data science for e-commerce
DESCRIPTION
Slidedeck from our seminar on "Data Science for e-commerce" (25/11/2014) Topics covered: - What is Data Science & Big Data? - Why is it relevant to your e-commerce business? - Recommendations - Physical shops vs e-shops - Dynamic pricing - Personalised offerings - Gathering external data - Anticipatory shipments - How to apply design science practices?TRANSCRIPT
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Data Science Company
DataScience for e-commerce
Infofarm - Seminar25/11/2014
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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?
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
About us
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Speakers
• Niels Trescinskie-commerce Consultant
– Fenego (Intershop)
– Elision (Hybris)
• Günther Van RoeyTechnical (IT) Consultant
– InfoFarm (BigData & Data Science)
– XT-i (software development and integration)
– PHPro (website development)
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
InfoFarm + Fenego & Elision – e-commerce!
Highly focused one-commerce
Business Knowledge
Highly focused on Data Science and
Big Data
Technical Knowledge
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Introduction: what is Data Science?
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Data Science & Big Data
• 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
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Data Science & Big Data
• 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!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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, …)
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Example Data Science applications:
#1: Recommendations
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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.
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
No product recommendations
at all(Link to category
without match with specific product)
Which roller would be appropriate?
No primer + paint combo?
Recommendations – Traditional approach
Showing (too)similar products?
No color alternatives?No glossy/matte
alternatives?
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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, …)
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Example Data Science applications:
#2: Physical stores vs webshop
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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/…
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Impact physical shops - example
• Analysis for a retailer: Physical shops vs online sales
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Impact physical shops - example
• Impact of opening a physical shop on local online sales
(brand awareness?)
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Example Data Science applications:
#3: Dynamic Pricing
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Dynamic prices
– End of life products?
– Relevancy of products.
– (Local) competition.
– Customer!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Example Data Science applications:
#4: Personalized offerings
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Personalized offering
– Loyal (online) customer vs new customers.
– Browsing habits and patterns.
– Spending patterns.
– Personalized discounts and/or content?
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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, …
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Example Data Science applications:
#5: Gather external data
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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?
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Example Data Science applications:
#6: Anticipatory shipping
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Anticipatory shipping
– Patent pending by Amazon.
– Ships an order before it is placed.
– Order history, search, wish list and click behaviour!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Example Data Science applications:
#7: Customer Service optimizations
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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?
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Applying Data Science
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
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 assumptionseg: trying to gather customer preferences when an order could as well have been a gift to someone else (perfume, …)
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Questions?