Mobile Engagement — In-App Messages as Main Driver for Increasing User Life Time Value — Explained
In-App Messages. Do they really help? Are they just pop ups in an app? No one believes such a simple and precise tool can really solve a huge and complex problem. Well, it does. But it’s only part of the solution.
I’ve been working on Abbi.io with Kobi Stok for almost two years now. It’s been a challenge building a massive mobile solution that actually does what we’re pitching it does — increase user life time value and general engagement — automatically.
Is it even possible to think that you can plug in a machine and just using some really nice in-app messages you’ll be able to make more money or solve that viral loop you’ve been aiming to close for ages.
Well, here goes.
In-app messages not only work, but they are the product managers best friend — hands down. I can guarantee an increase of any call to action for any app (granted a nice active user base) if they use IAMs wisely.
I’ve had the privilege of working with leading product managers from top US apps. Understanding their needs and day to day work are key metrics for me to figure out how to automate this process and produce a product that answers everyone’s needs. Once main thing that changed the way I thought before as a PM of smaller companies was the constraint of not being able to change the actual app flow or UI. If you’re a PM for Facebook, no way you’re playing around with the actual app structure.
Taking all the use cases and info from these product managers here’s the general process the tier 1 companies use for increasing engagement and user LTV:
- Identifying your goals — where are you hunting.
Before you can begin working, you’ll need to understand what you want to “fix”, what you want to improve. For example, let’s say I’m a product manager in charge of increasing sales for a certain category that attracts advanced users usually. So, my goal would be increasing conversions on the users who visit the category and complete a sale.
- Understanding the data — who & what are you hunting.
This is the most important part of the process. Knowing your data and user segments is crucial to a successful campaign. It’s a tricky process extracting the right data you need to complete your campaign. Did you setup the right events on your analytics software, is all the data accessible at one place, are you allowed to view everything?
Sifting and extracting the data is a lengthy process but must be accurate. If we’re at the example above, you’re looking for data around people who bought at that category and what correlations you can find that led them there. Sessions, screens, other features that led them to buy, etc.
Once you identify who are the users and what drives them to buy you can begin targeting them.
- Targeting the user — hunting the pray.
You got the where, who and what and now it’s time to hunt and increase sales. If you’re a smaller company, you can start playing around with the actual product layout, moving buttons around and fixing the user journey. But that may be a very complicated process to measure for a single use case and can hurt other use cases. The next plausible scenario is using in-app messages to direct the traffic that’s not converting to your goal.
It’s a complete waste of time developing in-app messages from scratch. There are some nice tools out there but nothing like we’ve created in Abbi.io.
Here’s the usual process of deploying IAMs:
- Ask a designer to create the design
- Ask a developer to place it somewhere in the application
- Ask a developer to setup events on your analytics so you can track clicks
- Submit to app store
- Redo the process over and over until you understand where and who in your user segment to target.
It’s a very complex process which usually yields results, but drains your entire time and team efforts.
- Automating the process — dinner at a click of a button.
We had an app. A music teaching app with over one million downloads, which was pretty awesome. Then we started going through this process and could never complete it successfully. The optimization process was a bitch.
So we built Abbi.io.
Abbi is simple to install SDK that requires one line of code. It grabs around 300 parameters from each session, each second. What screens are shown, what the user is doing, sessions, physical location (not GPS), all device sensors so we can know if the user is at home and lying on his bed or out running, and many many more parameters.
All these are used to build dynamic user profiles based on actual user behavior in the app (this automated the entire process of step 2 before). After identifying the right users for your goal, you can create an in-app message using Abbi’s drag & drop UI designer or choose from many prequalified and tested in-app message templates. Launching an IAM is as simple as a click and doesn’t require app store submission.
Then the machine (aka Abbi) will start doing all the A/B testing in the world to find the right user at the right time with the message you created. Basically, automating the process while predicting which users are likely and which are not to click and convert.
I know it sounds radical, but after a lot of heavy development we were able to automate the process and prove to our customers that machine learning and AI are not just cool buzz words, but actual tools for solving a problem.
Today, we’re seeing CTRs (Click Through Rates) for targeted audiences as high as 60–70% for in-app message campaigns launched through Abbi. I’ve yet to meet someone who achieved this manually using some manual IAM tool like Mixpanel (no offence, we love your analytics!) or others.
There’s another cool number we like to show — user promotion ratio. This is the percentage of users that were targeted for a campaign and have actually seen the in-app message. For example, if the number is 12% that means the machine identified that 88% of the user base is not likely to click and convert and avoided showing the “promotion” aka in-app message to them at all.
I can assure all app product managers, developers, marketers or whomever is in charge of pushing the conversions up and making more money that in-app messages are the best tool for a precise methodological approach of getting things done. Automating the process with what we’ve done at Abbi.io is making this tool accessible to many people who didn’t have the know how or time to deploy complex campaigns.
If you need more proof, I have endless of successful use cases which I’ll be happy to share upon request. Ping me firstname.lastname@example.org.