Everything you need to know about machine learning and digital marketing in 2021
From programmatic bidding to chatbots, the mobile marketing ecosystem has already been enhanced in several ways by access to machine learning (ML). Users are increasingly familiar with machine learning — even if they don’t know it — and seamlessly use it every day. For example, 97% of mobile users are using AI-powered voice assistants. The global artificial intelligence (AI) market is projected to reach $191.60 billion by 2025, ensuring that AI will become a more significant part of our lives and work. This guide shares how machine learning is changing the way marketers work, reach target audiences and provide the best service to their customers.
What is machine learning?
Machine learning uses AI to create a system that can automatically learn and improve its tasks. ML requires AI to access data and make analytical decisions to push toward a specific goal and become more competent at completing the required tasks. For example, businesses can use machine learning to automate tasks that would be otherwise impossible or take too much time to complete manually.
Why is machine learning important for mobile marketing?
Mobile marketers can use machine learning in several ways to improve the accuracy of their targeting, identify and reach the most valuable users, and provide a superior service to new and existing customers. For example, this technology enables you to provide 24/7 support to users, which can help them move further down the funnel and ultimately generate revenue. It is more important than ever to use machine learning tools to your advantage. It is no secret that machines can complete tasks that would be arduous if not impossible for humans, and ML is commonly being used to automate these tasks. Without these tools, it is significantly harder to stay competitive in your target market and scale your business.
When using machine learning to optimize your marketing strategy, it is essential to identify what can be automated and what still requires a human touch. In his article for Forbes, Founder and CEO of FiveChannels, Jason Hall, reminds readers that “an AI program might be able to generate a report using nothing but data [but] to truly connect with your customers, you'll still need the human touch. Empathy, compassion, and storytelling are all attributes that machines can't emulate, at least not yet.”
Machine learning and digital marketing: nine ways AI technology is changing the way marketers operate
1. Improve the user experience with personalization
Personalization is a powerful tool that can help you drive toward your most ambitious targets. Nearly three-quarters (71%) of consumers) get frustrated by an impersonal shopping experience and 91% of consumers are more likely to shop with brands that provide relevant recommendations and personalized offers. Machine learning is helping marketers create a unique customer experience that can drive sales, build brand loyalty, and increase lifetime value (LTV). For example, e-commerce apps can use machine learning to make purchase suggestions based on previous purchases and items in customers’ carts. Entertainment apps like Netflix and Amazon Prime Video can make recommendations based on a user’s viewing history.
Machine learning can analyze problems and identify ways to optimize. You can use personalization across several different types of marketing. For example, you can use this technology to curate content in-app and on your website, personalize emails to drive engagement, or serve a user the ads most relevant to their interests.
2. Superior customer support with chatbots
There are many ways machine learning can improve the user experience. For example, you can offer 24/7 support and eliminate customer wait times with chatbots. Chatbots are software that use defined rules and/or machine learning to identify what a human wants and deliver helpful information. This can be applied to channels such as SMS, website chat windows or social media business pages.
How you use your chatbot will depend on the nature of your app. In addition to the benefits above, you can employ chatbots to:
- Automate tasks that would otherwise be completed by your team, saving you time and money
- Increase conversion rates across several marketing channels
- Generate qualified leads and set meetings
- Reduce churn by managing high volumes of customer support requests
- Send follow-up messages to customers and assist with similar outbound marketing
- Announce new products and share discounts to drive engagement
It is important to note that not all chatbots use machine learning. There are live chatbots that send automated messages, rule-based chatbots that are limited to responses in their programming, and chatbots that can learn over time with the help of AI. Chatbots that have had time to learn human behavior require less specificity to answer complicated questions.
3. Use machine learning to develop products and services
AI can identify new products and services tailored to your target audience and existing customers. For example, 72% of people who use voice search devices claim they have become part of their daily routines. Voice commerce is projected to be worth $40 billion by 2022. This presents opportunities for brands to identify the needs of their customers, increase visibility and drive sales. AI-based voice assistants continue to learn from user behavior and provide data for hyper-personalization. You can also use AI-powered voice analytics to identify other areas for improvement, such as UX design and customer support.
