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Using Data Science to Drive Customer Insights

KARL RUMELHART

Gainsight

MICHAEL GELLER

BlueJeans

ASH MOHUIDDIN Salesforce

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Predictive Analytics Use Cases & A/B testing

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Math-Based Magic •  Data, data mining tools, and deep analysis can

predict the future.

•  How: For each KPI, build predictive models to identify predictive factors and predict performance.

•  Example: Which activities correlate with churn

Science-Based Experimentation •  Avoid preconceived notions. Form hypotheses and

test versions of one thing to know which is 'better'.

•  How: Run experiments to affect the predictive factors.

•  Example: Which button text converts “better”.

Predictive Analytics Testing

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Predictive Analytics Business Use Cases •  Attrition/Churn Prevention •  Propensity to Buy •  Customer Lifetime Value •  Customer Segmentation

Testing •  Validate Data Science Findings…

Types of Data Science Projects

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Leveraging Data Science Output

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ü  Always be cautious of the output and make sense out of it (don't trust blindly).

ü  Always track down reality and watch out for false positives and negatives.

High Activity

Low Activity

High Health

Low Health

Not OK False

Positive Churn Risk

OK

Not OK

OK False

Negative Time Suck

Health = Is their behavior different than their normal? Activity = How does their behavior compare to everyone else?

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Best Practices & Traps to Avoid

Best Practices •  Pilot a Study to Validate Findings •  Design from the Beginning •  Correlation vs. Causation •  Cross Functional Insights Action Team Traps to Avoid •  Not Communicating and Not Obtaining Buy In •  Not Understanding the Assumptions

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Data Science & Finding The “Why”

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There should be a balance between output of data science and intuition.

ü  Data Science can answer 'why.’

•  Example: What drives retention?

ü  Develop trust by: •  Doing it! •  Leaving your opinions at the door. •  Setting the expectations (on what DS can/can’t do). •  Rigorously testing and sharing results.

•  Example: Do support tickets indicate potential churn?

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Trusting Data Science

Finding the Why •  Simon Sinek’s Example “Start with the Why” •  Using Data Science to find a Common “Why” •  Start a Movement… Trust •  Data Science Trust Starts with “Trusting People” •  Business and Data Science Collaboration •  Must Test Findings and Pilot Small Changes

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Operationalizing Data Science

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Make the Time. Act Like a Lean Start Up.

Gauge The Right Amount of Effort

Don’t Be Afraid to Change

Shameless Plug: Free Booze on

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Best Practices •  Incorporate Data Science Into Existing Key “KPI’s” •  Quick Wins to Gain Buy In •  Socialize Findings to Get Buy In for a Larger Cross

Department Initiative •  Begin Building Foundational Models that can be Leveraged

Company Wide •  EDUCATE!

Turning Insights into Actions