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Fuel Cycle Blog: Using MaxDiff Analysis To Understand Consumer Preferences

Using MaxDiff Analysis To Understand Consumer Preferences

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The average consumer is inundated with marketing messages. In fact, people see anywhere from 4,000 to 10,000 marketing messages a day. Similarly, consumer purchasing options today are significantly more involved than walking into the nearest store and picking something off the shelf. Consumers now have over 16 ways of discovering new products including anything from online advertising to social media and everything in between, according to MarketingSherpa.

With so many marketing messages buzzing around, and so many ways people come in contact with products, it’s no wonder consumers are demanding personalized shopping and marketing experiences. In fact, 91% of consumers are more likely to shop with brands that provide personalization and relevancy.

While many marketers may believe they are already sending personalized and relevant messages, statistics say otherwise. Stats show 63% of consumers do not feel like retailers know them.

Companies that are truly looking to get to know their customers need the help of data-driven market research to get there. Thankfully, a well-crafted market research study, specifically a MaxDiff survey, can provide the insight marketers need.

Why MaxDiff analysis?

While it’s true there are several ways to learn about customer preferences (e.g. tracking consumer purchasing and behavioral data, likert-scale surveys, direct feedback, etc.), MaxDiff analysis is specifically important when marketers need to home in on one message–and one message only—that consumers care most about.

As a quick review, MaxDiff is a methodology that ask survey respondents to select the best and worst options out of a given list of three or more options. This methodology enables market researchers to obtain a relative ranking for each different option. These rankings tell researchers which attributes are more important to a consumer and which are least important.

Through MaxDiff analysis, marketers can identify the the most important attribute and personalize their marketing messages to highlight the benefits their audience not only cares about, but cares about the most.

MaxDiff analysis in practice

Here is an example of how a market researcher for an airline could use MaxDiff analysis to their advantage. Airlines offer any and all of following benefits (which would act as the named attributes in a MaxDiff survey):

  • No charges for checked bags
  • Ability to earn extra miles on flights
  • Free meal onboard
  • Free drinks on international flights
  • 4” more legroom than other airlines
  • In-flight TV Programs
  • Guaranteed aisle or window seats
  • Free upgrades for loyalty

It’s true that airlines offer all of these services, and customers probably enjoy all these benefits. However, imagine how absurd it would be for a marketing team to either try and write a personalized marketing message that speaks to all benefits or to just “follow their gut” and hope they select the attribute consumers care about most.

The data-driven approach to solving this marketing conundrum is to to send a MaxDiff survey to target customers. Survey results will quickly point out which one attribute resonates best with customers (e.g. “No charges for checked bags”). Then, marketers can craft a focused, hyper-personalized marketing message around the most important benefit, as reported by their target market.

Market researchers can use MaxDiff analysis in a number of different scenarios. This includes learning what customers most and least prefer in regards to:

  • Brand names
  • Attributes of a business
  • Elements of service
  • Product features
  • Color schemes
  • Ambiance
  • Advertising claims
  • Marketing messages
  • And more!

Wrap Up

MaxDiff Analysis is the perfect methodology for market researchers looking to understand which attributes customers care about most and least. With MaxDiff survey results, marketers can truly understand consumer preferences and create more personalized and highly-relevant messages.

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