David Perez of Convertro - What is Attribution at SIC2013

  • 1,411 views
Uploaded on

What is Attribution? …

What is Attribution?

This presentation will address the misconceptions about attribution solutions, and clarify how all-inclusive the term truly is. It will also cover how companies that implement proper attribution models can not only determine the most effective mediums in their campaigns, but can also establish behavior patterns in their consumers that will allow marketers to optimize their strategies, reallocate budgets as needed, and ultimately increase their ad spend to drive the highest possible ROI. The presentation will also include real world examples and stats that explain how these companies are benefiting from attribution.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
1,411
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
27
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Redo to be left-right and less dense
  • Bigger problem than last click is “last device”
  • 40% of touchpoints are in mobile but only 15% of conversions happen on a mobile device

Transcript

  • 1. What is Attribution?
  • 2. What is attribution? • Marketing activities = marketing touch-points • Desire outcome = sale, subscription 2
  • 3. Does it work? "A lot of that is around being able to allocate our spend where it's most effective." 3
  • 4. Will it really change my decisions? • Google AdWords Keyword: Job Positing Sites • Last click attribution:  CPA $378 & ROI -84% • Algorithmic attribution:  CPA $28 & ROI +229% 4
  • 5. Inadequate Attribution Models A simple customer path TV Commercial Mobile Search Laptop Coupon Site Tablet Website Laptop Website Desktop Website Laptop Search o Last-click: All exposures, except Coupon Site, assigned no value o Last-device: TV, Mobile, Tablet, Desktop exposures assigned no value o Digital-Media only: TV in this case, assigned no value 5
  • 6. Complexity – Offline Tracking • Offline Marketing spend & In-Store Sales Online Marketing Touch point 35% Spend TV Direct Mail Print Radio 65% Spend = Tracking = Missing In-Store Sales & Phone Orders 65% Sales User Visits Website.com User Closes Online 35% Sales
  • 7. Complexity – Media Fragmentation 7
  • 8. Complexity – Multi Device  Households have 5.7 Internet Connected Devices on average1 1 Source: https://www.npd.com/wps/portal/npd/us/news/press-releases/internet-connected-devices-surpass-half-a-billion-in-u-s-homes-according-to-the-npd-group/
  • 9. Case Study: Cross-device tracking, Indochino Problem: Unsure if mobile campaigns were producing results Result: Quantified how mobile campaigns were driving desktop sales 1. Discovered that of multi-device users were switching from mobile to desktop before converting 2. Optimized mobile based on overall results “When we initially decided to implement this, we were looking forward to improving the quality of our data, which we certainly did. The additional reporting and device data was a welcomed bonus! ” 9
  • 10. Complexity – Cookie Deletion Sweepers Blockers 10
  • 11. GIGO Problem 11
  • 12. Fixing GIGO Problem High Quality Data = Solid Foundation 12
  • 13. Fixing GIGO - Cookies 1st vs. 3rd Party • Third-party cookie is usually set by an analytics vendor. • First-party cookie is set in-house. • First Party Cookies are regarded as the most reliable method to measure visitor activity  Source: http://www.ogilvydma.com/2011/03/glossary-ana 13
  • 14. Fixing GIGO -3rd Party Cookies Bad • 3rd Party Cookies subject to 30% Greater Cookie Deletion 1st Party Cookies 3rd Party Cookies
  • 15. Fixing GIGO – Use 1st Party Cookies • Make sure your attribution solution uses:  JavaScript tag that serves a 1st party cookie on your site  1st Party View Pixel to track display impressions 15
  • 16. Fixing GIGO - Device Fragmentation • Email Address = The New Cookie… • User ID = The New Cookie 16
  • 17. Fixing GIGO – User ID Solution PII PII User ID Name email ft@gm.com 6805 6805 6805 6805 Laptop iPad iPhone PII Fred User ID Device 17
  • 18. Fixing GIGO – Cross Device 1st Party Data 1. Transaction – Get User ID 2. Login – Get User ID 3. ESP – Transactional Email – Get User ID 18
  • 19. Cross Device 2nd Party Data • Convertro  2nd party client device mappings • DoubleClick  Android + G+ + Gmail • Atlas  Facebook data 19
  • 20. Solving Offline Tracking • Offline Marketing spend & In-Store Sales Online Marketing Touch point 35% Spend TV Direct Mail Print Radio 65% Spend = Tracking = Missing In-Store Sales & Phone Orders 65% Sales User Visits Website.com User Closes Online 35% Sales
