June 18, 2020
Everywhere you look, marketers are talking about artificial intelligence (AI). And it’s no wonder why—according to Deloitte, leading financial services firms are looking for innovative ways to apply artificial intelligence to customer engagement opportunities.
Of course, we all know about the use of intelligence for things like robo-advisors, which are revolutionizing the way we think about wealth management. But marketers at financial services and insurance firms also have an incredible opportunity to leverage these technologies to improve their marketing performance.
But AI isn’t one-size-fits-all: machine learning, artificial intelligence, and intelligence are all related, but critical. What’s the difference between them? And how can these technologies help improve the performance of your marketing?
Intelligence in marketing technology applies solutions to known, repetitive problems based on data that’s structured and easy to model, in order to reduce manual work.
Imagine that the marketing team at a banking and payments firm has just launched a new credit card offering in partnership with an airline. However, soon after launch, the entire travel industry goes haywire due to a global pandemic, so the marketers and their partner have decided to amend some of the benefits of the offering to keep consumer attrition at bay. They want consumers who contact support about benefits or cancellation to get the latest information as quickly as possible.
To accomplish this, the company sets up their automated chat support tool with the latest and greatest benefits content and information, the goal of which is to decrease account closures. The reason this is at all possible is because the chat support software is integrated with a central digital asset management (DAM) solution. With the DAM, reps can easily update all chat workflows and swap assets to facilitate the automated solution.
Machine learning at its most basic is when advanced data analytics practitioners use algorithms (e.g., specialized math models created to evolve and adapt in a way that they become more accurate the more they’re used) to take in data, calculate an outcome, and then determine the most logical next step.
Let’s step back and imagine a wealth and asset management firm that’s launching a new type of robo-advisor service. This firm has a large marketing team handling huge amounts of content creation that they’ve decided to localize for each region—including translation into Spanish—to target investors they haven’t normally targeted. This company does hundreds of thousands of marketing projects every year, and this is one of many new marketing initiatives.
Rather than hire lots of project managers, this company has an advanced marketing operations solution that uses machine learning to route projects to the right resources based on a range of different attributes including expected skills needed, availability of resources given other project requests, ability to automatically flag if appropriate compliance needs aren’t met, and the ability to measure the actual performance metrics of the pieces in market.
The key here is that the more data that comes in about marketing results, the ability of resources to deliver on-time given existing tasks, or other parameters, the more the model can better route projects to the appropriate resources.
Building on the machine learning concept above, artificial intelligence (AI) is the higher level, broader category that seeks to enable machines to do critical thinking that normally has to be done manually. An easy way to think about this is by going step by step through some of your more tactical day-to-day tasks and then imagine a computer doing those exact steps—but the computer will do them to perfection every time without getting distracted or tired!
Imagine a car insurance company. They have millions of marketing and consumer experience assets—from TV ads and billboards to digital and social content. Instead of having someone manually tag these assets, they can use AI to automatically categorize each individual piece of content, no matter the type of content whether image, PDF, or video.
AI saves the teams at this insurance firm valuable time, so that they can focus on delivering richer consumer experiences rather than spend time on completing rote tasks. And it does more: as more and more data about the content is automatically exposed, it makes it easier to automate personalized delivery of content on digital channels. So if a machine automatically understands the subject of a video, say the video is about rates during a pandemic, when driving time has been cut down, the machine can then automatically match that content with someone who is searching rates during a pandemic on the insurer’s website.
These are just a few examples of how machine learning, AI, and intelligence can help improve your marketing activities. Interested in learning more? Take a look at Aprimo’s latest AI capabilities, how Aprimo helps financial services companies, and watch our on demand webinar, “Optimizing AI for Marketing: What’s the future and what’s just hype?”