Customer Data & Analytics Blog

AI For Everyone

Ed Wolf | 2 minute read

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Where Are We Now

In the world of marketing technology (MarTech), 2017 can best be described as the year of AI.   While artificial intelligence and machine learning have been around in various forms for decades, the last few years, and 2017 in particular, have seen a dramatic increase in automated and AI-powered marketing.  This new type of marketing technology is allowing many customer centric businesses to shift their focus to data-driven marketing, and to communicate more effectively with their customers.

Before AI came along, the collection, cleansing, deduping, and unification of large amounts of customer data – all essential to understand and predict customer behavior – were incredibly time-consuming and expensive, lowering the chances that they would be done successfully.  However, when all this data process is done by machines, efficient and accurate customer analysis and predictions can be made to determine the correct prescription for each individual customer. 

What Is The Problem

Despite this major advance, many small and medium-sized businesses are still struggling.  This is due largely to the complexity of data wrangling, as well as the marketers’ unfamiliarity with AI and machine learning as they often believe it requires the involvement of armies of data scientists and developers.  However, this is not necessarily the case for businesses operating on a modest budget: new AI-driven applications have been created to address specific marketing challenges, and are very user friendly for marketers with or without a data science background.

What AI Can Do

AI can be a tremendous boost for businesses that not only wish to keep pace, but actually thrive in the current landscape.  If these companies can take advantage of the latest in AI and machine learning, they can eliminate much of the manual work involved in the most important aspects of data-driven customer marketing.   This will allow for more time spent effectively personalizing messaging to individual customers, resulting in increased customer engagement, loyalty, and lifetime value.

Practical Examples of AI

Two examples where AI can make marketing more effective are data preparation and deep customer segmentation.  Regarding data, businesses today usually have customer touchpoints coming to them from multiple sources:  web browsing, mobile, in store/POS, transactional data, etc… Additionally, customers also have behavior and attributes outside of the company that would be great to know—things such as demographic data, household information, and other information of this type.

To consolidate, clean and enrich all of this data in a pre-AI world would an enormously expensive task, both in time and resources.  Now, with even a basic AI/ML system in place small or medium sized organizations can process this data like the big boys.

Another area where AI can help is customer segmentation.   The ability to quickly (even in real time) segment thousands or even millions of customers into distinct personas is an incredibly valuable process that enables business to understand everything about their customers, and how to best interact with them.   Again, to do this manually in a timely fashion is impossible.   There is simply too much data to process, even for legions of the best data scientists.   Conversely, machine learning based segmentation models allow marketers a deep understanding of their customers instantly.  At the core of this technology, analytic algorithms are identifying the key attributes that indicate relevant customer segments and even suggesting the appropriate customer communications.

Those are merely two of many examples of the tremendous power of AI.   And contrary to popular belief, these mighty tools are not only for the wealthy and powerful Fortune 500 titans.   Rather, the proliferation of AI and machine learning applications on the market today means that their use is within reach of even the small business marketers who struggle with personalized communications and vast amounts of customer data.

The Bottom Line

Although there is still a long way to go, AI-driven data and analytic platforms show how far the world of marketing technology has come.    As 2018 begins, perhaps we will see business of all types take advantage of these wonderful tools.

 

Topics: Marketing Technology Customer Analytics Customer Intelligence