Data science can provide benefits for digital commerce companies through personalized recommendations, dynamic pricing, and search engine optimization. At Info Edge India, which operates job, real estate, and matrimonial sites, data science is used for applications like real-time recommendation engines, lead scoring, price trend analysis, and semantic search. Data science techniques employed include machine learning, text mining, natural language processing, and using big data technologies and nosql databases.

1. Data Science for Digital Commerce
MANISH GUPTA, PhD
SVP-Analytics
Info Edge India Ltd. (Naukri, Jeevansathi,99acres,Shiksha)
Email: manish.iitdelhi@gmail.com

2. Outline
Why Data Science for Digital Commerce?
Issues & Challenges
Data Science Applications for Digital Commerce
– Recommendations
– Dynamic Pricing
– SEO
Data Science Work Overview @InfoEdge
– Live Demo
Data Science Techniques
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3. Why Data Science & Predictive Analytics for Digital Commerce
73% higher sales for companies which use Predictive Analytics
than those who have never done it?
65% of customers feel completely frustrated with the bad customer
experience, targeting/offers
60% increase in business margins and a 1% improvement in labor
productivity for retailers who started using Big Data (McKinsey)
45% of online shoppers are more likely to shop on a site that offers
personalized recommendation (invest Consulting)
68% in upsell and cross sell through Real time personalization.
Many more reasons…
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4. Social Media Statistics
As of July 2015, total worldwide population is 7.3 billion and The
internet has 3.17 billion users
There are 2.3 billion active social media users
91% of retail brands use 2 or more social media channels
Internet users have an average of 5.54 social media accounts
Social media users have risen by 176 million in the last year
1 million new active mobile social users are added every day.
That’s 12 each second
Facebook Messenger and Whatsapp handle 60 billion messages a
day
Social networks earned an estimated $8.3 billion from advertising in
2015
38% of organizations plan to spend more than 20% of their total
advertising budgets on social media channels in 2015, up from 13%
a year ago
Source: https://www.brandwatch.com/2016/03/96-amazing-social-media-statistics-and-facts-for-2016/ 4

5. Digital Commerce/Internet Companies
Search Based: Google, Bing
Market Place: Amazon, Flipkart, Snapdeal
Social: Facebook, Twitter, LinkedIn
Life Events: Naukri, Jeevansathi, 99Acres,
Shiksha
Peer2Peer: Uber, Airbnb, LendingClub
LifeStyle: Netflix, Zoomato,
Promotions: Groupon, CouponDunia
Many more…

6. Issues & Challenges
Data Explosion Problem (Volume, Variety & Velocity)
– Cloud-based data storage and low-cost, high-speed data
processing has become increasingly cheap.
– Naukri Example
Volume: Millions of Profile, Searches, Jobs Views, Applies
Variety: Structured, Semi-Structured, Unstructured
Velocity: Daily Volume of Applies, Views, Searches etc…
Scale & Performance
– Millions of Requests
– RealTime Recommendation Engines
5 Exabytes: All words ever
spoken by human beings.

7. Data Science Applications in Digital Commerce
Data
Science in
Digital
Commerce
Personalized
Recommend
ations
Semantic
Search
Contextual
Advertising
SEO
Customer
Retention
Dynamic
Pricing
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Customer Acquisition
• Funnel Optimization
• Lead Generation &
Scoring
• Telesales Prioritization
• Cross Selling &
Upselling
Customer Service
• Voice of Customer
Analytics Diagram
• Chat Bots
Data Driven Insights
• Price Trends
• Similar
Locality/Properties
• Content Creation
Fraud/Spam Detection

8. RECOMMENDATIONS
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9. Recommendations – Why? How?
– Why?
Attempt to cross-sell or up-sell
Provide customers with alternatives that might please them even more
– Traditional approach
No recommendations at all
Products in the same category
Manually managed cross-selling opportunities per product
– Why are these approaches fundamentally flawed?
They all start from the seller perspective, not the customer!
“We know what you should be buying”
Manual recommendations are too costly and time-consuming to
maintain – even impossible with large catalogs

10. Recommendations
– Online (RealTime) vs Offline
Main focus on online, but why?
Who knows best what products to recommend?
Learn from your data, don’t take decisions based on a feeling.
– Customer based recommendations
Learn from your customers and their past.
Understand Behavior, Segment
Android vs iOS smartphones.
– Time based recommendations
Recommend or cross sell different products depending on
– season?
– holiday?
– weather?

