If you had asked Jack Benny or Ted Williams their secret to a great performance, they would probably have said "timing."

As the old adage goes, "timing is everything." And if you are a marketer, especially, you most likely swear by it. But while the idealistic mantra of direct marketing has always been to make the right offer to the right customer at the right time, the reality is very different.

For all the talk surrounding it, time is actually the most underused and underappreciated marketing dimension. Effective segmentation can reveal the right customer, and good analytics (in the form of purchase probabilities) can match the customers to the right products; but the third one, the right time, is the often-neglected stepchild of effective marketing campaigns.

What many marketers fail to appreciate is that probabilities are perishable. They have an expiration date, just like a carton of milk. Markets change, and if data is not used within a certain time, it might go sour. The consequences of going to market based on data past its "use by" date are low response rates, wasted money, and poor ROI.

Understanding the Problem

Good market timing requires that you understand three kinds of behavior: customer cycles, product cycles, and marketing cycles.

Customer Cycles

Customer cycles vary with each customer. Some buy daily. Some stock up monthly. Others show no pattern at all. Marketers often fall back on recency (time since last purchase) but, as you'll see in a moment, recency can lead you astray.

Frequency can also be a misleading time metric. So recency and frequency are often combined with monetary value (revenue received) in an attempt to optimize the timing of marketing campaigns. This combination of recency, frequency, and monetary value (RFM) turns out to be the most popular segmentation method used today, primarily because it's marginally helpful and easy to calculate.

RFM actually does a decent job of identifying the very best and the very worst customers (the top 10 percent and the bottom 10 percent), but most marketers know who those are already. Unfortunately, RFM is a woefully inaccurate tool for predicting the behavior of buyers in the middle 80 percent of customer segmentation, the group that offers significant growth opportunity for most businesses. Here is an example of how RFM fails to predict customer behavior:

Customer Jan July Sep Dec Jan
Acme X $1,500 $1,000 $500 $200 $100
Acme Y $100 $200 $500 $1,000 $1,500

At the end of the year, these two customers have spent the same amount of money (same M). They have bought at the same monthly rate (same F). They each last bought in December (same R). Their RFM scores are identical, but it is clear that they are different customers moving in opposite directions. Understanding customer cycles is a critical piece of the time equation: a necessary piece, but not sufficient by itself.

Understanding product cycles

Does your appliance dealer know when the support contract on your washing machine is up for renewal, or is it up to you to monitor it yourself? Can your online retailer predict when that bottle of vitamins you bought last week is going to be used up? If they understood these product cycles, how could that knowledge generate more revenue for them?

For example, I continue to be impressed (and annoyed) by how my car dealer ignores my own product cycles. I bring my old but treasured sports sedan in for service. The service reps swarm over the car and read the vehicle identification number and current mileage. They go over to their computer and enter the numbers, and sure enough up pops my service record. I would bet that after doing this a few times they could predict to within a week or two when I should show up next for service. But have they ever offered me a discount or advised me of specials to keep me from defecting to my local gas station for repairs? Not once. Yet marketing to me effectively to retain my business means understanding my product cycles and applying that knowledge to a campaign.

Understanding marketing cycles

Data-driven marketing is fueled by the one reliable source of data that can be used to predict future behavior: customer transactions. And only four pieces of data can tell you almost everything you need to know: transaction date, transaction amount, customer ID, and product information such as the SKU or product group description.

Let us walk through the whole process.

Assume that your accountant closes the books on the last day of the previous month and the data is delivered for analysis within five days. In the best of worlds (and it is being done), data is analyzed overnight and marketing campaign lists are built the next day.

If your company is efficient and the data set is not too big, de-dupe and suppression will take another week. Let's say the campaign has been already developed and you are ready to match offers with customer purchase probabilities. You will still need to plan time to get the message in front of your customer:

  • Email: another one-to-two weeks to campaign launch

  • Direct mail: another month for printing

  • Telesales: within one week

If your campaign will run for a month, you must know the probabilities in that month. You need to coordinate and synchronize your marketing cycle to the period when the probability of the customer making a purchase is highest.

Fixing the Problem

To get optimum results from a campaign, you need to make the time dimension an integral part of your planning:

  1. Calculate the probability for every customer's buying every one of your products or services.

  2. Select the customers who have the highest probabilities for the products you want to feature.

  3. Identify the dates when those probabilities are highest for each selected customer and corresponding product.

  4. Also identify the dates when those probabilities drop to 50% and 10% of their peak values.

  5. When you put all this in a database, you've systematized your marketing for the next many months or year—you can look up when and what to market to each customer.

  6. Regular analysis tied to your typical business cycle will keep your data refreshed.

You haven't made a great campaign yet, but you've optimized the opportunity for a great product, a great offer, and a compelling contact strategy by making sure that your timing is impeccable.

Companies that follow this data-driven approach are the leaders in their market niche. They are able to turn that idealistic mantra into profitable reality.

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Timing Is Marketing's Stepchild

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Dr. Mark Klein is CEO and founder of Loyalty Builders LLC (loyaltybuilders.com) and three other companies. He blogs frequently on mathematical marketing and recently published his first novel. You can reach him at 603-610-8800.