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Remember sitting in the back seat of the family car and watching an adult wrangle a huge map, turning it this way and that, as you continued on your road trip? Or perhaps it was a visit to AAA to print out step-by-step directions. It seems so antiquated now that our smartphones have built-in GPS and voice command anticipating our next turn. As drivers, we welcome this technology that saves us time and makes us more efficient, not to mention safer drivers!
But there is reluctance when it comes to trusting that same technology, namely machine learning, in other domains like our professional lives. Let “the machine” decide the quickest way to get home? Absolutely. Let “the machine” decide when to deliver a marketing campaign, with what message, on what channel to thousands of customers? Not so simple. Is it a trust issue? A lack of understanding the technology? A fear of being replaced by a machine? Or perhaps it’s a bit of everything.
We’re living in an interesting time, to say the least, where people are still confused/ afraid/ doubtful of machine learning (ML), and yet we all use this technology every day whether we’re aware of it or not. Google Maps, when we were driving to that new restaurant, Netflix, thank goodness for those recommendations now that we are home-bound, Spotify, soothing music anyone? Siri, Alexa…the list goes on. So why are there such stark differences in adoption for certain tools and services compared to others when they all use machine learning at their core?
There are many possible explanations, but one could argue it primarily comes down to our perception of control. As humans, we like to feel in control. If a technology supports us by eliminating certain manual tasks while still letting us feel in control, we readily adopt it. Back to the GPS example: you’re still driving the car, you still decide if you want to go one route or the other. Machine learning simply suggests routes and dynamically adjusts its recommendations based on your actions. So you’re ultimately in control, but your decision making is facilitated by algorithms that you never see “under-the-hood” in applications.
Guess what? Machine learning can help marketers in a similar way. Think customer journeys and personalization. Rather than manually building complex journeys that rely on arbitrary, time-based rules (such as send email A after 3 days, if not opened, send email B after 7 days) you can create dynamic, one-to-one customer experiences using machine learning.
Let’s look at an example. While many activities and purchasing habits have completely stopped during the COVID-19 quarantine, others have surged such as pet adoption. Being isolated at home for weeks on end, many people have decided it’s a good time to get a puppy or kitten. In fact, Petfinder.com saw a 122% increase in adoption inquiries between March 15 and April 15 as social distancing became the norm. Nestlé Purina, which owns Petfinder, uses Lytics to create personalized recommendation offers for current or future pet owners looking to adopt a new furry friend.
By leveraging Lytics behavioral scoring and content affinities, Petfinder was able to identify highly engaged users and the specific breeds they were interested in. Targeting those users on Facebook, Petfinder achieved 3X the conversions at 1/10 the cost. Combining the right data with the right data science resulted in a more engaging customer experience and drove results towards Petfinder’s top-level marketing conversion goals.
True one-to-one personalization at scale is possible but it requires a fundamental shift in methodology and mindset. The road to personalization does not happen through segmentation as we know it today. Just as printing out driving directions instead of using a smartphone hinders your ability to respond to course corrections like wrong turns or construction, relying on traditional, heuristic-based audience segmentation hinders your ability to make the leap to more meaningful personalization. Using ML-powered tools enables you to pivot your plans based on real-time activity instead of looking in the rear-view mirror as most marketing campaigns are monitored.
Adding machine learning to your campaign decision making process does not replace marketers’ jobs. Instead, it frees marketers to focus on what they do best: the creative aspects of figuring out what resonates with people. Let machine learning handle the nitty-gritty of which message to send, at what time, and in what format based on customer behavior. You get to focus your energies on test driving new creative, new messaging, new offers, or giving a tune-up to your most successful campaigns.
So you can think of machine learning as your assistant. It will spare you manual tasks and help you make decisions, both in your personal and professional life. You’re still in the driver seat and get to pick the destination, but machine learning can help you get there and maybe even find your dream dog to be your companion for the ride.