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Contextually Relevant Retail APIs for Dynamic Insights and Consumer Experiences
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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
3
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
9
Orders & Stores API > Queries = context (user purchases “now” by “location”)
……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
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
11
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
13
Grouping “like items” (search terms, items, people, etc).
Dynamically, application of clusters as behavioral changes (clicks) occur
Algorithm API – Clustering
Visualization
14
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
15
Jason X X X
Jessica X X
Kin X X X
Steve X X X
Sarah
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 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