#35 What are Actionable Insights and How to Generate Them

  1. the result (customer does Carrot Trial) is more explainable by our action (engage the customer with our marketing programs). This means we are more likely to get statistically significant results.
  2. It is easier to form treatment group and control group. Why? Because the problem changed. The factors we need to control is only the ones that will significantly influence the customer’s likelihood to do a trial, such as “Already a customer of us or not” (assuming existing customers are much more likely to buy more)
  • To measure a marketing program’s effectiveness on generating opportunity is too far to reach. It may include selection bias, so that taking the same action (engage customers who are not as interested and determined with the same programs) will not generate the same result (have high probability of opening opportunity).
  • Marketing programs’ effectiveness should be measured again more within-reach outcomes, such as “Carrot Trial”.
  • We should develop A/B test on randomized treatment and control groups to find out what works the best to generate engagement in Carrot Trial
  1. Put “Trial” as a Button
  2. As a Link
  3. As a Pop up window
  1. Try Carrot Analytics for Free for 30 days!
  2. Try Carrot Analytics for Free for 30 days! (no credit card info required)
  3. Here is a one-minute video about Carrot Analytics: What you can do and Why they are valuable to your business. Try for free for 30 days. Call us anytime.
  4. Thinking about ROI — is Carrot Analytics worth it? Here is what our customer say: one-minute illustration about the “R” and the “I”. Try for free for 30 days.
  • E-book 1
  • E-book 2
  • E-book 3
  1. What tactics to use in E-book programs to generate more click in Trial
  2. Which E-book programs are most effective in converting leads to Trial
  3. More Trials lead to more Opportunities (not covered in this blog)
  • In order to generate actionable insights, we need to know not only what works, but also why it works.
  • If the reason why “it” works is not that “it” is great, but other noise factors, such as “it” introduces selection bias, we will not be able to say do more of “it” will generate more results (causal effect). Thus this insight is not actionable.
  • If we can’t prove causal effect from the action to the end result, we can use middle ground. Action 1 has proven causal effect on Action 2. Action 2 has proven causal effect on End Result. Then use Action 2 is the proximal metric for Action 1. In programming language, it is called divide and conquer.



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Gloria Xiaolu Zhang

Gloria Xiaolu Zhang


A data scientist in digital marketing. Love blogging and coding. On a quest of posting 52 blogs in 2019. www.gloriablog.com