How Machine Learning Uncovers the Best Customer Insights
Data is the new oil. Insights derived from massive amounts of information can go beyond planning, budgeting, and forecasting, it can define a brand’s competitive advantage. Big data processed using machine learning can identify emerging opportunities, simulate events and help executives make better business decisions.
In fact, the strategic importance of shifting to data-driven decision making (DDD) is becoming more real. You may have experienced it already without consciously knowing. Remember the time when you visited Amazon, put an item in your cart, changed your mind and eventually abandoned it? Bizarrely, the product you left suddenly keeps popping on every website you visit.
No, that’s not a plain coincidence, and certainly, your mind is not playing tricks on you. That’s what advertisers called retargeting. Through data-driven advertising, potential customers who abandoned their carts are treated as data points in an algorithm that will be targeted to see hyper-relevant offers to close a sale eventually.
The best customer insights are right under your nose. Social media chats, comments, photos, and videos; corporate presentations and project documents; audit and project completion reports. The insights are all there just waiting to be crunched and interpreted. Take the case of LinkedIn, this data-driven company power up to 300 billion events in which data plays a significant role. Uber is another perfect example. This data-driven tech giant leverages big data to track thousands of cars and drivers and match them up with the best route.
Certainly, data coming from different sources, both structured and unstructured is seemingly impossible to process and much even correlate to derive predictions and insights. This is where machine learning comes in. Big data analyzed through machine learning using products such as WorkFusion Smart Process Automation (SPA) can provide more in-depth customer insights that traditional business intelligence reports cannot provide. Using artificial intelligence, algorithms from SPA can make sense of billions of data regardless of its state or format. This is done through a data mining process that involves data conversion, removing or inferring missing values and data normalization that transforms disparate datasets into a homogenous database. From here, a carefully identified machine learning algorithm from SPA will perform descriptive, predictive and prescriptive analysis, providing customer insights that are deeply buried for years.
Combining big data and machine learning to uncover the best customer insights is done through a six-step process.
Start with understanding the business problem that needs to be solved together with the strengths and limitations of existing data sources. The data scientist’s creativity is vital at this stage, because of the need to carefully transform business problems into data science problems.
