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Data Science for e-commerce

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?

Data Science for e-commerce

  1. 1. Data Science Company DataScience for e-commerce Infofarm - Seminar Veldkant 33A, Kontich ● ● 25/11/2014
  2. 2. Veldkant 33A, Kontich ● ● 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?
  3. 3. About us Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  4. 4. Veldkant 33A, Kontich ● ● 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)
  5. 5. Veldkant 33A, Kontich ● ● 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
  6. 6. InfoFarm + Fenego & Elision – e-commerce! Highly focused on e-commerce Business Knowledge Highly focused on Data Science and Big Data Technical Knowledge Veldkant 33A, Kontich ● ●
  7. 7. Introduction: what is Data Science? Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  8. 8. Veldkant 33A, Kontich ● ● 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
  9. 9. 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 ● ●
  10. 10. Data Science & Big Data • Most of us don’t run a business like the ones referred to in Veldkant 33A, Kontich ● ● 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
  11. 11. Data Science & Big Data • Non-structured data: weblogs, social media content, … • Secondary use of data sources is the key Veldkant 33A, Kontich ● ● – 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!
  12. 12. Topics not covered in this seminar • Very interesting topics that we will gladly Veldkant 33A, Kontich ● ● 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, …)
  13. 13. Example Data Science applications: #1: Recommendations Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  14. 14. Recommendations – Why? How? – Why? • Attempt to cross-sell or up-sell • Provide customers with alternatives that might please them even more Veldkant 33A, Kontich ● ● – 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
  15. 15. Veldkant 33A, Kontich ● ● 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.
  16. 16. 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? Veldkant 33A, Kontich ● ●
  17. 17. 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 ● ●
  18. 18. Recommendations – Other ideas Veldkant 33A, Kontich ● ● • 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, …)
  19. 19. Example Data Science applications: #2: Physical stores vs webshop Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  20. 20. 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 ● ●
  21. 21. 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 ● ●
  22. 22. Impact physical shops - example • Analysis for a retailer: Physical shops vs online sales Veldkant 33A, Kontich ● ●
  23. 23. Impact physical shops - example • Impact of opening a physical shop on local online sales Veldkant 33A, Kontich ● ● (brand awareness?)
  24. 24. 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 Veldkant 33A, Kontich ● ● brand presence – Customer segmentation based on this information
  25. 25. Example Data Science applications: #3: Dynamic Pricing Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  26. 26. Veldkant 33A, Kontich ● ● Dynamic prices – End of life products? – Relevancy of products. – (Local) competition. – Customer!
  27. 27. Dynamic Prices – some ideas • Auto-combination special offers based on cross-selling Veldkant 33A, Kontich ● ● 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!
  28. 28. Dynamic Prices – some ideas Veldkant 33A, Kontich ● ● • 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!
  29. 29. Example Data Science applications: #4: Personalized offerings Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  30. 30. Veldkant 33A, Kontich ● ● Personalized offering – Loyal (online) customer vs new customers. – Browsing habits and patterns. – Spending patterns. – Personalized discounts and/or content?
  31. 31. Veldkant 33A, Kontich ● ● 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, …
  32. 32. Personalized offerings – some ideas Veldkant 33A, Kontich ● ● • 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
  33. 33. Example Data Science applications: #5: Gather external data Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  34. 34. 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 ● ●
  35. 35. Gather and use external data Veldkant 33A, Kontich ● ● • 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?
  36. 36. Example Data Science applications: #6: Anticipatory shipping Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  37. 37. Veldkant 33A, Kontich ● ● Anticipatory shipping – Patent pending by Amazon. – Ships an order before it is placed. – Order history, search, wish list and click behaviour!
  38. 38. Anticipatory shipping • High-tech? Actually not complex at all … Veldkant 33A, Kontich ● ● • 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
  39. 39. Example Data Science applications: #7: Customer Service optimizations Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  40. 40. Veldkant 33A, Kontich ● ● 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!
  41. 41. Customer Service – Some Ideas Veldkant 33A, Kontich ● ● • 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?
  42. 42. Applying Data Science Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  43. 43. Veldkant 33A, Kontich ● ● 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, …)
  44. 44. Questions? Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye