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Using the Right Technology and Data to Keep Mobile App Users Engaged

November 22, 2017 | Marketing Technology | Mobile


Lou Orfanos
Head of Sales and Marketing
Localytics

When marketers first started creating their own branded apps, user acquisition was their top priority. But they eventually realized that a huge user base doesn’t neccesarily translate to high app engagement, and many users abandon an app after a few tries. eMarketer’s Tricia Carr spoke with Lou Orfanos, head of sales and marketing at mobile engagement platform Localytics, about tactics and technologies that are helping marketers build up their apps’ active and engaged user bases.

eMarketer: What’s the state of the mobile app market right now? What does it take to keep app users engaged?

Lou Orfanos: Marketers are starting to dig in on user engagement. Typically, user engagement was driven by the product team, which thought, “We need to have an app that’s elegant and has a delightful experience without any bugs.” Now marketers who are focused on growth are getting involved with their company’s app and looking at how the experience can be augmented with different pieces of messaging, offers and personalization that are relevant to each user. This is what will make apps stickier.

eMarketer: What tools and technologies are absolutely required to do this?

Orfanos: There’s a certain technology stack needed for mobile apps. You need the competency to track user acquisition. You want the ability to do quality assurance and user feedback testing, which requires a user testing mechanism. You want a general set of analytics for both behavior analytics and user profile analytics to be captured, as well as something that captures and leverages location data.

You also want a tool to do the actual engagement, including push messaging, in-app interstitial messaging and an inbox you can augment your app with, which is really valuable. You want a remarketing tool to export your mobile audiences and find other audiences elsewhere. You might also want an A/B testing framework to test experiences within the app.

eMarketer: What’s the argument for using a mobile app engagement platform to help marketers meet their goals?

Orfanos: If you’re a mobile-first company, it’s a no-brainer to be doing everything you can to optimize your app. But for more traditional companies where mobile is not as big a part of the business, they don’t have the luxury of spending a lot of money on acquiring a constant flow of users. Instead, they probably have a core app audience, and they need to extract the most possible value out of it. The only way to do that is to deeply understand your users and be able to interact with them in a smart way that drives toward the goals you care about.

eMarketer: What are the most valuable types of data that marketers look for to create an engaging user experience? And how can that data be acquired and managed?

Orfanos: There are two sides to this. An underrated piece of data that a lot of marketers don’t always have great access to is what’s working and what’s not once they start engaging users. Of course you need the ability to get rich data together and trigger things based off of it to create an engagement with a user, but you also need to be able to rapidly identify whether or not it’s moving the needle toward the goal you set.

Marketers should also use something beyond the initial vanity metrics around opens or clicks. You hope that what you’re doing moves the needle along your onboarding goal or retention goal. Then you want to be able to use that data to calibrate for the next time, and make sure you refine your audience and your message so that you’re optimizing toward that goal more effectively.

eMarketer: As marketers work toward goals like these and make improvements, what are the biggest challenges on the horizon for them?

Orfanos: One challenge is trying to make artificial intelligence [AI] and machine learning real when it comes to mobile. And there’s another challenge that goes along with that—the danger of going too far with personalization. The industry hasn’t set boundaries yet that define the right level of personalization. We want to get machine learning working in a smart way, and brands can test and learn their way in.

Another challenge is around getting omnichannel right. A lot of marketers are rushing to make sure they’re touching their users on every possible channel, but they’re ignoring the efficacy of each individual channel. This is a challenge where machine learning and AI can teach us what works best, and the quicker that iterates, the better we’ll get at omnichannel.

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