05 Jan What is conversion optimization?
It’s about growth.
The question to ask is this: How do we optimize our website so that our business will grow?
If you can acquire customers more cheaply – from any channel- than your competitors, you can grow faster. It’s about better marketing
There are 2 approaches to improving a website:
- You go in and change what you think might be a good idea to change – mainly on the Home Page – and hope the sales will go up.
- You start by figuring out which pages cause the biggest drop-offs – where the flow is stuck. Once you understand WHERE the problem is, you proceed to identifying WHAT the problem is.
You seek to understand your customers better – their needs, sources of hesitation, conversations going on inside their minds. You gather whatever quantifiable data you can to understand what people are doing on the site, and the impact each individual widget or form field has on the revenue.
You need to move away from random guessing, and focus instead on KNOWING what’s happening, and understanding WHY.
Conversion optimization will be very effective once you move away from testing crap that doesn’t matter, and start approaching it like the process that it is:
Set goals → Set up measurement and gather data → Analyze data → Turn data into insights → Turn insights into prioritized hypotheses → Test your hypotheses → Get data from tests → Back to data analysis. And round and round we go.
So how do we go about gathering and analyzing data?
For every piece of data that you gather, you need to know exactly how you’re going to use it. Forget “nice to have” data. “Must have” only. If we have too much data, it causes analysis paralysis – too overwhelmed by the sheer volume of data – so we won’t do anything.
There are 6 steps of data gathering and analysis, followed by creating a master sheet of all found issues that we then turn into action items:
Step 1. Technical analysis
· Cross-browser testing
· Cross-device testing
· Conversion rate per device / browser
· Speed analysis
Step 2. Heuristic analysis
· Identify “areas of interest”
· Check key pages for relevancy, motivation, friction issues
Step 3. Web analytics analysis
· Analytics health check: is everything being measured, is everything accurate
· Set up measurement for KPIs
· Identify leaks
Step 4. Mouse tracking analysis
· Heat maps & click maps
· Scroll maps
· User session video replays
Step 5. Qualitative research / surveys
· Customer surveys
· Web traffic surveys
· Chat logs
Step 6. User testing
· Identify usability & clarity issues, sources of friction
Step 7. Sum-up
· Categorize and prioritize each issue, translate into a test hypothesis
*Guide by Peep Laja