The difference between rewards and business rules, and why it matters.
Last time, I reviewed basic reward schedule categories as defined by B. F. Skinner: continuous reinforcement, fixed/variable interval, and fixed/variable ratio. I also provided real-world examples and outlined reward schedule measurements: learning curve, frequency, and decay.
In the next few columns, I'll weigh the pros and cons of each reward schedule type. I'll discuss when each type is good to use, which should be used with caution, and which should be avoided. Today, we'll explore continuous rewards, the scariest of the bunch.
The Continuous Reinforcement Reward Plague
Each reward schedule varies in effectiveness. A continuous reinforcement reward schedule always occurs. In Skinner's experiments, food came down the chute every time the mouse pressed the lever. Free shipping on every purchase would be a real-world example. Both Amazon.com and Barnesandnoble.com tried this reward in the late '90s, later restricting free shipping to certain order sizes. Many online companies today still offer free shipping, even more offer it during the holiday shopping season.
Let's measure continuous reward schedules using our three criteria:
What do these measurements mean? Picture a bell curve. The first third (in which the curve starts at zero and ascends) is the learning curve. It's how quickly users alter their behavior based on the new promotion.
A steep learning curve shows people understood the reward quickly and altered their behavior in a relatively short time. (People usually use the term "steep learning curve" to indicate something is difficult to understand, but it has the opposite meaning here.) This reward schedule has a steep learning curve, which is good. It means the user's behavior alters almost immediately when the promotion is put in place. If you want higher short-term numbers quickly, use a program with a steep learning curve.
The middle part of the bell curve denotes frequency. Frequency shows how often the user interacts with the system once the reward is in place. The frequency of a continuous reward system isn't as good as those of other reward schedules. If the reward is always there, a user must only interact with the system when he wants the reward.
If Amazon offered free shipping with every fourth order, the customer would purchase more frequently to build up to that fourth order. With constant free shipping, there's no incentive for frequent purchases. The customer is rewarded whenever he buys. This isn't to say you won't see a marked increase in usage when you offer free shipping. But frequency won't be as high as it could have been with other reward schedules.
The bell curve's fall-off is decay. It's the measure of how quickly users stop interacting with you once the reward ends. A continuous reward system shows the worst (fastest) decay of the various reward schedules. Decay is perhaps the most important metric for these programs. It identifies how loyal people will be after the promotion is over.
Decay is partly why businesses slump in Q1. They used continuous reward programs so heavily during Q4, there's a very quick decay once those rewards are over. Users who were attracted to you because you offered free shipping on every purchase, 10 percent off every purchase, or other such rewards are alienated once these rewards are removed.
Continuous Reinforcement Becomes a Business Rule
It's important to understand the difference between a reward and a business rule. A reward occurs occasionally, is basically unexpected by the user, and is a competitive advantage. A business rule is an ongoing part of how your business operates.
The problem with a continuous reinforcement schedule is over time it becomes a business rule. It always happens. It's always there, so it isn't special. It's simply how you do business. And when you remove the reward, you effectively change your business rules, which alienates customers in a much more extreme way than simply removing a promotion does.
Some rewards become business rules by accident. When Urban Fetch was still around, it included little giveaways with orders (branded Post-it notes, kitchen magnets, etc.). Though this loyalty idea wasn't intended to become a continuous reward schedule, it did. A little goodie came in every order, not just some orders. I came to expect them. When an order arrived one day with no freebie, I was disappointed. Once a user expects something, it's no longer a reward; it's a business rule.
Free shipping became a business rule for many online companies in the late '90s. They had a very difficult time lifting it without alienating customers. Those that didn't always offer free shipping weren't affected. Likewise, online companies that always charged sales tax weren't affected by the backlash when companies that never charged sales tax began to.
Some companies risk teaching users new behaviors that will hurt them in the long run. Several companies have considered emailing customers a week after they've abandoned their shopping carts. These companies offer 10 percent off to purchase the products left behind. This may seem like a good idea at first, but it's a costly mistake. Over time, you train users to abandon shopping carts to get a discount. High-value customers become low-value customers, and low-value customers become even lower-value.
Be Wary of Continuous Reinforcement Rewards
A continuous reinforcement schedule is dangerous. At best, the promotion shows a little lift in the short term and a huge decay in the long term (doing nothing in terms of real loyalty). At worst, you unintentionally create new business rules for your company. When those rules inevitably change, you alienate people who migrated to your company because of them. You even risk teaching users new behaviors that are bad for business.
In upcoming columns, we'll look at other reward schedules and how they stack up to continuous reinforcement. We'll also look at ways to compete against those who have a schedule in place.
As always, please let me know your thoughts!
Until next time...
Vote for your favorite products, services, and campaigns! The ClickZ Marketing Excellence Awards recognize ClickZ readers choices for achievement and innovation in online marketing technology, solutions, and execution. Voting runs until Wednesday, June 22 (EOB, EST).
Join the Industry's Leading eCommerce & Direct Marketing Experts in Chicago
ClickZ Live Chicago (Nov 3-6) will deliver over 50 sessions across 4 days and 10 individual tracks, including Data-Driven Marketing, Social, Mobile, Display, Search and Email. Check out the full agenda and register by Friday, Oct 3 to take advantage of Early Bird Rates!
Jack Aaronson, CEO of The Aaronson Group and corporate lecturer, is a sought-after expert on enhanced user experiences, customer conversion, retention, and loyalty. If only a small percentage of people who arrive at your home page transact with your company (and even fewer return to transact again), Jack and his company can help. He also publishes a newsletter about multichannel marketing, personalization, user experience, and other related issues. He has keynoted most major marketing conferences around the world and regularly speaks at Shop.org and other major industry shows. You can learn more about Jack through his LinkedIn profile.
IBM Social Analytics: The Science Behind Social Media Marketing
80% of internet users say they prefer to connect with brands via Facebook. 65% of social media users say they use it to learn more about brands, products and services. Learn about how to find more about customers' attitudes, preferences and buying habits from what they say on social media channels.
An Introduction to Marketing Attribution: Selecting the Right Model for Search, Display & Social Advertising
If you're considering implementing a marketing attribution model to measure and optimize your programs, this paper is a great introduction. It also includes real-life tips from marketers who have successfully implemented attribution in their organizations.
September 23, 2014
September 30, 2014
1:00pm ET/10:00am PT
October 23, 2014
1:00pm ET/10:00am PT