  • 21. Offline Step 1 – Identify File Leads file tied to PII 1 In-Store Sales, Catalog, Direct Mail, & Soon Print Email john@gmail.com jane@gmail.com Name Postal Address 3 1st Street, San Francisco, CA John Doe 94105 520 Powell Street, New York, NY, Jane Doe 10001 Order ID In-Store Sale 1111 $440 1112 1113 1114 $133 $410 $640
  • 22. Offline - Step 2 – Move File Send file to LiveRamp 2 Secure FTP Email john@gmail.com jane@gmail.com Name Postal Address 3 1st Street, San Francisco, CA John Doe 94105 520 Powell Street, New York, NY, Jane Doe 10001 Order ID In-Store Sale 1111 $440 1112 1113 1114 $133 $410 $640
  • 23. Offline - Step 3 – Activate Associate purchase data with cookies 3 LiveRamp provides masked Walmart lead information to Convertro tied to cookie IDs Convertro Cookie ID 11212 12312 41928 123912 Order ID 1111 1112 1113 1114 In-Store Sale $440 $133 $410 $640
  • 24. Offline - TV Method #1 – Set-top Box Fusion • Use set-top box data to understand Brand commercial viewership • Match set-top box data to look-a-like cookies • Sync cookies w/ LiveRamp • Import sources into Attribution Tool at user level 24
  • 25. Offline - TV Method #2 (DR) • DR TV Lift (~90 minutes)  Measure the pre-baseline site traffic before a given spot and post-baseline • TV Drag Effect (~1 week)  Secondary method establishes a drag for each daypart/day of week combination to determine the latent lift from TV © Convertro, Inc. | Confidential and Proprietary 25
  • 26. Offline - DR TV Lift
  • 27. Offline - DR Visitor Histogram
  • 28. Offline - TV Drag Effect 28
  • 29. Offline - TV Drag Effect Response Density Index by hour since TV Airing 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Sunday 1 Monday 2 Tuesday Wednesday 3 Thursday 4 Friday Saturday
  • 30. Offline Case Study: Dollar Shave Club Problem: Where to profitable acquire customers on TV for online business Result: TV works, but only on specific channels with specific creative 1. Reduced cost per spot by 2. Expanded its TV budget by 30
  • 31. Attribution Model Evolution • Single Click Rules Based - First click / Last Click • Multi-Click Rules Based - U-Shape or Even Click • Algorithmic Models 31
  • 32. Convertro Algoritm Probability of Conversion 1 0.9 Logistic Function Events 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 Exposure to Marketing 32
  • 33. Convertro Algoritm In Matrix Format (Example: Four Unique Paths) Y Conversion & Frequency Counts of Paths X Prob Conv Convs Total Org. PPC TV Email Disp. 1% 10 1000 1 0 1 1 0 5 100 0 1 1 0 0 10% 10 100 1 0 0 0 1 3% 30 1000 0 1 1 1 0 5% 1 = i Resulting Weights .10 .20 .30 .10 .30 33
  • 34. Advanced Modeling: Event Chaining • Addresses repeat purchases and multiple steps in conversion funnel • Attribute preceding events  First conversion gets credit for the next conversion 34
  • 35. Advanced Modeling: Source Decay • Estimate period over which source exposure effect decays over time • Plot for views shows that < 25% of conversions occurred by end of 1 day 35
  • 36. Model Validation • Randomly splitting user click trails into training and test sets (80/20 split) • Train a set of models on the training set • Then Run a prediction on the previously-unseen test sets (with the actual conversion events removed from the clicktrail).
  • 37. Model Validation Results
  • 38. Apply Cost Data "A lot of that is around being able to allocate our spend where it's most effective.” Attribution + Cost = ROI, CPA 38
  • 39. Optimizing - Cross-Channel Allocations • Determine the optimal spend against each marketing channel to maximize the number of conversions
  • 40. Optimizing - Sub-Channel Allocations • Then determine optimal allocations at sub-channel level to maximize conversions
  • 41. Optimizing - Tactical Allocations • Then determine optimal allocations at tactical level (e.g. publisher) to maximize quotes
  • 42. Automating Attribution • Feed properly allocated conversion data into programmatic buying tools:  Bid Tools – Kenshoo, Marin, AdLense, etc.  DSPs – Media Math, X+1, etc. 42
  • 43. Attribution Companies • Adometry • Convertro • Google Analytics Premium • Visual IQ 43
  • 44. Key Attribution Takeaways • Be Holistic – Track online offline touch-points & events • Avoid attribution solutions that rely on 3rd party cookies • Leverage a User ID to track cross-device & have more persistent tracking • Use algorithmic attribution model 44
  • 45. Key Attribution Takeaways • Validate that your attribution model is accurate against your data • Attribution data is useless without cost (need CPA & ROI) • Feed attribution data into programmatic buying tools • Determine optimal cross-channel spend allocations • Determine what intra-channel optimizations need to be made 45
  • 46. Thank You! NYC: 11 West 42nd Street New York, NY 10036 HQ: 1453 Third St Promenade Santa Monica, CA 90401 (888)308-9896 perez@convertro.com www.convertro.com 46