1. SwiftIQ provides contextual retail APIs that power omni-channel personalization, smarter analytics, and category management for retailers.
2. The APIs allow retailers to activate insights and digital experiences across channels by enabling data from different sources to be machine readable and accessible via queries.
3. SwiftIQ's platform includes data APIs, query APIs, algorithm APIs, and applications that use predictive algorithms and machine learning to extract insights from retail data.
Don’t Get Showroomed- Are you frustrated with showrooming?
Contextually Relevant Retail APIs for Dynamic Insights & Experiences
1. Jason Lobel, CEO
@jasonlobel
Contextually Relevant Retail APIs
for
Dynamic Insights and Consumer Experiences
September 2014
2. Primary Use Cases for Contextual Relevance
Omni-Channel Personalization
Smarter Analytics
Category Management
3. SwiftIQ: End-to-End Data and Analytics API Infrastructure & Applications
3
Data APIs Query APIs Algorithm APIs
4. Contextual APIs Activate Insights and Digital Experiences From One Platform
Embed data into digital apps easily
Activate digital personalization
efficiently via web, mobile, in-store
(beacon), ads and other channels
Unify data from disparate sources
Enable data to be machine readable
Critical data sources
Visually interpret data
Queries on demand
Predictive applications
5. 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
Product Category Captains
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
6. Why APIs?
Mandate for APIs: “Anyone who doesn’t do this will be fired.
Thank you; have a nice day!”
http://apievangelist.com/2012/01/12/the-secret-to-amazons-success-internal-apis/
Value of Retail APIs
Contextual Insights
Contextual Experiences
Omni-Channel Agility
Predictive Analytics
Optimize Supply Chain
Partnerships
Open API
7. Leading Retailers Leverage APIs for Omni-Channel Agility
Some even publish open APIs for partners and 3rd party developers
9. ……Day/Week Parting
9
Orders & Stores API > Queries = context (user purchases “now” by “location”)
10. ……Facets/Tags = Semantic Context
10
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
11. 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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12. Algorithm Type Application
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
Applying Machine Learning to Extract Insights
13. Algorithm API – Pattern Mining
Compute all permutations of behavior (e.g., basket patterns)
APIs facilitate three-tier access
REST API = developers
+angular = interface
+angular+d3 = visualization
Visualization Layer FPM Interface
API Output
"name":"GENOVA TUNA IN OIL",
"itemsets":[
"items":[
"CDF ITALIAN BREAD",
"PLNTRS LT SLT MIX”
"count":8,
"support":0.04,
"confidence":100.0
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14. Algorithm API – Clustering
Grouping “like items” (search terms, items, people, etc).
Dynamically, application of clusters as behavioral changes (clicks) occur
Visualization
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API Sample
15. Algorithm API – Item Recommendations
Recommender algorithms (user, item, anonymous)
API Sample User Matrix
Post algorithm Logic layer is very important
Add human layer
Suppress bad output
15
Jason X X X
Jessica X X
Kin X X X
Steve X X X
Sarah
16. Use Case: Interactive Visualizations
16
API + Open Source (D3) = interactive dashboards
Easy to interpret large data sets (~20-40 hours per application)
Enable access to decision makers faster
Interactive Open Source (D3) Libraries Dashboards
17. Use Case: Web, Email, Ad Personalization
Data Collection
• Data storage
• Reports
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)
Dynamic Web/Email Templates
utilizes Predictive Algorithm to pull in
the relevant coupons, upsells, etc
Logic to determine
title to display
Hero
Image
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
18. 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
Engage at shelf
Welcome content is
pushed by Bluetooth
beacons a t s t o re
entrance
At shelf engagements
are delivered through
BLE, NFC and QR
Beacon pulls contextual content (recipe
content, real-time web trends, POS
affinities, coupons)
Trending products
Item affinities
Recommendations
Items
Coupons
Offers
Source: Thinaire