Methods to Systematically Reduce Customer Choice

grocery storeNew research regarding online dating websites shows that when it comes to presenting customers with choices, in fact “less is more.” And while marketers inherently know that too much choice leads to cognitive meltdown, sometimes we’re confounded with the best way to remove options presented to customers. Is there an ideal way to cull customer choice?

Sometimes marketers believe that customers want more choice. According to an MIT Technology Review article, however, in the online dating market new research shows that, “users presented with too many choices experience cognitive overload and make poorer decisions as a result.”

The Technology Review article cites research from two professors from National Sun Yat-Sen University in Taiwan where in an experiment they presented online dating users with wide and deep selection of potential matches. After all, customers want more choice—right?

Not so according to the study: “More search options (led) to less selective processing by reducing user’s cognitive resources, distracting them with irrelevant information, and reducing their ability to screen out inferior options.” In effect, users suffered from data overload where too many choices prohibited them from making an optimum decision.

Here’s where statistical analysis can help reduce choice overload.

Online dating sites often attempt to use sophisticated computer applications and proprietary algorithms to divine appropriate partner matches based on user inputs such as preferences for race, religion, eye or hair color and more. EHarmony’s matchmaking algorithm, for example, helps select potential partners based on a 258 question personality test.

With a deep historical data set of what EHarmony determines as “success” (236 marriages a day according to the site) this online company believes they can predict matches with a high degree of probability.

For sites like EHarmony, Match.com or others, the challenge isn’t showing all relevant results (like a Google search that delivers 2,000 hits) but just the top ten and maybe worst case—twenty. This of course, assumes that the algorithm is based upon the right “predictors” of successful match making, and that the science behind the scenes can be trusted.

The lessons for online dating companies—much less any business—is due to carrying costs or customer confusion, less choice can actually increase sales and profitability! In fact, some large U.S. retailers are starting to investigate this idea by reducing the variety of different products carried by up to 15%.

The experiment run by the Taiwanese professors shows that when it comes to choice – less is more. If this hypothesis is true, how then does a marketer decide which choices to reduce?

Reduction in customer “options” must be based on carefully considered variables and analytical analysis. For example, a category manager at a retailer—let’s say the toothpaste aisle—shouldn’t automatically assume that the products with the lowest sales should be removed.

Careful analysis including variables such as year-over-year sales comparisons, seasonality, pricing, profitability, trade promotion dollars, etc should be considered. In addition, market basket analysis may inform the retailer that a brand of slow selling toothpaste is in fact often purchased with other very profitable items. Indeed, assortment optimization and shelf space allocation can be a very scientific exercise. A company should also set up control groups for experimental purposes to test a hypothesis before making any permanent changes.

The use of algorithms and careful analytical analysis are two ways that companies are reducing and optimizing customer choice. In the end, customers may not ultimately want more choice—just more relevant options.

Questions:

  • In online dating, some users may want more results (choices) presented because they feel that they can judge a “match” more effectively than a computer. Are there instances where delivering more choice options is the better strategy?
  • Are there dangers of optimizing customer choice—especially when it comes to encouraging new innovative products/services with no sales history?
  • Predictors of a “good match” in online dating can be highly subjective. How would you counsel online dating companies to improve their matchmaking capabilities?

Related article: Less is More in Consumer Choice

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