How to optimise conversion rate by leveraging machine learning and predictive segmentation
•
0 likes•412 views
Cost Per Click and Marketing Spend have increased 2-3x over the year. Brands can not control the digital war zone between Google and Facebook. However, brands are sitting on a big trove of their customer data that they can leverage to create competitive advantage with powerful machine learning to increase efficiency, return on ad spend and lift conversion rate. Long gone the day of manual segmentation!
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to How to optimise conversion rate by leveraging machine learning and predictive segmentation
Similar to How to optimise conversion rate by leveraging machine learning and predictive segmentation (20)
Recently uploaded
Recently uploaded (20)
How to optimise conversion rate by leveraging machine learning and predictive segmentation
- 1. Marketer + AI: How to optimise conversion rate by leveraging machine learning? Jack Nguyen | Country Manager Vietnam Mobile Day 2018
- 2. 16 Country, 250+ Employees, 5 Continents Trusted by Singapore Seoul Dubai Tokyo London Sydney Istanbul Taipei Kuala Lumpur Jakarta Taiwan Ho Chi Minh City All-in-One Platform Increase User Engagement Across All Touchpoints
- 4. in 30 minutes, 50% percent? How to increase your conversion rate
- 5. Kidnap your CFO … and decrease the prices by 100%
- 6. It’s a Disconnected Digital World “Hey, I want to buy something One click to an eCommerce, I'm on heaven…” “Hey, we have amazing things to sell…” Hello … Can You Hear Me?
- 7. Digital marketing is a war zone where Google and Facebook always win.
- 8. Low Conversion Rate High Marketing Cost Huge Challenges ● Too many choices ● Who to best target? ● Not relevant experience ● Instant Gratification ● Low attention ● Who to best to NOT target? ● How to do it automatically? ● How to do it in real-time?
- 9. LTV Lifetime values are relatively low because of the competition & winner takes it all Retention Many online businesses are failing because of low LTV/CAC ratios. Marketing is Expensive! Acquisition CPC & CAC CPCs increased 288% from 2010 – 2017
- 11. 891$ GLOBAL 704$ APAC Lower Average Revenue Per User (ARPU)
- 12. 0.29$ GLOBAL 0.42$ APAC Higher Average Cost Per Click in eCommerce
- 13. ACTION without DATA is a nightmare! John Wanamaker “Half the money I spend on advertising is wasted; the trouble is I don't know which half.”
- 15. Effective Usage What If ? Minimize Waste
- 16. Predictive Segmentation through AI + Machine Learning
- 17. Machine Learning (ML) Advanced area of AI Absorb huge amounts of data and create predictive algorithms that improve over time. Marketers can offer more personalized content and product suggestions than ever before. Makes machines do things that would require intelligence if done by a human. E.g: learning, seeing, talking, socializing, reasoning, or problem solving. Artificial Intelligence (AI)
- 18. With customers creating more valuable data at every stage of their journey, AI and Machine Learning brings endless opportunities for the brands. A Discount Chaser’s Shopping Journey 1. She comes from a search for “sales” on Google 2. Then quickly looks for Seasonal Offers on Homepage
- 19. A Discount Chaser’s Shopping Journey 3. Filters Sales & Price Low on Listing Page 4. Ony View Discounted Items on Product Page 5. Use voucher code to make purchase
- 20. Understand Your Users Accurately “I always look for great discount offers and try to find the best-valued products.” “I generally buy expensive products but buy them very occasionally.” “I do a solid research about the product before buying, so that I can buy from the best supplier” “I shop very much and very frequently.” DISCOUNT CHASER PREMIUM SHOPPER INSPIRATION SEEKER RESEARCHER
- 21. Recency Product Category Landing Page Type Frequency Session Duration Add to Cart Device Type Traffic Channel Purchase History Purchase History Training the algorithm with data
- 22. Insider’s AI Algorithms Across All Users Touch Points Predictive Segmentation 1. Likelihood to Purchase 2. Discount Affinity 3. Interest Clustering 4. Predicted Conversion Date 5. Likelihood to Churn 6. Customer Lifetime Value Advertising Desktop Web Mobile Web Messaging Mobile App ACQUISITION ACTIVATION RETENTION REVENUE Predictive Audience DATA without ACTION is a daydream!
- 23. 1. Likelihood to Purchase Continuous update on the Predictive Model Anonymous users are also segmented Real-Time Predict the chance of users buying in the next 7 days
- 24. Target users who are likely to buy and searching for competitor keywords on Google with higher bidding Use Case on Ad Channels 1. Likelihood to Purchase
- 25. Understand your users’ affinity toward discount rates to distinguish “discount” and “premium” shoppers. Use Case on Ad Channels 2. Discount Affinity
- 26. Use Case on Desktop Web Exit Intent Recovery For Premium Shopper: Sending Survey to their get feedback For Discount Chaser: Sending voucher with countdown timer 2. Discount Affinity
- 27. Use Case on Email Channel Cart Abandonment Emails 2. Discount Affinity For Premium Shopper: Warm reminder of items in cart For Discount Chaser: Sending voucher with countdown timer
- 28. Competitor 3. Interest Clustering Discover distinct clusters and deliver targeted content to meet their interests and needs
- 29. Competitor 3. Interest Clustering Discover distinct clusters and deliver targeted content to meet their interests and needs
- 30. 3. Interest Clustering Rearrange navigation bar based on Most Interested Category to increase engagement & reduce bounce rate Use Case on Mobile Web
- 31. 4. Predicted Conversion Date Range Monitor and analyze each visitor’s purchase history to predict their conversion date range. 1-3 days10-15 days 3-7 days Use Case on Web Push
- 32. 5. Likelihood to Churn Identify visitors who are more likely to churn and create retention strategies before it’s too late. Use Case on Desktop Web
- 33. High (VIP) Low 6. Customer Lifetime Value (CLV) Identify visitors who are more likely to return and buy more from you vs those who never come back
- 34. Users who are predicted as high LTV users and did not make purchase in last 30 days Use Case on Web Push Segment Automatically send a price-drop web push to note the item they viewed has been discounted Action 6. Customer Lifetime Value (CLV)
- 35. Success Stories
- 36. ROASCONVERSION RATE Success Story - Top 3 Biggest Fashion Retailer in Europe & MENA Apply high likelihood to purchase audience for dynamic remarketing campaigns. UPLIFT 160.71% UPLIFT 629.73%
- 37. ROASCONVERSION RATE Success Story - Top 3 Biggest Online Travel Agency in Taiwan Apply high likelihood to purchase audience for Google AdWords campaign of hot items. UPLIFT 407% UPLIFT 350%
- 38. Predictive Segmentation will Increase Return on Ad Spend Increase Conversion Rate Decrease Customer Acquisition Cost in an Automated & Accurate way
- 39. 3 Key Things To Remember Build a Machine Learning that: ● Understand different things about each user ● Update real-time ● Strong action layer at all users touchpoints
- 40. www.useinsider.com jack@useinsider.com London | Moscow | Singapore | Tokyo | Seoul | Dubai | Warsaw | Istanbul | Kuala Lumpur Jakarta | Ho Chi Minh City | Sydney | Bangkok | Hong Kong | Taipei Thank you.