Think visually.

7 Lessons of Starting a Growth Team

May 11, 2016

Why start a growth team?

Historical approach to growing 100%+ YoY

100+% increase

100+% growth

Top of funnelvisitors

$$$

Why start a growth team?

Proposed approach to growing 100%+ YoY

50+% increase

10+% improvement

20+% improvement

100+% growth

Top of funnelvisitors

Visitor --> registration rate

Registration--> conversion rate

$$$

x

x

Would require dedicated growth team to achieve significantimprovements in lower-funnel conversion rates

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…

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

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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…

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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”

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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?

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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

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Next, use data to drive the entire process, including ideation

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Next, use data to drive the entire process, including ideation: an example

0 5 10 15 20 25 30 35

Trial Conversions 2/23/15 - 3/6/15

T-AT-D

Days since registration

Conv

ersio

n ra

te

• 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

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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!

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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

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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

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Finally, beware of long-tail or other unintended consequences

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• 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

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

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dave@lucidchart.com