Conversion Rates and The Types of Visitors

Wendy Moe and Peter Fader published a paper in March 2003 titled "Dynamic Conversion Behavior at e-Commerce Sites". In it, they talk (among other things) about the types of visitors to e-commerce sites. This struck me because in analytics we tend to lump all visitors together, just because we can't easily define the segments. Moe and Fader mention the obvious: conversion rates on e-commerce sites are spectacularly low compared with physical stores, below 5% in most cases. Any brick-and-mortar store would have closed up within a week at that rate. They analyze why rates are so low, mostly because so many visitors aren't really immediate buyers. Moe and Fader classify visitors into four groups, only one of which is the get-in-get-out immediate buyer type. Note that I've added notes from my own perspective, so Moe and Fader may take issue with how I'm using their categorization.

• Direct buyers. They come, they choose, they slap down the plastic. They enter knowing what they're looking for. The site can be marginally usable and unattractive, and they'll still probably buy.

• Indirect buyers. They know generally what they want, but they're browsing. Probably will buy, but will take a while and lots of pages. May be influenced somewhat by site characteristics, but not extensively.

• Threshold buyers. These aren't ready to buy, but they're curious and skittish. They're window-shopping. For these visitors, site elements are everything. If the site isn't sticky, they'll leave. Likely influenced by usability and attractiveness. Store impression is as important as product.

• Never-buy visitors. These are seeking knowledge, not product. Not likely to be influenced by site appearance or usability. May look at lots of pages or very few. No intention of buying.
  

Now, these aren't mutually exclusive. I switch modes. I may go to Amazon to order a book I've been wanting, or I may go there just to see what's inside of a particular book that my campus will order.

The relative size of each group has a huge bearing on site owner strategy, but all four groups are typically crammed into the data and dashboard indiscriminately. In statistics, we call this "conflating populations". We see it often in distributions with multiple "hills". And it makes analysis almost a blind operation. For example, if you're seeing a large per-session page view rate, but the conversion rate refuses to rise from 2%, is it because you haven't satisfied the threshold visitors, or because you have too many never-buys, or because the population is mostly indirect buyers? One solution won't hit all of them, so choosing to optimize something on the site may not be the answer. For example, if you streamline the checkout, that may help to capture more of the threshold buyers, but if they're actually only a small percentage of the visitor count, you won't see much of a bump in conversion.

For very large sites, picking the right optimization strategy may make the difference between a huge loss and massive improvement, six figures or more. So how do we segment these populations? Surveys always beckon to us, but I'm skeptical. Surveys online are always self-selected, and self-selection seems to me to invalidate most survey data. There may be clues in the analytics data itself, but I have yet to find a formula. Moe and Fader propose a formula (in fact that's the major purpose of the paper). It would provide a good basis in the real world if only our figures for new and returning visitors were accurate, and they're decidedly not. Several studies have confirmed that cookie-based figures for new visitors can be off by a factor of 2 or more. Unfortunately, if we don't know who's coming to the site, we can't segment them, and without registration we just can't be sure.

Still, keeping these four categories in mind will help when doing site analysis and optimization. If we can make an informed guess about which category of visitor is dominating, we can advise the client accordingly. For example, if we get a lot of visitors to particular pages that have a lot of information, and the pages are obviously being read, and the product is unusual or truly new, then we may have a lot of never-buys. This intuitive approach isn't completely satisfying to me, but it may be all we have.