When you hear “artificial intelligence” or “machine learning,” what comes to mind? A complicated technology that demands deep domain experience or a degree to use?
This was once the way technology worked; only a select few had access. But innovation has a funny way of changing things. What might seem out of reach today can become widely accessible tomorrow — just look at the GPS system, or drones.
We’re seeing this now with marketing technologies. Machine learning has made it so that marketing automation platforms can be predictive — able to learn, think and act without explicit instructions. The result: more precise, efficient marketing that adapts to the journeys customers travel.
What used to be nice only in theory (predictive analytics, often complex and expensive) can now be put into practice, via features that allow for real-time data optimization, automated workflows and personalized content.
Think of it as a team of robots at your disposal — at the ready to evaluate, modify and optimize your company’s programs so you can better anticipate and respond to the customers in your care. Here are three specific benefits:
On any given day, most marketers are up to their ears in data — data from the programs they run, the buyers they court and track. Marketing automation enables them to separate signals from noise, wheat from chaff, so that they can orchestrate specific actions based on the stories data tells them.
In fact, some marketing automation platforms have evolved to the point of predicting the best times to engage specific buyers, using past behaviors and actions to identify an optimal time for sends and engagements (a window when opens and click-throughs are likely to be highest).
The technology has also gotten smarter in the lead scoring and weighting department and can enable marketers to go beyond conditional scoring rules, prescribing scoring values for behaviors and actions across different segments, industries and buyers.
And it can help marketers with segmentation by dynamically creating lists of engaged contacts by any combination of factors; these segments can automatically adapt as new information is provided.
A more tailored approach to outreach Marketers have long relied on A/B tests to optimize their programs and strategies, to ensure the most effective message is used. But this method has always resulted in a one-size-fits-all approach, in much the way segmentation has — assuming that the behavior of a set of buyers tells us everything we need to know about a whole swath of buyers.
The modern customer engages with a business across multiple digital touch points, and it’s crucial that brands personalize their experience at every stage, from first interaction to purchase through ongoing customer care. Machine learning allows for this, finding patterns at faster speeds and on far greater scales. It provides an immediate, high-definition view of customers and their behaviors — allowing us to find smaller micro-segments of customers that have shared interests and needs. It immediately connects dots that traditional methods are slow to recognize, if they ever do at all.
Source: Marketing land