Customer Data & Analytics Blog

How machine learning solves your customer data quality issues

Ariella Brown | 3 minute read

Zylotech_Customer experience is the new battlefield_072519_headerMachine learning helps marketers find potential customers, understand customer purchasing patterns, and even predict and prevent churn. This blog explores where machine learning fits in around customer data quality issues.

As we saw in 3 Tips for achieving customer data quality, poor data quality causes losses of $3.1 trillion annually in the US alone, according to IBM’s 2016 estimate. Based on projections, the cost is even higher now. Concern about data quality has been increasing, according to Dunn & Bradstreet’s 6th Annual B2B Marketing Data Report. It grew from 75 percent in 2016 to 89 percent this year.

It’s not so surprising that concern about data quality rises as the amount of data keeps increasing at an exponential rate. IDC has predicted that the Global Datasphere has expanded from 33 zettabytes in 2018 to 175 zettabytes by 2025.

To understand just how much that is, you have to picture a number followed by a lot of zeros.  The storage capacity of a zettabyte with the unit symbol ZB is 2 to the 70th power bytes, which can also be represented as 1021 (1,000,000,000,000,000,000,000 bytes) or 1 sextillion bytes. Another way of putting it is that one zettabyte is about equivalent to a thousand exabytes, a billion terabytes, or a trillion gigabytes.

David Reinsel, senior vice president at IDC, offered a visual reference of 175 ZB contained in “BluRay discs, then you’d have a stack of discs that can get you to the moon 23 times.” If it were possible to download it all to hard drives, he added, “it would take 12.5 billion drives.”

Having data, of course, is good, but such large numbers are likely to prove overwhelming and too much of a good thing. Indeed, Matthew Rawlings, Head of Data License at Bloomberg observed that “it takes a lot of manual effort to clean and run that data and add some business intelligence on top of it.”

Added to the burden of simply taking in, cleaning, and running huge quantities of data is the demand for doing so in real time. IDC forecasts that by 2025, close to 30 percent “of the Global Datasphere will be real-time.”Zylotech_Customer experience is the new battlefield_072519_sub

This is where machine learning comes into play. One of the advantages it offers is assurance that the data is complete and accurate. It can also identify patterns and pull out discoveries on a scale that is simply not humanly possible. And it can accomplish these processes quickly enough to meet increasing demands for real-time responses. 

As explained in Why machine learning matters to B2B companies, machine learning fills in the information that may be missing in a customer listing and fill it in. It also unifies the data, “matching data by leveraging AI, so that match rates improve as more data is processed.”

It also makes it possible to run through analysis much more quickly than what is humanly possible. That includes finding key correlations in the patterns associated with your customers. 

On that basis you can more accurately segment and anticipate the moves of your customers, which enables much more accurate and effective personalization. Thanks to machine learning’s pattern recognition, it is able to direct personalized marketing to deliver the right marketing messages to the preferred channel of the right customers at the right time.

While Amazon was among the pioneers of building its business through predictive analytics, other brands have successfully adopted that approach. In the past few years, even B2B businesses have started to realize they have to adapt to the new normal of customer expectations. 

Rawlings offered the example of data normalization in which a large bank wants to identify a particular client that can appear under various different names. While reviewing them all manually would be arduous and time-consuming, it’s possible to apply AI to bring up all the matches quickly. 

With AI, observed Rawlings, you can reduce “what was a year-long process” into as little as a day, which makes it possible to “test hypotheses and act on them much more quickly.” As a result, machine learning “enables faster, better decision-making.”

Ariella Brown is a Zylotech contributing writer.

If you liked this post, check out our recent blog post: How to rev up your revenue engine. 

 

 

Topics: machine learning