Document version Extended user-centric analyses

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1 Document version.0 08 Extended user-centric analyses

2 Table of contents User-centric data. Calculation of frequencies. Calculation of intervals. Overview on dimensions and metrics.4 Use cases Evaluating users on the basis of the RFM and RFE model. General. Setup. Predefined Segments

3 User-centric data. Calculation of frequencies

4 . Calculation of frequencies Which information can be generated by a user? st visit 0- nd visit 0-4 rd visit 0-5 For the example of frequencies of visits the following information could be relevant: What was the number of the visit at a date (e.g. of the first order)? How many visits did a user make in total? How many visits did a user make in the analysis time period?

5 . Calculation of frequencies Besides the regular metric customer and customer profile views are differentiated: Customer: Customer Profile: [Regular]: The value a user had at a specific date. Focusses on the current status of the users (until his last visit). For doing so, the last visit does not necessarily have to be within the analysis time period. How often did the value occur in the analysis time period? Example: Customer Visits vs. Customer Profile Visits vs. Visits st visit 0- nd visit 0-4 rd visit 0-5

6 . Calculation of frequencies Even though the last visit was not made within the analysis time period, Customer Profile shows the value of the last visit. Example: st visit 0- nd visit 0-4 rd visit 0-5 Customer profile views are not suitable for a time-based analysis.

7 User-centric data. Calculation of intervals

8 . Calculation of intervals Webtrekk calculates intervals of visits and orders. Calculation of visit intervals For each visit the amount of days that passed since the previous visit is calculated. The days are calculated based on the date, the time is not taken into account. The first visit is not considered for calculation. Visit 0-0 Visit 0-0 A Visit 0-04 B A B By using cohorts Webtrekk provides the possibility to analyze days/weeks/months since the first visits (e.g. Cohorts (Lifespan Weeks) ).

9 . Calculation of intervals By using the filter engine the analysis can be limited to specific events/visits. Example: Calculation days between contacts with filtering Visit Visit Visit Visit How many days prior to the last order users accessed the website? How many days prior to the last visit users accessed the website?

10 . Calculation of intervals Calculation of order intervals For each order the the amount of days that passed since the previous order is calculated. The days are calculated based on the date, the time is not taken into account. For the first order the amount of days passed since the first visit is depicted. A Visit 0-0 Visit 0-0 B Visit 0-05 A B

11 User-centric data. Overview on dimensions and metrics

12 . Overview dimensions and metrics The following dimensions and metrics are calculated automatically: Traffic view Available as Metric/value Designation Description Metric Dimension Page impressions URM Customer Page Impressions URM Customer Profile Page Impressions Total page impressions to date in intervals of ten Total page impressions (Dimension: intervals of ten) Visits Days Yes No URM Customer Visits URM Customer Profile Visits URM Days since Contact URM Customer Profile Days since Last Contact URM Customer Profile Days since First Contact URM Customer Profile Visit Frequency Avg. (Days) URM Days between contacts URM Days between contacts Avg. URM Last Visit Total visits to date Total visits Days since a visit Days since the last visit to date Days since the first visit to date Avg number of days between visits Number of days between visits Avg number of days between visits Shows whether something occurred within a visitor s last visit

13 . Overview dimensions and metrics Order view (/) Available as Metric/value Designation Description Metric Dimension Orders Order value Discount value Discount rate % URM Customer Orders URM Customer Profile Orders URM Customer Orders w. Discounts URM Customer Profile Orders w. Discount URM Customer Order Value URM Customer Profile Order Value URM Customer Profile Order Value Avg URM Customer Discount Value URM Customer Profile Discount Value URM Customer Profile Discounts % Total orders to date Total orders Total number of discounted orders to date in intervals of ten Total number of discounted orders (Dimension: intervals of ten) Total order value to date Total order value (Dimension: intervals of ten) Avg total order value Total discount value to date Total discount value (Dimension: intervals of ten) Percentage of discounted orders out of total orders

14 . Overview dimensions and metrics Order view (/) Available as Metric/value Designation Description Metric Dimension Customer Lifetime Value Days URM Campaign New Visitor CLV URM Days since Order URM Customer Profile Days since Last Order URM Days between orders URM Days between orders Avg. Total order value to date for new visitor with campaign click Days since the previous order (or first visit in case of the first order) Days since the last order Days between two orders Yes No URM Last Visit with a Purchase URM Customer Profile Last Customer Micro Journey Shows whether an order was placed during the last visit Allows to restrict on the last cycle of the Customer Micro Status stretching across multiple visits.

