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Anna-Katharina Knarr & Tobias Roth - Addressing Users Successfully with the Help of Dynamic Segmentation

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Anna-Katharina Knarr, Head of Account Management at Trbo, and Tobias Roth, Digital Conversion Manager at Galeria Kaufhof, present "Addressing Users Successfully with the Help of Dynamic Segmentation".
They explain and demonstrate how bolstering segmentation with cutting-edge technology can make mass messaging feel personal and relevant.

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Anna-Katharina Knarr & Tobias Roth - Addressing Users Successfully with the Help of Dynamic Segmentation

  1. 1. Galeria Kaufhof & trbo Addressing Users Successfully with the Help of Dynamic Segmentation Anna-Katharina Knarr, trbo GmbH Head of Account Management Tobias Roth, Galeria Kaufhof Digital Conversion Manager
  2. 2. 2 Agenda 1) Introduction (Dynamic) Segmentation 2) (Dynamic) Segmentation in Action: Galeria Kaufhof 3) What’s next?
  3. 3. Introduction (Dynamic) Segmentation
  4. 4. 4 Issues in E-Commerce
  5. 5. 5 Segmentation in Personalization ❖ Personalization does not always have to mean addressing each individual user 1:1 ❖ Classification of users with similar interests, preferences, behaviour into (dynamic) segments ❖ Machine learning is used to distinguish between permanent and temporary interests (dynamic segments)
  6. 6. 6 Dynamic vs. Static Segments ❖ Static segments are used to group users according to their previous actions ❖ Dynamic segments are formed in realtime. A user can move from one segment to the other with a single click.
  7. 7. Dynamic Segments Use Cases
  8. 8. 8 Brand Affinity ❖ If the user is interested in certain brands, they can be clearly highlighted when entering the shop. Use Case Adjustment of the start page through teaser areas that reflect the user behaviour by means of dynamic segments in order to pick up the user both during and after a session and thus acquire a loyal customer.
  9. 9. 9 Brand Affinity
  10. 10. 10 Dynamic Purchasing Behavior ❖ The change between the individual segments depends on various factors, such as the length of stay and other aspects. Use Case Dynamic segments distinguish the purchasing behaviour of the user into impulsive, temporary and permanent interests. e.g. a man is looking for a gift for his girlfriend.
  11. 11. 11 Dynamic Purchasing Behavior
  12. 12. 12 Affinity Main Category ❖ The dynamic segment assigns user behavior to certain top categories. Use Case Certain content is shown to the user according to his segment.
  13. 13. 13 Affinity Main Category
  14. 14. 14 Further Use of Data ❖ Push the dynamic interests as Custom Audiences to Google Analytics for retargeting or also for transmission to other partners, such as FINDOLOGIC for optimizing the search or to CrossEngage for data orchestration.
  15. 15. (Dynamic) Segments in Action: Galeria Kaufhof
  16. 16. 16 Example 1: Daily Deals (“Tagesdeals”) ❖ Ongoing campaigns ❖ Special offers on Sundays and Tuesdays ❖ Offers on certain categories, teased by newsletter ❖ Banners with offers specifically tailored to the segment are shown only to certain segments (e.g. children's fashion).
  17. 17. 17 ❖ Daily Deal: 20% on the categories watches, suitcases, sports, jackets and women’s shoes, 15% on kitchenware ❖ Segments were used to address relevant target groups → 3 campaigns on the basis of these segments ➢ Control group: No segmentation ➢ 1st Segment: User with interest in watches ➢ 2nd Segment: User with interest in sports ❖ The numbers prove that segmentation is worth it Example 1: Daily Deals (“Tagesdeals”) on Sunday
  18. 18. Example 1: Daily Deals (“Tagesdeals”)
  19. 19. 19 Campaign Addressed Users Conversion Rate User Value First Segment over 60.000 1.87 % Double that of third segment 2nd Segment over 20.000 3.3 % Double that of Top Segment Control Group (no segments) over 450.000 1.37 % Example 1: Daily Deals (“Tagesdeals”) on Sunday
