Jason Lobel, CEO @jasonlobel

Contextually Relevant Retail APIs

for

Dynamic Insights and Consumer Experiences

September 2014

Primary Use Cases for Contextual Relevance

Omni-Channel Personalization

Category Management

Smarter Analytics

SwiftIQ: End-to-End Data and Analytics API Infrastructure & Applications

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Data APIs Query APIs Algorithm APIs

Contextual APIs Activate Insights and Digital Experiences From One Platform

  Critical data sources

  Unify data from disparate sources

  Enable data to be machine readable

 Embed data into digital apps easily

 Activate digital personalization efficiently via web, mobile, in-store (beacon), ads and other channels

 Visually interpret data

 Queries on demand

 Predictive applications

Adaptive Intelligence

Data > Insights > API > Activation

Retailer (Data)

Unified Data / Algorithm / API Platform

Point of Sale Transactions

-  Data Storage -  Query Explorer -  Algorithms -  Applications (Alerts,

Dashboards, etc)

Data Scientists

Suppliers

Category Captains Product Catalog

Internal (API)

Media Buying/Marketing

Digital (eCom) & In-Store (BLE, NFC)

Locations

Promotions

Internal

CRM (Web/Email)

Marketing Assets

Suppliers (API)

Public (API) 3rd Party Developers

Data Scientists / Research

Category Managers

Media Buying (DSP)

Inventory Deliveries

Why APIs?

http://apievangelist.com/2012/01/12/the-secret-to-amazons-success-internal-apis/

Mandate for APIs: “Anyone who doesn’t do this will be fired. Thank you; have a nice day!”

Value of Retail APIs

 Contextual Insights

 Contextual Experiences

 Omni-Channel Agility

 Predictive Analytics

 Optimize Supply Chain

 Partnerships

 Open API

Leading Retailers Leverage APIs for Omni-Channel Agility

 Some even publish open APIs for partners and 3rd party developers

What Retail APIs are Relevant?

Core Retail

 Products

 Orders

 Prices

  Inventory

 Categories

 Shopping Cart

 Customer History

 Loyalty

Marketing

 Advertising Assets

 Promotions

 Coupons

Company Information

  Stores / Locator

  Brand Assets

  Events

Contextual Retail

  Item Recommendation

 Affinities

 Clusters

  Item Tags/Facets

 Product Reviews

 Search Results

 Queries (Top Clicked)

……Day/Week Parting

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 Orders & Stores API > Queries = context (user purchases “now” by “location”)

……Facets/Tags = Semantic Context

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 Products are complex to “describe” to a machine

  Facets/Tags/Linked Data is mission critical context

Source: Jay Myers (BestBuy) www.slideshare.net/jaymmyers/better-business-through-linked-data

Clam Chowder   Category: soup, appetizers   Season: winter, fall   Ingredients: Crème, corn, carrot, onions   Pairs: seafood, red wine

Predictive Targeting – Crawl……Walk……Run……Repeat

 Most enterprises will start small with low sophistication targeting

  The degree of individualization can vary significantly

Source: Forrester Research

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Frequent Pattern Mining

Product Associations: if X is bought, what else is likely to be bought (e.g. men that buy diapers also buy beer)

Recommendation Item/Offer Recommendation:

suggest products a consumer may like based on known interests

Clustering Discover Customer Segments:

examine purchasing habits to identify clusters of shopper segments

Algorithm Type Application

Applying Machine Learning to Extract Insights

  Compute all permutations of behavior (e.g., basket patterns)

  APIs facilitate three-tier access

  REST API = developers

  +angular = interface

  +angular+d3 = visualization

Algorithm API – Pattern Mining

FPM Interface Visualization Layer

"name":"GENOVA TUNA IN OIL", "itemsets":[ "items":[ "CDF ITALIAN BREAD", "PLNTRS LT SLT MIX” "count":8, "support":0.04, "confidence":100.0

API Output

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  Grouping “like items” (search terms, items, people, etc).

  Dynamically, application of clusters as behavioral changes (clicks) occur

Algorithm API – Clustering

Visualization

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API Sample

  Recommender algorithms (user, item, anonymous)

  Post algorithm Logic layer is very important

 Add human layer

 Suppress bad output

Algorithm API – Item Recommendations

API Sample User Matrix

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Jason X X X

Jessica X X

Kin X X X

Steve X X X

Sarah

Use Case: Interactive Visualizations

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 API + Open Source (D3) = interactive dashboards

  Easy to interpret large data sets (~20-40 hours per application)

  Enable access to decision makers faster

Interactive Dashboards Open Source (D3) Libraries

Use Case: Web, Email, Ad Personalization

Apply Algorithms •  Train models •  Generate recommendation scores per user •  Output sent to web/mobile site, ESP, etc.

Data Logic & Verification •  Ensure correct language •  Ensure copy exists •  Suppress previously-presented items/offers •  Suppress inappropriate items (logic-based)

Data Collection •  Data storage •  Reports

Hero Image

Dynamic Web/Email Templates utilizes Predictive Algorithm to pull in

the relevant coupons, upsells, etc

Logic to determine title to display

Ad Tiles or Custom Messaging

Data Import  Purchase Behavior (real-time/next-day)

 Web actions, reviews (real-time)

 Loyalty (real-time/next-day)

 Email History (one-time)

 Product catalog (as changing)

 CRM/Ad Segments (weekly)

 Logic Exclusions (one-time) via API to

Front-End Experience

Engage at shelf

Welcome content is pushed by Bluetooth b e a c o n s a t s t o r e entrance

At shelf engagements are delivered through BLE, NFC and QR

Beacon pulls contextual content (recipe content, real-time web trends, POS affinities, coupons)

Use Case: Mobile In-Store (Beacons, NFC, QR, SMS) Personalization

 APIs to deploy content to beacon/NFC partner platforms  Deliver contextually relevant experiences upon entrance or down the aisle

  Trending products   Item affinities  Recommendations   Items  Coupons  Offers

Source: Thinaire