15 . Overview dimensions and metrics Why is much information available as dimension, even though figures are depicted? For lots of user-centric data it can be helpful to use it as dimensions, even though figures are depicted. Only by doing so further metrics and formulars can be viewed in this context. Example for using the dimension URM Customer Profile Visits : Allows the analysis of the question: How many users did make only visit in total? Example for using the metric URM Customer Profile Visits : Allows the analysis of the question: How many visits did users make in their lifetime, when they had at least one access via the channel Direct?

16 User-centric data.4 Use cases

17 .4 Use cases How many days pass between visits? Analysis: Individual Analysis Reading example: Visits: For,649 visits the last access happened 4 days before. Visit %: For.6 % of all visits the last access happened 4 days before. The first visit is not taken into account for calculation.

18 .4 Use cases How many days pass between visits, depending on the number of visits? Analysis: Individual Analysis Reading example: Visitors:,60 users made their second visit one day after their first visit. Visits %: 5.74 % of all users with a second visit made it one day after their first visit.

19 .4 Use cases How many days pass between orders? Analysis: Individual Analysis Reading example: Qty Orders: For,578 orders the last order was made days before. For the first order the days that passed since the first visit are depicted.

20 .4 Use cases How many days pass between orders, when users purchased at least twice? Analysis: Individual Analysis Reading example: Visitors: 8 visitors made their second order on the same day as their first order.

21 .4 Use cases How many users had more than 0 visits in the analysis time period and generated more than 00 Page Impressions until this point in their lifetime? (/) Analysis: Individual Analysis Reading example: Visitors: 79,65 visitors accessed the website in March. Visitors > 0 visits and > 00 PI in lifetime*:,7 visitors accessed the website in March, had more than 0 visits in their lifetime and until then generated more than 00 Page Impressions.

22 .4 Use cases How many users had more than 0 visits in the analysis time period and generated more than 00 Page Impressions until this point in their lifetime? (/) Used filters:

23 .4 Use cases What revenue was generated in long-term via new visitors per campaign channel? (/) Analysis: Marketing > Campaign Categories > [Name of the campaign channel] Reading example: URM Campaign New Visitor CLV: Order Value: 8,057.0 order value was generated by the new visitors tracked in the analysis time period via the channel Display up to the current date. 5.64,0 order value was assigned to the channel Display according to the default attribution model.

24 .4 Use cases What revenue was generated in long-term via new visitors per campaign channel? (/) Example: Visitor with campaign channels and default attribution last-campaign-wins Display 0-0 SEA Direct Direct 0-06

25 .4 Use cases How many visits are made long-term by new visitors via campaign channels? Analysis: Marketing > Campaign Categories > [Name of the campaign channel] Reading example: Visits: URM Customer Profile Visits: Further lifetime visits per visitor*: The channel SEO was used in 4,059 times by new visitors in the analysis time period. New visitors generated 88,605 visits up to the current date. On average.7 further visits were generated by new visitors up to the current date. Further lifetime visits per visitor is a custom formula. It is configured as follows:

26 Evaluating Users on the basis of the RFM and RFE model. General

27 . General The RFM and RFE models are a proven scoring system, that can be used for the evaluation of a visitor. The models focus on the visitor s behavior in different contexts. RFE Evaluates the usage behavior RFM Evaluates the purchase behavior Recency How many days passed since the last visit? Recency How many days passed since the last order? Frequency How many visits were made in total? Frequency How many orders were made in total? Engagement How many Page Impressions were generated in total? Monetary What total revenue was generated?

28 . General values are assigned for each of these measurement criteria: (bad), (medium) or (good). RFE Evaluates the usage behavior Recency = How many days passed since the last visit? Frequency = How many visits were made in total? Engagement = How many Page Impressions were generated in total? The underlying setup can be configured individually.

29 . General Therefore, ³(=7) combinations are available in each model. Example: Possible combinations in the RFM model R F M R F M R F M

30 . General The clustering allows to take targeted measures for selected user groups. No Google Adwords or Facebook ads. Seize upsell chances. Use cross sell chances. Advertize. Provide additional purchase incentives.

