Data Science for e-commerce

Data Science Company
DataScience for e-commerce
Infofarm - Seminar
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
25/11/2014

Agenda
• About us
• What is Data Science?
e-commerce vs Data Science vs BigData
• Example Data Science applications in e-commerce
some inspiration to see your opportunities…
• Applying Data Science
how to get started with all this?

About us
Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye

Speakers
• Niels Trescinski
e-commerce Consultant
– Fenego (Intershop)
– Elision (Hybris)
• Günther Van Roey
Technical (IT) Consultant
– InfoFarm (BigData & Data Science)
– XT-i (software development and integration)
– PHPro (website development)

InfoFarm - Team
• Mixed skills team
– 2 Data Scientist
• Mathematics
• Statistics
– 4 BigData Consultants
– 1 Infra specialist
– n Cronos colleagues
with various background
• Certifications
– CCDH - Cloudera Certified Hadoop Developer
– CCAD - Cloudera Certified Hadoop Administrator
– OCJP – Oracle Certified Java Programmer

InfoFarm + Fenego & Elision – e-commerce!
Highly focused on
e-commerce
Business Knowledge
Highly focused on
Data Science and
Big Data
Technical Knowledge

Introduction: what is Data Science?

What is data science?
• Data Scientist: “A person who is better at statistics than
any software engineer and better at software
engineering than any statistician”
- Josh Wills
• “Getting meaning from data”
Finding patterns (data mining)
• Complementing business
knowledge with figures

Data Science & Big Data
• Relevance for e-commerce - use data to:
– Increment conversion
– Increment operational efficiency
– Understand your customers’ needs
– Make better offers
– Make better recommendations
– …
• Many successful online business thank their position to
smart data usage:
– Google was the first search engine that didn’t index by keyword
– Amazon is the e-commerce leader thanks to BigData
– NetFlix is a world leader in personalized recommendations

• Most of us don’t run a business like the ones referred to in
stereotypical Big Data cases
• Big Data does not necessarily means or requires much data
• Data Science is very affordable to companies of all sizes
• Typical Data Science projects are 10’s of man-days of work

• Non-structured data: weblogs, social media content, …
• Secondary use of data sources is the key
– eg: Weblogs
• Are there to log webserver activity
• But can also tell you how people find, compare and choose products!
– eg: ERP / Cash register software
• Prints bills
• But can also tell you what products are typically bought together in a shop
• Many data is present, valuable information is hidden in it!

Topics not covered in this seminar
• Very interesting topics that we will gladly
elaborate upon another time:
– Statistical Tools (R, SPSS, …)
– Mathematical models
– Machine Learning Techniques (Clustering, Classification, …)
– BigData Tools & Platforms (Hadoop, Spark, …)
– Data processing tools (Pig, Hive, …)

Example Data Science applications:
#1: Recommendations

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

Recommendations
– Online 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.
– Time based recommendations
• Recommend or cross sell different products depending on
– season?
– holiday?
– weather?
– Customer based recommendations
• Learn from your customers and their past.
• Android vs iOS smartphones.

Showing (too)
similar products?
No color alternatives?
No glossy/matte
alternatives?
No product
Recommendations – Traditional approach
recommendations
at all
(Link to category
without match with
specific product)
Which roller would be
appropriate?
No primer + paint
combo?

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!

Recommendations – Other ideas
• Data Science ideas
– “x % of the people who looked at this item eventually bought product X or Y”
– Get cross-selling information from ERP in the physical shops and let this feed the
webshop recommendations!
– Similar product in different price ranges
(“best-buy alternative”, “deluxe alternative”)
– ...
• This is very achievable for a webshop of any size
– Just generate ideas, and test to see what actually increases sales!
• Secondary use of various kinds of non-structured data = BigData !
– Weblogs of e-commerce site (use to deduct customer journeys)
– ERP info with bills and/or invoices (use to deduct cross-selling in physical shops)
– Product information (product categorization, …)

#2: Physical stores vs webshop

Impact physical store on online?
– Are online sales higher when physical store is nearby?
– Where to open a new store?
– How to approach your customers to motivate

Impact physical shops - Why bother?
• Determine strength of online brand vs physical brand
– Is online sales driven by brand awareness?
– Or is there quite a balance between the two?
– Omni-channel strategy?
• Know what would be the impact of opening/closing a
physical shop, also on the online business
– Support management decisions with facts & figures
• Depends heavily on sector/product/case/…

Impact physical shops - example
• Analysis for a retailer: Physical shops vs online sales

Impact physical shops - example
• Impact of opening a physical shop on local online sales
(brand awareness?)

