UX research plan that defines an effective CRO program
How to create an effective CRO program that consistently results in more than 5% total revenue per user growth from a winning experiment?
If you ask to choose only one most critical factor of a conversion rate optimization program, that would be a depth of analytics and UX research plan. So that you back up your hypotheses with strong, statistically valid data on the actual reasons behind the main conversion barriers.
Michal Parizek, Growth Product Manager at Smartlook formulated that in a way more clear way:
Every CRO agency claims a depth of data behind hypotheses and CRO plan. And we are no exception! But what does “scientifically arriving at a hypothesis” actually look like?
Well, turn Slack and email notifications off for a couple of minutes and read our checklist of UX research plan needed to build an effective CRO program.
CRO program: UX research plan
- Data tracking setup audit.
- Event mapping.
- Marketing analytics. Top performing traffic sources and its scalability bottlenecks.Marketing analytics. Top performing traffic sources and its scalability bottlenecks.
- Keywords and user-intent analysis.
- Top performing creatives and insights for UX.
- Top landing pages.
- Segment analysis.
- ABC analysis.
- Funnel analysis.
- Cohort analysis.
- LTV, usage and transaction frequency.
- Personalization opportunities.
- User journey map.
- User flows.
- Main drop-offs and bottlenecks.
- Behavioral patterns.
- Event correlation and feature usage. Regression analysis.
- CTR analysis.
- Competitor UX analysis.
- Competitor UVP and features analysis.
- Competitor A/B tests, product and website changes.
- User personas. Persona testing.
- “Jobs to be done” research. User tasks research.
- Audit of UVP and it’s perception.
- NPS analysis. Customer satisfaction survey questions.
- First time user experience (FTUX).
- Bounce-rate analysis.
- Relevancy of user intent, keywords and ad messages to landing pages.
- Loading speed analysis and correlation with conversions.
- Screen sizes, cross-browser, cross-device and conversion correlation.
- Onboarding audit.
- AHA moment and conversion to activation analysis.
- Conversion barrier research.
- UX content audit.
- Unanswered user questions.
- Conversion barriers.
- User rejections, fears and concerns.
- Core purchase motivation and triggers research.
- UX heuristic analysis.
- Usability audit.
- UI audit.
- Form analytics.
- UX tests. User testing questions.
- Video session recordings.
- Online polls. Open-ended and closed-ended questions. Poll targeting and triggers.
- User interviews. Respondents recruiting based on data, user poll answers and visitor session recordings.
- Heatmap analysis. Scroll depth, correlation of scroll and funnel progression.
- User feedback analysis.
- Customer support feedback.
- Sales team interview and questionnaires.
- Audit of business model and monetization tactics.
- Potential pricing experiments. Price elasticity.
- Upsell, cross-sell and down-sell opportunities.
- Post-conversion behavior research.
- Thank You page marketing audit. Referral tactics optimization.
- Technical audit.
- QA and bug detection.
Sounds like plenty of homework, right?
Based on our experience of running A/B tests on 127 million of our clients’ users per month, we see that hypotheses without direct interconnection of cause and effect data tend to have 2-10x lower growth and win rates (if you follow cro program best practices listed above).
But how do we actually come up with designs and content for alternative versions?
Based on our experience, depth of UX research has an inverse correlation with the uncertainty of how to design alternative versions. Meaning, if the 57 steps for creating a successful cro program are done right then it’s obvious what and how to test.
Сonclusion
To sum up, these 57 CRO program steps could dramatically increase the growth and success rate of your experiments if done right. If you’re launching a CRO program for the first time or for a new product, then it’s critical to go through all of the steps.
Product managers often don’t have the resources, time, patience or expertise to execute that to a full extent, leading to hypotheses with a bunch of unknowns, like no exact data on cause and effect, no exact quantitative prioritisation, etc.
When you guess the biggest problem, then guess the reasons for that, and afterwards assume the solution, then the probability of winning is lower than when you have the exact data on this.
So “Arriving at hypotheses, scientifically” as Michal said is the core thing that defines an effective CRO program.