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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

Table of Contents
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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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5. Import the file in Braze by clicking on create a segment and selecting the CSV file

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6. Export all the cohorts (opted out, opted in, control) in Amplitude by:

  • Clicking on User Data and then CSV Export Email Addresses

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  • In the export, only keep the User ID column

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  • Open Amplitude and click on Create New

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  • Click on User Cohorts

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  • Select import from cohort file

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  • Select User ID

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  • Create the cohort and when you are done click on Save

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  • Give your cohort a title and click on Save

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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

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7. Export all the cohorts (opted out, opted in, control) in Amplitude by:

2. Click on Create New

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3. Click on User Cohorts

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4. Create the cohort targeting the user properties or events you would like to filter for and when you are done click on Save

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5. Give your cohort a title and click on Save

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6. After you have saved click on Sync

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7. Choose your destination - in this case it will be Braze

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8. Choose the frequency you would like this Amplitude cohort to sync into Braze then click Sync. The frequency could be:

  • a one time sync

  • hourly sync

  • daily sync

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9. After this under Sync tab, you will see the destination set as Braze and the option to sync the Braze cohort again

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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.

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Info

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

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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)

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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

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

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

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