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Key Lessons from Starting a Growth Team (David Grow, COO, Lucidchart)

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In 2015, we kicked off a growth team (2 analysts, 3 engineers) with the goal to increase revenue and funnel conversion rates on the website and in the product through A/B experimentation. This has been incredibly successful at Lucidchart - come learn some keys to success and pitfalls to avoid.

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Key Lessons from Starting a Growth Team (David Grow, COO, Lucidchart)

  1. 1. Think visually. 7 Lessons of Starting a Growth Team May 11, 2016
  2. 2. Why start a growth team? Historical approach to growing 100%+ YoY 100+% increase 100+% growth Top of funnel visitors $$$
  3. 3. Why start a growth team? Proposed approach to growing 100%+ YoY 50+% increase 10+% improvement 20+% improvement 100+% growth Top of funnel visitors Visitor --> registration rate Registration--> conversion rate $$$ x x Would require dedicated growth team to achieve significant improvements in lower-funnel conversion rates
  4. 4. However, simply creating a growth team does not guarantee success Late 2013 – Early 2014 2015 - Present • Fizzled after 4-6 months • Few concrete ‘wins’ • Going strong after 18 months • Major contributor to the business What’s been the difference? Here’s 7 quick lessons…
  5. 5. Need to assemble the right team – dedicated to growth 2013 2015 • Business - Me (CRO): 10% of time - Director of Product: 20% of time • Engineering - 1 full-time engineer - 2 part-time engineers • Business - Director of Growth: 100% of time - Analyst / PM: 100% of time • Engineering - 3 full-time engineers If it’s not yet important enough to truly dedicate resources, don’t do a growth team 1
  6. 6. Need to assemble the right team – dedicated to growth • No ‘growth’ experience • No ‘marketing’ experience Director of Growth Engineering Team Lead • Incredibly smart • Most analytical in company • Driven by results • Incredibly smart and talented engineer • Strong interest in business and understanding users but… and… 1
  7. 7. Next, set a clear objective and goal… 2013 2015 • (None) • Drive $X.X million of incremental revenue in 2015 Without clear objective, the implicit one will probably be like ours in 2013: “Run some cool A/B experiments” 2
  8. 8. Next, set a clear objective and goal…and evaluate that goal against a few criteria • Opportunity cost - If you don’t have product-market fit, don’t invest here yet • Financial cost - Standard SaaS metrics apply! (e.g., cash payback; LTV/CAC) • Motivational power - Does it motivate the team and the organization? 2
  9. 9. With a dedicated team and clear goal in place, first invest in data infrastructure and process 2013 2015 • Heavily reliant on homegrown, internal analytics system • Too many material errors as a result • Robust implementation of third-party analytics software (Kissmetrics) • Confidence in data, though never perfect Unless you are a data analytics company – buy, don’t build 3
  10. 10. Next, use data to drive the entire process, including ideation 4
  11. 11. Next, use data to drive the entire process, including ideation: an example 0 5 10 15 20 25 30 35 Conversionrate Days since registration Trial Conversions 2/23/15 - 3/6/15 T-A T-D • Analysis of our 14-day trial showed that: - Usage declined day-over-day - 30% more users active on day 7 - 80% of total trial activity (e.g., diagrams created, shared, downloaded) happened by day 7 • Hypothesis: Shortening trial length to 7 days will still allow users to experience significant value but may incentivize 30%+ more users to subscribe Following the data yielded 20%+ increase in trial conversion rate – one of our biggest wins in 2015 4
  12. 12. Create a culture of informed risk-taking and pursue “needle-moving” ideas • Our team has tested things like: - Pricing - Paywalls - Onboarding - Requiring credit card for trial • Our success rate is <30% for our A/B experiments, but tend to be big wins • And don’t forget to run sensitivity analysis before investing: If this test increased the key metric by XX%, how much would it be worth? Needle-moving ideas often make you uncomfortable…and excited. Don’t play it too safe! 5
  13. 13. Understand how to accurately value the results of an experiment Example: After 30 days, “B” is producing more subscriptions with 95% statistical significance. WIN! MAYBE 6
  14. 14. Understand how to accurately value the results of an experiment • What levels did the customers subscribe at? • What is the average payment value? • Are the subscriptions monthly or annual contracts? • Are there any early indicators of usage, upgrades, or renewals? Don’t fall into the trap of only looking at short-term revenue… … Customer Lifetime Value should be key metric 6
  15. 15. Finally, beware of long-tail or other unintended consequences 7 • In our experience, a successful experiment usually stays a success upon later analysis • However, results can occasionally flip given enough time - Why? Like most freemium products, significant percentage of subscriptions come months after initial registration • We now perform 90-day and 180-day analyses on original cohorts to ensure results haven’t changed Don’t be too narrow when evaluating the success of an experiment; check other metrics and occasionally revisit big changes
  16. 16. Starting a growth team can be a huge win, just don’t forget to… • Assemble the right team • Set a clear objective and goal • Invest first in data infrastructure and process • Use the data to guide efforts, including ideation • Create a culture of informed risk-taking and big bets • Understand how to accurately value the results • Beware of long-tail or unintended consequences 1 2 3 4 5 6 7
  17. 17. dave@lucidchart.com

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