How to increase mobile app engagement

In the past, brick-and-mortar store owners had to remember names, choices, purchase preferences, and lifestyles of their most loyal customers, leveraging all this information to provide a compelling and memorable experience.

Today, technology enables businesses to collect customer data, such as demographic data, anniversary dates, and purchase history, to deliver personalized customer experiences. Mobile app brands use unified data to address customers by their first name and recommend products based on their gender, location, and past purchase behavior. 

We’ve entered into an advanced mobile marketing era where customers are more aware of how brands communicate and have the power to choose which apps communicate with them. Customers today expect personalized and targeted marketing from their favorite brands. Sending the same push notifications or generalized discount offers to everyone is no longer an effective strategy. 

That’s why mobile app marketers consistently try to restructure their tech stack to deliver best-in-class and individualized experiences across channels. When evaluating different marketing tools and technologies, discerning marketers focus on data and analytics capabilities, personalization technologies, and customer data profile management capabilities.

Evolution of data-driven marketing

Marketing has come a long way. With its evolution, marketers have become increasingly creative, data-driven, and more answerable for results than ever before.

Globally, marketers track their lead generation activities and understand how much pipeline they need to generate for sales to hit their projected numbers. But from there, things can get fuzzy, with various teams across sales and marketing using different tools and methods to generate the ideal formula for accurate numbers.

When marketing technologies were first introduced, tools focused narrowly on a single marketing activity—such as individual social media channels or on-page website analytics—and produced data in numerous standalone dashboards. However, these days, data-driven marketers are more likely to choose marketing technologies that can easily integrate with other platforms for a seamless and consolidated view of the customer journey. The concept of the floating sales funnel indicates that every customer goes through the same buying process. But today’s data-driven marketer knows that in the age of omnichannel marketing, the only constant is your customer’s expectation of consistency on every channel. For business, this means bridging the gap in understanding between marketing channels to gain a complete picture of an individual’s buying cycle. 

What is personalization?

Personalization is a marketing technique that uses customer data to deliver messages to a targeted audience segment at the same time. Personalized marketing typically uses general customer data such as user logins, visit history, most visited category, and past purchases to split users into target groups. Instead of sending random broadcast messages to the entire user base, personalization sends highly contextual messages at the right time.

Customers have long-expected brick-and-mortar businesses to cater to their needs. Today, most companies can analyze online and offline data, so basic personalization is no longer a differentiator—it’s what customers expect. 

In the not too distant past, customers expected nothing more than seeing their first name in an email. Today, every business has some level of personalization in place. The bare minimum will not make your brand stand out or forge ahead of the competition. To survive and build lasting relationships, you’ll need to tap into advanced personalization methods that bring you closer to your customers’ wants and needs. 

Individualization: Redefining personalization to deliver 1:1 experiences 

Individualization is a technique that automatically personalizes and optimizes the customer experience for each person. It requires greater data unification and analysis, multichannel deployment, and artificial intelligence to predict customer behavior and adjust to their real-time needs.

Let’s say a user visits your eCommerce app to buy a smartphone. Using individualization techniques, you can send the shopper a push notification with an offer, including their latest browsed products, related recommendations, top-selling phones, and other incentives to increase engagement and achieve higher conversions. 

Taking this a step further, if a user adds an out-of-stock phone to their wishlist, using geofencing, you can send them a push notification when they’re near a physical store carrying the product.

What’s the difference between individualization and personalization?

Every time a customer visits your app and searches for a product, they share data about their current interests and needs. This data can tell you what they are looking at and provide insights into why they are looking for a particular product. Armed with this information, you’ll create a valuable individualized customer experience that captivates users at every point of the journey.

However, Personalization and Individualization are often used interchangeably. Personalization was initially defined as the process of delivering individualized experiences to each person. But at the time the term was coined, few technologies could live up to the intended definition.

Marketers embraced and promoted a term that only partially captured the potential of customized experiences with segmentation and product recommendation algorithms.

Today, the marketing industry is in a golden age of innovation. New technologies make it possible to deliver experiences that closely resemble personalization’s original intent. With new tech came new terms such as 1-to-1 Personalization, Personalization 2.0, Individualized Personalization, or Individualization.

Individualization is an advanced form of personalization strategy

Individualization does not replace segmentation-based marketing or personalization. It adds significant value to your overall personalization strategy. Brands that successfully deliver a seamless customer experience and personalization often use three different personalization strategies to achieve long-lasting customer experiences. 

1. Optimization – Continuously test your marketing messages to your app user base and optimize them to get the best result every time.

2. Segmentation – Improve your messaging relevance by taking into account key distinctions in user segments.

3. Individualization – Connect and engage with customers individually to deliver a seamless user experience and increase the use of your app.

When used in the right combination and in the right context, these three strategies can create opportunities and help you achieve your brand’s overall personalization goals.

In comparison to optimization and segmentation strategies, individualization goes one step further by offering a solution to two limitations:

• Inability to deliver immediate results 
• Inability to scale 

Individualization represents a fundamentally different approach. This unprecedented level of relevance, tailored to each user, translates into higher revenue and increased customer loyalty. It’s not surprising that individualization has become what app-first businesses aspire to adopt.

How does individualization work in mobile marketing?

Individualization is built on various layers of personalization that your customers receive and respond to. It considers all types of data that you know about the customer, such as user profile attributes, location, and past actions. Individualization changes significant parts of the messaging such as content, visuals, and timing to deliver an engaging and individualized app user experience. 

Individualization heavily relies on artificial intelligence (AI) powered technology to deliver a personalized experience to each customer based on all available data. The customer data is continuously updated according to their current preferences, browsing patterns, and action-driven behavioral data. As the data is continually aggregated, AI makes it possible to improve each individual’s personalized experience consistently and at scale. 

