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Dec 17, 2015 · 21m · mad

B2B Big Data Challenges, Nick Mehta, Gainsight (Data Driven NYC / FirstMark Capital)

Nick Mehta · 18m spoken Matt Turck · 10s spoken
0:00 / 0:00
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At Data Driven NYC, Gainsight CEO Nick Mehta examines the fundamental differences between B2B and B2C big data, outlining five key operational challenges SaaS companies face when leveraging analytics and offering practical strategies to solve them.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 1.1% of the talking time here. How this is scored →

Matt as informed peer 0.3 Guest teaching 0.5 Guest disagreement 0.2 Matt pushing back 0.2
05100:0010:0020:000:23–3:19 · Matt as informed peer 0/10 Contextualizing B2B Big Data and Gainsight Overview As a solo keynote presentation segment, the host does not speak, requiring host-side scores of 0. Nick introduces Gainsight and frames the tension between big data analytics and human sales intuition in B2B context.3:19–7:07 · Matt as informed peer 0/10 Challenge 1: Messy Data and Business Schema Drift Monologue segment with no host involvement. Nick outlines the first major B2B data challenge involving schema drift, legacy field naming in CRMs, and key matching across disparate enterprise systems.7:07–9:51 · Matt as informed peer 0/10 Challenge 2: B2B Customers as Unique Snowflakes Monologue segment without host interaction. Nick explains high data variance, custom contract sizes, and small sample sizes in B2B retention modeling.9:51–13:10 · Matt as informed peer 0/10 Challenge 4: Explaining Complex Predictive Models Monologue presentation segment. Nick breaks down the challenge of explaining complex statistical models (like AUC) to business users and overcoming user resistance when edge cases arise.13:10–15:39 · Matt as informed peer 0/10 Five Practical Suggestions for B2B Big Data Success Monologue presentation segment. Nick shares five practical recommendations for successful B2B predictive analytics deployment, focusing on simplified metric explanations and human process removal.15:39–21:21 · Matt as informed peer 2/10 Audience Q&A Session Matt Turck opens audience Q&A and facilitates questions. Nick politely corrects an audience member regarding Gainsight's market category and answers technical stack questions.0:23–3:19 · Guest teaching 0/10 Contextualizing B2B Big Data and Gainsight Overview As a solo keynote presentation segment, the host does not speak, requiring host-side scores of 0. Nick introduces Gainsight and frames the tension between big data analytics and human sales intuition in B2B context.3:19–7:07 · Guest teaching 0/10 Challenge 1: Messy Data and Business Schema Drift Monologue segment with no host involvement. Nick outlines the first major B2B data challenge involving schema drift, legacy field naming in CRMs, and key matching across disparate enterprise systems.7:07–9:51 · Guest teaching 0/10 Challenge 2: B2B Customers as Unique Snowflakes Monologue segment without host interaction. Nick explains high data variance, custom contract sizes, and small sample sizes in B2B retention modeling.9:51–13:10 · Guest teaching 0/10 Challenge 4: Explaining Complex Predictive Models Monologue presentation segment. Nick breaks down the challenge of explaining complex statistical models (like AUC) to business users and overcoming user resistance when edge cases arise.13:10–15:39 · Guest teaching 0/10 Five Practical Suggestions for B2B Big Data Success Monologue presentation segment. Nick shares five practical recommendations for successful B2B predictive analytics deployment, focusing on simplified metric explanations and human process removal.15:39–21:21 · Guest teaching 3/10 Audience Q&A Session Matt Turck opens