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prefeye_investor

Published on Nov 19, 2015

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PRESENTATION OUTLINE

PREFEYE

REINVENTING ONLINE SHOPPING...IT'S ABOUT TIME. 
Photo by StuartWebster

prefeye is a discovery engine for apparel

think Pinterest meets Pandora 
Prefeye helps people find clothes they love online. We match a person's taste & style profile to a piece of clothing's profile for a better Preferece-based fit.

Prefeye helps people discover clothes they love.
You Touch It, We Match It, You Buy It

Prefeye's matching algorithm creates a taste and style profile

Helping people discover clothes they'll love, buy and keep!

Discovery Engine
Discovery + Recommendations Engine
Recommendations Engine
we walk new users through a “style game” in order to customize their preferences, turning the online store into a unique experience personalized to every visitor.

Search for Women
Apparal focused Search for Women

unpack Discovery Engine v Search Engine

Hunt is Searching v Discovery is Finding

Inverting the power structure and not just getting what the corporation wants to show you but allowing the customer to define their own experience. Their own discovery experience. That's why

Own personal shopper. We know our selves best. Not personal shoppers. Have their own agenda.

Our own personal shopper Me+ Time
reread trust article on nytimes
DNA





a smart personalized

Pinterest meets Match.com
Photo by *nacnud*

YOU'RE GOING TO WHAT. IN 15 YEARS

No one has cracked the conversion nut  for apparel 
background on fashion + tech - canyon of a divide

amazon created first ecommerce retail experience. everyone else (fashion included) copied their experience from Search to Personalization engine.

Problem: amazon and others built for high transaction low emotional load. think buying a book or a hammer, person knows coming into what the general specs are for what they're searching for.

vs. Fashion where there people don't know exactly what they're looking for. Offline we solve this problem by self-curating out what we aren't interested in by walking up and touching what we are interested in/drawn to - led by our emotional, visual, sensory preferences....
Photo by katerha

PREFEYE IS

  • A preference-based Discovery Engine for Apparel
  • An Algorithm: we create a Preference profile for each Person & Product
  • A Match: We preference match the Person to the Products
Sensory, visual and emotional preferences

Product personality meaning the product attrubutes that match our preferences. Things like how does it fit, look and feel

No one else is looking at the product

personalized
Photo by P!XELTREE

THE OPPORTUNITY

Vs 30%

Women's pants go up to 60%
$$ how much money is being lost to returns specific to apparel

CE market

WE can do better

It's Time
online retail (search to personalization) has not been changed for 10+ years.

It's time
Photo by Adam THEO

Problem

  • Customers can't find what they want to buy when they Search
  • Too many results
  • System built for buying (spec based), not shopping
  • Clothing is personal, current system not built for that
1. Clothing is personal


3. 999 versions of jeans - options - option fatigue - people are frustrated

4. shopping discovery emotional journey not 'i want a 2.5 inch heel'

We shop with our eyes, not with words

Use the timeline created by Jackie to
created clone and clone going to find 5 dresses

using 5

Solution

  • Mimic the real world shopping experience
  • Create a Personal Preference + Product Profile (asking what + why)
  • We match the Person to a Product Profile (personality of the product)
  • We recommend matches that are unique and personalized by extracting nuanced
  • We mimic the real world shopping experience
A lightweight process that asks the user to Touch What They Love, it's fun, it's interactive

Screenshot of TWYL -

Going from Search (word based/spec based) to Discovery

Modernizing Search to Personalization
It's Time

extracting and extrapolating a layer of human intelligence

taking product attributes and humanizing the product

anthropomorphizing - giving human attributes to non-human thing


Preference-First Platform

Graph with Old Internet to What is now
Search and Personalization much the same

input Solution part of Jackie's chart
then show stick figure



We're smarter than that

we know what we like but technology isn't asking why people like it...only asking what we like, but with clothes we can't capture it with words...need to shift the paradigm

why apple garners so much respect is it's visual/design-led












we're building this for retailers

Preference has higher impact on Sales than Brand
who are we building this for: largely women

women shop, men buy
men buy based on specs. women (largely) do not.

women enjoy the 'thrill' of shopping. the discovery process.
Photo by visitflanders

We will build an audience

by Partnering with the Top Online Retailers 
Go to Market

Examples:*Of 1000 unique visitors, converting $50 more in sales, pays for Prefeye. Because Prefeye individually, is the only personalization product Preference individual customer the benefits to retail clients go beyond PST Inc. launches early 2014 via on

exclusive partnership with one retailer.

Expansion begins in 2015, acquiring

new retail clients each quarter. Prefeye

markets directly to the Top 500 Internet

Retailers, including Nordstrom, Saks

Fifth Avenue, Neiman Marcus, Macy’s.

$$

revenue model

  • SaaS Powered by Prefeye Model
  • Retailer pays a licence fee per 1000  visitors (CPM)
  • $50 per 1000 to launch
  • Prefeye delivers personalized product recommendations
Revenue is derived via CPM [cost per 1000]. Each retailer pays a license fee per 1000 website visitors. In exchange Prefeye delivers individually personalized, customer product recommendations and retargeted ads.

prefeye revenue example

style seek numbers? who else is seeing 2x conversion?

why us: fashion + tech

  • Cynthia  : molecular knowledge of how and why people shop
  • Stacey: deep understanding of how to make customers happy
  • Cynthia: seasoned Retail veteran
  • Stacey: seasoned Tech veteran who builds great teams and products
  • Together we are greater than the sum of our parts 

COMPETITION+

  • Google's "Visually Alike" Shopping feature
  • Behavioral based recos like Rich Relevance, Monetate
  • Can we build the platform in a scalable, optimizable way
Current competing recommendation technologies offer recommendations based on: 1. “If You Like That, You Will Like This” [Amazon - based on other shopper’s purchase behavior]
2. ”Visually Alike” [Google Shopping ]
3. ”Behavioral” based recommendations inferred by a shopper’s click stream [Rich Relevance, Monetate].

Our VISION is to create

  • A nuanced algorithm using a powerful combination of Tech + Taste
  • An indispensable service to Retailers + Consumers
  • A unique, personalized experience in the online world, for every visitor
  • An experience that gets smarter the more it's used
  • THE NEXT GENERATION RETAIL PLATFORM
"powered by" model. behind the scenes, shaping a highly personalized, unique experience for every visitor - just like we create for ourselves in the physical world. from discovering (replacing search) to p14n. and a guided, curated experience that gets better the more you use it (the smarter we get).

we know that

launch plan/next steps

  • Hire the best people on earth to build this
  • Build out MVP, Test, Measure, Build
  • Rinse & Repeat

our ask - your opportunity

  • Convertible debt note up to 1.5M
  • Use of funds: key hires, patents, technology build out
  • Get in on defining the next generation retail platform
  • Create a needed solution for an untapped market
Current Round
Convertible Note - Bridge Financing - Up to $1.5 M
Notes Convert to Series A-1 Preferred at Series A
[funding minimum of $2M ]
Use of Funds
Enterprise Platform
Software
Patents
Prefeye Retailer Customer Satisfaction Team
Series A
2015 Q1 –Q2
Photo by ttstam

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