Can we really know what outcomes are likely? It may not be as far-fetched as it sounds. Predictive analytics can give us a highly accurate “Crystal Ball,” allowing us to see into the future, leveraging insights gleaned from large data sets and advanced machine learning (ML) algorithms.
Predictive analytics is the use of data, algorithms, and ML techniques to assign ‘scores’ to various user segments based on historical data. Its goal is to assess a likelihood of future events — such as a purchase or customer churn — so that a specific action can be taken. Using predictive analytics, we can know with a high degree of certainty the outcomes for future customers and business activities.
Identification of Customers Likely to Churn
In modern growth marketing efforts, churn is a crucial statistic. The old axiom rings true, “It’s cheaper to keep an existing customer than to find a new one.” Predictive retention models can identify which customers are most likely to churn — and companies can respond by reaching out to them with education on product benefits or other promotions. Predictive scoring can also identify a set of behaviors in customers who are less likely to churn. Messaging likely churners and steering them to adopt behaviors of customers who are less likely to churn is a valuable outcome for any business.
Recommendations for eCommerce Cross-selling and Upselling
If you’re a retailer selling a variety of products, predictive scoring can help you tailor your ‘recommended for you’ product placements by analyzing historical customer data and applying customer profiles to offer look-alike targeting for optimal conversion. For example, someone who has purchased hiking boots might be shown advertising for other outdoor gear — while someone who has bought kitchenware might be shown ads for kitchenware.
The increased pressure to implement enterprise data management is not the result of a fad or herd mentality. There are real operational needs driving this movement. Chief among these is the urgent demand to make data accessible and useful. Businesses need to be able to put their data to work fueling decisions, empowering efficiencies, and shaping company direction. That means data must be standardized, converted to useful forms, and stored where it is secure but still accessible to users.
If you are lucky, your company was able to get a jump on its data management, implementing proactive measures over the last few years to handle the swelling data tide. Unfortunately many enterprises weren’t quite so vigilant and now find themselves scrambling to get a handle on their data, which is growing and changing by the day.
The Many Sides of Enterprise Data
According to IDC, businesses are managing a volume of data that is growing at an average of 40% a year. Not only are companies handling more data, but the types of data are expanding as well. Data streams contain everything from inventory figures and financial information to videos, images, and other unstructured data coming in from social media, mobile, and the Internet of Things (IoT). All of these varied data types need to be centralized, organized, and made accessible and useable to the business. That is the true mission of enterprise data management.
We all have that place where we end up stashing those things we think we’ll need or want someday. Some of us throw the stuff in a junk drawer in the kitchen. Others squirrel it away to the attic or into a closet in the spare bedroom.
On occasion, we do venture into these storage spaces and unearth certain items that prove to be extremely beneficial in solving a problem. But in most instances, those things we deemed essential in the moment are left in a jam-packed drawer or dark corner of a closet — forgotten and worthless, yet taking up valuable space that could be utilized in some other way.
This is precisely the situation many organizations face today with their data.
A Junk Drawer Full of Data
Today, the amount of data produced by businesses continues to increase at a dizzying speed. Most organizations migrate their data into a Data Lake, thanks to its inherent scalability and flexibility. What goes in the lake, stays in the lake. On the surface, this appears to be a smart business move since data is their most valuable asset.
But dive beneath the surface and you’ll discover that using a Data Lake as a repository without giving consideration to its usage makes it no better than a junk drawer. Sure, the lake may store a vast amount of data, but all of the raw data in the world is of little worth if there isn’t a process in place for unlocking its value. Even worse, there may be private information in that unopened letter that you don’t want others to see.
The vast majority of businesses have Data Lakes that are little more than virtual junk drawers: reservoirs that house data from disparate sources across enterprise. The problem is, most of this data isn’t accessed. In fact, it’s not uncommon for the majority of users to find only a small percentage of truly valuable data sets. The remainder of it is submerged in the lake, an uncataloged, useless jumble of data sets taking up costly space without providing the ROI businesses expect. Users don’t know how to find data sets in the lake — or if they can, it’s difficult and time intensive to distinguish which ones are the best … or if they should have access to it.