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Isolate CRM incremental impact on total sales from loyalty incrementality by relying on the control group methodology |
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The document describes how to calculate CRM Incrementality on topline sales, isolate this impact from Loyalty, understand specific Campaign & Journeys performances & drivers |
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Which analysis can we run?
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.
1. CRM Control Group Pre-Post:
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
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. This proves the overall value of CRM (how do we impact same guests’ sales if we send communication vs if we don’t)
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Methodology
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
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How to set up the groups
1, Open your Braze account and click on Segments
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Test: 10k
Control: 1K
Unique per week
Week 1 | Week 2 | Week 3 | Week 4 | |
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Test | 120 | 121 | 130 | 133 |
Control | 12 | 11 | 11 | 12 |
Events Total
Week 1 | Week 2 | Week 3 | Week 4 | |
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Test | 140 | 141 | 160 | 173 |
Control | 12 | 12 | 11 | 14 |
Monetary Value
Week 1 | Week 2 | Week 3 | Week 4 | |
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Test | 800 | 810 | 780 | 790 |
Control | 80 | 81 | 79 | 80 |
Frequency = events total by week by cohort / fixed cohort size
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Monetary value = frequency x check
2. CRM Behavior Trend
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
Methodology
Check test group frequency, and check, on a monthly - weekly basis
Example: https://analytics.amplitude.com/burgerkinguk/notebook/qk83d5g/breadcrumbs/chart/wxgl7wc/edit/2gs1j4j
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3. Specific Ad Hoc Campaign & Journeys Incrementality:
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
Methodology
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)*(average check)
This information is available here: add nena's dashboard once ready
4. Overall Ad Hoc Campaign & Journeys Incrementality:
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:
Weighted average for all ad hoc campaigns incrementality
Weighted average for all journeys incrementality
Example:
Journeys | Test Group | Control Group | Conversion Test | Conversion Control | Incremental Conversion |
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Welcome Flow | 10,000 | 100 | 300 (30%) | 20 (20%) | +10% |
Activity Flow | 5,000 | 50 | 100 (20%) | 5 (10%) | +10% |
Journeys | Test Group | Control Group | Check Test | Check Control | Incremental Check |
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Welcome Flow | 10,000 | 100 | 8 | 8.5 | +6.2% |
Activity Flow | 5,000 | 50 | 9 | 9 | +0% |
The impact is the weighted average (weighted for the people who received the campaign) of conversion, and check, for all the campaigns & journeys.
Deployment
The methodology has been deployed in:
EMEA:
BK UK
BK DE
BK NL
BK CH
BK AT
BK France (partially)
APAC:
BK Korea
BK NZ
LAC
BK Mexico