Introducing Uplift: Measure the Revenue Impact and Optimize for it
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MoEngage currently delivers more than 4 billion personalized interactions per month for our customers through engagement channels like push, in-app, email, and web push.
We have always focused on end-to-end attribution for app marketing campaigns and were the first product to provide transparency on the entire funnel (Sent -> Received -> Clicks -> Conversions -> Converted Users). From our interactions with customers, we realized that marketers still have the dilemma of whether their marketing campaigns are working and what’s exactly working & what’s not.
They would specifically want to know, “Is the communication that I am sending to my customers making a business impact?”
While creating campaigns, various other questions come up to them like:
- What kind of messaging works better for the target segment, and where the user should land?
- While MoEngage supports advanced options like images, carousels, action buttons, etc., would using them make a better impact?
- Would personalized messaging make a better impact, and what would the impact be?
Keeping this in mind, we are proud to unveil Uplift today to help marketers measure the business impact of each interaction they deliver to their users, and also optimize for it.
Uplift enables marketers and product managers to:
- Analyze campaign performance (from impressions to conversions) with multiple attribution models like View-through attribution (x hours from impressions), Click attribution (x hours from clicks), and In-session attribution
- Create and validate experiments using Multi-variate testing
- Measure true uplift on business metric against a Control Group
Uplift in action
Here’s an example of how Uplift helped one of the largest travel companies in India experiment with multiple variants of customer interactions and understand the business impact of each:
Following were the results of the experimentation:
As seen above, a conversion rate of 10% is achieved in the ‘Control Group’ where no communication is sent to the users. However, in the case of ‘Variation A’ and ‘Variation B’, where a push notification is delivered to the users, the results vary drastically. While the message in Variation A garners 13% conversion (Uplift of 30% over Control Group) Variation B garners a conversion of 16% (Uplift of 60% over Control Group) compared to Variation A. Clearly, with a marketing objective of generating higher conversions, Variation B is the winner.
Conclusion
Experimentation with your marketing campaigns is the next big step in building effective engagement with your audience and Uplift allows marketers to achieve exactly that. We go beyond A/B Variations and allow you to experiment with as many as 6 variations per campaign. Hope you have as much fun using Uplift, as much as we did in creating it.
To get started with Uplift, click on the button below
To learn more about how Uplift can help drive engagement for your brand, click on the button below and speak to our engagement experts today.