11. Recommendations – what does Amazon do?
Cross-selling
as realized with
other (similar?)
customers
Starts from
customer point of
view!
Recommendations
based on perceived
customer journeys
Re-use the product
comparisons that
previous customers
did!
DATA
DRIVEN!

12. Personalizations
– Loyal (online) customer vs new customers.
– Browsing habits and patterns.
– Spending patterns.
– Personalized discounts and/or content?

13. Personalizations
Customer should be central
– Provide a truly personalized shopping experience
– Like high-end physical shops with personal approach to VIP
customers
Gather data about your customer
– Surfing history – what products where looked at? How long? …
– What products were bought? When?
– Brand preference?
– Product-segment preference? (budget, high-end, best-buy?)
– Abandoned shopping carts
Take action based on information mined from this data
– Triggered e-mails, personal recommendations, …

14. DYNAMIC PRICING
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15. Dynamic prices
– End of life products?
– Relevancy of products.
– (Local) competition.
– Customer!

16. Dynamic Prices – some ideas
Auto-combination special offers based on cross-selling
info
Monitor stock & manage promotions accordingly
– Example: stock of calendars in December
(value decreases over time…)
– Example: Customer history: needs incentive to buy?
Why not give a small
discount if bought
together?
Testing will show if
and for which
products and
customers this
increases revenue!

17. Dynamic Prices – some ideas
Pricing vs competition
scraping competition websites
Analysis of tenders vs deals
– What type of deals do we typically win, and which not?
= Data mining on CRM data!
– How can we optimize our chances to make a deal?
Which tenders should we invest in? What offer should we make?
Remark: in B2C scenarios, can be difficult / unwanted to
use dynamic prices. Mind the legal impact!

18. SEO
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19. How Search Engine Works
1. Gather Content
– Crawler or spider moves recursively downloading
content
2. Builds sophisticate index
3. Individual web searches run against index
– Results are retrieved and ordered
PageRank & Relevance

20. Search Engines

21. Google Search Placement
Placement: importance and relevance
PageRank (importance)
– Counts links
– Weights links
Query matching (relevance)
– sophisticated text-matching techniques
– examines all aspects of the page's content (and the
content of the pages linking to it)

22. SEO Optimization Categories
Keywords
– Keyword selection and keyword-rich text
Crawler
– A crawler-friendly site navigation scheme
Links
– Link popularity

23. Keyword Recommendations
Meta tags: use but don’t stuff
– <meta name="description" content="Free Web
tutorials on HTML, CSS, XML, and XHTML">
Alt tags: use for graphics
– <IMG src="star.gif" alt=“star logo">
Content is king
– Write good content with relevant and important
keywords in mind.
Geo Targeting
– Add geocentric terms to target local areas Domain
Names
– Use keywords as part of domain name

24. Crawler-friendly
Engine spiders are primitive beings
– choose simplicity over complexity
Goal
– All your web pages seen by crawlers
– Google: enter in searchbox “allinurl:utexas.edu”

25. Link Development Inbound Links Impact PageRank
PageRank (Popularity, importance)
Number and quality of links pointing to a website
Measure of usefulness of site
Link Development Tradeoffs
Advantage
– it is dynamic, cumulative, and difficult to imitate
Disadvantage
– takes time (vs. advertising)

26. Link Development Approaches
Quality content to start with
Cultivate quality link (not quantity)
Begin with web directories
Harness online publicity
Use Blogs and forums wisely
Investigate competitors
– Understand their strategy
– Online publicity, blogs and forums
– See inbound links ("link:domain.com" in Google,
"linkdomain:domain.com" in Yahoo)

27. Data Science Work Overview @InfoEdge
Naukri
Real Time
Recom.
Engine
Job Alert
Mailers
Recruiter’s
Relevance
Candidate
Search to
Recruiter
Suggestor &
SEO
Semantic
Personalized
JobSearch
Candidate Services
• Lead-Scoring Engine
• E-Learning
• Recruiter Connection
• Parser
Naukri Gulf
• Job Alerts
• Real Time
Recommendations
• Recruiter Relevance
JeevanSaathi
• Profile Scoring basis
Payment Propensity
• Recommendation Engine
• Spam Detection
99Acres
• Price Trends
• Similar Localities

28. Intelligence Powered by Analytics – Naukri Applications

29. Data Science Techniques
Machine Learning
Text Mining
Natural Language Processing
Semantic Technologies
Information Retrieval
Information Extraction
BigData Technology like spark, mahoot
No-SQL DBs like MongoDB
Scripting Language- Python, Shell, R
Lucene, Solr & Elastic Search

30. Credits to those authors who shared slides on internet and I own all the errors
on this deck.