Success turning Data into Insights that drives Sales?

Doug CavinessDoug Caviness VP, B2B Strategy & PartnershipsMember | Scholar ✭✭

Would you have a success you can share about converting raw customer data into insights to inform the work of your sales reps and CSMs to drive sales (renewal, expansion, etc.)? 


I enjoyed participating in a very informative Business Challenge Accelerator last week at Interact, The Digital Renewal Experience co-led by @Jack Johnson of the TSIA and Rob Rosa of Extreme Networks. In our technology breakout, participants shared that aggregating data and turning it into actionable insights was a major challenge and they were hungry for success stories in overcoming this challenge. 

Best Answer

  • Steven ForthSteven Forth Managing Partner Founding Partner | Expert ✭✭✭
    Accepted Answer

    A couple of thoughts.

    The tools for this are increasingly available, from Google, Amazon, Microsoft, IBM, Open Text and many other more specialized vendors. BMC also has some good tools for orchestrating the data flows.

    I think there are two blockers: (i) poorly articulated processes that try to jump to the end point and (ii) a failure to understand value to the customer and its role in upsell, cross sell and renewal.

    On the process side, start with something simple. We have had a lot of success with something we call Predictive Engagement. Take all of your current engagement data (you are probably collecting this real time) and run it through one of the Deep Learning engines (I don't think it matters much which one) and see if you can use current engagement data to predict future engagement. This is a nice simple way to get started and it let's you begin to predict future engagement. Once you can predict it you can start to take steps to improve it.

    From predictive engagement you can begin to look for the types of engagement that predict renewals. This is more work, and will likely require some support by a data science team, internal or external.

    The real challenge though is to understand and monitor value. This requires a value model that uses data (as far as possible data collected from your own system). The value model is also a good guide to the kinds of data you should be collecting. Once you have a baseline way of monitoring value you can then layer in predictive value, which uses predictive engagement as an input into predicting future value.

    That is the goal anyway.

    The value model should also be linked (through something like a benefit ladder) back to functionality. It should become possible to predict what additional value could be created by adding additional functionality, data or services.

    I think of this as the next generation of configure-price-quote, which can be rethought as value-configure-price-deliver ...

    We have done predictive engagement and value models but have not yet tried to connect them. We are small and I doubt that we are collecting enough data to do this yet. But larger companies here could move forward quickly.

Answers

  • Spencer HancockSpencer Hancock Senior Member Success Manager Moderator | mod

    Hey Doug,

    Great question, I am eager to hear some successes in this area. @Anthony Medeiros @Joe Thomas @Saurabh Sharma; I would be interested to hear from each of you on Doug’s question.  

  • Doug CavinessDoug Caviness VP, B2B Strategy & Partnerships Member | Scholar ✭✭

    @Steven Forth thanks for your insights!

  • Saurabh SharmaSaurabh Sharma Manager Data and Analytics Founding Analyst | Scholar ✭✭

    We have recently developed a Customer Churn prediction model based on historic Customer engagements. It helps Renewal team to focus on high risk deals. It also provides an insight of high risk customers in 6 months advance. Currently we are taking up same and bringing the health sore to all steps of Customer lifecycle.

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