Impact physical shops – now what?
• Use this correlation information:
– As extra input for determining new shop locations
– Publish folders focusing on online in non-covered areas
– Use popup-stores to get brand awareness and drive online sales
– Discounts per region
– Google Adwords campaigns focusing on regions with limited
brand presence
– Customer segmentation based on this information

#3: Dynamic Pricing

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

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!

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!

#4: Personalized offerings

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

Personalized offerings
• Customer should be central in the webshop
– 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, …

Personalized offerings – some ideas
• Imply social media
– Are there any connections of our customer that wrote product
recommendations that might convince him to buy?
– Do we know the shopping behaviour of some of the customers’
connections? Are they in line with his/hers? Can we use this to
make better recommendations?
• Anticipate customer behaviour
– Use all customer contact moments
eg: if customer calls customer service, they should know what
products the customer was looking at during his last visit to the
webshop
– Prediction model (surfing behaviour vs % deal making)
eg:
Low chance? Go to checkout immediately.
High chance? Offer extra cross-selling opportunities

#5: Gather external data

Gather external data, zoom & magnify
– Explore search trends within Google.
– Detect what is hot on social media.
– Magnify to the results and set clear goals/actions.
– Take action!

Gather and use external data
• example: how to sell a Smartwatch?
– It’s a new product, how to market it effectively?
– eg: SEO in line with trending topics on twitter, facebook posts, …
– eg: SEO in line with used search terms
• Added value: combining external data sources with own data
• Some ideas
– Find and follow your contacts on LinkedIn
previous/future employers of your contacts may be great prospects for
your B2B business!
– Use weather info to adapt the featured product offering
Data Science exercise: do we find any correlation between the weather
and the product sales figures?

#6: Anticipatory shipping

Anticipatory shipping
– Patent pending by Amazon.
– Ships an order before it is placed.
– Order history, search, wish list and click behaviour!

Anticipatory shipping
• High-tech? Actually not complex at all …
• Steps:
– Gather many info on past orders
(customer info, country, product info, price, product group,
product combinations, time of day, season, …)
– Build a prediction model predicting “cancelled or not” based on
all this information
– Assess the quality of the model by training it with 90% of your
historical orders and testing it with 10% of your historical orders
– Pass each new order’s info and predict the likelihood of this
order getting cancelled (0 .. 100%) and act accordingly

#7: Customer Service optimizations

Customer service
– Losing sales/conversion/money by poor customer service.
– Optimize information for all communication channels.
– Which issues are your customers concerned with?
– Allocate resources better!

Customer Service – Some Ideas
• Text mining
– Mood analysis: detect negative messages on social media, forum, …
Put TODO on action list of customer care to contact with certain priority
– Auto-classification of e-mails, letters, messages:
Is this e-mail a question or a complaint?
Is it about the quality of the product or financial (wrong invoice, …)?
Automatic routing of messages to the right person! (operational optimization)
• Social media
– Social media status of customer (scoring based on profile)
What’s would be the impact of this customer being unhappy about our service?
• Omnichannel insights
– What did this customer buy of look at?
– How did he rate the last bought products?
– Which contacts (mail, phone, …) did we have and what seems to be the most
effective deal trigger?

Applying Data Science

Applying Data Science
• Data Science does not replace business knowledge
– Need to find balance between the two
– Confirm or deny assumed business knowledge
– Detect changing trends early (customer behaviour, …)
• Not a development cycle, rather exploratory process:
– Formulate hypotheses
– Data mining and modeling
– A/B testing (test new idea on x % of your customers/products/…)
– Conclusions: did the test group show better conversion?
– Rollout or cancel and start over!
• Potential issues
– Privacy law and other legal restrictions
– Feedback loops, information leakage, wrong assumptions
eg: trying to gather customer preferences when an order could as well have
been a gift to someone else (perfume, …)

Questions?

Data Science for e-commerce

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