audience Q&A and facilitates questions. Nick politely corrects an audience member regarding Gainsight's market category and answers technical stack questions.0:23–3:19 · Guest disagreement 0/10 Contextualizing B2B Big Data and Gainsight Overview As a solo keynote presentation segment, the host does not speak, requiring host-side scores of 0. Nick introduces Gainsight and frames the tension between big data analytics and human sales intuition in B2B context.3:19–7:07 · Guest disagreement 0/10 Challenge 1: Messy Data and Business Schema Drift Monologue segment with no host involvement. Nick outlines the first major B2B data challenge involving schema drift, legacy field naming in CRMs, and key matching across disparate enterprise systems.7:07–9:51 · Guest disagreement 0/10 Challenge 2: B2B Customers as Unique Snowflakes Monologue segment without host interaction. Nick explains high data variance, custom contract sizes, and small sample sizes in B2B retention modeling.9:51–13:10 · Guest disagreement 0/10 Challenge 4: Explaining Complex Predictive Models Monologue presentation segment. Nick breaks down the challenge of explaining complex statistical models (like AUC) to business users and overcoming user resistance when edge cases arise.13:10–15:39 · Guest disagreement 0/10 Five Practical Suggestions for B2B Big Data Success Monologue presentation segment. Nick shares five practical recommendations for successful B2B predictive analytics deployment, focusing on simplified metric explanations and human process removal.15:39–21:21 · Guest disagreement 1/10 Audience Q&A Session Matt Turck opens audience Q&A and facilitates questions. Nick politely corrects an audience member regarding Gainsight's market category and answers technical stack questions.0:23–3:19 · Matt pushing back 0/10 Contextualizing B2B Big Data and Gainsight Overview As a solo keynote presentation segment, the host does not speak, requiring host-side scores of 0. Nick introduces Gainsight and frames the tension between big data analytics and human sales intuition in B2B context.3:19–7:07 · Matt pushing back 0/10 Challenge 1: Messy Data and Business Schema Drift Monologue segment with no host involvement. Nick outlines the first major B2B data challenge involving schema drift, legacy field naming in CRMs, and key matching across disparate enterprise systems.7:07–9:51 · Matt pushing back 0/10 Challenge 2: B2B Customers as Unique Snowflakes Monologue segment without host interaction. Nick explains high data variance, custom contract sizes, and small sample sizes in B2B retention modeling.9:51–13:10 · Matt pushing back 0/10 Challenge 4: Explaining Complex Predictive Models Monologue presentation segment. Nick breaks down the challenge of explaining complex statistical models (like AUC) to business users and overcoming user resistance when edge cases arise.13:10–15:39 · Matt pushing back 0/10 Five Practical Suggestions for B2B Big Data Success Monologue presentation segment. Nick shares five practical recommendations for successful B2B predictive analytics deployment, focusing on simplified metric explanations and human process removal.15:39–21:21 · Matt pushing back 1/10 Audience Q&A Session Matt Turck opens audience Q&A and facilitates questions. Nick politely corrects an audience member regarding Gainsight's market category and answers technical stack questions.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 0% · guest 100%0:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%12:00 · Matt 0% · guest 100%12:00 · Matt 0% · guest 100%15:00 · Matt 6% · guest 94%15:00 · Matt 6% · guest 94%18:00 · Matt 1.5% · guest 98.5%18:00 · Matt 1.5% · guest 98.5%21:00 · Matt 5.3% · guest 94.7%21:00 · Matt 5.3% · guest 94.7%
Sharpest disagreement ▶ 16:54 Categorical correction during Q&A

