In the export, only keep the User ID column
Calculate CRM Incrementality, isolate this impact from Loyalty, understand specific Campaign & Journeys performances & drivers |
CRM Control Group Pre-Post: this proves the overall value of CRM (how do we impact same guests’ sales if we send communication vs if we don’t)
CRM Behavior Trend: for the test group, affected by CRM communication, we should be looking at their average frequency and check on a monthly basis, to ensure that we are maintaining & sustaining the growth
Specific Ad Hoc Campaign & Journeys Incrementality: on a single campaign and journey level, we will use the delta between test group conversion & control group conversion to understand the incrementality. This is helpful to assess the performances of a specific campaign, or incentive. Conversion is defined as loyalty online and/or in-store transactions in the next 5 days
Overall Journeys & Ad Hoc incrementality: we will take the weighted average of the conversion delta between control & test group for all the ad hoc campaigns, as well as for the full journeys.
Through this methodology, we want to measure the impact of Loyalty & CRM over total sales. On top of that, we want to isolate the impact of CRM from the one of loyalty. This proves the overall value of CRM (how do we impact same guests’ sales if we send communication vs if we don’t)
Create 2 control groups:
Control group for opted in people, to measure CRM incrementality over total sales
Control group at campaign level, to assess efficacy of individual campaign & incentives and optimize the CRM strategy
1, Open your Braze account and click on Segments
2. Select from the drop down list Custom Attributes
3. Create a new segment with the following characteristics:
MarketingEmail = True OR
MarketingPush = True
By relying on the OR logic, we are selecting all guests who have opted in to at least one channel (either email or push, or both)
4. Export this cohort and randomly select 10% of the users. This 10% will be the fixed control group
5. Import the file in Braze by clicking on create a segment and selecting the CSV file
6. Export all the cohorts (opted out, opted in, control) in Amplitude by:
Clicking on User Data and then CSV Export Email Addresses
In the export, only keep the User ID column
Open Amplitude and click on Create New
Click on User Cohorts
Select import from cohort file
Select User ID
Create the cohort and when you are done click on Save
Give your cohort a title and click on Save
7. Braze: Include the cohort as a control group for all campaigns, both automated campaigns & journeys. To exclude the control group from the campaigns, in the Target Audience select Consumer Membership and Is Not part of the control group
8. Amplitude: Create a new analysis, selecting segmentation and use as event Online Loyalty Transaction Claimed & In Store Loyalty Transaction Claimed all together (all RS). You can adjust the timeframe where the condition have to be verified, and the period you want to look at.
By selecting “daily” the unique users will be counted each day. By selecting weekly, the unique users will be determined on a weekly basis. Therefore the sum of the weekly unique does not correspond to the monthly unique |
9. Include as segmentation criteria where cohort = test group and control group
10. Extract unique, event totals and monetary value (note: to extract monetary value, select group by subtotal and then click on properties, and select sum of property value)
11. Calculate frequency, check and monetary value per user by using as denominator the fixed cohort
12. Calculate the pre-post NoC by comparing the behavior of the test group with the behavior of the control group. Eg:
Fixed cohorts
Test: 10k
Control: 1K
Unique per week
Week 1 | Week 2 | Week 3 | Week 4 | |
|---|---|---|---|---|
Test | 120 | 121 | 130 | 133 |
Control | 12 | 11 | 11 | 12 |
Events Total
Week 1 | Week 2 | Week 3 | Week 4 | |
|---|---|---|---|---|
Test | 140 | 141 | 160 | 173 |
Control | 12 | 12 | 11 | 14 |
Monetary Value
Week 1 | Week 2 | Week 3 | Week 4 | |
|---|---|---|---|---|
Test | 800 | 810 | 780 | 790 |
Control | 80 | 81 | 79 | 80 |
Frequency = events total by week by cohort / fixed cohort size
Check = total sales by week by cohort / total events by week by cohort
Monetary value = frequency x check
We want to monitor the evolution, for both check & frequency, of our test group to ensure that, through activations, we are sustaining growth month over month
Check test group frequency, and check, on a monthly - weekly basis
Example: https://analytics.amplitude.com/burgerkinguk/notebook/qk83d5g/breadcrumbs/chart/wxgl7wc/edit/2gs1j4j
We want to understand the impact of specific journeys or initiatives on overall conversion & user engagement. This can help us identify where we should allocate our resources and which are the initiatives that drive sales within the overall CRM strategy
Each campaign has a control group. Note: The control group for journeys is 10%, the control group for ad-hoc campaigns is 5% (as they have a broader audience reach). The control group does not receive any communication from that specific ad hoc campaigns or journey.
Therefore, we can compare the average conversion rate (online or in-store loyalty transaction) in the next 5 days for the 2 groups. The delta between test & control is the incremental sales
CRM Campaign or Journey Incrementality:
% incremental conversion = % conversion rate test group - % conversion rate control group
On top of that, we should also monitor the average check of test group vs control group to understand the $ value of the campaign.
Incremental value:
$ value = (% incremental conversion)*($ average check test group - $ average check control group)
This information is available here: add nena's dashboard once ready
It applies the same methodology listed above (so delta between test & control for both conversion rate and check) but instead of doing it on a campaign level, we will implement it on