I shall take your advice and start with the simple segmentation first and then move upwards.

I cant help but link this excellent analytics tip to the Eisenberg’s book when they describe how impulsive purchases (purchase on first visit) happen on low-risk/cost items, where as for costier items - with more risk involved - purchases usually happen after 3-4 visits.

Of course this is made very visible with tools such as SiteCatalyst or GA :-)

An interesting tool would be defining expectations for the number of visits for a given product, by products class.

“Cars are bought after 4 to 5 visits, toasters after the second visit, usb flash drives upon the first visit”

Just a thought.

For different categories converting at a different “pace” segmentation is the answer. In the above example I segment by “traffic source” you can just as well segment by product category (and then segment one more level by source for some great insights that you can action).

For the second part, same person but multiple attributable sources, two thoughts:

1) Decide on a overall rule for the business. Give credit to the first source or the last one or the one in the middle. Have one rule that is right for your business. This is rife for personal interpretation and since there is no wrong answer people apply their own interpretation, avoid that. Usually people give “credit” to the last one.

2) What many people are doing with source attribution is computing and assigning a value to each source using a model that works best for them.

So if this is the behaviour: I came to you website from a search engine, then a affiliate, then price comparison site and purchased. Let’s say that my order was for $100.

Given the % of people who come from search and affiliate and then price comparison you can assign a value to the search key term and the affiliate.

Each can get “credit” for some part of the conversion. Depending on the number of people moving from one stage to next, number of bailing,
how soon/late you can come up with a model that assigns the value to each source.

But it would depend on what is going on in your site at a aggregated macro level and there is a very strong gut level “business smarts” that are required.

My overall thought would be: start simple, start small and work your way to complexity. I know this sounds obvious.

I hope this is of some help.

Sorry about the earlier comment that I had written in a hurry. When I read it again, I realised that it does not make sense for someone reading it out of context.

I have rephrased the same as below and would like to know your response to the same. There is problem that I have in hand that is with regard to the days/ visits to conversion. Ours is a large retail site and we sell different categories. The problem arises on who do we assign the conversion, the search engine that first got the customer to the site or the price comparision site that got the last visit before the purchase. This problem gets compounded if you have different categories that you sell and gets converted at different periods of days/ visits.

We currently just switched analytics vendors and are in the middle of implementation but I’m looking forward to adding these KPI’s to our list once we get up and running.

As usual, an excellent post. I especially like how you referened what _actions_ can be taken based on the data.

I would like to point out that Google Analytics has a number of reports that address time to e-commerce conversion. The E-Commerce > Loyalty & Latency report section contains the following reports:

- New vs Returning
- Time to Transaction
- Visits to Transaction

Unfortunately only the New vs Returning visitor transaction report can be segmented easily. The Time to Transaction and Visits to Transaction report simply show a pretty graph that can not be segmented. I’ll see if I can’t figure out a nice way to dig deeper into the information and write up a blog post.

Have a great week,

Justin

Appreciate your response.

Cheers

jaisri