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Data Science and the Future of Customer Success
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Data Science and the Future of Customer Success

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Everybody seems to be talking about big data, data science and predictive analytics these days, but what does it really mean? How does it apply to your business? Can it help you identify the early ...

Everybody seems to be talking about big data, data science and predictive analytics these days, but what does it really mean? How does it apply to your business? Can it help you identify the early indicators of churn and growth in your customers? And if you had the data, what can you do with it?

In this webinar, you will learn all about data science from David Gerster, former head of data science at Groupon. Mike Stocker, from the Marketo Customer Success team, will then dive into how Marketo is using data science to drive success for its customers. Finally, the Gainsight data science team will share best practices on turning analytics into Customer Success actions.

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Data Science and the Future of Customer Success Data Science and the Future of Customer Success Presentation Transcript

  • Monthly Webinar August 20, 2013 #customersuccess
  • Some Housekeeping 1 We will have a Q&A session at the end, so keep your questions and comments coming Video recording and slides will be made available soon after the webinar Please fill the survey at the end – we promise it’s short! It’ll help us bring you more #CustomerSuccess events and content 2 3 4 Use the GoToWebinar chat box and/or tweet using #CustomerSuccess
  • Dan Steinman Chief Customer Officer David Gerster VP Data Science Mike Stocker Team Lead – Customer Success Our Panelists
  • David Gerster VP Data Science Introduction • I’m David Gerster, and I led the Mobile Data Science team at Groupon • Spent 3 years applying Data Science to practical business problems • Example: predicting customer churn on Groupon’s iPhone app – Concept of customer churn also applies to B2B companies • Recently joined BigML as Vice President of Data Science
  • David Gerster VP Data Science What Is Data Science? • Your business has gathered large amounts of data • How do we find meaningful patterns in all this data? – Which of my customers are most likely to churn? • How do we take action on these patterns? – If I know a customer is likely to churn, what do I do about it?
  • David Gerster VP Data Science Simple Churn Example • You’re a daily deal site with an iPhone app • You log three types of user events: deal views, deal clicks and deal purchases • Based only on a user’s activity in the first month after downloading the app, how do we predict if that user will still be using the app in six months?
  • David Gerster VP Data Science Simple Churn Example There’s a pattern in this data: “Users who do 1 or more clicks in Month 1 come back and use the app in Month 6.” Even this simple pattern is challenging for a person to find. Why not have machines do it instead?
  • David Gerster VP Data Science Using BigML to Predict Churn • BigML is a cloud- based tool that finds meaningful patterns in data • Instead of three users, let’s try 243,000! • Instead of three columns, let’s try 10! • BigML finds useful patterns that predict which users will churn
  • David Gerster VP Data Science Using BigML to Predict Churn • This segment of users is 80% likely to churn in 6 months: – Doesn’t use the iPhone app in last 2 weeks of the first month, and – Doesn’t log in during the first month. – 32% of users fit this description! • This segment of users is 75% likely to come back in 6 months: – Logged 5 or more events in the last 2 weeks of the first month, and – Had an average gap between visits of 2 days or less in the first month. – 11% of users fit this description! • [Quick glimpse of SunBurst visualization]
  • David Gerster VP Data Science Acting on Predictions • These predictions only need data from the first month of activity, so we can act quickly when we see a user who’s likely to churn • Reach out to that high-risk 32% of users using marketing promotions, better daily deals, etc. • Analyze the 11% of users who do come back in more detail. What are we doing right that makes them so active? • More info: gerster@bigml.com
  • Mike Stocker Team Lead – Customer Success Introduction Marketing software from Marketo automates lead scoring, email nurturing, landing pages, events, social campaigns, and ROI reporting from a single, integrated platform. It’s not just about automating tasks; it’s about making marketers better. Marketing automation software is fundamentally different from other kinds of business applications, like CRM or ERP. Marketing is much more dynamic – users need to constantly conceive, build and launch new marketing campaigns every few days or weeks, with minimum effort and minimal IT support.
  • Mike Stocker Team Lead – Customer Success Marketo CSM Goals To become a long-term partner and grow with our customers To have a closed-loop feedback system in place To bring customer data and workflow front and center to the whole organization To have more relevant and strategic engagements with our customers To be more proactive and improve customer experience
  • Mike Stocker Team Lead – Customer Success Success Across the Enterprise CSM Team Installed Base Sales Team Renewals Team Marketing Department
  • Mike Stocker Team Lead – Customer Success Important Features Used Everyday Alerts: Provide early warning for customers who are at-risk helping to prioritize Customer Success outreach. Alerts may be triggered by survey scores, observations of usage, support tickets, etc. Customer 360: Holistic view of customers’ health—includes adoption, support tickets, NPS score, and Services engagements, all in one view. Adoption: Quick and easy way to identify out-of-compliance and underutilizing customers based on product adoption metrics. Survey/NPS: Results are pushed across the organization; alerts are triggered based on NPS score. Insights: Executive Dashboards to monitor customers’ health, CSM status, up-sell/cross-sell opportunities.
  • Mike Stocker Team Lead – Customer Success Alerts Applied Before Data Science Logins • No logins last X days • Logins have not increased in last four weeks • Logins have dropped by 10% in the last two weeks Adoption • # of emails sent dropped by 10% vs. last week Support Tickets • No support tickets in the past 30 days Survey Scores • Low survey score
  • Mike Stocker Team Lead – Customer Success Alerts Applied Before Data Science
  • Mike Stocker Team Lead – Customer Success Alerts Applied Before Data Science
  • Dan Steinman Chief Customer Officer Data Science @ Gainsight Intuition Best Practices Data Science Alert Rules and Playbooks New Insights Counter Intuition Confirm Intuition
  • Dan Steinman Chief Customer Officer Data Science Approach Phase Hard Problems Data science strategy What data to start with? Feature extraction How do group / aggregate data? Feature selection Which data matters? Machine learning Which algorithms to use? Iteration Where do we go next? Alert configuration How do we operationalize this?
  • Dan Steinman Chief Customer Officer Case Study Data Set Selection • Isolated data to customers with at least 9 months of history • Split customers into two – ―Test‖ and ―Control‖ groups • The ―Control‖ group compared Churn vs Active • We back-test the prediction model against the ―Test‖ Time Selection • Narrowed focus to ―normal‖ active periods • Compared 3-month time frames to find secular trends
  • Dan Steinman Chief Customer Officer Case Study Variables Analyzed • > 40 usage features • Support data • Account attributes • NPS data Best Practices • Remove ―noisy‖ data (e.g., big companies with large ACV) • Remove incomplete or inconsistent data (e.g., manually-coded Account attributes) • Remove variables that are ―too heavy‖ (ones that are true for >80% of all customers)
  • Dan Steinman Chief Customer Officer Generate New Alert Rules Alert Rules and Playbooks No proactive outreach within 6 months of renewal 53% decline in number of sessions in middle of subscription 15% decline in key usage metric
  • Dan Steinman Chief Customer Officer Counter-Intuitive Findings Average NPS score of churned customers = 7.2 (scale is 1 to 10) Only a small number of churned customers had highest level Support tickets
  • Questions?
  • Thank You