Always Be HelpfulNOTE: Case study with test results included below.

When I was doing my MA thesis on persuading users in ecommerce environments, I focused on converting in online “catalogs” (by which I mean any page that lists your products, services, packages or plans).

As is required for any thesis, you have to read everything anyone has ever written that’s even remotely related to your research question.

So I did a lot of reading…

And I found great studies revealing insights like:

  • Consumers use peripheral cues to form attitudes when motivations and abilities are low: “under some conditions (e.g., high arousal), only cues perceived to be diagnostic are used” (Johar et al., 2006, p. 141)
  • Humans do not evaluate options in isolation but rather with the aid of diagnostic cues, such as context effect, and non-conscious processes (Martin, 2008; Fitzsimmons and Shiv, 2001)
  • Steckel et al. showed that although “more information [creates] the potential for customers to make more informed choices… [t]he downside is that information overload can lead to decision biases due to selective processing of information” (p. 310-311)
  • Disruptive delays during information-foraging are common in e-commerce environments, where abundant information, product choices and lengthy page load times are common (Huang & Chen, 2006; Steckel et al., 2005; Yang & Wu, 2007)
  • Steckel et al. (2005) found that “the arrangement of multiple items [in catalogs] impacts choice” (p. 314)
  • In their studies of list-sorting in online product catalogs, Cai and Xu (2008) showed that the order in which multiple items are listed is key to decision-making. Some users are willing to sort through lists to find information, but the majority are “unwilling to spend more cognitive power to reorganize information [in lists]” (Cai & Xu, 2008, p. 705)

What does all of that really boil down to?

Simply this: Help your visitors understand their options. Because they haven’t evolved to fully help themselves.

After years of putting research like this into play with clients, I’ve found that the “Always Be Helpful” rule not only applies to websites but continues to knock shizzle out of the park…

…as you’ll see in the test creative I’m about to show you.

How Helpfulness Increased Paid Conversions by 61% in My Client’s Ecommerce Catalog is this great resource for non-designers that are developing their own apps. (It’s also used by app designers who want more options.)

Basically, if you want to make your app look great + not code everything from scratch, they’re the guys to go to. They have gorgeous app templates – and lots of ’em. …….And, in fact, that was the problem.

(Not the gorgeous part. The lots of ’em part.)

See, people really do have the damnedest time making decisions. Even the most basic decisions. Our tiny but powerful lizard brains look for the slightest cues to help us 1) eliminate options and 2) compare between what’s left. Take these classics for example:

“Tom, Ugly Tom & Jerry
I first read about this in this Human Factors International whitepaper. In this MIT study, women were asked which of the following 2 dudes they found most attractive, and they answered pretty equally – half thought Tom was attractive, and the other half thought Jerry was.

Narrowing option sets is helpful UX When an ‘ugly’ version of Tom was introduced, as below, more women chose normal Tom as the attractive one in the set. They rarely even considered Jerry to be an option.Narrowing option sets is helpful UX

Wassup with that? When making decisions, we tend to group similar things mentally. In this case, the women mentally narrowed their options into the smallest set – the one that would allow for the easiest comparisons – and decided on the best in that option set.

“The Cheaper Breadmaker”
This is a classic; I though it was in Cialdini’s Influence, but I just skimmed my hard copy and couldn’t find it, so read more from Harvard instead. Anyway, the study goes that Williams-Sonoma was selling a breadmaker ($275) in their catalog for years; then they introduced a more expensive breadmaker ($429) and positioned it next to the standard one. With the introduction of the expensive one, sales of the cheaper one nearly doubled.

Williams Sonoma breadmaker example persuasion contrast

So much of our decision-making comes down to this: creating the smallest possible set, and then making a decision by contrasting what’s in that small set. In the Ugly Tom example, people had to narrow their options and then contrast; in the Williams-Sonoma example, people had to have an option added to create a ‘set’ that would help them contrast and decide.

