Improved effectiveness of loyalty campaigns for one of the largest airline in the US
Case Studies:Mu Sigma
Published On: 11 August 2015
What We Did: Developed an improved email targeting mechanism using test and learn engine for better engagement.
The Impact We Made: The developed email targeting mechanism helped the client run targeted campaigns with increase in both response and conversion rates by approximately 20%. Additionally, the client also witnessed increased redemption on secondary promotions, such as hotels and cars, offered to customers.
Summary - Analytical framework for improved targeting
Mu Sigma developed an analytical framework for email targeting using test and learn approach. This helped the client roll out an org-wide campaign design module for use in different regions, which led to improved targeting and ROI.
About The Client - Leading airline in the U.S.
The client is one of the largest airlines in the United States with more than 700 fleet size and 250 destinations.
The Challenge - Low marketing campaign effectiveness
Client had an e-mail targeting algorithm which bombarded the customers with an endless stream of e-mails. As a result, relevant e-mails were being ignored. Overall effectiveness of the marketing campaigns was reportedly very low.
The Loyalty Team, catering to Customer Relationship Management, wanted to optimize their e-mail targeting framework through an extensive focus on customer behavior and preferences. Existing campaign management framework was not suitable for designing relevant offers for different types of customers. On the whole, the client was looking for a framework that could improve the effectiveness of their Design-and-Execution trigger as well as Business as usual (BAU) campaigns.
The Approach – Iterative test and learn
Mu Sigma took a 3-pronged approach to build an e-mail targeting framework for the client.
Phase 1: Laying the foundation
Mu Sigma evaluated multiple KPI’s around customer preferences, offer types, etc. to identify for an effective e-mail campaign, and hypothesized around them.
Historical campaigns were analyzed to understand the issues across the existing campaign management cycle. The areas analyzed included offer design, audience building approach, and measurement modules.
Phase 2: Test and Learn
Post the foundational exercise, multiple scenarios were developed to map offers and audience characteristics
Multiple statistical techniques were leveraged
Depending on the KPI type, intensity of the campaign, and significance levels were varied to achieve better precision
Phase 3: Execute and Scale
Mu Sigma collaborated with the client’s Loyalty Management Team to roll out nationwide campaigns based on the new design. Under first phase of rollout, the design was leveraged across all possible offer types.
The analytical framework leveraged continuous inputs from the campaigns which were then fed into the test and learn engine to account for newer patterns.
The Outcome - 20% increase in audience response and conversion
There was approximately 20% lift in the client’s targeted campaign in terms of response as well as conversion
Value added content as well as social shares were added to e-mails for improved engagement with the audience
To account for evolving audience preferences and enable better campaign design and measurement, a superior, more statistically robust engine was deployed