Where Customer Success and Data Science Meet

Posted by Kenny S. on Nov 27, 2017, 8:46:45 AM

4 minute read

November 2017 (8).jpg

Customer success (CS) means different things to different people.

On an enterprise level, it involves a great deal of human-to-human heavy lifting. The CS professional in that environment looks a lot like an account manager, customer service representative and consultant all rolled into one.

But in the SaaS industry, CS is different. To be sure, the human connection doesn’t disappear entirely. But when you're trying to scale a business on $5/month subscriptions, it's neither right nor necessary to assign every customer a personal CS agent.

In the fast-paced world of SaaS, CS means early acquisition, automated engagement and maximized LTV. What’s more, all of this has to happen in a competitive marketplace where customers jump ship and swim to competitors at the drop of a hat.

Enter Big Data

For the past few years, SaaS companies have been leaning hard on data science to optimize onboarding, kill churn and streamline just about every aspect of their business.

In subscription-based SaaS business models, quantification is the name of the game. For this reason, data analytics make the critical difference between a model that works and one that peters out in the first few months.

Think about it from this angle: how would it impact your business if you had definitive answers to the following questions:

  • Where do we lose potential customers in the onboarding process?
  • Why do customers churn?
  • Who’s at the highest risk of churn?
  • When's the right time to cross- or up-sell?

Data science can answer all of these questions for you with stunning precision... as long as you’re willing to implement them widely and consistently. But how do you do that?

Bob Hayes—a data science and customer experience expert—offers three suggestions:

  • Train Your CSMs in Data Analysis –
    Long-term, this option will give you the best results. Be prepared, however, for a steep learning curve.
  • Hire a Data Scientist –
    Shorten the learning curve by hiring an expert to come in. The only trouble may be facilitating seamless communication between your CSMs, product experts and data scientists.
  • Hire Outside Third-Party Vendors –
    An entire industry has spawned to address the growing data needs of both enterprise and SaaS businesses.

Which option you choose will depend on the size of your company,  available resources and data needs. But with the number of DaaS providers out there ready to plug into your existing operation, option 3 looks quite attractive.

Whichever route you go, Appduri—itself a DaaS provider—offers 4 data science imperatives for CS teams of any size and scope:

  • Integrate Customer Data –
    Pull every scrap of data you have on your customers and house it in one central location.
  • Adopt Machine Learning –
    Utilize current AI technology to sift through customer data and provide more in-depth insights than you ever could on your own. Use predictive modeling to understand the various aspects of your customer’s journey.
  • Adhere to the Scientific Method –
    Think back to your high school days and pull out the scientific method. Ask questions about the data. Then, formulate a hypothesis. Gather relevant data. Analyze your conclusions. Take action. Repeat.
  • Develop your CSM Team’s Stats Skills –
    Your analyses are only as good as the people who wield them. Teach your CSM team to understand what they’re looking at and use it for the benefit of the organization.

Customer success is becoming more and more data-driven, particularly in the SaaS industry. If your team doesn't learn to employ data science in its work, you won't just fall behind; you'll fall right off the map. Don’t let that happen.

Topics: Support

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