Tad Miller has a great post about how to combat some of the issues with advertising dollars shifting to PPC in  (Advertising Dollars are Shifting to PPC.  Now What?) One of his recommendations is to

…re-allocate the extra dollars to optimizing your website or landing pages to improve your conversion.

I couldn’t agree more.

For over a decade, I’ve struggled with gathering the right tool-set to provide me with the “complete picture” of my site’s traffic in an effort to optimize the site and increase the return on investments from my marketing campaigns.

No Complete Solutions

To my knowledge, there are no solutions that provide the 360 degree view of site visitors, but several solutions may be combined to get it.  This post isn’t about the solutions themselves, but in what I’ve modeled as the information needed to provide optimal conversion.

Note: Obzervant is building software to serve this need and more.  But in the meantime, a good review of the currently available tools  is warranted and planned for near-future posts.

What You Need to Know

To recommend and prioritize changes that improve conversion rates, 4 questions need to be answered about your traffic:

1. Where did they come from? (source)
2. Why did they come? (motivation)
3. What did they do? (activity)
4. What did they think? (perception)

Model Overview

The model requires both quantitative and qualitative information.  It also separates intent from result to achieve the information required to make recommendations for optimal conversion.  Keep in mind that data changes based on your visitors.  Constant analysis and monitoring of the data provides a good basis for perpetual and iterative improvements.

Source

Answers Question: Where did they come from?
Type of Data: Quantitative
Intent or Result: Intent

Web analytics solutions provide information about the domain and exact url that brought the visitor to your site.  You can also define and  append parameters like source and campaign (e.g. sr=linkedin, cm=banner ad) to urls to learn even more about the referring site, creative element, etc.

(Learn more about traffic sources in my follow-up post, “Analyzing Traffic Sources: Where are your visitors coming from?“)

Motivation

Answers Question: Why did they come?
Type of Data: Qualitative Data
Intent or Result: Intent

Understanding your visitors’ reveals purpose… intent.. motivation.

Laboratory usability testing involves participants that are given scenarios and are asked to perform tasks.  And while this type of usability testing is  a very effective tool in some instances, users are not self-motivated.

Quick polls asking the user of their intention is a valid means of determining intent.  Only after establishing intent can we measure whether the user completed their intended goal.

(Learn more about motivation in my follow-up post, “Finding Motivation: Why Did They Visit?“)

Activity

Answers Question: What did they do?
Type of Data: Quantitative
Intent or Result: Result

Again, web analytics provide information about the visitors’ journey while on your site.  Data collected may include page views, time on page/site and clicks.

If the solution provides an ability to set up goals, you can also look at the number of visitors achieving a business goal.  By establishing intent from the user, you can analyze the percentage of visitors achieving their goal as well as the effort involved (time to complete, number of pages and effort).

Perception

Answers Question: What did they think?
Type of Data: Qualitative
Intent or Result: Result

Triggered, exit and follow-up surveys provide key information to understand visitor satisfaction.  There are methods to gathering each of these that are user-friendly.  I personally hate surveys and close them whenever I see them.  However, there are ways to gather qualitative information without the users perceiving that the effort involved in providing you with that information will be arduous.

Challenge the Model

There you have it: a quick overview on a model for gathering information needed to increase conversion rates through site usability and traffic quality recommendations.

Over the next several posts  I will explain each section of the model.  Until then, I’d really appreciate your feedback.  Help me fine-tune the model by challenging it!

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