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Machine Learning & AI in e-commerce
The document discusses the impact of AI and machine learning on e-commerce, highlighting a significant shift in retail sales and competitive dynamics. It emphasizes the importance of data for business growth, focusing on customer retention and profitability in the face of rising advertising costs and competition. Key strategies for leveraging AI include enhancing user experience, optimizing customer engagement, and utilizing data-driven insights for better decision-making.
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Machine Learning & AI in e-commerce
- 1. AI & MACHINELEARNING IN ECOMMERCE Andraž Štalec
- 2. 25 % increasein retail e-commerce sales in 2017 RECORD NUMBER OF TRANSACTIONS & REVENUE Statista 2018
- 3. WHY IS THISHAPPENING?
- 4. Need to understandthe bigger picture THE TIDE IS SHIFTING
- 5. Media consumption vsspend 2016 eMarketer 4/16 & IAB 2016 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Print Radio TV Desktop internet Mobile internet Time Spent Ad Spend
- 6. 3 things thatwill redefine your digital focus BUDGETS WILL SHIFT TO ONLINE BUILD AWARENESS AND INTENT ONLINE DELIVER BETTER ROI
- 7. TO SUSTAIN THESESHIFTS COMPANIES WILL RELY ON DATA TO GROW AND OPTIMIZE THEIR BUSINESSES
- 8. ENTERING THE 3RDPHASE OF DIGITAL TRANSFORMATION
- 10. HUGE ADVANCEMENTS INARTIFICIAL INTELLIGENCE
- 12. AI AS AUTILITY Brains not distracted as humans are
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- 16. AI is nota plug-in Enhance Scale Automate Build processes manually
- 17. 4 pillars ofAI future Data Technology Process People
- 18. 4 pillars ofAI future Data Technology Process People
- 19. The problem ofNo Data vs the problem of Big Data TRADITIONAL RETAIL VS ECOMMERCE
- 20. Entry barriers offlineare much higher than online Entry barriers Number of competitors Number of competitors is rapidly increasing Growth of online user population is slowing down Number of users Returning customers Number of returning customers is in decline as new competitors are entering the market Cost of advertising is rising and one-time customers are not profitable anymore One-time customers Main difference between offline and online retail
- 21. One-time customer revenuestream CPA GM CPA <= GM The only way your business could be profitable is achieving CPA much lower then your Gross Margin.
- 22. One-time customer revenuestream CPA GM CPA > GM Due to increased competition CPCs are increasing and CRs are decreasing.
- 23. Shift to retentionmodel CAC GM CLV <= CLC Sum of GM from all customer transactions should be higher then sum of all costs connected to the customer GM GM GM GM GM MRC MRC MRC MRC MRC MRC MRC MRC MRC MRC MRC CLV CLC
- 24. CLVP = (P× AOV) × AGM) × ALT The tricky part is calculating your CLV
- 25. The SaaS EcommerceFunnel Acquire Activate Retain Grow Referrals 2nd purchase1st purchase
- 26. How do youmeasure churn in ecommerce? Customer aquisition Retention Customer churns
- 27. Retention for otherecommerce sites 3 months 6 months
- 28. The SaaS EcommerceFunnel Acquire Activate Retain Grow Referrals 1st purchase 2nd purchase Customer Engagement Score
- 29. Customer Engagement Score 01 02 03 043% 35% 10% 50%05 10% Days since last session Sessions with product views Interest-based browsing Adds to cart Email engagement rate
- 30. Active vs. churnedcustomers
- 31. Active vs. churnedcustomers active churned
- 32. WITH NEW FOCUSNEW KPIs ARISE
- 33. Top Ecommerce metrics 231 4 5 6 7 CLV CAC Customer profitability score Repeat rate Retention rate AOV CES
- 34. UNDERSTAND YOUR DATA Beforeyou begin
- 35. Gather and preparethe data Transactional data from client‘s ERP 3rd party data for enrichment Behavioral and demographic data from Google Analytics
- 39. Ecommerce segmentation K-means clusteringbased on RFV Create distribution for new features like ALT Add them to clusters to create more acurate segments Clustering: 2 3 1 High value High Frequency Grey zone Home run Grey zoneDead zone
- 40. WHY THE HELLALL THIS TALK ABOUT DATA?
