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Mar 3, 2014 · 23m · mad

Corey Pearson, Custora // Data Driven NYC 23 // January 2014 (Hosted by FirstMark Capital)

Corey Pearson · 20m spoken Jonathan Taku · 40s spoken Matt Turck · 20s spoken
0:00 / 0:00
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

At Data Driven NYC, Custora co-founder Corey Pearson demonstrates how predictive analytics and probability modeling can be operationalized to optimize retail customer acquisition and retention. By combining predictive algorithms with friction-free workflow automation and human marketing strategy, retail brands can achieve measurable increases in customer lifetime value and campaign ROI.

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 1.9% of the talking time here. How this is scored →

Matt as informed peer 0.2 Guest teaching 2.6 Guest disagreement 0.2 Matt pushing back 0.4
05100:0010:0020:000:29–7:42 · Matt as informed peer 0/10 Presentation Overview: Big Data: Will It Blend? Corey Pearson delivers an uninterrupted presentation introducing Custora and predictive analytics frameworks for retail. As a pure monologue segment, host expertise and pushback scores are zero.7:42–11:47 · Matt as informed peer 0/10 Example 1: Customer Lifetime Value and Acquisition Optimization Pearson explains customer lifetime value calculations and acquisition channel optimizations. This segment is a monologue, resulting in zero host involvement scores.11:47–14:28 · Matt as informed peer 0/10 Case Study: Optimizing Customer Acquisition at Bonobos Pearson discusses Bonobos case studies and introduces probability-based churn detection in retail. The segment remains a monologue presentation without host participation.14:28–18:13 · Matt as informed peer 0/10 Overcoming Action Friction in Re-Engagement Campaigns Pearson details operational friction in win-back email campaigns and automated integrations. Monologue rules apply, keeping host scores at zero.18:13–23:12 · Matt as informed peer 1/10 Case Study: Automated Customer Retention at LivingSocial Pearson concludes his talk and answers an audience question about uplift modeling before host Matt Turck briefly moderates and wraps up the session. The interaction is polite and collaborative.0:29–7:42 · Guest teaching 2/10 Presentation Overview: Big Data: Will It Blend? Corey Pearson delivers an uninterrupted presentation introducing Custora and predictive analytics frameworks for retail. As a pure monologue segment, host expertise and pushback scores are zero.7:42–11:47 · Guest teaching 3/10 Example 1: Customer Lifetime Value and Acquisition Optimization Pearson explains customer lifetime value calculations and acquisition channel optimizations. This segment is a monologue, resulting in zero host involvement scores.11:47–14:28 · Guest teaching 2/10 Case Study: Optimizing Customer Acquisition at Bonobos Pearson discusses Bonobos case studies and introduces probability-based churn detection in retail. The segment remains a monologue presentation without host participation.14:28–18:13 · Guest teaching 3/10 Overcoming Action Friction in Re-Engagement Campaigns Pearson details operational friction in win-back email campaigns and automated integrations. Monologue rules apply, keeping host scores at zero.18:13–23:12 · Guest teaching 3/10 Case Study: Automated Customer Retention at LivingSocial Pearson concludes his talk and answers an audience question about uplift modeling before host Matt Turck briefly moderates and wraps up the session. The interaction is polite and collaborative.0:29–7:42 · Guest disagreement 0/10 Presentation Overview: Big Data: Will It Blend? Corey Pearson delivers an uninterrupted presentation introducing Custora and predictive analytics frameworks for retail. As a pure monologue segment, host expertise and pushback scores are zero.7:42–11:47 · Guest disagreement 0/10 Example 1: Customer Lifetime Value and Acquisition Optimization Pearson explains customer lifetime value calculations and acquisition channel optimizations. This segment is a monologue, resulting in zero host involvement scores.11:47–14:28 · Guest disagreement 0/10 Case Study: Optimizing Customer Acquisition at Bonobos Pearson discusses Bonobos case studies and introduces probability-based churn detection in retail. The segment remains a monologue presentation without host participation.14:28–18:13 · Guest disagreement 0/10 Overcoming Action Friction in Re-Engagement Campaigns Pearson details operational friction in win-back email campaigns and automated integrations. Monologue rules apply, keeping host scores at zero.18:13–23:12 · Guest disagreement 1/10 Case Study: Automated Customer Retention at LivingSocial Pearson concludes his talk and answers an audience question about uplift modeling before host Matt Turck briefly moderates and wraps up the session. The interaction is polite and collaborative.0:29–7:42 · Matt pushing back 0/10 Presentation Overview: Big Data: Will It Blend? Corey Pearson delivers an uninterrupted presentation introducing Custora and predictive analytics frameworks for retail. As a pure monologue segment, host expertise and pushback scores are zero.7:42–11:47 · Matt pushing back 0/10 Example 1: Customer Lifetime Value and Acquisition Optimization Pearson explains customer lifetime value calculations and acquisition channel optimizations. This segment is a monologue, resulting in zero host involvement scores.11:47–14:28 · Matt pushing back 0/10 Case Study: Optimizing Customer Acquisition at Bonobos Pearson discusses Bonobos case studies and introduces probability-based churn detection in retail. The segment remains a monologue presentation without host participation.14:28–18:13 · Matt pushing back 0/10 Overcoming Action Friction in Re-Engagement Campaigns Pearson details operational friction in win-back email campaigns and automated integrations. Monologue rules apply, keeping host scores at zero.18:13–23:12 · Matt pushing back 2/10 Case Study: Automated Customer Retention at LivingSocial Pearson concludes his talk and answers an audience question about uplift modeling before host Matt Turck briefly moderates and wraps up the session. The interaction is polite and collaborative.

