A wise person once said: “Don’t believe what a fortune teller says, they can’t even predict the weather.” And, when it comes to meteorological forecasts, even the most advanced atmospheric specialists on the planet get it wrong.
Don’t misunderstand me! I am an avid cloud watcher and have a huge amount of respect for those that have the final say on my daily choice of wardrobe. But there are so many factors to take into consideration, so many historical statistics and patterns to analyze…
The bottom line is that Mother Nature can be fickle. And, with the help of El Niño, La Niña or, dare I say the two words “climate change,” all the weather data collected since the beginning of time would still not be able to tell me whether I should wear snugly fitting Espadrilles or breathable wader boots in fourteen days’ time.
What has this to do with marketing? Well, I want to go out on a limb here and say that humans are often more predictable than the mighty elements. Of course there are many documented examples of just how random people can be but please stick with me as I carry out the highly dangerous task of combining hypothesis with generalization and tying them both into consumer behavior trends.
We are creatures of habit. Over a period of time, we tend to eat the same types of food, wear the same type of clothes, watch the same movies and so on.
When it comes to collecting human data, we have it so much easier. Or do we?
Let us peruse the following basic example showing the complexities of human behavior.
Collecting all this data into an understandable and actionable format used to be a Herculean task but, with the advent of technology, life suddenly became far easier. For those seeking the ultimate overview, the all-knowing, ever-seeing eye, collection is just the beginning of an extremely complex story.
I can sense vague mental mutterings from those of you that have read this far, it could be something in the order of “Get to the #*!$# point!” So I will.
Let’s return to the theme of prediction. Using our aforementioned customer profiles, it is technically possible to capture all online behavior. As soon as the data is available the process can begin.
Machine Learning is Key
Creating real-time, personalized content based on the ever-changing behavior of an entire customer database is a challenge for both humans and machines. But, over time, even the most gregarious (random) individual shopping behavior can be logged, stored and analyzed.
But how does it work behind the scenes?
Machine learning software processes diverse behavioral data including page views, check-outs, add-to-cart events and search queries on a website. Customer interactions with thousands of products are constantly processed, giving up-to-the-minute, individual recommendations with every page refresh.
The best solutions use artificial intelligence and are continuously testing and updating technology directly in the core product. The platform is learning and evolving throughout every cycle.
Understanding the Buying Processes
Recommendation models can be designed specifically for each respective stage of the buying process (research and discovery, cart, purchase, post-purchase) and are tailored to take into account channel-related behavior.
The more advanced solutions use complex learning algorithms that filter out irregular online activity to ensure only genuine crowd behavior is captured. In other words, non-human visitors are ignored (sorry, bots!).
These algorithms can also deal with long-tail items with little or no behavioral traffic which is a boon for clients with large or frequently changing catalogs.
Think this sounds like trying to predict the future? Think again. It’s the reality of marketing today. If you are not already using this technology in your current digital marketing workflow, there could be a storm heading your way.
Learn how Emarsys can help to give your customers a personalized experience across all channels.
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