  20. 20. 20 ❖ CTR, Conversion Rate and Revenue increased through segmentation ❖ Setup effort for campaign is low → creation of a campaign for the top segment, then adjustment of date, wording and other segments ❖ In most day deals we achieved better results with segmented playout than with non-segmented playout → overall the Conversion Rate was increased by 20% Example 1: Daily Deals (“Tagesdeals”)- Insights
  21. 21. 21 ❖ Learnings from daily deals ➢ Adjust the workload according to projected uplift for the segments: e.g. graphics vs. text-teaser ➢ If segments are too similar, the uplift is reduced ➢ Schedule as a weekly task Example 1: Daily Deals (“Tagesdeals”) - Learnings
  22. 22. 22 Example 2: Pre-Christmas ❖ 3 campaigns during pre-Christmas time 2018 ❖ Challenge: Communicating 3 campaigns at the same time ➢ Christmas Service-Banner: Punctual delivery for christmas (order by...) ➢ Favourite discount (“Lieblingsrabatt”): Discount campaign for Christmas shopping showing a promo code ➢ Payback campaign: Customers using Payback but have not entered their number are specifically being addressed
  23. 23. 23 Example 2: Pre-Christmas “Lieblingsrabatt”
  24. 24. 24 Example 2: Pre-Christmas “Weihnachtsservice”
  25. 25. 25 Example 2: Pre-Christmas Payback
  26. 26. 26 ❖ Campaigns ran simultaneously ❖ Prioritized playout: If a user fell into all segments, measures were played out according to priority ❖ Capping the showing to five times ❖ Users can be picked up with different intensity, depending on the length of stay and clicks on the page Example 2: Pre-Christmas
  27. 27. 27 ❖ No A/B-Test for these campaigns ❖ Setup effort: Playout on desktop/tablet/mobile; predefined look & feel → approx. 1 h ➢ Payback User segment was set up approx. 1 month before campaign ➢ Favorite discount was filled adhoc (basing on current surfing behavior in defined categories) ➢ Christmas Service segment was filled ad hoc and containing all users with +3 page views Example 2: Pre-Christmas
  28. 28. 28 Example 2: Pre-Christmas Campaign Addressed Users Conversion Rate User Value Favorite Discount over 3 Million + 1.3 % Analogue to users without address Christmas Services over 200.000 + 1.38 % Analogue to users without address Payback over 2.000 + 7.89 % 3-fold increase
  29. 29. 29 ❖ It is worthwhile to address users who have a special interest with this campaign setup ➢ The target group may be small, but the uplift is enormous ➢ Despite the simultaneous use of special actions in the same period of time Example 2: Pre-Christmas
  30. 30. (Dynamic) Segments at Kaufhof: What’s next?
  31. 31. 31 Kaufhof: What’s next? ❖ We have established and are further working on interfaces to other providers to access segments from their systems. Challenge: Mapping
  32. 32. 32 ❖ Mapping of the different segmentation types (women vs. Damen) in all systems ❖ A Customer Data Platform (CDP) can be used to cleanly merge elements into a main system Kaufhof: What’s next?
  33. 33. 33 ❖ New teaser areas: ➢ Teasers are re-sorted and selected according to segment preferences ❖ Completely Dynamic Segments: ➢ Each time a page is accessed, a new and dynamic decision is made as to which segment the user is in Kaufhof: What’s next?
  34. 34. 34 ❖ Shop the Look & Inspiration Kaufhof: What’s next?
  35. 35. 35 ❖ Shop the Look & Inspiration Kaufhof: What’s next?
  36. 36. 36 ❖ Shop the Look & Inspiration Kaufhof: What’s next?
  37. 37. 37 ❖ Shop the Look & Inspiration Kaufhof: What’s next?
  38. 38. 38 ❖ Shop the Look & Inspiration: Advantages ➢ BNDLA users have a higher conversion rate than the shop average ➢ Interest-pages offer added value for users and is rewarded with a conversion rate increase of up to 20% Kaufhof: What’s next?
  39. 39. Dynamic Segments Learnings
  40. 40. 40 Learnings ❖ Dynamic segments are formed in real time ❖ In e-commerce, together with personalization algorithms, they can have a positive effect on ■ turnover ■ conversion rates ■ customer relations ■ average purchase value
  41. 41. 41 ❖ Dynamic segments can be prioritized using the ■ Search depth ■ Time on Site ■ According to fixed specifications ❖ A segment must be defined in such a way that it has a meaningful size. ❖ However, small segments can lead to significant uplifts in individual cases and should not be generally excluded. Learnings
  42. 42. Any Questions? Thank You!

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