $55, and high-end tiers $85, it doesn't take long to see that net profits would take a major hit once the costs of the $50 gift card were factored in. The tire manufacturer would lose $20 on every set of low-end tires it sold and earn a slim $5 on every set of medium-end tires.
Translation: The tire company might win some quick market share, but it would have to sell six times more sets of tires than normal just to break even on its campaign.
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