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

0:00 · Matt 8.6% · guest 91.4%0:00 · Matt 8.6% · guest 91.4%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 0% · guest 100%15:00 · Matt 0% · guest 100%18:00 · Matt 4.4% · guest 95.6%18:00 · Matt 4.4% · guest 95.6%21:00 · Matt 2.9% · guest 97.1%21:00 · Matt 2.9% · guest 97.1%
Sharpest disagreement ▶ 22:30 Reframing black-box modeling limitations

Pearson offers a mild counter to pure automated response modeling, arguing that fully black-box solutions miss human marketer creativity and intuition.

 Hardest push from Matt ▶ 23:06 Host closes Q&A session

Host Matt Turck playfully cuts off further debate on response modeling by directing the guest and audience member to settle it over wine after the talk.

 Biggest teaching moment ▶ 9:30 Explaining acquisition LTV vs CAC

Pearson educates the audience on why conventional acquisition channel optimization fails when marketers ignore predicted customer lifetime value.

 Matt holds his own ▶ 20:18 Audience question on uplift modeling

Audience member Jonathan Taku demonstrates strong industry knowledge by pressing Pearson on whether Custora predicts incremental response rather than simple churn.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Presentation Overview: Big Data: Will It Blend? 0200 Corey Pearson delivers an uninterrupted presentation introducing Custora and predictive analytics frameworks for retail. As a pure monologue segment, host expertise and pushback scores are zero.
Example 1: Customer Lifetime Value and Acquisition Optimization 0300 Pearson explains customer lifetime value calculations and acquisition channel optimizations. This segment is a monologue, resulting in zero host involvement scores.
Case Study: Optimizing Customer Acquisition at Bonobos 0200 Pearson discusses Bonobos case studies and introduces probability-based churn detection in retail. The segment remains a monologue presentation without host participation.
Overcoming Action Friction in Re-Engagement Campaigns 0300 Pearson details operational friction in win-back email campaigns and automated integrations. Monologue rules apply, keeping host scores at zero.
Case Study: Automated Customer Retention at LivingSocial 1312 Pearson concludes his talk and answers an audience question about uplift modeling before host Matt Turck briefly moderates and wraps up the session. The interaction is polite and collaborative.

Statements from this episode (5)

Disclosure
Custora relies heavily on R for statistical analysis and predictive modeling
“Just like Rent the Runway, we're built, we use a lot of R to do a lot of this, ah, heavy lifting with statistics”
Corey Pearson Mar 3, 2014 ▶ 4:54
Insight
Analytics dashboards are useless unless they directly drive action
“The dashboard, the insight's never enough. You need to get either to the decision, or you need to get to the doing of the thing.”
Corey Pearson Mar 3, 2014 ▶ 11:17
Insight
Retail churn is fuzzy and non-deterministic compared to subscription businesses
“And in the world of retail, this concept of churn is really fuzzy. So Netflix knows when their customers quit, you know, because you actually cancel. But a retailer, you know, your big retail brand, Amazon or something, and you had this customer who was orderi…”
Corey Pearson Mar 3, 2014 ▶ 12:53
Assertion Not checkable as stated
Retailers lose 80% of customers who go inactive for 4-5 months
“What you can do is say, hey, a customer that kind of looked like this, that had this pattern, that ordered with this frequency, that bought this kind of stuff, if they go quiet for four or five months, 80% of the time, they never return.”
Corey Pearson Mar 3, 2014 ▶ 13:31
Insight
Algorithms cannot replace human marketers trying novel creative ideas
“There's something very, ah, helpful about having marketers in the mix there where they say, well, you know what, you know, that, that model will never realize necessarily, well, maybe we should try a funny email, or maybe we should try this type of communicati…”
Corey Pearson Mar 3, 2014 ▶ 22:21
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