63% of consumers now expect personalization as standard, and businesses need to reflect that in their marketing efforts. But delivering a truly personalized experience means collecting and analyzing a ton of data in order to understand your customers’ needs and find the most effective ways to engage them.
That’s where AI comes in.
The latest artificial intelligence and machine learning technologies enable you to personalize marketing messages at scale, and use real-time information to optimize campaigns on the fly. They also harness data to help you predict future behaviors.
Let’s take a look at what marketing personalization involves, and all the ways AI can help.
Customers have come to expect that brands, particularly the ones they use most frequently, are able to understand their needs and preferences on a personal level. Along with marketing intelligence, marketing personalization is an important part of a business's marketing campaign. Prospects want to receive customized messages, recommendations, and offers at every stage of their journey.
If your business can’t manage that, you could be losing out on serious revenue. In fact, 45% of consumers say they’re less likely to purchase from a brand after a non-personalized experience, and 27% would stop using that brand altogether.
Personalization is a way for you to demonstrate that you’re listening to what individual customers need. It helps you to establish a connection with each customer, leading to increased satisfaction and long-term loyalty. This is especially important for account-based marketing (ABM), which relies on individually-targeted marketing.
Apart from delivering the best experience to all customers, companies can use customer data to improve their marketing efforts. By analyzing customer feedback and using things like email tracking tools, you’ll know which type of ads and messages are most likely to resonate.
These days, it’s not enough just to deliver marketing content tailored to a buyer persona or a group of customers with the same demographic, such as people who want to know how to create their own internet radio station, or people of a set age or background. Especially when consumer preferences and behaviors are constantly changing. But personalization on an individual basis is time-consuming and labor-intensive—unless you’re using AI.
Back in the day, marketers had to use assumptions and trial and error to figure out what customers wanted. Even with the advent of rule-based personalization, you could use a customer’s name in an email but they’d still see the same content as everyone else. AI, however, is capable of learning what content works best for each customer, and delivering it automatically.
Another challenge of personalization at scale is the sheer amount of data available to marketers: browser history, previous purchases, social media posts, customer support interactions, etc. Luckily, AI can gather, process, and analyze huge amounts of behavioral data, giving you real-time insights.
For example, data virtualization lets you access and centralize virtually limitless data from anywhere and everywhere, while Azure instance types, such as memory-optimized and GPU-optimized virtual machines (VMs), enable machine learning-based segmentation and analytics.
As well as maximizing efficiency, AI brings new ways to experiment, test, and learn from the data. With AI and machine learning, you can instantly produce hundreds of iterations, such as multivariant A/B testing to see exactly what works best for a certain type of user. You can optimize your marketing strategy even while a campaign is underway.
AI and machine learning help marketers to collect more data and analyze customer preferences to send out truly personalized emails and messages. It can predict the type of content that will engage particular individuals under different conditions and automatically create an appealing message for each one.
AI also ensures that emails and messages are sent at the most suitable time of day for each customer, using automation rules based on historic and real-time data. It can also deliver targeted messages responding to specific behaviors, such as the customer’s current online activity or location.
These include push notifications, which grab the users’ attention by appearing on their mobile screen and making suggestions such as “Still interested in [product]? Head back to our site for 10% off.” It’s like a personalized text, resulting in a higher click-through rate.
Another way that AI helps to engage your customers is by making recommendations for the products and services they actually want. Again, AI models learn this through user behaviors and site traffic data, getting smarter the more they process.
For example, companies like Spotify and Amazon have built recommendation engines that dynamically suggest certain options to customers. They use content-based filtering and collaborative filtering to recommend similar items based on their characteristics or how groups of users have interacted with them.
AI can also make personalized recommendations to your retail website visitors. A chatbot might offer a selection of items based on keywords in the conversation and previous shopping history and then offer a loyalty-based discount and recommendations for complementary items when the customer goes ahead with the purchase.
Sending personalized messages and making recommendations is one thing, but how about letting each customer see a customized version of your website? AI makes that possible, using live behavioral signals and past browsing or purchase history.
Dynamic websites adapt to individual users, showing them different products and a custom layout. For example, if you and I log into our Netflix accounts on our laptops, we’ll see different homepages because it uses our past actions to recommend a movie or show we’ll like.
Machine learning also helps you to customize the content, using data such as trending topics, common searches, or user location. Some sites use local weather conditions to recommend related items, such as ads for sunscreen in a heatwave.
AI is great at predicting what customers will do in the future. The more data the machine learning engines collect, the more accurate those predictions will be. Marketers can then analyze the data using tools such as pandas DataFrame (click here for a pandas DataFrame example by Databricks).
This knowledge helps marketing teams to optimize individual customer journeys and adjust to customers’ next actions. With predictive marketing, you can also make better forecasts about which products are most likely to sell, which is important for budget allocation and improving ROI. You can optimize future campaigns and experiment with personalized experiences.
Machines are now capable of picking up on our mood through the language we use, which has several marketing applications. An AI model can listen to conversations and messages and scan through customer reviews, and determine whether the sentiment being expressed is positive or negative. Other than that, a business that relies on voice communications, such as a cloud-based contact center or a telemarketing company, will primarily benefit from this technology.
It can also help marketers judge customer reactions to an ad, product or service and use the information to improve the campaign. For example, a wavering customer might be won back with a personalized special offer on their favorite product.
Chatbots are being programmed with more natural language to deliver human-like interactions, so they can respond to certain words or phrases with predefined actions such as making recommendations or mitigating a problem. It’s often possible to order products directly from a chat.
Advances in natural language programming are also driving the move toward assistive search. This is where a search engine guides users through the search process, presenting them with results that match their personal preferences and behaviors—which makes them more likely to buy.
So-called “smart search” plays a role here, providing customized autosuggest and even adjusting for misspellings by recognizing context. (If someone searches for “contract management system” but they type “cntract” by mistake, it will still know what they meant.)
Image recognition or machine vision technology means a customer can take a photo of an item they want to buy and feed it into a search engine, tracking down similar-looking items for them. Pinterest’s Chrome extension effectively does this already. This technology can also help brands see where people use their product or service online.
Here are some extra tips to help AI-based marketing personalization work for you.
AI speeds everything up by processing large datasets quickly, such as RDD (Resilient Distributed Dataset) for unstructured data. It then puts the information in front of you, but you’ll still need to utilize your marketing experience to decide how that info can optimize your campaigns.
Although machines can learn, they’ll still need you to point them in the right direction and tell them what to look for. Plus, chatbots and assisted searches are great, but make sure you don’t rely entirely on automation—customers still appreciate a human touch.
When you’re using customer data, it’s crucial that you comply with data regulations like GDPR. Keep users informed about what you’re collecting and tell them why it’s important—in many cases, customers are happy to give you their information if it’s going to help them get personalized recommendations. It’s all about trust.
AI-based marketing personalization is increasingly necessary when customers have access to so many choices. With a data-driven approach, you can ensure your users receive highly-targeted marketing messages and predict their future behaviors, keeping them engaged with your brand.
AI also makes marketers’ lives easier, saving time by eliminating time-consuming tasks such as manual segmentation. But you’ll need the right tools to make it a success. Fewer than 1 in 4 businesses have the required technology to deliver consistent, personalized experiences across channels.
Don’t underestimate the power of personalization. Learn how to apply AI to your marketing and enjoy improved customer loyalty and revenue.