How Mobile Growth Leaders Are Using Real-Time Data and AI to Deliver Personalized Human Experiences

It’s easy to get swept away in the hype over real-time computing and artificial intelligence (AI). But one thing is clear: today’s most successful mobile apps have cracked the code for bringing personalized customer experiences using actionable, data-driven engagement tactics.

In a talk at Brand Growth Summit 2021, CleverTap CMO Dave Dabbah discussed how high-growth mobile apps can harness the power of real-time data and AI to deliver data-driven engagements during the moments that matter most to their customers.

Data-driven engagement is the single biggest game-changer for Mar-tech

We all react to personalization in different ways. Much of the differences in reactions can be attributed to generational divides. As Dabbah noted in his presentation, the Baby Boomer generation often finds targeted marketing “creepy” and doesn’t have much interest in utilizing online engagement tools. Meanwhile, his own generation has become comfortable with the way that companies are personalizing advertising experiences to individuals. To an even greater extent, studies have shown that Gen Z would prefer personalized marketing above anything.

Says Dabbah, “There has been a significant mind shift in how people are viewing engagement and how the importance of real-time data can change the game if you understand your users at that level.”

Data-driven engagement is the single biggest game-changer in marketing technology and something that has (and will continue to) fundamentally change how brands provide value to their users. Here are just a few examples of how machine learning and data-driven engagement can reshape the user experience for customers and brands alike.

• Delivering personalized recommendations based on browsing history and previous purchase behavior improves conversions and delivers a more relevant experience for the customer.
• Providing proactive support by identifying leading indicators that suggest a user might need assistance to help customers through onboarding and customer support processes.
• Re-engaging users that show signs of fading away so that a brand can deliver the right message at the right time, improving retention and advocacy.
• Driving personalized customer experiences requires access to actionable data

Before the internet, the three classic B2B marketing touchpoints were direct mail, fax broadcasting, and telemarketing. Despite the digitization of nearly all B2B marketing touchpoints, the goal remains the same: reach the right person, with the right content, at the right time, on the right medium. The biggest difference between these two eras, however, is the access to data that digital marketing allows for.

Driving personalized customer experiences requires access to actionable data. It’s this data that allows for the restoration of intimacy and humanity in the seller-buyer relationship that is far more personalized than cold calling, for example. That’s the goal of data-driven engagement — just at a massive scale.

Now, if you’re already in the mindset of a modern marketer, this all might seem like a no-brainer. So why isn’t every mobile app blazing a trail with data-driven engagement?

It turns out that it requires a very specific set of capabilities. And it’s not enough to have just one of them — a brand has to have all of them, since they form a virtuous cycle.

1. Sense: The ability to ingest user behavioral data in real-time to see how users are engaging in any given moment. In our modern real-time world, it’s no longer enough to receive batch data updates.
2. Recognize: The ability to use machine learning and AI to identify pattern recognition on an individual level and go beyond basic user segmentation.
3. Respond: The ability to orchestrate intelligent cross-channel campaigns to reach users at the “moment of truth.”
4. Improve: The ability to continuously self-optimize through testing and learning.

The most common pitfalls for brands on the road to personalization

It’s easy to get excited about the potential of data-driven engagement when huge improvement metrics are associated with an introduction of AI and machine learning strategies into a brand’s marketing tech stack. One CleverTap customer saw a 43% improvement in retention rate across its install base of more than 30 million users.

But it’s also worth sharing some of the pitfalls that commonly occur for organizations as they move towards this new paradigm. Here are five common challenges that brands often face when adopting AI strategies — and how to avoid them:

1. There is no one-size-fits-all solution for data-driven engagement. In the incredibly diverse world of mobile apps, your “true north” is unique to your specific business objectives. Before thinking about fancy AI or machine learning models, make sure you clarify what your business goals are.
2. You need a full picture of your individual users — not just across one channel or over the last 30 days, but an individual’s lifetime actions across all platforms. One of the first and best use cases of AI in your marketing stack is deploying it to unify customer data across all touchpoints.
3. Using historical analytics to drive personalization means you’re operating off of stale and potentially irrelevant data. User behavior is changing at an unprecedented rate due to COVID-related disruptions, tech developments, emerging channels, and an exploding app economy — to name a few. Use AI and machine learning to harness predictive analytics instead of historical data to inform a dynamic understanding of an ever-evolving user journey.
4. The benefit of AI and machine learning is the ability to dynamically learn from what’s working and what’s not. If your personalization platform isn’t getting smarter based on what’s resonating with your users, it’s fundamentally letting you down. Ensure that there’s a rock-solid pipeline that’s feeding experiment results back into the prediction engine.
5. Trust the data. Data-driven decisions can only be made if there is complete buy-in on what it’s telling you. It’s vital for organizations to have a customer data infrastructure that treats governance and security as first-class citizens and that’s built for scalability and reliability.

The bottom line

Thinking big means making digital relationships more human. The point of real-time data and AI is to restore the human element to relationships, but at a massive scale. Any mobile app that’s serious about data-driven engagement must invest in real-time data ingestion, machine learning, campaign orchestration, and continuous optimization capabilities. While it’s easy to get swept up in the AI hype, the most important thing you can do before diving in is define what your business-specific objectives are and what success will look like for your app.

Watch the full presentation on demand , and check out the official CleverTap blog for more mobile marketing news and best practices.