How to create Individualized Mobile App User Experiences?

A Personalization Engine makes it easy to test and optimize, segment, and target, and even create true 1-to-1 experiences for customers. With all this in one personalization platform, mobile marketers have the flexibility to leverage proven strategies and practices to improve the customer experience, not only on the mobile app, but across all digital touchpoints.

To enable brands to create meaningful individualized experiences, a personalization platform should deliver on the following key promises:

1. Customer Data Platform (CDP)

A CDP is a new necessity and the lifeblood of every app-first brand today. However, one of the major obstacles to advanced personalization is inaccurate or incomplete customer data used as the foundation for delivering an individual customer experience. Unified customer data is a critical resource for individualization. Creating this unified data is a major task, and most brands have a tough time doing it. Having holistic and complete customer data—which consists of all the interactions, transactions, and other data beyond marketing—holds significant value in individualizing a customer’s journey with your app. 

Using a Customer Data Platform (CDP) as part of your app marketing and engagement efforts enables you to:

• Develop customer intelligence at an individual level.
• Deliver insights from your customers’ experiences across multiple channels.
• Turn raw data inputs into standardized data which is accessible to your entire marketing tech stack.
• Create a closely-targeted, individualized multichannel experience in real-time that goes beyond plain marketing.

2. Predictive Audiences

Real-time Predictive Audience Segmentation helps marketers optimize their personalized campaigns for the right target audience and engage those customers through the right channels and devices. It has the power to help businesses identify the target audience with the highest potential for conversion to a sale, click, or install, whatever your KPIs.

When armed with AI-powered Predictive Audiences, you can:

• Leverage advanced algorithms to predict each customer’s likelihood to perform any action: Likelihood to Purchase or Likelihood to Uninstall
• Leverage RFM (Recency, Frequency, Monetary) segments to target customers
  • who come back frequently but spend very little (high frequency, low monetary),
  • who ordered once but whose expenditure was above average (high monetary, low frequency)
  • who used to have high frequency and monetary value but have stopped ordering from you (low recency)
• Send personalized push notifications to engage each customer with a higher intent to convert.
• Automatically adapt the journey for each individual customer along a predefined funnel.
• Deliver the best next product, content, or offer – every time to each individual user based on their current in-app behavior and historical data.
• Send individualized notifications to the audience most likely to engage.

3. Real-time Messaging

Mobile devices are inherently personal, and it is extremely rare for customers to not have their smartphone within arm’s reach. Mobile messaging makes it easy for brands to engage with potential and existing customers in a one-on-one way. But broadcasting messages at random won’t serve the purpose. You have to send customers the right content at the right moment. Individualization is all about engaging customers at the right time with the most preferred content. 

Imagine you are an eCommerce brand, and a customer has been browsing blue jeans on your mobile app. They find a pair they like and add it to their cart, but leave your app before they complete their purchase. This creates an opportunity to send them an abandoned cart reminder message with a picture of the jeans they selected and a direct link to complete their purchase. Real-time interaction with customers helps you build stronger bonds with them and cultivates stronger brand loyalty.

4. Mobile App Analytics

Once a user has downloaded the app, businesses need to activate them and deliver a seamless onboarding experience. It doesn’t just stop there; you need to regularly engage your app users to build habits, personalize their journey, and ensure you retain them for a longer time. To personalize user journeys and engagement, you need to understand what your users do after installing your app.

This is where a Mobile App Analytics platform helps you to deliver individualized experiences to a greater level. It also helps in creating individually targeted user experiences at scale. Using these analytics, you can create different segments based on user actions and behaviors. Targeted and individualized push messages delivered to these users’ segments can significantly uplift open rates. 

Using analytics is a great way to get to know your new and existing users, including those who have fallen out of the marketing funnel, and how to win back your churned users. By analyzing the data retrieved from in-app user behavior and profile data, you can inform your marketing strategy to win users back with individualized messages and in-app experiences.

5. AI-Powered Recommendations

The longer you get your users to stay on your app, the higher the chances to convert them. One way of doing this is by delivering smart recommendations that help users search for a product that fits their needs. To deliver an individualized app experience, make sure to use contextually relevant recommendations based on users’ current in-app behaviors.

According to a study by Salesforce, customers who clicked on product recommendations are 4.5 times more likely to add items to their shopping cart and 4.5 times more likely to complete their purchase. This ability to orchestrate customer lifecycles with the right marketing technology stack to provide the most relevant product recommendations to an individual in real-time is invaluable.

For example, suppose a customer is looking for a given type of television set. In that case, you can send recommendations of products based on the size of the display, the technology browsed, and the best sellers in the category. To achieve this level of personalization, you would need to leverage an AI-driven recommendation platform that can help you deliver an individualized app experience. 

Rethinking Mobile App Engagement With Individualization

There has never been a bigger opportunity to build brand preference and increase engagement by delivering individualized experiences across channels. But marketers should recognize that personalization isn’t just a marketing tactic. It’s a strategy that focuses on the entire business-to-customer relationship and manifests not only on the mobile app but across touchpoints at every stage of the customer journey. However, the strategy becomes challenging when mobile marketers mistake true 1:1 personalization with traditional, segmentation-based personalization that fails to deliver the individually relevant experiences their audiences expect. 

That’s why brands at the forefront of innovation are taking personalization to a new level by delivering hyper-personalized or individualized experiences. Experiences can only be as powerful as the data and tech underlying them. Therefore, brands raising the bar in customer experience delivery are leveraging artificial intelligence and big data to deliver real-time experiences that truly engage and convert.