6 Ways to Up Your App Commerce Game Now
Today’s world is truly going mobile-first, with more than half of all web traffic (50.81 percent) now coming from mobile devices. And when it comes to activities we like to do on our devices, shopping is making its way up the list.
Typically, mobile devices, such as smartphones, have been used for research rather than purchase. However, now more than half of all internet users buy products on their mobile phones, with two-thirds doing so through shopping apps – a trend that continues to grow.
According to recent figures, global mobile commerce app revenue is set to reach US$3.56 billion by 2021. COVID-19, which caused the shutdown of physical stores, has played a big role in the acceleration of online shopping, but other longer-standing factors have also contributed.
Largely, it is connected to how people are using the internet – they are increasingly turning to smartphones for convenience. Consumers, in particular millennials, are becoming more comfortable using mobile devices and apps to complete transactions.
Why Consumers Love Shopping Apps
Shopping apps offer multiple benefits for consumers. The biggest is that they are faster and easier to navigate than websites. In fact, research has suggested that people view 4.2 times more products per session in apps compared to mobile sites. Mobile apps also have 3 times higher conversion rates than mobile sites – great news for brands.
App commerce allows for more personalized content – a big tick for the customer experience. Apps also deliver instant on and offline access, making browsing and shopping possible almost everywhere consumers go.
For brands, another bonus is that shopping apps have the highest retention rates among all app categories. A recent report shows that it takes a full week for retention rates to drop to 14 percent.
The benefits of mobile apps and the increasing sea change in attitudes towards shopping apps have lowered acquisition costs, increased engagement, and made conversion rates more promising. Because of this, brands are investing more in apps. According to McKinsey, about 45 to 50 percent of retailers planned to prioritize a mobile app or point-of-sale experience in 2020.
This investment in apps is essential as consumers become increasingly demanding, but what can brands do to cash in on the app commerce trend and stay ahead?
How to Maximize Your App Commerce Efforts
Acquisition is crucial to maximizing your app commerce efforts. Without downloads, conversions aren’t possible. Equally as important is retention. By keeping your app users happy and engaged, you can drive more conversions and sales – something data and technology can help you do more effectively.
1. Identify and target high-value app users
According to Statista, the average install-to-purchase rate for shopping apps was 14.7 percent as of April 2020, while for brand commerce apps, the rate was 13.6 percent. To lift these figures, brands are increasingly using data and deep learning tools to identify the traits and behavior of high-value app users.
Looking at how consumers behave outside of your app based on third-party data, deep learning can help you segment and rank them according to the likelihood to convert. It can also help you identify topics and products people have shown an interest in, pinpointing high-value keywords, which you can use to target potential app users via search and social ads.
2. Deliver hyper-personalized content
While shopping apps have the highest retention rate compared to apps in other categories, churn remains an issue, with many shoppers disappearing after download. One effective way to increase app stickiness is by delivering personalized, meaningful content to increase engagement.
By looking at how customers interact with your app using machine learning, you can better understand their behavior and preferences, and then tailor your content accordingly, such as product recommendations, tips, videos and app-specific rewards.
3. Seize the moment, and the channel
Not only is it important to target the right users and to engage them with personalized content, when and where you engage them is also critical.
What channels are your users most active on, and respond best to? What is the best time to reach out? You can leverage artificial intelligence-powered marketing automation tools to better understand user behavior and intent, in order to send out automated, timely marketing messages through their preferred channels, such as in-app messages, push notifications, emails or SMS.
4. Entice hesitant users with intelligent promotions
Shoppers love promotions. By hitting the right customers with the right app-only promotion, you can push people to purchase.
Using advanced machine learning can help you analyze users’ in-app behavior, such as how they tap and swipe, to accurately assess their purchase intent, and identify hesitant, coupon-responsive customers for effective targeting. You can then use predictive A/B testing to define the most appropriate promotion to maximize your efforts.
5. Use deep links to streamline customer experiences
Running campaigns to drive users back to your app is a great way to increase engagement and conversions. However, if the journey from the ad to the app isn’t smooth and simple, you can lose customers with a high potential to convert.