As intelligent marketers, we know that eliminating the cognitive burden of our users can help increase our conversion rates. The less they have to think or do mental labor, the better it is for everyone involved. So if you KNOW that people will group options into the smallest groups they can – so that they can then compare and contrast to reach a decision – it behooves you to group those options FOR THEM.

That was our hypothesis with the Gallery page of, at least. We wanted to find out if taking a massive set of options and narrowing them into smaller sets for site visitors would truly minimize their cognitive effort, maximize their understanding of the options, and allow them to contrast as easily as possible.

So we took this control view:

App Design Vault Gallery Split Test

And we tested this treatment against it:

App Design Vault - Home Page Split Test - Recipe B

And our statistical winner was… can you guess?

It was the new treatment (or Variation 1). It produced a 61% increase in paid conversions, as shown in Visual Website Optimizer here:

App Design Vault winner (Almost as good? Revenue Per Visitor was higher on Variation 1 than on the control throughout the test. But that goal didn’t reach significance, although it was trending up when we stopped the test. So more people were buying, and they were paying more, too!)

Now, what did we do there… and what does it have to do with helping people narrow their options so they can make decisions?

As you can see if you compare the control and Variation 1, we took a big lump of options… and split them out into smaller sets.

  • The default view for each set of options showed just 3 options, which you could then scroll along if you wished to see more
  • We gave each set a title to help visitors make sense of the option sets themselves… so they could narrow the sets
  • We added short descriptions to support the name of each of the apps; for example, we added “For a feminine UI” to the app named Moments
  • We positioned similar apps near each other to facilitate further option-narrowing; for example, next to Moments we put Gunmetal, which is described as being “For a masculine UI” (easy to compare masc vs fem)

Yes, we did more to the treatment than SIMPLY reorganizing all the options into more helpful, smaller sets…

We also got strategic about organizing apps with the most expensive ones to the left, tapping into something called Primacy Effect; this may have driven to the higher but not significant RPV. We also enhanced the buttons, changing the copy and the look and feel. We also added a “bundle” set for price-conscious visitors. We did lots of other little things that surely contributed to the great results here.

But the core of what we did – and the biggest change by far on this page, without which none of the other tweaks would have made a difference – was helping the user by narrowing their many options for them.

Do I believe that you could see a similar increase in paid conversions if you narrowed your visitors’ options for them in a meaningful, helpful way? Yes I do.

I have no video for you today – because I don’t think this post is asking for one. There’s enough to chew on here already. And, after all, I’ve got a handful of videos I have to edit for my soon-to-launch course on exactly how I optimized the home page of App Design Vault to get a 51% paid lift… so I’m all video-ed out for today. Do leave a question or comment, though, and I’ll gladly chirp back at you.


Cai, S., & Xu, Y. (2008, September). Designing product lists for e-commerce: The effects of sorting on consumer decision making. International Journal of Human-Computer Interaction, 24(7), 700-721. Retrieved May 10, 2009, doi:10.1080/10447310802335730
Huang, J., & Chen, Y. (2006, May). Herding in online product choice. Psychology & Marketing, 23(5), 413-428. Retrieved May 10, 2009, from Communication & Mass Media Complete database.
Johar, G., Maheswaran, D., & Peracchio, L. (2006, June). Mapping the frontiers: Theoretical advances in consumer research on memory, affect, and persuasion. Journal of Consumer Research, 33(1), 139-149. Retrieved June 4, 2009, from Business Source Elite database.
Martin, N. (2008). Habit: The 95% of behavior marketers ignore. New Jersey: FT Press.
Steckel, J., Winer, R., Bucklin, R., Dellaert, B., Drèze, X., Häubl, G., et al. (2005, December). Choice in interactive environments. Marketing Letters, 16(3/4), 309-320. Retrieved May 10, 2009, doi:10.1007/s11002-005-5894-
Yang, C., & Wu, C. (2007, February). Gender and internet consumers’ decision-making. CyberPsychology & Behavior, 10(1), 86-91. Retrieved May 10, 2009, doi:10.1089/cpb.2006.9988