I was recently working on an interesting project where I was estimating the demand impact of a change that we had implemented to our site. Without getting into the details, a change was made so that the customer would be less distracted during their shopping experience. This then – hopefully – keeps the visitor more engaged with the site and if everything else goes well, they will then buy.
The tricky part is that the likelihood to convert changes at different points on the site, though it is a bit difficult to get it.
For example, if all a visitor sees is the homepage, they are going to convert at some percent. Assume 10% for easy math. If a visitor doesn’t bounce – meaning come to the site, see one page, and leave – say the percent to convert increases to 20%. If the visitor then sees a product page they are now going to convert 30% of the time. And finally if they enter the checkout process they will convert 40% of the time. The point is that the level that the customer is at in the site changes the likelihood of conversion.
This seems like it would be a very obvious thing, and to a certain extent it is. The key component here is not that these differences exist and you know about them. The key is taking that knowledge into account when making an estimate for demand impact of a change.
If visitors have all of these different conversion points and a change is made that causes 1,000 visitors to not leave the site you need to take these conversion points into account. Saying that the 100 more people will buy (using 1,000 visitors * 10 % conversion from homepage) is just as misleading as saying that 400 people will buy (using 1,000 visitors * 40% conversion rate from checkout pages). When making a demand impact, make sure that you include a few inputs for these different areas.
For example: 100 visitors * 10% + 300 * 20% + etc. As long as the percents add up to 100% and the visitors add up to your total you are in good shape. You can then take this number times your average order value and you now have a demand estimate. Note that you could even take this a step more and apply a different average order value to people who have been in different areas of the site. For instance someone who is shopping for Outerwear or A laptop will probably have a different average order value then an individual looking at flip-flops or computer cables.
Ultimately you can segment this to any level that you are able to get. Just make sure that the work that you put into arriving at the final number is worth it – especially if you are using the Omniture Excel Client. Make a judgement call. If it is just going to be small dollars or you really just need a ballpark then take the 1,000 visitors * 25% or something like that. It is a guess, but it should be an educated guess. Each different analysis will require varying levles of confidence.
Have you done anything like this before? How did it go?
This has been a Thought From The Cake Scraps.