- 41. 2 main usecasesof AI Insights Use AI to deliver better and deeper actionable insights for your business. User experience Use AI to deliver better experience to users, automate processes and scale the business.
- 42. Customer clusters segmented bypurchase behavior, CES & LTV Size of clusters How are your customers segmented? Roadmap to ML in Ecommerce Prevent churn and re-engage users based on deviation in their behavior Predict LTV of newly acquired users and bid accordingly Customer transitions Between clusters SaaS Ecomm metrics like churn, retention, ALT, CES
- 43. Anomaly Detection Customer Support Product Recommend ation Personalizati on SearchDynamic Pricing 06 01 02 0304 05 6 usecasesof AI for enhanced User experience
- 44. BARS ARE RISING Productquality + Customer support + Transparency
- 45. GOOD ENOUGH ISJUST NOT GOOD ENOUGH!
- 46. 2 areas thatwe need to focus on for growth in 2018 and beyond EXCEPTIONAL LIFETIME EXPERIENCE BETTER PERFORMANCE AND ROI
- 47. Thank you 01 0203 04 Email andraz@red-orbit.com Twitter @andrazstalec LinkedIn Andraz Stalec Web www.red-orbit.com Red Orbit, Jožeta Jame 12,1000 Ljubljana, Slovenia W: www.red-orbit.com |E: info@red-orbit.com | T: +386 (0)590 75 680
Editor's Notes
- #3 We live in a tremendous time … yet, retailers find it harder to sustain growth & profitability Source: https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
- #5 Our world is becoming fully digitalized Consumers are moving from offline to online
- #9 Entering the 3rd phase of digital tranfsormation: 1st = making information accessible 2nd = mobile & social media + how they connect people https://www.youtube.com/watch?time_continue=31&v=shDMy232MAA
- #10 3rd = world of AI Leading companies moving from mobile first to AI first strategy
- #17 Road to AI ni implementacija AI toolov ampak postaviti sistem (procese) na roke (manual) in jih potem avtomatizirati in izboljšati z AIjem; tako kot postavljaš GH in potem avtomatiziraš
- #18 Enter the AI era: There are 4 pillars of AI future
- #20 With explosion of data, online advertising and ecommerce are becoming increasingly measureable and actionable. eCommerce, like traditional retail involves taking in a lot of data and trying to make the best decisions based on the data that you have. In a retail store data is relatively limited, you might know how many customers come in the store each day (or at least be able to estimate it), but it’s hard to keep track of how many customers try things on, how long the average customer stays in a store, or how many items they look at during each visit. Online, it‘s much easier to track customer metrics because the data is easily accessible. You can tell exactly how long the average person spends on your site, which items they look at, what they add to their cart, and what they end up buying in the end. Unlike brick-and-mortar retail where the complexity comes in when you’re trying to collect data, online the complexity is making sense of all the data this is coming in.
- #24 Need to shift to retention model, where customer profitability is a key metric CLV = SUM of all GMs CLC = CAC + MRC CAC MRC = cost of running business – servers, licences, client retention, staff One key component is keeping MRC as low as possible -> renention should not depend on advertising
- #25 P = average monthly purchases AOV = Average Order Value AGM = Average Gross Margin ALT = Average LifeTime in months
- #32 Steep function
- #40 Create clusters using RFV or AOV and Freq Add ALT so you get segmnets based on LTV
- #44 https://techblog.commercetools.com/top-5-machine-learning-applications-for-e-commerce-268eb1c89607
- #46 Section titles.