This document discusses the history and evolution of personalization from manually targeting small groups to using a single platform and machine learning to personalize experiences at scale. It provides examples of how Dynamic Yield's platform is used by companies to automatically personalize webpages, recommendations, messages and more based on user attributes. Machine learning algorithms help identify the most effective personalization strategies and segment audiences for predictive targeting.

1. Craft of Personalization
Andrey Tyschenko
Country Director, CEE, Russia, CIS, Dynamic Yield

2. History of Personalization.
4 Season Series.

3. Personalization Season 1. Show everything to everybody
If I and Warren Buffet were site visitors, average user wealth is $45 Billion

4. Personalization Season 2. Manual Personalization
Ok buddy, I’ll change text on that button in 3 weeks

5. Personalization Season 3. Stack of Separate Tools for Each Task
Banners, Recommendations, Popups, Emails, Push, AB-testing… what
else should we personalize?

6. Personalization Season 4. Single Platform for Personalization

7. … exactly what Dynamic Yield is

8. About Dynamic Yield
Dynamic Yield helps marketers increase revenue by personalizing each customer
interaction across web, mobile web, mobile apps and email.
500 MILLION
unique users per month
10 BILLION
page views per month
600 BILLION
events captured per month
BACKED BY:

9. Trusted by the World’s Leading Brands
Athletics & Outdoors
eCommerce
Fashion & Apparel
Beauty & Personal Care
Food & Beverage
Home & Furniture
Other eCommerce
Horizontal Platforms
Luxury & Jewelry
Footwear & Accessories
Consumer Electronics
Media & Publishing Business to Business
Betting & Gaming
Listings & Classifieds
Travel & Hospitality

10. DY is a Single Technology Stack for Personalization
• Front-end personalization
• Product & Content Recommendations
• A/B Testing and ML-Powered Experience Optimization
• Message Triggering
• Customer Data Management
• User Segmentation
Across Any Channel: Web Mobile Email APIs

11. Let’s see some real-life cases!

12. Demo Use case #1: Homepage Hero Banner
Point and click the element you want to replace!

13. Demo Use case #1: Homepage Hero Banner

14. Use Case #1: FjallRaven Homepage Banner - Default

15. Use Case #1: FjallRaven Homepage Banner - Women
Winter is coming…

16. Use Case #1: FjallRaven Homepage Banner – Cyber Monday

17. Use Case #2: FjallRaven: Category Recommendations at HP

18. Use Case #2: FjallRaven: Category Recommendations at HP
Looked for Women parkas? Hey, we remember that!

19. Use Case #2: Tottanham Football Club: Category Recs at HP

20. Use Case #2: Tottanham Football Club: Category Recs at HP

21. Use Case #3: Urban Outfitters: Packs of auto AB-tested banners
auto-changed weekly
When 10 banners per week route like trains with a schedule

22. Use Case #4: Lamoda: Changing the whole Homepages: Default

23. Use Case #4: Lamoda: Changing the whole Homepages: Women

24. Use Case #4: Lamoda: Changing the whole Homepages: Men

25. Use Case #4: Lamoda: Changing the whole Homepages: Kids

26. Use Case #5: Ozon category banners based on user interests
returned by 3rd Party data provider (Mail.ru)
Warm trends in
the air

27. Use Case #5: Ozon category banners based on user interests
returned by 3rd Party data provider (Mail.ru)
Oh no, I have
never looked
for women
spring fashion,
honestly!

28. Demo Use case #6: Page Action to change page layout

29. Use Case #6: Juniqe: Product Page Layout Optimization
Product description under or to the right
of Product name?
Show all Product attributes or only some?
Show Product details closed or open?
What is the best placement for Add To Cart
button?

