Have you ever wondered if we can forecast the future with accuracy?
I'm not talking about psychic ability now, but rather taking a client or customer, or even a portion of them, and projecting how they're going to behave based on their past behavior.
This is what many companies do to help gain a deeper understanding of general customers and their own clients and use those experiences to build strategies for industry, marketing, and product / service to deeper satisfy their needs. The brief response to the title question, then, is: yes. Indeed, to achieve a better understanding of consumers, we can use predictive analytics-but only when used correctly.
Predictive analytics are used on a regular basis to predict potential trends, opportunities and challenges; these observations are fantastic for key plans for potential-proofing, but do they also help us better understand customers?
On a regular basis, most companies use predictive analytics to impact decision-making processes around the organisation. When energy providers apply it to determine how much energy you would need and when, even if you've just moved into a new location, one practical example of how predictive analytics benefits both consumers and businesses.
What is Predictive Analytics?
Predictive analytics is a data analytics field that specializes in 'predicting' the future based on insights created from user experiences from current and historical data. A very neat idea with a great deal of promise for all sectors.
Data mining, machine learning, and statistical analysis techniques are usually used to gather and sort this knowledge from a range of platforms covering the many channels through which brands communicate with clients and consumers, including but not limited to:
· Trawling social media for user reactions, patterns, and opportunities-social media is a ready-made online forum that the consumers and customers use to communicate 24/7 with each other-generating more information as a sole means of communication now in this pandemic than ever before.
· Trawling live chat customer service transcripts, or captured customer service telephone line data-this is usually used to identify pain points and address problems that arise during everyday encounters with customer service, but may also be used for predictive analytics purposes.
· Using the IoT to collect passive app usage data, frequently visited locations, frequently googled questions, and much more to build a better image with contextual insights that better educate the reasons behind past customer actions and therefore share insight into how customers will act in the future when faced with similar circumstances.
There are several more avenues from which this knowledge can be accessed, of course. Through this, both consumers and customers may shape an inherent fundamental understanding. There will always be surprises, but predictive analytics is one of the best opportunities for organizations to plan themselves for possible future scenarios when supplied with enough data; future-proofing their plans so that they can come stronger and intact from the other side.
Predictive Analytics: Tactics, Tools, and Techniques
One of the best ways to create future-proof strategies is to use predictive analytics to construct a deeper understanding of customers-but only if it is used to educate rather than completely guide them.
It is difficult for a research team to trawl through almost anything because of the vast amount of data we create on a daily basis-this is where machine learning and automation come into the picture. Automated algorithms can easily pattern and sort data into key themes ready for the expertise of a researcher when configured correctly; they can then use their choice of statistical analysis tools that use data visualization and modeling techniques to analyze the data and draw up key insights that help inform organizational decisions on a range of different topics.
There are several different models of predictive analytics that assist with corporate decision-making processes, but these are the ones that are most important to improving customer understanding:
· The Customer Segmentation Model lets researchers group customers into groups based on whatever they choose, but buying habits or how they communicate with products will be the most beneficial for the company.
The Algorithm of the Random Forest, which is best used for large datasets. This is a model that attempts to forecast consumer behavior in the form of a flowchart, based on historical data, and to present the range of potential results. A substantial number of variables and