Your Data is Screaming at You. Uncover the Science of Retail.
Transcript of Your Data is Screaming at You. Uncover the Science of Retail.
Speakers
Todd HarrisDirector of Marketing
© 2015 Celect, Inc. All Rights Reserved.2
Vivek FariasCTO, Co-founder
Agenda
• An Intro to Celect• Current State of Retail• Why Comparisons and Choice Matters• Demo Scenario
© 2015 Celect, Inc. All Rights Reserved.3
An Intro to Celect
• Who we are‒ Predictive analytics platform focused on the
retail sector‒ Based in Boston, MA‒ Venture-backed, founded by two tenured
MIT Professors
• Awards & Recognitions‒ DEMO Fall 2014 – DEMO God Winner‒ MIT Computer Science and Artificial
Intelligence (CSAIL) Top 50 Greatest Innovations
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Predictive Analytics Solutions for Retail
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Right product in the right place at the right time across all channels.
Current State of Retail
• Retailers are awash in data‒ Inventory, PoS, browse history, transaction logs, etc
• Online / offline are blurring‒ Merchants often have responsibility for both online and in-store
• Merchants, buyers, planners under pressure to be smarter with allocations and forecasting
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Do These Concepts Still Hold Up?
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I like blue, so my customers will like blue also.
We sold 100 droids two years ago, 200 last year,so will forecast 300 for this year.NO
There’s a Better Way.
• Use the data you already have‒ Leverage predictive analytics to make better,
more informed decisions‒ Supplement your experiences, knowledge, and
intuition
• Realize one size doesn’t fit all• Understand choice – products interact
and influence each other
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An Important Question
Comparisons are eas(ier) …
… but also, comparisons are everywhere.
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Comparisons Are Everywhere
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Traditional Reviews: Peter prefers Tamarind to Cafe Boulud
PeterG.
PeterG.’sProfile
AboutPeterG.
Peter G.
Comparisons Are Everywhere
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Click Stream Data: Vinay is more in the mood for The Expendables 3 vs. Cosmos or The Bank Job or ... ?
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Comparisons Are Everywhere
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(Brick and Mortar) Retail Data
Comparisons Are Everywhere
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(Brick and Mortar) Retail Data: I prefer Neutrogenato Dove, and Aveeno, and …
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Demo Scenario
• Mid-Size Retailer • Task
‒ Optimize assortment using our approach to learn choice patterns at individual stores
• Data‒ In-store inventory and transactions over time‒ Online browse / transactions
• Controlled experiment at 10 representative stores
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Demo Summary
• Celect modifiers were applied to a limited portion of each test store
• Adjusting against control stores and for trailing 6-wk trends:‒ Celect modified departments yielded an incremental 6.65% increase in
revenue‒ We predicted 6.43%, so our performance is predictable
• Test stores in total yielded an incremental 2.98% increase in revenue potentially due to positive spillover effects of a better assorted store
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