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“Artificial intelligence” (AI) is such a common buzzword in marketing that it can sometimes feel like an elusive fly zooming around your ears.
But just like “mobile” and “responsive design,” this term is here to stay, and it’s redefining the marketing landscape as we know it.
According to Henry Schuck, CEO of DiscoverOrg, “Any part of the marketing world where a marketer has to read data and make decisions based on that data will be affected by AI in one way or another in the near future.”
From the connoisseurs of spreadsheet caches to those daunted by overwhelming dashboards of data, we could all use the power of artificial intelligence to help us make more intelligent marketing, sales, and service decisions.
AI is already being used by key brands in ways that affect all of us as consumers, even if we don’t realize it. Have you asked Siri for directions this week, watched a movie Netflix suggested, or used Facebook’s facial recognition tool to tag friends in a photo? Natural language processing, product suggestions, and deep learning are all examples of AI being used to create intelligent customer journeys.
Here are some ways brands are using AI to build intelligent customer experiences.
Predictive scoring can give you confidence that you are channeling your marketing efforts toward the right potential customers. It can also save you a great deal of time over traditional lead scoring, which requires constant hands-on attention to maintain. Knowing the likelihood of a potential customer taking an action lets you discover patterns and trends in customer behavior that you can use to plan more targeted messaging and campaigns.
Sales Cloud Einstein, for instance, uses machine learning to analyze leads based on historical sales data to discover the top factors that determine whether a lead is likely to convert to an opportunity. Imagine being able to find out that VPs in a certain industry who view product demos two times in one month are the most likely leads to convert. That knowledge can help marketing and sales teams create and distribute tailored content for that key audience.
Ninety percent of marketers agree that machine-based predictive scoring provides more value than traditional lead scoring, where marketers and sales teams score leads manually.
In a slightly different take on predictive scoring, AI can predict the discrete actions individual audience members are most likely to take, empowering marketers to employ smarter audience segmentation and personalization to guide messaging.
For instance, with Einstein Engagement Scoring in Marketing Cloud marketers can filter and segment subscribers into target audiences based on their likelihood to take a given action, such as likelihood to engage, likelihood to unsubscribe, and likelihood to purchase.
By removing complexity and automatically optimizing audience segmentation, tools like Marketing Cloud and Salesforce DMP that use machine learning to build segments and analyze customer patterns can save marketers time while building more successful campaigns.
To drive successful engagement, the content you share must be relevant to the audience you’re sharing it with. Using a tool like Einstein Recommendations, marketers can use aggregated web, email, mobile, and offline behavior data to predict the products, content, and offers audience members want to discover.
This works by 1) uncovering insights about customers’ preferences and tastes, 2) predicting which content customers are most likely to engage with next, and 3) recommending the right content or products — all automatically.
Large and growing brands use automation to scale personalization to thousands of site visitors and millions of email subscribers so they can deliver amazing experiences to every customer.
Think about your last Amazon visit. Unless it was your first time using the site, you probably saw products recommended for you based on your recent purchases and searches. Amazon uses predictive intelligence to identify trends and characteristics from across an entire database, which it then uses to automatically recommend relevant products based on your personal shopping behavior.
Successful email marketing is about delivering the right content, to the right audience, at the exact right time.
Typically, send-time optimization solutions look at your subscriber data to determine the best time, overall, to push a send to everyone on your list at once. Machine learning can go further by drilling into each individual subscriber’s engagement data to identify patterns and behaviors and queue up emails to send when that specific person is most likely to engage.
Using predictive analytics can earn the top spot in every inbox, increasing the likelihood that users open your email and click your links.
Social media is a treasure trove of data that brands can use to better understand their audiences and real-time trends. By putting AI to work, brands can collect and analyze social media data to discover hidden insights like audience sentiment, save valuable time by differentiating between spambots and actual users, and efficiently manage a global presence by automatically routing conversations based on language.
Artificial intelligence is not limited to reading words; it can also glean rich insights from images shared across social channels. More than three billion images are shared on social every day. AI can recognize logos, and product and brand interest even when a brand’s name is not mentioned — giving brands a deeper understanding of their audiences and allowing them to identify product or service issues that may have otherwise gone unnoticed.
Understanding your audience and building better customer journeys is possible with AI tools like Salesforce Einstein right now. As machine learning continues to grow and influence marketing decisions, brands will come to rely on it as a vital leg of any successful marketing, sales, and service strategy.
Explore our resource center to discover even more about how artificial intelligence is impacting intelligent customer experiences across the world.