4. Optimize website design and UX
Developing and optimizing your UX and website design is critical to reaching your targets. By generating useful indicators such as heat maps and other analytics, machine learning can help marketers optimize a website’s design with a data-driven strategy. Moreover, machine learning can be used to run A/B tests and continually optimize your UX.
5. Leverage marketing automation
Machine learning and marketing automation enable you to decrease time spent on repetitive tasks and instead focus on strategic campaign management. This is the use of software to automate processes such as email marketing, social media management and ad campaigns. For example, Adjust Automate is our automation tool that lets you effortlessly generate cross-app, cross-partner, and cross-network reports. This centralizes your data from different ad campaigns, countries, and networks on one dashboard so you can analyze your most important KPIs. Immediate access to this data empowers you to make real-time changes to bids and budgets and to optimize cross-platform campaigns from the same dashboard. You can also leverage this automation technology by setting rules that automatically change the attributes that don’t meet your benchmarks.
Optimize ad targeting with automation and machine learning
Mobile marketers need to know which channels to choose, how to distribute their ad spend, when to run campaigns, and for how long. Marketers can manage this with assistance from machine learning. For example, you can target lookalike audiences to acquire more high value users with tools such as Facebook’s Lookalike Audience. This enables you to reach a new group of potential customers who have similar attributes to your existing audience. You can use multiple audiences simultaneously for a single campaign and serve your ads to users who are in any of your selected lookalike audiences.
For Adjust clients, our Audience Builder leverages your Adjust data to automatically group users that fit your criteria, saving you time and effort so you can realize revenue gains faster. The feature updates every audience in real time so you don’t have to add or remove users who no longer fit the audience criteria. We also have direct integrations with major partners like Facebook and Snapchat, along with an easy dynamic webhook to which any ad network can connect. Audience Builder is a smart way to perform A/B testing because you can segment audiences into sub-groups to create controlled samples.
6. Programmatic media buying
Programmatic media buying is the automated process of buying and selling ad inventory through an exchange. This process uses ML to improve efficiency and make better decisions for the advertisers over time. When using machine learning for programmatic advertising, algorithms analyze large volumes of data from several sources. This means you can use ML to help you predict, plan and optimize your media strategy. Your experienced media buyer will still need to be in charge, but AI enables them to focus on strategy and spend less time on laborious tasks.
7. Automated email marketing
Machine learning-powered email marketing allows you to create personalized emails that drive engagement and help you reach your marketing goals.You can use this technology to segment audiences, curate your library of content and gather data that can be used for optimization. This all contributes to hyper-personalized emails that engage users and build long-term brand loyalty.
Software companies such as MailChimp, Moosend and SendinBlue offer machine learning capabilities to clients. In addition to curating content, machine learning can also be used to determine the best response times for sending emails to your users. It can also help you enhance the reputation of the sending domain to ensure that your emails are successfully delivered.
8. SEO analysis at scale with machine learning
Machine learning helps marketers perform SEO analysis at scale. Tools such as SEMrush and WordTracker provide insights into rankings of search engines and which terms and keywords you should target. You can also use these tools to identify links to build and pages on your website to be optimized. SEO analysis tools can quickly present reports that can be valuable in driving growth and increasing exposure.
You can also use machine learning to create content for personalized user journeys. For example, you may implement content creation for on-site SEO at scale with machine learning tools.
9. Fight ad fraud and in-app bot fraud
Ad fraud is a widespread issue within the mobile marketing ecosystem. Our research shows that fake users are still the most prevalent fraud type in the mobile ad ecosystem, making up 54.6% of all fraudulent activity worldwide. Global fraud rates in gaming increased by 172.95% between August 2019 and 2020, with the U.S. seeing a surge of 310.29% in the same timeframe. Without adequate prevention, this activity takes money from advertisers’ pockets without giving anything in return. AI and machine learning, such as Unbotify, can combat ad fraud by learning user behavior and identifying real users and bots.
If you found this article useful you may also be interested in How marketing automation platforms are helping B2C and B2B companies. We also have resources for email marketing and how to master social media for niche apps.