Transforming Prospects Into Customers With Desire Data

How do you entice someone to buy a certain product or service? Of all the information surrounding a product, what do you put in front of the user? How do you prioritize the information? Is price more or less important than a product picture?

Marketers have studied this for a long, long time. Today, I offer my take on what information is the most enticing, based on several years of my own research.

When I create a Web or e-mail marketing strategy, I classify data into three major groups: identification data, contextual data, and desire data. Understand these groups and how data in the groups work together, and you’ll optimize screen real estate and maximize selling potential.

Identification data is objective, atomic data that helps a user identify a product or a service. In music, identification data includes artist name, album name, record label, and cover art. In a service business, it includes the name of the service (“E-mail Strategy and Design Workshop”) and a list of deliverables. This data addresses one question: Does this product meet the most basic criteria of what I am looking for?

Contextual data is objective information to help identify a product as the “correct” one, beyond the atomic name/artist. Contextual data addresses, “Between two seemingly identical products, which is the correct one?” Examples include table of contents, track list, book excerpts, format (hardcover, audio cassette, DVD), instruction manual, and a description of a company’s methodology around a given service.

A search for The Lord of the Rings: The Fellowship of the Ring would return at least six items: DVD and VHS formats of the theatrical, extended, and extended collector’s releases. All are identical, in that they share the most basic identification data and fill the same basic need. Contextual data shows which contains extra scenes or includes collector’s items.

There may be 15 books with titles that are a variation on Learn C++. Contextual data (a table of contents or an excerpt) reveals what’s actually inside the book.

The final, most important group is desire data. Unlike identification and contextual data, desire data is generally nonobjective. Desire data contains primarily subjective information about a product or service. It includes published reviews, user reviews or testimonials, or collections the product fits into. Collection examples include Web sites’ “People who bought [x] also bought” listings, or Amazon’s “Listmania.” All these data points lend desirability to a product or service.

If I can’t decide between three different books called Learn C++, I’ll be heavily swayed if one has several reviews saying, “This book is terrible!” Even if I decided to buy that book before looking at desire data, I’d likely be swayed by customer reviews.

If a toy’s review reads “breaks easily,” I won’t buy it. Price and availability are generally the only objective data in this category.

Now, the challenge: How much data from each group is required for a given product or service, and in what combination?

My theories stem from my own research. Feel free to agree or disagree. Test them yourself to see if they work for your products or services before accepting anything as dogma. Here are my findings.

Every product or service must display at least two units of identification data. More than three units is a waste of real estate and won’t help the sale. In addition, every product requires at least one unit of contextual data. I don’t believe a product needs more than that. Following this, the real estate is better served with desire data.

Desire data is better when varied. Different personality types respond to different desire data. Some people, to whom peer community is important, respond to user reviews. Others care only what an expert, such as a newspaper critic, thinks. It’s important to vary desire data to address all user types. It’s a better use of space to have two expert reviews and two user reviews than to employ four user reviews, which half your visitors will ignore.

To complicate matters slightly, contextual and desire data overlap. Some information fits into both groups. The important distinction is a single unit of data (like a table of contents) may have one effectiveness rank in the contextual group but a different one in the desire group. The table of contents might rank third as a book’s most effective contextual information but much lower as desire data. Pick the best contextual data first. Then, the remaining data points can be moved to the desire group. There, it’s sorted with the rest of the entries.

Sound confusing? It isn’t if you understand the relative weight of each data point and whether that weight differs depending on group. This is where much of my own research time has been spent.

This was only an overview of identification, contextual, and desire data. I’ve touched the tip of the iceberg, but hopefully enough for you to start your own research. Correctly combining the data points maximizes real estate (you can’t make room for everything known about a product on one page) and selling potential.

Understand the data combinations for your products. You’ll enable customers to answer the three basics: Do you have the product I’m looking for? Is it the right product variation for my needs? Am I convinced I’ll like the product?

Answering “yes” to any of these questions mean you’ve closed the sale.

Adapted from

Jack Aaronson is the CEO of the Aaronson Group as well as a corporate lecturer. The Aaronson Group works with companies to increase the loyalty and satisfaction among their customers, increasing their profitability. Focusing on multi-channel user experiences, Jack and his team makes sure that your users have a fulfilling and enjoyable user
experience across all of your touchpoints. As a corporate lecturer, Jack also travels around the world teaching companies how to effectively implement personalization, loyalty programs and multi-channel CRM. He also publishes a newsletter about Personalization, User Experience and other related issues.

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