Nick directly refutes the audience member's assumption that Gainsight operates in the lead scoring or lead spend space, setting a firm boundary on Gainsight's actual category.

 Hardest push from Matt ▶ 15:44 Refocusing on Gainsight product

Matt Turck prompts Nick immediately after the talk to redirect from abstract industry problems to Gainsight's specific commercial solution.

 Biggest teaching moment ▶ 16:54 Educating audience on Customer Success Management

Nick educates the audience member on the distinct differences between top-of-funnel predictive lead scoring and post-sale Customer Success Management.

 Matt holds his own ▶ 15:44 Host steering presentation to commercial offering

Matt Turck demonstrates host authority by steering the speaker straight into explaining Gainsight's concrete value proposition for the audience.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Contextualizing B2B Big Data and Gainsight Overview 0000 As a solo keynote presentation segment, the host does not speak, requiring host-side scores of 0. Nick introduces Gainsight and frames the tension between big data analytics and human sales intuition in B2B context.
Challenge 1: Messy Data and Business Schema Drift 0000 Monologue segment with no host involvement. Nick outlines the first major B2B data challenge involving schema drift, legacy field naming in CRMs, and key matching across disparate enterprise systems.
Challenge 2: B2B Customers as Unique Snowflakes 0000 Monologue segment without host interaction. Nick explains high data variance, custom contract sizes, and small sample sizes in B2B retention modeling.
Challenge 4: Explaining Complex Predictive Models 0000 Monologue presentation segment. Nick breaks down the challenge of explaining complex statistical models (like AUC) to business users and overcoming user resistance when edge cases arise.
Five Practical Suggestions for B2B Big Data Success 0000 Monologue presentation segment. Nick shares five practical recommendations for successful B2B predictive analytics deployment, focusing on simplified metric explanations and human process removal.
Audience Q&A Session 2311 Matt Turck opens audience Q&A and facilitates questions. Nick politely corrects an audience member regarding Gainsight's market category and answers technical stack questions.

Statements from this episode (11)

Assertion Supported
Mehta: Gainsight has around 250 customers and 275 employees
“And today about 250 customers, 275 employees.”
Nick Mehta Dec 17, 2015 ▶ 1:40
Insight
Mehta: B2B big data's hardest problem is changing front-line behavior
“The last mile of all this cool technology and what we've struggled with just in terms of how do you take that and turn that into changes in behavior for sales people, for customer service people, that type of thing.”
Nick Mehta Dec 17, 2015 ▶ 3:00
Insight
Mehta: Calling B2B data 'garbage in' ignores past business logic
“Just saying garbage in, garbage out, is kind of like just throwing your hands up, right? The reality is, there's actually a good reason why this happened. At every given point, there was logic why they made the changes.”
Nick Mehta Dec 17, 2015 ▶ 5:11
Insight
Mehta: Long-term historical B2B data models often yield useless results
“So you look at a whole bunch of data over five years, and the customer says, I've got five years worth of data, right? But in those five years, they got bought by a private equity firm, They introduced new products. They fired their CEO. They redid their prici…”
Nick Mehta Dec 17, 2015 ▶ 6:40
Assertion Not checkable as stated
Mehta: The top B2B customer retention predictor is CEO friendship
“So in our business kind of predicting churn, you know what the number one predictor of customers retaining is? If the customer's CEO is friends with the vendor's CEO, right?”
Nick Mehta Dec 17, 2015 ▶ 8:26
Insight
Mehta: Most B2B companies lack sample size needed for predictive analytics
“The reality is, most companies, the sample size is far too small. They're just few, too few data points over too little time.”
Nick Mehta Dec 17, 2015 ▶ 9:13
Insight
Mehta: B2B conversion funnels make predicting bottom-of-funnel events like churn difficult
“This waterfall means It's hard to do predictions further down the stack”
Nick Mehta Dec 17, 2015 ▶ 9:45
Insight
Mehta: B2B data projects fail when users reject models over single counter-examples
“Where a lot of projects fail is you do all the work, and you get all the way to the end of the users, and then the users are like, okay, I found one counter example.”
Nick Mehta Dec 17, 2015 ▶ 12:35
Insight
Mehta: Driving analytics adoption requires taking humans out of the execution loop
“At the end of the day, you've got to figure out how you take the humans out of it, to be honest. It's a lot of, like, how do you make it more automated?”
Nick Mehta Dec 17, 2015 ▶ 15:03
Prediction Didn’t hold up
Mehta: Customer success software market is becoming a power law
“Some spaces tend to you end up being a power law where one company ends up getting more of the space than the others. In other spaces, there's a lot of players, right? Ours is probably ending up a little bit more of a power law”
Nick Mehta Dec 17, 2015 ▶ 17:47
Insight
Mehta: Enterprise focus early forces startups to build sophisticated products
“We went after larger customers early on, and that's allowed us, it forces you to build a bigger product, right? There's pros and cons to it, by the way. There's lots of companies that get killed by going after larger customers, but by, for us, we built a very …”
Nick Mehta Dec 17, 2015 ▶ 18:28
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