Unless you’re well-versed in computer science and mathematics, the word “algorithm” can seem a bit abstract. Most of us are familiar with the term and even know some of its applications: curating social media feeds to specific users, photo facial recognition on Facebook, the underpinnings of machine learning (AKA, artificial intelligence), and something that helps solve complicated math problems. Many of us, however, would be hard-pressed to define what algorithms are and why they might just make for marketing superpowers.
Algorithms are responsible every time we Google “cross-country skis for women” and end up with a specific array of ski choices that show up in our Facebook ads the next time we log on. They’re also responsible when we buy a sheet pan on Amazon and receive an email for just the right spatula to use with it the following day. Algorithms empower marketers to deliver personalized content to clients and prospects in a more powerful, efficient, and meaningful way than ever before, and that’s what makes them crucial to have in your tech toolbox.
One of the most powerful things about using algorithms and machine learning in marketing is the ability to truly learn about your customers – what stage of the buyer’s journey they’re in, how they like to shop, what they like to do, how they’re likely to make major financial decisions, and what they’re likely (and able) to invest in. This is particularly salient in financial services, since financial decisions carry with them a great deal of stress and complexity for many clients. The more we’re able to understand our customers, the more we’re able to empower their purchases.
Let’s define algorithms a bit more…
Here’s the short of it: An algorithm is a sequence of steps or rules that takes a set of inputs or data points and creates a specific outcome out of them. Artificial intelligence uses algorithms to allow for machine learning; much like the human brain, AI takes large amounts of data, learns to interpret that data with repeated exposure, and makes meaningful predictions based on what it’s learned. Thanks to the specific algorithms AI uses, its able to adapt its customer recommendations in real-time.
Algorithms are what allows Google News to determine what kinds of articles you like and how Spotify can make almost-too-accurate music suggestions. They make certain platforms seem like they know you. Part of what makes algorithms so powerful in marketing is their ability to scale. Not only can algorithms glean patterns from large amounts of data and make predictions much, much faster than human cans, but they can also use this data to deeply personalize content.
Perhaps one of the simplest examples of real-time learning is Pinterest; it can adjust all the “pins” in your feed to be more like the ones you click on as you’re scrolling, creating a dynamic feed that constantly learns about your preferences as you browse.
Beyond personalization, algorithms can help marketers make major decisions, like the best time to send sales emails to specific clients, how to reduce waste by guiding ad buys, and reverse engineering a great customer experience. By collecting individual user data and drawing conclusions based on that data, algorithms are able to suggest relevant products to specific customers.
For example, a single young professional who consumes a lot of travel content may be in the market for a credit card with incredible airline benefits, while a parent with three small kids who frequents interior design blogs may want one that offers cash back for everyday household purchases. Instead of placing ads for these products in front of large demographics, algorithms allow marketers to zero in on exactly what customers are looking for.
In fact, this kind of precision can even influence purchase decisions. Sometimes customers don’t consciously know they’re looking for a product yet, or they might not be quite sure what they’re looking for, but with the help of algorithms, marketers are able to beat them to the punch. Amazon capitalizes on this tech advantage in a big way — 35% of purchases made through this platform are a direct result of suggested products. That’s some major revenue!
With so much of the data collecting, interpreting, and predicting done at light speed by computers, more time is freed up for marketers to, well, market. Here are a few major marketing categories that can be streamlined with the help of algorithms:
- Campaign strategy: This includes the right time to send specific kinds of emails, the audience selection for specific campaigns, the right places to display ads, and how to allocate ad spend.
- Customized campaigns: Like we mentioned earlier, this is the ability to personalize marketing to specific individuals instead of broader demographics. Customized campaigns can include dynamic content – webpage content that changes depending on who the user is.
- Tracking the consumer path: The path to purchase varies widely, and some touchpoints (such as Instagram ads vs. marketing emails) can yield very different customer behaviors. Algorithms make determining which touchpoint(s) specifically results in a purchase possible.
Crunching the Data
What makes an algorithm such a successful marketing tool is the scope of data available and how they’re able to organize and make meaning of that data. Data such as user demographics and location might be more obvious inputs used to predict customer behavior, but it’s behavioral data that really allows for the algorithms’ precision.
Brand preferences, product usage, posts and comments on social media, websites frequented, and so much more are included in this category; and if that wasn’t enough, algorithms that interpret an individual’s data includes data from others as well. This means that algorithms are learning about people at large while they’re learning about individuals. You might call it automated insight.
Thanks to platforms like HubSpot, you don’t actually have to know the nitty-gritty computer science of how algorithms work to use of them in your marketing – they’ll do that work for you. In case you’re curious, here are a few examples of algorithms commonly used in marketing:
- Decision trees: These algorithms make predictions, like predicting customer churn or suggesting products a customer might like. Again, these predictions can be self-fulfilling – sometimes customers don’t know they’re looking for a product until they see it.
- K-Means: This algorithm finds clusters of data with similar attributes and then creates distinct groups out of it. This is useful way for quick, efficient, precise customer segmentation that will come in handy with campaigns and dynamic content.
- Naïve Bayes: These evaluate marketing campaigns and help make pricing decisions, eliminating wasted time and money.
One last example of deep personalization is dynamic content: algorithms that change not just recommendations or suggestions for customers, but entire web pages or page modules. This means that some customers visiting your site can be taken to completely different landing pages or product page than others, streamlining their customer journey and increasingly the likelihood that a sale is made. In some instances, this dynamic content can remember that a customer has visited your site before and send them straight to their carts.
Of course, this is just the beginning. As algorithms and machine learning continue to develop, so will the potential of marketing.
You may also be interested in:
- Optimizing Your Financial Services Website for Voice Search
- A Customer’s Journey Is Not Linear: How to Connect Dots to Optimize Marketing
- How to Make Marketing Automation in Financial Services More Human