30. Use Case #7: Under Armour Mobile App: Homepage personalization
A whole page re-built
personally for you

31. Use Case #7: Under Armour Mobile App: Homepage personalization
A whole page re-built
personally for you

32. Use Case #7: Under Armour Mobile App: Homepage personalization
A whole page re-built
personally for you

33. Use Case #8: Lamoda: Highlight Menu Items
Struggle to find your favorite brands in a long long menu?
Follow the white rabbit ;)

34. Use Case #9: Hollar.com: Product Listing Auto Personalization by
User Affinity

35. Use Case #9: Hollar.com: Product Listing Auto Personalization by
User Affinity
Is it a coincidence?
Somehow I start
seeing more and more
relevant product items
in the listing!

36. Demo Use case #10: Create Personalized Overlays

37. Use Case #10: Sephora: Mobile Homepage sequence of 3
Subscription Overlays
Tap here

38. Use Case #10: Sephora: Mobile Homepage sequence of 3
Subscription Overlays
Enter email
here

39. Use Case #10: Sephora: Mobile Homepage sequence of 3
Subscription Overlays
Only subscribe?
Hey, hold on, sign up
for the loyalty club!

40. Use Case #11: Ozon.ru: Social Proof – rocket-science logic behind
a super simple message

41. Use Case #12: Fjall Raven: Product page Low-stock Messages
I feel pretty much like at Booking.com J

42. Use Case #13: Lamoda: Countdown timer to create urgency
Order within 20 minutes and get 10% off!

43. Use Case #13: Lamoda: Count-down timer for an Urgency Offer
Hmm, looks like I should decide quickly and not let 10% off go!

44. Use Case #14: Barnes & Noble: Category context-based promos
Same static UI element. No popups. Always relevant.

45. Use Case #15: Barnes & Noble: Product Page Exit Intent
Recommendations with Similar products
Give us another
chance – maybe you
like that one better?

46. Demo Use case #16: Recommendation Widget

47. Test Algorithms and Visual Design in Real-Time

48. Use Case #16: LuisaViaRoma: Product Page Recommendations

49. Use Case #16: LuisaViaRoma: Cart Overlay Recommendations

50. Use Case #16: LuisaViaRoma: Check-out Recommendations
Oops… No
recommendations!
But wait… is it really
worth to show Recs
at Check-out?

51. Let’s do an exercize

52. Example: A Sports Team Tests a New Recommendations Widget
Strategy A : Similar Items
Strategy B :
Similar & Bought Together
Control No Recommendations

53. Baseline
Strategy A: +27.1% ARPU
No Recommendations
Strategy B: +34.4% ARPU
A Sports Team Tests a New Recommendations Widget Placement

54. Dynamic Yield’s Predictive Targeting
Recommended:
Use Strategy A for “Kit Buyers” Segment
Use Strategy B for All Other Visitors

55. Revenue per User:
+54.1%
Predictive Targeting
Revenue per User:
+34.4%
A/B Test

56. Use Different Merchendizing Strategies for Different Segments

57. 3 people have purchased this item today. Don’t miss out!
Another Example: Impact of ‘Popularity Messaging’

58. Does ‘Popularity Messaging’ Work?
Segment Results
Italy +9.40% Purchases / User
+14.98% Revenue / User
Rest of the World
+0.60% Purchases / User
But negative revenue impact

59. Breaking Down ‘Rest of World’
+7.5%
-10.6%

60. Color of Buy Now button on PDP
+7.8% 98%0.10755,919523,480Pink
-6.5% 0%0.086838,551444,183Pink

61. eCommerce Manager, Last Week

62. Run: Switching to Machine-Learning is Simple

63. Predictive Targeting Drives Massive Uplift
Create a few
variations
Apply Targeting
with One Click
Profit!Predictive Targeting
identifies statistically significant
targeting opportunities
* Patent Pending

64. Machine-Learning Engine Alerts you upon Recognizing a
Segmentation Opportunity
Serve Control to all users
Serve Variation A to Audience Direct Traffic, and Control to the rest of the users
Serve Variation A to Audience Paid Search Traffic, and Control to the rest of the users

65. Learning:
Ignore Personalization and Leave
Millions on the Table

66. Thank You
Want this deck? Email me: andrey@dynamicyield.com