By Srujan Akula
I am pleased to announce that Apsalar has added revenue and engagement lift analysis to its fast-growing Mobile Marketing Cloud suite. This addition is a huge step forward for providing value to clients because it enables them to fully assess the incremental impact of custom-targeted campaigns on their mobile businesses.
As part of the lift measurement process, Apsalar also employs a multi-touch attribution methodology to understand how different audience-driven campaigns and ad networks contribute to these revenue and engagement gains.
A Little Background on Apsalar Mobile Marketing Cloud
As readers of this blog know, Apsalar recently launched a Mobile Marketing Cloud suite. This powerful, integrated mobile app marketing toolset offers everything app developers need to create and fulfill powerful app marketing strategies. Specifically, mobile app marketers benefit from an end-to-end offering that encompasses:
~ Mobile Attribution: Our industry-leading mobile attribution platform reveals how well their marketing and ad spending are growing their businesses.
~ App User Analytics: Analysis and customizable reports empower app marketers to understand their users and what will motivate them to make purchases.
~ User Audience Segmentation: Tools to create high-performing user audiences based on their behaviors. For example, creating an audience of best “buyers”.
~ Secure Audience Distribution: Ability to safely share the audiences with your media partners so they can deliver custom advertising campaigns to them.
Market response has been very strong. Also, Apsalar has radically expanded the number of data integrations we have with networks and publishers, so sharing audiences with more media partners is fast, easy and secure.
Our New Lift Reporting – Next Gen Cloud Marketing
Today, we announced that we’ve added revenue and engagement lift analysis to our Mobile Marketing Cloud. With these new tools, CMOs measure the incremental impact of custom marketing campaigns that respond to specific app user behaviors. This is a major advance that keeps our solution out in front of the industry.
With lift reports, CMOs and their teams can conduct audience analysis on multiple dimensions for both install and remarketing campaigns. By understanding how such campaigns impact business results, as compared to more mass-targeted efforts, app publishers can calculate the incremental ROI of marketing efforts specifically tailored to user groups.
The core idea behind audience-based marketing is to proactively guide users through the traditional marketing funnel from user acquisition to repeat purchases. See picture below:
Audience programs do this by delivering custom messages that get users to take their specific next step in their path to conversion. With lift analysis, app developer marketers can actually pinpoint the incremental sales impact of these campaigns. These reports quantify the impact of such proactive app promotion efforts as:
~ Using “block lists” to ensure that app user acquisition (UA) spend does not get wasted on existing app users.
~ Getting users of one of a brand’s apps to install another brand app via targeted user acquisition efforts.
~ Getting lapsed app users to return and engage
~ Encouraging first-time users to return to the app often and make their first purchases
~ Driving uninstallers to reinstall brand apps
~ Dynamically retargeting cart abandoners to encourage them to return to the app to finish their in-app purchase.
~ Stimulating past buyers to make incremental purchases
Metrics include reach, revenue, reactivations (relaunches), daily active users, average revenue per user, uninstall and reinstall rates, and more. These different views help marketers gain a superior understanding of their consumers, and assess the material impacts of their marketing strategy.
Understanding Lift Reports
We’ve designed our lift reports to be both high informative and easy to use and customize.
Step One: Creating Your Audiences
This post helps explain the ins and outs of using our Audience Builder segmentation tools.
Step Two: Setting your Baseline or Control
To measure lift, the user must choose a baseline group/campaign against which to compare the custom audiences/campaigns. Simply choose one of the campaigns you input into the platform. All inputted campaigns are available for use as your baseline or control cell.
In this screenshot, the marketer has chosen to compare the campaigns to the performance of the standard audience for a fictional app called Tipsy Samurai.
Step Three: Choose the Time Period for the Lift Report
As with all Apsalar reports, you can choose any period you wish, up to two years. In this case, the marketer has chosen Past 30 Days
Step Four: Choose the Metric to Examine for Lift
Here the marketer chooses the metrics to examine for lift. In this case, the user has selected BOTH DAUs (Daily Active Users) and Revenue.
Step Five: Examine and Compare Percentage Lift for Each Campaign
The marketer can then compare for every campaign that they wish, as compared to the control sample.
~ Reach: How many people were reached by each campaign during the selected period.
~ Reactivations: How many people touched by each campaign relaunched the app during the period.
~ DAUs has two columns. The first shows the percentage of app users that were daily users throughout the period. The second shows the incremental lift in the number of DAUs caused by that campaign
~ Revenue also has two columns, one showing the actual dollar sales made to people who were exposed to each campaign during the period. The second shows the percentage lift caused by this campaign over the control campaign.
~ ARPU shows to columns as well, one indicating the ARPU of the people who saw the campaign and the second showing the percentage lift versus people who saw the control campaign.
~ Uninstall shows the percentage of people exposed to a campaign that uninstalled during the period, and the percentage illustrate the percentage difference between uninstall rate for the campaign versus the baseline.
Market Forces Driving Custom Ad Campaigns
The mobile app ecosystem is experiencing rapid change as app marketers shift their focus from vanity metrics like high app install counts to measures like revenue and engagement. Years ago, marketers focused almost exclusively on measuring how many installs they drove at the app stores. Virtually all mobile ad spending in the app category was focused on driving new users. What happened after those installs wasn’t something that marketing people spent much time focusing on.
Since mobile devices now account for over 60% of total connected consumer time, it’s natural that marketers have shifted focus from app downloads to post-install monetization. After all, revenue and profit are what drive business, not app download counts.
Further, mobile ad spending is skyrocketing, particularly in support of mobile applications. And ad spend tells only part of the story. Many enterprise app businesses have added marketing automation platforms to their stacks so that they can deliver push notifications and emails that remind users to return to the app to make in-app purchases. Lift analysis can help there as well.
More and more marketers and app developers are recognizing the need for custom marketing efforts that help better monetize app users. Across the Apsalar client footprint, re-engagement now represents about 15% of all paid attributions (app installs make up the balance), up from just 1% two years ago.
That growth is expected to accelerate in the years ahead. According to a recent industry survey, 58% of enterprise app marketers report that they are already testing and/or fielding post-install campaigns to their app users. Another 28% expect to begin re-engagement efforts in 2017. Apsalar’s Cloud offering is purpose built to power such data-driven marketing efforts for mobile applications while simultaneously protecting brands’ first party data assets.
Learn More About Lift Analysis and the Apsalar Mobile Marketing Cloud
To learn more about Apsalar Mobile Marketing Cloud and Lift Analytics, get in touch today.
Thanks to all the employees that made lift analytics possible, and to the clients that help us road test the concept and who provided critical field input.