Advances in marketing technology have made it possible for companies to collect or purchase significant amounts of prospect and customer data. What to do with this data – how to transform it, disseminate it, and render it usable – is the topic du jour for marketers, and this is where artificial intelligence, or AI, enters the conversation.
Artificial Intelligence is defined by Merriam-Webster as “the capability of a machine to imitate intelligent human behavior.” Given the vast amount of data available and the limited hours in the work day, the concept of using machines to execute human tasks, particularly repetitive or highly technical tasks, is desirable.
In fact, 55% of CMOs surveyed by PR firm Weber Shandwick last year believe AI will transform global marketing on an even grander scale than social media. It makes sense: AI is one logical solution to the mountain of data we're able to collect from the various channels and platforms that exist today. It has the ability to revolutionize the speed at which we process data, and use that information to take insights to the next level.
What Constitutes Artificial Intelligence
ArtificiaI Intelligence itself is a broad term, which encompasses multiple technologies and capabilities, including intelligent automation, machine learning, and predictive modeling.
- Intelligent automation refers to activities in which the human brainpower behind specific tasks is substituted for machine compute power. Typical marketing automation software is a good example of this. Marketing departments could have their staff compile campaign lists, put together an email for each prospect on the list, and send said email to the respective email address. Or, they could put their staff to better use, and transfer these tasks to a marketing automation tool, such as Salesforce's Einstein ABM, which identifies the best accounts to target and creates personalized content and email subject lines at scale.
- Machine learning refers to technology that uses algorithms to detect patterns and applies logic from those patterns in future iterations, hence the learning component. For instance, in the aforementioned marketing automation example, how does the software know when to send each email? Machine learning allows it to run through billions of data points to understand patterns around the best time to make contact and apply this knowledge in sending emails to boost open rates and engagement. To take it even further, machine learning has the ability to target the best contact time at an individual level, personalizing send times for each recipient to go one step deeper at optimizing for success.
- Predictive modeling applies statistical models to data to either predict given events (propensity), identify like-groups (segmentation), or apply filters based on feedback (recommendation). Social media platforms provide a great example for recommendation modeling. Marketers generate relevant content, but most find it difficult to predict exactly what will resonate with social media followers. Hootsuite Impact helps marketers quickly evaluate their social media content by categorizing content topics, and measuring and providing feedback on performance to influence future content decisions.
Using AI to Drive Personalization
One of the most useful benefits of AI for marketers is the ability to personalize conversations at scale. This allows digital conversations to more naturally unfold in the way a human conversation would.
Recent advances in technology have allowed marketers to store AND retrieve massive amounts of data at amazing speeds. This enables them to capture just about every aspect of their relationship with their audience, including current and past customers. When companies can combine current digital engagements with offline interactions, including salesforce tools, customer service applications, and financial systems, a wealth of information becomes available from which to draw context and react more intelligently. Layering AI on top of this wealth of information allows moment-specific messaging to be introduced into each individual conversation with a high degree of accuracy.
For CMOs looking to leverage in-depth behavioral analytics and customer data to drive an individualized customer interaction experience, a number of real-time personalization tools exist in the market today. The variety of channels that can be customized, and specifically customized in real time, spans the gamut. Personalization in email has moved well beyond the simple ability to chose which version to send to a given individual. It is now possible to alter the actual message itself based on when the customer opens the email, taking into account current inventory levels, or location—even weather at the individual’s location. When highly relevant content, be it information or an offer, is delivered at the optimal moment, it makes a connection in the same way a human could.
Getting Started with AI-Driven Personalization
AI-driven personalization can be a huge asset for those marketers who want to stand out from the noise. Using intelligent processes and predictive models allows organizations to realize the full potential of their data, turning it into valuable insights that can be utilized to, for instance, ensure that you are providing contextually relevant content to engage prospects and customers in the moments that matter.
Getting started with AI-driven marketing techniques shouldn’t be intimidating, but it takes more than just technology and data. It requires understanding the outcomes that are desired and selecting the right partner whose AI engine is tailored to drive those outcomes. And behind any good AI engine is quality data, so ensuring the data being fed in is a clean and complete as possible is a top priority for those marketers looking to move to artificially assisted marketing. But with a keen focus on data quality and desired outcomes, any marketer can take their personalization to another level—a more human level—using AI.
If you enjoyed this post, you should also check out this martech white paper I helped to develop: The Martech Maturity Model.
About the Author
BiographyMore Content by Brett Eckrich