Mobile app deep linking is a great way to avoid this. By directing a user who clicks your ad or email campaign to your app and opening up the exact page that the user was viewing, deep link technology can make the user experience more seamless and boost app conversions.
6. Improve the UX of your e-commerce app
With consumers looking for ease and simplicity in m-commerce, it is important to make sure the user experience (UX) of your shopping app is spot on.
How do customers use your app? What do they like? What features would make the experience smoother? Don’t rely on guesswork. Ask customers for feedback on your app with short questionnaires so you can tap into their mindset, build empathy, and create an easy-to-use, intuitive app they want to continue using.
With app commerce on the rise, staying ahead of the game is crucial to growing sustainable market share. Using data, technology, and smart tactics, you can bring consumers to your app, boost app engagement, and make your shopping app more profitable.
* Looking to boost your app engagement and m-commerce efforts? Download our white paper ‘From Download to Favorite: How to Engage Users Across the App Life Cycle’ to learn how to make your app a favorite! Have a question? Contact our team today to schedule an exclusive consultation.
WE ARE HERE TO HELP
YOU MIGHT ALSO LIKE
In the world of online retail, knowing who your customers are is only half the battle. Equally as important as knowing their demographic information is understanding how they behave as customers, so you can tailor your marketing offering to their specific needs. What Is Behavioral Segmentation? Behavioral segmentation is not the same as market segmentation. While market segmentation encompasses behavioral segmentation, it also includes your customers’ demographic, psychographic and geographic information. Behavioral segmentation, however, focuses specifically on patterns of behavior customers exhibit as they interact with brands and make purchasing decisions. By leveraging artificial intelligence (AI) to study past behaviors, you can predict customers’ future actions with a high degree of accuracy. Once you have identified your relevant behavioral methods, you can segment your customers accordingly. Then it is possible to tailor your e-tail marketing to address each segment’s needs. Why Is Behavioral Segmentation Important? Behavioral segmentation gives you a more intimate knowledge of your customers. Dividing your overall customer base into more effective segments allows you to market to them with a greater degree of personalization, meeting their needs more accurately and creating more compelling content. This means you are allocating your resources more efficiently, ensuring a
Marketers have more data than ever at their fingertips, but how to make sure you are using the data to its full potential? Big data analytics can unlock the power of data, and offer insights on customer habits, interests and trends, helping you predict their future actions and plan your marketing campaigns accordingly. What Is Big Data Analytics? Big data refers to not only the huge amounts of data, but also the varied and numerous different types of data sets. This data includes customer behaviors like spending habits and interests, consumer trends, as well as hidden patterns and correlations that can help illuminate current market fluctuations. Marketers can make sense of this data through the methodology of big data analytics, which is a form of advanced analytics, requiring high-performance analysis systems. For many companies, the investment can really pay off. What Are the Benefits of Big Data Analytics? Knowledge is power, as the old adage goes. Big data analytics can unlock an enormous amount of knowledge about what your customers want, do, fear and dislike on your channels, but also on external websites. It can also provide unique insights on seemingly unrelated aspects of your customers’ lives –
You may have heard the term ‘predictive analytics’ being often used when talking about digital marketing or data science. So, what does it mean, how will your business benefit from it, and how does it work exactly? Read on for everything you need to know. What Is Predictive Analytics? Put simply, it is a way of analyzing historical data in order to predict future events. Typically, companies will utilize historical data to build a mathematical model. Given data and the model, the computer can make a prediction of the future. These trends can refer to the immediate future, just seconds, minutes, hours or days ahead, or can look much further into the future. This model can then predict what will happen next, or suggest steps a business can take in order to best meet its goals. Predictive analytics is a subset of data analytics, and within data analytics, there is also descriptive analytics, which is to leverage historical data to better understand what has happened in a business, meaning paint the picture of the past. Predictive analytics uses a wide range of technologies like big data, data mining, statistical modeling and machine learning to crunch data in order to