Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Analytics – The next Level
Marcel Martschausky / Kay Lehmann / Markus Nagel
Levels of Data Driveness
Analog – Multi Retailers are here
TRANSFORMATION
Decision Preparation
OUTPUT
Marketing Actions
INPUT
Data Collection
Websi...
TRANSFORMATION
Decision Preparation
OUTPUT
Marketing Actions
INPUT
Data Collection
Website
Mobile
Apps
Offsite
CRM
SCM/ERP...
Digital Tier 2 – Some really developed Players are here
TRANSFORMATION
Decision Preparation
OUTPUT
Marketing Actions
INPUT...
Digital Tier 3 – This is an Unicorn right now
TRANSFORMATION
Decision Preparation
OUTPUT
Marketing Actions
INPUT
Data Coll...
Improve Your Analytics to become a strategically relevant asset
 The race is won, if Analysts are
able to turn optimizati...
How to find Target Groups
Individuality comes with Combinations
ATTRIBUTES ENGAGEMENT
REVENUE
Individuality comes with Combinations
ATTRIBUTES  Gender
 Age
 Location
 Demographic Info
Individuality comes with Combinations
 New User
 Customer
 Lead
 Micro Conversions
Individuality comes with Combinations
 Page Groups
 Product Groups
 Brands
 Price Groups
 Channels
Individuality comes with Combinations
ENGAGEMENT
REVENUE
Our
Focus
Today
Groups for Engagment and Revenue
Improve Activity Improve Performance Top Groupof no Interest
currently inactive
low perfo...
Groups for Engagment and Revenue
Improve Activity Improve Performance Top Groupof no Interest
RFE-/RFM-Groups
113 / 122 / ...
Groups for Engagment and Revenue
Improve Activity Improve Performance Top Groupof no Interest
We want to keep
them and get...
Combination
ENGAGEMENT
REVENUE
REVENUE ENGAGEMENT
engagement activity
engagement performance
order activity
order performa...
Combination Examples
Browsers
Browsers with Potential
Sleeping Top Customers
Churning Top Customers
inactive low revenue
a...
 highly engaged with the website
 motivated to come back
 no order intent
 stuck in the process
 can be targeted onsi...
Example: Browsers
Scenario:
Reducing the Basket Abandonment
Rate by 15% for 30% of these visitors
would lead to an overall...
Example: Browsers
€
What and how
did they buy last
time?
What are they
interested in
now?
What is the
problem?
Browsers
in...
Example: Browsers
€What and how
did they buy
last time?
What are they
interested in
now?
What is the
problem?
Discover our...
Let‘s get active!
Use Case 1
Target Group
Goal
Channel
active low revenue
inactive high engagement
We want to get 10 more visits from
25% of...
Use Case 2
Target Group
Goal
Channel
inactive low revenue
active high engagement
We want to get an average CR for
20% of t...
Use Case 3
Target Group
Goal
Channel
inactive low revenue
active high engagement
We want to get an average CR for
20% of t...
Incentives for newsletter and social media
Use Case 4
Target Group
Goal
Channel
inactive high revenue
active high engageme...
Product reco for category 1
Use Case 5
Target Group
Goal
Channel
inactive high revenue
inactive high engagement
We want 25...
Appendix
Example Data: User Groups
Revenue Engagement Visitors % Visits % PI per Visit CR
Basket
Value Avg.
Product
View per
Visit
...
Example Data: Scenarios
Revenue Engagement Visitors % Scenario Revenue Increase
No Interest Performance 2,73% 20% with 20%...
Example Data: Product Views
Product Views (last 30 Days)
Category
No Interest /
Super User
Activity /
Super User
Performan...
Example Data: Purchased Products
Purchased Products (last 180 Days)
Category
No Interest /
Super User
Activity /
Super Use...
Example Data: Product Type
Purchased Products (last 180 Days)
Product Type
No Interest /
Super User
Activity /
Super User
...
Example Data: Channel
Entries (last 180 Days)
Channel
No Interest /
Super User
Activity /
Super User
Performance /
Activit...
Example Data: Checkout
Conversion Rate (last 30 Days)
Funnel Step
No Interest /
Super User
Activity /
Super User
Performan...
Thank you for your attention
Marcel Martschausky / Director Consulting / marcel.martschausky@webtrekk.com
Kay Lehmann / CE...
Upcoming SlideShare
Loading in …5
×

[WUC Workshop 2016] Kay Lehmann, CEO, converlytics | Markus Nagel, Senior Consultant Digital Analytics, Webtrekk | Marcel Martschausky, Director of Consulting, Webtrekk | Analytics - The Next Level

644 views

Published on

Webtrekk User Conference 2016, 1 and 2 June, Berlin.

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

[WUC Workshop 2016] Kay Lehmann, CEO, converlytics | Markus Nagel, Senior Consultant Digital Analytics, Webtrekk | Marcel Martschausky, Director of Consulting, Webtrekk | Analytics - The Next Level

  1. 1. Analytics – The next Level Marcel Martschausky / Kay Lehmann / Markus Nagel
  2. 2. Levels of Data Driveness
  3. 3. Analog – Multi Retailers are here TRANSFORMATION Decision Preparation OUTPUT Marketing Actions INPUT Data Collection Website Mobile Apps Offsite CRM SCM/ERP Payment 3rd Party Market Research Marketing Teams Onsite Mobile Apps Display SEM/SEO TV Email Social Media Data transformation process needs weeks here and is driven only by humans
  4. 4. TRANSFORMATION Decision Preparation OUTPUT Marketing Actions INPUT Data Collection Website Mobile Apps Offsite CRM SCM/ERP Payment 3rd Party Data Warehouse Business Intelligence Marketing Teams Onsite Mobile Apps Display SEM/SEO TV Email Social Media Digital Tier 1 – Most E-Commerce Players are here Data transformation process needs days here and is driven by humans (BI for decision) and technology (DWH for transforming the data)
  5. 5. Digital Tier 2 – Some really developed Players are here TRANSFORMATION Decision Preparation OUTPUT Marketing Actions INPUT Data Collection Website Mobile Apps Offsite CRM SCM/ERP Payment 3rd Party Data Management Platform Marketing Teams Onsite Mobile Apps Display SEM/SEO TV Email Social Media Data transformation process will be done in realtime here and is driven by technology (DMP). BI is only needed to set up the technology in the right way.
  6. 6. Digital Tier 3 – This is an Unicorn right now TRANSFORMATION Decision Preparation OUTPUT Marketing Actions INPUT Data Collection Website Mobile Apps Offsite CRM SCM/ERP Payment 3rd Party Marketing Teams Offline Data Stores Cars … Onsite Mobile Apps Display SEM/SEO TV Email Social Media Offline Retail Callcenter Cars … Data Management Platform Data transformation process will be done in realtime here and is driven by technology (DMP). Additional offline touchpoints will be digitalized.
  7. 7. Improve Your Analytics to become a strategically relevant asset  The race is won, if Analysts are able to turn optimization potential into actions and measure there success or adjust plans  Analysts are not a Cost Center! We as Analysts are not Controllers for data.  Seeking for more optimization potential is the first half of the race  Hard skills could be learned, Soft Sills are unbelievable more important  Avoid discussions about data quality and have your pitchdeck always ready  Make sure the marketing department has the right people on board
  8. 8. How to find Target Groups
  9. 9. Individuality comes with Combinations ATTRIBUTES ENGAGEMENT REVENUE
  10. 10. Individuality comes with Combinations ATTRIBUTES  Gender  Age  Location  Demographic Info
  11. 11. Individuality comes with Combinations  New User  Customer  Lead  Micro Conversions
  12. 12. Individuality comes with Combinations  Page Groups  Product Groups  Brands  Price Groups  Channels
  13. 13. Individuality comes with Combinations ENGAGEMENT REVENUE Our Focus Today
  14. 14. Groups for Engagment and Revenue Improve Activity Improve Performance Top Groupof no Interest currently inactive low performance currently inactive high performance currently active low performance currently active high performance
  15. 15. Groups for Engagment and Revenue Improve Activity Improve Performance Top Groupof no Interest RFE-/RFM-Groups 113 / 122 / 123 / 133 / 212 / 213 / 222 RFE-/RFM-Groups 111 / 112 / 121 / 131 / 132 RFE-/RFM-Groups 211 / 221 / 231 / 311 / 312 / 313 / 321 / 322 / 331 RFE-/RFM-Groups 223 / 232 / 233 / 323 / 332 / 333
  16. 16. Groups for Engagment and Revenue Improve Activity Improve Performance Top Groupof no Interest We want to keep them and get them to buy/visit more Let us try to keep our costs down with this user type How do we get these guys back? Reward them as you want to keep them and get more people like them
  17. 17. Combination ENGAGEMENT REVENUE REVENUE ENGAGEMENT engagement activity engagement performance order activity order performance 4x4 Groups
  18. 18. Combination Examples Browsers Browsers with Potential Sleeping Top Customers Churning Top Customers inactive low revenue active high engagement inactive high revenue active high engagement High Potential Customers Churned Customers active low revenue active high engagement active high revenue active low engagement inactive high revenue inactive low engagement inactive low revenue inactive high engagement
  19. 19.  highly engaged with the website  motivated to come back  no order intent  stuck in the process  can be targeted onsite  good potential with low costs Example: Browsers Browsers inactive low revenue active high engagement
  20. 20. Example: Browsers Scenario: Reducing the Basket Abandonment Rate by 15% for 30% of these visitors would lead to an overall revenue increase of 2,2% Visitors: 8% Conversion Rate: 5% Avg. Basket Value: 100€ Product Views per Visit: 4 Basket Abandonment Rate: 70% Avg. Product Value: 40€ Browsers inactive low revenue active high engagement
  21. 21. Example: Browsers € What and how did they buy last time? What are they interested in now? What is the problem? Browsers inactive low revenue active high engagement
  22. 22. Example: Browsers €What and how did they buy last time? What are they interested in now? What is the problem? Discover our Sale Products You might be interested in… Get 10% off! € You forgot something Browsers inactive low revenue active high engagement
  23. 23. Let‘s get active!
  24. 24. Use Case 1 Target Group Goal Channel active low revenue inactive high engagement We want to get 10 more visits from 25% of this user group. Newsletter Content Product reco with products from category 2 This would bring us 1,07% more revenue overall WHY? We want to reactivate them and it is a good performing channel for this group. This category got many product views from this group but wasn‘t but that often Addition Check payment obstacles This group has a low conversion rate for the payment step
  25. 25. Use Case 2 Target Group Goal Channel inactive low revenue active high engagement We want to get an average CR for 20% of this user group. Onsite Content 1 Product reco with products from category 5 This would bring us 3,18% more revenue overall WHY? This user group is already very active on our website. This category has the best conversion rate for this group Content 2 Discount in basket This group has a high basket abandonment rate that needs to be improved
  26. 26. Use Case 3 Target Group Goal Channel inactive low revenue active high engagement We want to get an average CR for 20% of this user group. Onsite / Retargeting Content 1 This would bring us 3,18% more revenue overall WHY? This user group is already very active on our website. Content 2 Retargeting of basket abandoners This group has a high basket abandonment rate that needs to be improved Reco for category 5 at category 1 pages The group shows high interest for category 1 but has its highest conversion rate for category 5
  27. 27. Incentives for newsletter and social media Use Case 4 Target Group Goal Channel inactive high revenue active high engagement We want 25% of them to become part of the top group Onsite Content This would bring us 2,40% more revenue overall WHY? This user group is already very active (interested) on our website. Although they come very often to our website we have more chance to guide them via e-mail or social media
  28. 28. Product reco for category 1 Use Case 5 Target Group Goal Channel inactive high revenue inactive high engagement We want 25% of them to do 10 more visits Newsletter Content This would bring us 1,20% more revenue overall WHY? We have to bring them back to our website and newsletter has a good performance for them Although they have not bought much from this category it‘s the one they are most interested in Next Step Find regular activites for them (content, competitions, social media etc.) It is a good performing user group with lack of activity. So we have to keep them active
  29. 29. Appendix
  30. 30. Example Data: User Groups Revenue Engagement Visitors % Visits % PI per Visit CR Basket Value Avg. Product View per Visit Add to Cart Rate Aban- donment Rate Avg. Product Value No Interest Super User 15,69% 38,65% 13,88 5,94% 116,42€ 6,20 11,00% 61,61% 26,40€ Activity Super User 5,63% 17,57% 17,22 10,78% 173,42€ 9,01 14,32% 49,63% 28,76€ Performance Activity 4,42% 1,53% 22,69 25,14% 117,81€ 9,92 16,46% 34,34% 27,64€ Activity Activity 3,19% 3,18% 24,30 22,91% 201,72€ 12,97 17,23% 32,87% 30,80€ Performance Super User 2,92% 5,23% 16,98 8,86% 115,18€ 7,70 12,04% 55,86% 24,95€ Performance Performance 2,87% 0,79% 20,81 27,49% 129,85€ 9,77 16,16% 22,87% 29,30€ No Interest Performance 2,73% 1,48% 15,30 15,41% 126,59€ 6,33 15,11% 29,71% 29,00€ Total Total 100,00% 100,00% 16,98 11,85% 141,33€ 8,02 13,69% 46,16% 28,34€
  31. 31. Example Data: Scenarios Revenue Engagement Visitors % Scenario Revenue Increase No Interest Performance 2,73% 20% with 20% more PI per Visit 0,07% No Interest Super User 15,69% 20 % with Average CR 3,18% No Interest Super User 15,69% 30% with 15% lower Basket Abandonment Rate 5,94% Activity Activity 3,19% 25% with 10 more Visits 1,20% Activity Activity 3,19% 10% with 30% more Visits 0,26% Activity Super User 5,63% 30% with Average CR 0,58% Activity Super User 5,63% 25% becoming Top Group 2,40% Performance Activity 4,42% 25% with 10 more Visits 1,07% Performance Activity 4,42% 20% with 20% more Basket Value 0,11% Performance Performance 2,87% 15% with 25% more Product Views per Visit 0,06% Performance Super User 2,92% 25% becoming Top Group 2,99%
  32. 32. Example Data: Product Views Product Views (last 30 Days) Category No Interest / Super User Activity / Super User Performance / Activity Activity / Activity Performance / Super User Performance / Performance No Interest / Performance Cat 1 17,09% 18,79% 7,34% 12,95% 17,77% 8,17% 12,04% Cat 2 7,51% 6,91% 12,20% 11,11% 6,93% 13,73% 11,29% Cat 3 8,52% 8,97% 7,33% 7,18% 8,88% 8,03% 7,74% Cat 4 8,14% 8,40% 7,48% 7,61% 8,29% 7,08% 7,36% Cat 5 7,76% 7,93% 7,08% 6,98% 8,05% 7,18% 7,34%
  33. 33. Example Data: Purchased Products Purchased Products (last 180 Days) Category No Interest / Super User Activity / Super User Performance / Activity Activity / Activity Performance / Super User Performance / Performance No Interest / Performance Cat 1 7,00% 13,01% 3,31% 5,29% 5,99% 4,32% 3,84% Cat 2 3,18% 3,91% 5,21% 5,59% 2,82% 7,67% 6,41% Cat 3 4,76% 5,33% 6,51% 4,48% 8,91% 9,85% 5,57% Cat 4 3,61% 3,89% 5,68% 3,57% 5,17% 6,40% 3,65% Cat 5 7,32% 7,96% 7,74% 7,07% 7,85% 8,61% 6,77%
  34. 34. Example Data: Product Type Purchased Products (last 180 Days) Product Type No Interest / Super User Activity / Super User Performance / Activity Activity / Activity Performance / Super User Performance / Performance No Interest / Performance Regular 43,51% 51,02% 50,67% 48,95% 49,49% 56,81% 47,87% Sale 37,60% 29,02% 21,83% 28,59% 27,89% 15,40% 28,18% New 18,88% 19,95% 27,49% 22,46% 22,62% 27,73% 23,93%
  35. 35. Example Data: Channel Entries (last 180 Days) Channel No Interest / Super User Activity / Super User Performance / Activity Activity / Activity Performance / Super User Performance / Performance No Interest / Performance Direct 33,54% 36,61% 28,97% 32,19% 36,62% 28,68% 23,76% SEO 15,79% 13,80% 26,48% 18,96% 13,87% 29,37% 27,43% SEM 8,27% 6,53% 10,30% 9,52% 6,21% 10,80% 14,82% Display 10,77% 7,41% 10,61% 7,86% 8,43% 10,58% 18,25% Newsletter 6,38% 7,32% 10,70% 13,02% 5,78% 7,15% 7,79%
  36. 36. Example Data: Checkout Conversion Rate (last 30 Days) Funnel Step No Interest / Super User Activity / Super User Performance / Activity Activity / Activity Performance / Super User Performance / Performance No Interest / Performance Basket 45,59% 55,85% 85,55% 82,10% 67,34% 85,73% 70,36% Payment 73,13% 75,73% 76,42% 77,96% 75,97% 76,90% 74,12% Confirmation 75,71% 77,63% 89,32% 88,07% 84,75% 90,98% 76,74% Thank you - - - - - - -
  37. 37. Thank you for your attention Marcel Martschausky / Director Consulting / marcel.martschausky@webtrekk.com Kay Lehmann / CEO Converlytics / kay.lehmann@converlytics.com Markus Nagel / Senior Consultant / markus.nagel@webtrekk.com

×