31 . General The same visitor can belong to a RFM as well as a RFE group. RFM RFE The RFM model solely focuses on the buyer, the RFE model focuses on all visitors. RFE Online- Shop RFM

32 Evaluating Users on the basis of the RFM and RFE model. Setup

33 . Setup In the setup you configure the model s threshold values. Webtrekk Q > Configuration > System Configuration > Account Changes in the setup affect all tracked users. Thus, you stay flexible and can approach your perfect setup in small steps.

34 . Setup The spread of the RFE groups can be viewed in the following analysis: Analysis path: Visitors > URM - User Relationship Management > URM - Customer RFE Group Reading example:,08 visits were made by visitors, that belonged to the RFE group in the analysis time period. This analysis calculates the assignment to the model based on the calendar time period. Alternatively, the user s current status can be determined. For doing so, the dimensions URM - Customer Profile RFE Group or URM - Customer Profile RFM Group can be used. Please note, that you choose a meaningful time period in the analysis. If you, for example, choose Today as calendar time period, all users will have a Recency of.

35 . Setup Example for the assignment of a user to the URM - Customer Profile RFM Group : Configuration: Accesses of a user: Time of the visit Qty orders orders order Order value Value: 649 Value: 49 Analysis: Date of analysis Recency Frequency Monetary Group Less than 0 days Between and 0 orders More than,000 More than 90 days Between and 0 orders More than,000

36 . Setup The challenge of the configuration is to find the right model. The setup can vary heavily depending on the industry sector. Our Webtrekk consultants gladly assist you with finding an individually matching setup! The RFE/RFM dashboard (Webtrekk Q > My Reports > Predefined Reports) can be used for configuring the threshold values.

37 .. Example setup based on the Pareto principle By using the Pareto principle visitors can be differentiated: Only the best 0 % of all visitors are business relevant. Because Webtrekk works with groups, the pareto principle has to be applied twice. Either the group of bad or good users can be split up again. The following example shows segmentation of users by the number of overall visits to determine the frequency in the RFE model. Group = 4 % No. of visits Group = 0 % No. of visits Group = 6 % Group = 80 % Group = 80 % Share of visitors. Segmentation of all users Segment corresponds to all business relevant users. Share of visitors. Segmentation of all business relevant users. Segment b corresponds to best users.

38 Frequency group assignment.. Example setup based on the Pareto principle Webtrekk can be configured for the example as follows:. In the predefined report Recency, Frequency and Engagement in Webtrekk Q click on the arrow symbol near Frequency, Customer Visits to open the corresponding analysis.. The values can be determined using the metric Visitors cumulated %. Read out the values at 80 % and 96 % and insert them into the configuration mask.

39 Evaluating Users on the basis of the RFM and RFE model. Predefined Segments

40 Predefined segments Based on the RFM/RFE model segments are set up, that depict important user groups. Example: Configuration of the segment Flop Buyers In Webtrekk Analytics reports, analyses or specific metrics can be filtered by segments. In Webtrekk Marketing Automation segments can be used for defining the target group. Segments use the profile view. Thus, only the current status of the user is taken into account.

41 Predefined segments Example for predefined RFM segments: Flop Buyers Big Spenders **

42 Predefined segments Segments based on the RFM model (/) Based on an example configuration the operating principle of the segments is described. Example configuration for the RFM model Segment name Filtering Description based on example configuration Big Spenders ** More than,000 revenue was generated. Churned buyers ** The last order was made more than 90 days ago. Flop Buyers The last order was made more than 90 days ago. In total less than orders were made and less than 00 revenue was generated. Frequent Buyers ** In total more than 0 orders were made.

43 Predefined segments Segments based on the RFM model (/) Based on an example configuration the operating principle of the segments is described. Example configuration for the RFM model Segment name Filtering Description based on example configuration High Potential Buyers The last order was made less than 0 days ago. In total less than orders were made and more than,000 revenue was generated. Inactive Top Buyers The last order was made more than 90 days ago. In total more than 0 orders were made and more than,000 revenue was generated. Small Buyers The last order was made less than 0 days ago. In total more than 0 orders were made and less than 00 revenue was created. Top Buyers The last order was made less than 0 days ago. In total more than 0 orders were made and more than,000 revenue was created.

44 Predefined segments Segments based on the RFE model Based on an example configuration the operating principle of the segments is described. Example configuration for the RFE model Segment name Filtering Description based on example configuration Churner ** The last visit was made more than 0 days ago. Frequent Users ** In total more than 60 visits were made.

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