Drawing more examples from Avinash Kaushik’s book, Web Analytics 2.0, we learn that there are other opportunities, other than Conversion Rate, to help us improve our bottom line (e-commerce speaking, that is).
Cart and Checkout Abandonment
He identifies this calculation as critical in determining the number of people who commit to buying something from you site (eg. have added it to their basket), but then bail and don’t follow through with the order.
So Cart Abandonment is: the total Visitors who start checkout divided by the total number of add to cart clicks.
- helps you understand the effectiveness of your merchandizing efforts on a site. There could be a number of reasons why people don’t actually start the checkout process, so first determine where they have been (pages visited) before adding to cart.
And Checkout Abandonment is: the total Visitors who complete checkout divided by the total number of people who start checkout.
- helps you understand how many people bail on the 2 or 3 pages used for the checkout process. With few pages to analyze, you should be able to figure out where the problem exists. Maybe they are too long? Maybe too many steps? Maybe buttons not clear?
Basically, it helps you fine narrow in on where the abandonment is really taking place. As Avinash says, “any improvement in either of these tow Abandonment Rate metrics is money directly in to your pocket.”
Visits to Purchase
Shockingly, not all Visits to a website convert into a sale! Some of us need a bit of time, to think it over, check finances, do a little price shopping, etc. Visits to Purchase tells us how many visits someone needs before they can commit.
- Shows the distribution of the number of visits it took for someone to purchase from your website.
- Note, a “purchase” can also be submission/lead gen activity
- You could then call it, “Visits to Lead Submission”
Days to Purchase
Days to purchase, which is similar to the above, determines how many days it takes someone to make an online purchase.
- When you intersect this report with Visits to Purchase, you can understand how many days pass between visits.
- Helps you understand customer behaviour in a very “actionable way”: you can go back and optimize how you sell each item and how you advertise/market it. Avinash says you can even optimize your inventory based on this information .
Average Order Value
Our guru calls this a “simple metric”, and that it is. Total Revenue divided by the total number of orders received…simple, non? The reason why we are focusing on this instead of a plain and simple Conversion Rate is because of the following scenario:
If you get 200 orders from 10,000 unique visitors = a 2% conversion rate
If you reduce the traffic to 100 unique visitors adn get 20 orders = 20% conversion rate
Wow, that’s awesome, what a HUGE improvement. But wait, 20 orders is less than 200 orders which means revenues are also LESS. So Conversion Rate goes up, but revenues are less.
With Average Order Value, it helps to compare the results to your Conversion Rate and also acquisition sources (eg. where is the traffic coming from?). So high Conversion Rates and low Average Order Value might end up telling you that the money and effort spent on getting people to the site did not convert into very high sales.
Primary Purpose
Another great scenario by Avinash helps us understand the real size of our buying market. If we convert 2/100 visits into sales, we have a 2% conversion rate. But does this mean that we have to convince the other 98% to buy? No. Why? Because that 98% doesn’t represent everyone with an intention to buy. Many people come to a site to look for a job, read a press release, gaze at the financials. By surveying our users, only then can we determine how many might have come with an intention to buy. It could be a lot less, like 20%.
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