×

Once registered, you can:

  • - Read additional free articles each month
  • - Comment on articles and featured creative work
  • - Get our curated newsletters delivered to your inbox

By registering you agree to our privacy policy, terms & conditions and to receive occasional emails from Ad Age. You may unsubscribe at any time.

Are you a print subscriber? Activate your account.

How the Subscription Economy Will Change the Price We Pay

By Published on .

Your Amazon subscription may say more about you than you know.
Your Amazon subscription may say more about you than you know.   Credit: Amazon
Most Popular

Should a customer in New York pay a higher price than someone from Ohio for the same service? And should a doctor in Ohio pay a higher price than a New York taxi driver? Should a Mac user be steered to a pricier hotel when shopping online? Hint: It's already happening.

A growing number of companies have already shifted to a subscription model, where customers pay a monthly fee. Now, more firms are experimenting with charging customers different prices and using alternate pricing plans, based on data and analytics that can maximize revenues and customer satisfaction.

American consumers increasingly use subscriptions for things once bought one transaction at a time, such as watching movies on Netflix or paying one annual fee to Amazon for all their shipping costs. Subscriptions are in vogue for everything from buying razors (Harry's or Dollar Shaving Club) to buying underwear (MeUndies) to beauty products (Birchbox).

Charging different prices for subscriptions is the latest development. And to find the cutting edge of where that experimentation is taking place, look at how companies handle subscription cancelations.

Subscription companies often make big mistakes when customers try to cancel. They either try to hide from the cancellation (by making their number hard to find or difficult to get through to someone). Or they try to get the customer to reconsider their decision without changing the price or the service level.

Smart companies find that customers who want to cancel automatic monthly payments will often accept a different offer. For example, one customer might be offered three months free followed by a one-time payment for the balance of the year. Another customer might be offered one month free followed by a six-month subscription, with a one-time payment, followed by another free month. A third customer might be offered a different product bundle at the same price.

Different customers respond differently to various offers, based on everything from demographics to location to time of year. Orbitz experimented with this concept back in 2012, pitching higher quality hotels to Mac users after learning that they spend, on average, $20 to $30 more per night for a hotel room.

Maximizing revenues

As companies gather more customer data, they can maximize revenues by trying different subscription offers. A/B tests can prove which offer generates the best retention percentage for each customer demographic; real-time analytics can suggest different offers for different customer types, serving them up in real time. For instance, a call center employee would have a tailored offer when a customer calls, created by underlying software -- ditto automated phone systems and web interactions. Based on our experience working with companies, such strategies can help a company retain as many as 15% of customers who would otherwise cancel.

Rather than hiding from customers trying to cancel their subscription (too many companies make it difficult to find the right phone number to call), companies should use those calls as an opportunity to make a strong offer, such as offering a lower price or a higher service level. Imagine if JUST 100 out of every 1,000 cancellations accepted the new offer -- it'd be a huge revenue boost. At the same time, companies should be using these calls to learn about selling and pricing to different demographics.

Possiblities at the POS

As marketers learn the value of charging different prices from handling how cancellations are processed, the next logical step is varying in price for different audiences at the original point of sale, based on aggregated marketing data. Example: a software engineer in Boulder, Colorado making $100,000 a year will be offered a higher price, with a premium bundle of green-emphasized products or services as compared to a waiter earning $40,000 in Omaha.

Of course, the underlying concern in all of this is, how does the doctor in New York feel about paying more than the nurse in Omaha? In today's world of Facebook, Twitter and Linkedin, nothing stays secret for long, so customers will eventually learn that they are receiving different offers. However, companies can disguise various prices by associating each offer with a slightly different product offering or service level, making the price differentiation more justified. Or at least better masked. And in general, customers tend to focus on the overall product and experience to assessing their satisfaction. There has been no great hue and cry from site users to this information, perhaps trusting primarily in their own ability to judge the service they are receiving.

Using data such as home values and taxes, incomes, online habits and automotive records enables a three-dimensional picture of each customer. As companies refine their use of such information, they will market more effectively, developing pricing and service offerings that feel tailor-made for each customer.

Note: This approach is legal, so long as different prices are not based on ethnicity. Also, marketers cannot charge different prices between men and women in Los Angeles, San Francisco, Chicago and some smaller cities.

Ultimately, companies that embrace this trend will see increased profits, and consumers who are aware of what is happening can also use their leverage to negotiate pricing plans that better fit their needs.

In this article: