According to Wikipedia, the definition of conversion rate ” is the ratio of visitors who convert casual content views or website visits into desired actions based on subtle or direct requests from marketers, advertisers, and content creators.”
Deriving a conversion rate may be a matter of doing a simple calculation, but understanding the basis and accuracy of your measurements can be a little challenging.
Take for example an ecommerce retail store selling shoes. If one hundred shoppers click on a featured product banner ad and ten of those shoppers go from there to ultimately buy the shoes featured in that ad, then the conversion rate is 10%. Easy enough, right?
But what if 8% of those converting shoppers return their merchandise? What if your conversion rate changes from season to season? What if all 10% of those converting shoppers used a discount code they saw in a print ad instead of being initially attracted by the homepage banner ad? What if the landing page from the banner ad actually contains products not listed in the featured products banner ad and a few shoppers went ahead and added those items to their order?
There’s a lot of what ifs out there that can skew conversion rate validity.
It can get even tougher if conversion rate is set to measure actions that can be potentially subjective. Take for example, measuring the value of “contact us” inquiries for a service based business. If ten out of one hundred shoppers write in for an estimate, yes, you have the same 10% conversion. But then the important question is not so much about conversion but how many of those who wrote in were qualified buyers. Other confounding issues include determining the navigation pathway shoppers took before getting to the “contact us” page and how long it took them to “convert.”
Conversion rates are, no doubt, important to know, as they often times provide us great insight into how our sales are trending, but it is also vital to understand the latent components affecting our rates. If we can understand the hows and whys of conversion, the process of improving it becomes increasingly more manageable.
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Jonathan, This is a very relevant line of thought. My first reaction is to ask you – what is the the purpose of the conversion rate and what actions is it feeding into.
1. conversion rates of a product based on banner clicks vs. product page offer clicks
the measure of this is likely to indicate interest in purchase and a strong intent of making or suggesting the purchase. From the marketing view point, this is the first major hurdle in connecting to the customer or passerby. This also represents the conversion to a “warm lead”.
Being an Analytic person, to me this represents a subset of prospects which can then be linked to buyer analysis profiles.
2. Click on banner for brand a and purchase brand b
The question here is what are the margins on brand A and Brand B and the net business impact of the purchase of brand B. Was it an upsell or cross sell? In either case the conversion rate might have changed against the original offer, but the ROI of the offer has also changed.
From the marketing point of view, I would stick to the attributing Brand B conversion to the original 10% rate. That is conversion is resulting sale associated with the banner placement.
– The semantics around which brand should be given credit or what revenue sharing model should support the next round of campaign .. that is a different discussion.
3. when there are returns and refunds. With the increase in online purchase ..this information is more quickly available than physical transactions so I do sense the need to ‘correct” the measurements.
I would suggest doing a Wave 1 and Wave 2 conversion rates. The adjusted conversion rate is a leading indicator about the product value realized, while the wave 1 rate is an indicator of the ‘customer’s perceived product value’.
If Wave 2 rates rise significantly, then it is a sign for the product team to follow up. Given the availability of ‘customer reviews’ the returns by customers might trigger a wave of negative feedbacks left online.
So While you are right about the dilemma you face, i think the message that you get from each metric is unique and important to track.