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Retail Detail OmniChannel Congress 2015 - Data Science for e-commerce
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Transcript of Retail Detail OmniChannel Congress 2015 - Data Science for e-commerce
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Data Science Company
Data Science for e-commerce
RETAIL DETAIL – OmniChannel Congress05/02/2015
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Agenda
• About InfoFarm & Elision
• What is Data Science?e-commerce vs Data Science vs BigData
• Example Data Science applications in e-commerce
some inspiration to spot your opportunities…
• Applying Data Science
how to get started with all this?
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
InfoFarm + Elision – e-commerce!
Expert Hybris partnerin omnichannel
solutions in B2C & B2B
SAP/Hybris Nominee Service Delivery Partner of the year 2015 EMEA
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,
enabled by IT practices
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Data Science & Big Data
• Relevance for e-commerce - use data insights to:
– Increase conversion rate
– Increase operational efficiency
– Understand your customers’ needs
– Make better offers
– Make better recommendations
– …
• Many successful online businesses thank their position
to smart data usage:
– Google was the first search engine that didn’t rank by keyword
– Amazon is the e-commerce leader thanks to BigData practices
– NetFlix is a world leader in personalized recommendations
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Data Science & Big Data
• Classical presentation : the V’s in BigData
• Can be very hard to make the leap from this technical
capability towards the added value for your business!
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 mean or require much data
• Data Science is very affordable for 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
– Example: Weblogs
• Are there to log webserver activity
• But can also tell you how people find, compare and choose products!
– Example: ERP / Cash register software
• Prints bills
• But can also tell you what products are typically bought together in a shop
• Much data is present, valuable information is hidden in it!(aka “dark data”)
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
OmniChannel considerations
• Customer intimacy leads to customer loyalty, referrals, …
Combine data from online and offline channels for a holistic view
• There is a lot of unused information on customers !
– Does your store personnel have any idea if a customer has bought things via
your online touchpoints? Or has been looking for specific items recently?
– Do you take contact moments (in-store, callcenter, …) into account for the
personalisation of the online touchpoints?
• Key enablers:
– Ability to ID customers in-store (purchase? visit?)
(personal contact, CRM, ibeacons + mobile app, loyalty card, …)
– Ability to ID customers online (quite obvious if logged in)
– Ability to combine / integrate both
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Data Science – some key techniques
Data Mining - Machine Learning algorithms applied to business data
• Clustering data – finding similarly behaving/interested customers…eg: can be used to determine marketing segments
– Do we segment our customers on (assumed) business knowledge?
– Or do we cluster them in segments based on their behaviour?
• Predictive modeling – finding factors and their weightings that predict customer behaviour with a certain probability
eg: can we predict the probability that a sale will be made, an order will be cancelled, etc… based on historical information.
• Classification algorithms – auto-classify data in predefined labels.
eg: Can be used to auto-classify messages in question, complaints, etc… or products in budget-class, high-end, etc …
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• Making no recommendations at all
(would you be happy with such a salesperson? why then online?)
• 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
– Product based recommendations
• Main focus on online, but can be elaborated to in-store?
• 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.
• example: Android vs iOS smartphones.
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Recommendations – Omnichannel
– It’s the same customer (more or less) online and in the store
– It’s about the same products!
– How to align:
• In-store recommendations and personal, professional advice
• Online automated recommendations and advice
– Some ideas:
• Analyze cross-selling realized in-store and online
• Are there any differences? How come?
• Can sales person use the information from the online touchpoints to their
advantage? (browsing history, …)
• Can cross-selling information realized in-store by used to optimize the online
touchpoints?
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Recommendations – example
• Ex: Buying paint material: paint, roller, brush, primer, tape, …
• Omni-channel: try to combine advantages of both for all customer profiles (Online viewers, in-store buyers and vice-versa)
• Customer perspective– I hate painting - I don’t want to spend much time on this
– What did others buy? I can’t be the first to do such a paint job?
– I don’t want to forget an item I’ll need
• Customer wants information on all needed products that fit together– In-store: professional advice
– Online: <here you can still make a real difference>
• Fact: most paint-selling sites don’t even do recommendations!The ones that do could go a lot further if it wasn’t so labor-intensive to maintain.
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Recommendations – many questions/opportunitiesWhich
similar productsto show?
Color alternatives?Glossy/matte alternatives?
Cheaper/better?
Which complementary
products to show?Link to category
without match with specific product?
Which brush would be appropriate for this
paint?Which primer?
Related Products
Similar / alternative ProductsCurrent Product
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 made!
DATA DRIVEN!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Recommendations – other ideas
• Data Science ideas
– “x % of the people who viewed this item eventually bought product X or Y”
– Get cross-selling information from ERP in the physical shops and let this feed the
online 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)
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Recommendations – other ideas
• Auto-combination special offers based on cross-selling
infoWhy 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.beData Science Company
Example Data Science applications:
#2: Personalized offerings
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Personalized offering
– 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 approach– Provide a truly personalized online shopping experience
– Like high-end stores with personal approach to VIP customers
• Gather data about your customer (on- and offline)– Surfing history – what products were viewed? For 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
• 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
– Build prediction models
(surfing behaviour vs % deal-making)
eg:
Low chance? Go to checkout immediately.
High chance? Offer extra cross-selling opportunities
Possible with well-known machine learning techniques!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Example Data Science applications:
#3: Anticipatory shipping
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Anticipatory shipping
– Patent pending by Amazon.
– Ships an order before it is placed.
– Based on order history, search, wish list and click behavior!
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Anticipatory shipping
• High-tech? Actually not that 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 potential orders’ info and predict the likelihood of it
getting cancelled (0 .. 100%) and act accordingly
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.beData Science Company
Example Data Science applications:
#4: 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.
– What issues are your customers concerned with?
– Allocate resources in a better way
Veldkant 33A, Kontich ● [email protected] ● www.infofarm.be
Customer Service – Some Ideas
• Text mining– Mood analysis: detect negative messages on social media, forum, reviews, …
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 or view?
– How did he rate the last products (online) he bought (in-store)?
– Which contacts (brochure, 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, …)