Your Data is Screaming at You. Uncover the Science of Retail.

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Your Data is Screaming at You.

Transcript of Your Data is Screaming at You. Uncover the Science of Retail.

Your Data is Screaming at You.

Speakers

Todd HarrisDirector of Marketing

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Vivek FariasCTO, Co-founder

Agenda

• An Intro to Celect• Current State of Retail• Why Comparisons and Choice Matters• Demo Scenario

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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

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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

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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!

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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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Thank you!

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