With the advent of more information being available to companies than ever before, and coupled with the ability to process it, companies are starting to make better use of business intelligence. Up until now, this has been something more for the major corporations with billions of dollars in operating budgets to invest in the R&D. However, as software companies look to expand their markets, software solutions to help process data are becoming available to SMBs too.
In this article, we consider the use of big data and applied statistics to see how it can improve the way companies operate in the future.
The Chatbot Revolution
A slow revolution is happening in customer service teams across the country. While CRM software is very useful in improving the management of incoming customer queries, this is only the beginning.
The next generation is with chatbot technology. These are apps running off artificial intelligence that are able to create a planned, initial conversation with a customer before passing them off to a member of the customer services team.
These apps use natural language processing to break down responses from the customer to better understand questions and responses. What can be learned about the customer from past purchases and other customer information datapoints informs the bot, enabling it to ask questions or understand what the person is referring to when complaining about their recent order.
In the advent of Alexa, Google Assistant and Siri, people are lowering their resistance to dealing with AI-powered systems that speak or respond to you. This has provided an opportunity for companies to introduce chatbots into the customer service realm using customer data to make their responses more human-like. It also takes the burden off the human operators too.
Media, Entertainment and Shopping Industries
Big data is being analyzed in the entertainment, broad media and online shopping industries.
With entertainment, big data is often guiding new ideas about content to create. For instance, there’s a surprising number of reboots of previous TV series presently that have part of their origin from marathon watching of classic TV series on streaming services. Even the occasional meet up of the original cast of a favorite movie like Goodfellas demonstrates the endearing nature of some much-loved classics which encourages more remakes.
Recommendations online are often powered by big data. Spotify matches up previous songs played by customers using Hadoop data tricks to find other listeners of similar albums or songs and then make recommendations.
Amazon has also done the same thing for many years across their music, movies and TV catalogs, to name a few. Most recommendations to Amazon Prime subscribers are less about what’s been newly released and more about what matches up with their demographic for media. People with similar tastes see recommendations that other likeminded people enjoyed before them. Customers can even help the system by rating movies, TV series and music, which makes the prediction engine even more precise.
Routing Out Fraud with Insurance Claims
Insurance companies are using previous valid and fraudulent claims as a source of data to use in their industry too.
Valid insurance claims tend to have similar characteristics. With fraudulent ones, there are also some tell-tale signs that something might not be quite right with the information presented in the claim form. While a claims officer might do well in looking for and spotting these issues, they might miss them too. Alternatively, the personnel could be newer with less experience of these types of cases and not know what to look for.
By using data processing to examine current insurance claims, companies make use of statistics, data crunching and complex algorithms to derive meaning behind the sea of digitized claim forms. Because of these efforts, insurers that take advantage of big data use these systems to spot a fraudulent claim even if the claims officer fails to do so. This helps all insurance agencies from home and car insurance to gun liability or workplace injury liability.
Also, in cases where it’s not as clear cut, claims can be highlighted that have tripped a flag, look suspicious, and require more background information to confirm the information supplied is valid.
How Do Statistics Fit into It?
Statistics involves working with numbers to draw meaningful conclusions from them. Each data set can be analyzed to see what can be learned. With a larger sample size, it’s possible to draw different conclusions or be more certain about them. This works similarly to surveys where larger datasets are better for getting reliable responses.
Applied Statistics as a subject relies on being highly capable in mathematics with a strong grasp of linear algebra and calculus too. These topics work well to take datasets and run them through different types of analysis to determine whether there’s a strong correlation or likelihood of something being true.
For instance, with insurance companies, statistical analysis helps to identify commonalities with previous fraudulent claims. This analysis is used to scan new unsettled claims for identifiers that suggest they’re problematic. Statistical data considers how likely it is that this type of claim is valid. Or, if there’s an uncommonly high incidence of accidents in a particular ZIP code that is a statistical anomaly vs the rest of the country, what’s the probability that this is an outlier?
How Does a Person Get Involved in Big Data?
An employee in this field would have a strong head for numbers. Often, they have an affinity for them and find meaning in them. For companies looking to work through customer data to obtain uncommon findings that provide real insight, then these types of employees are invaluable in that pursuit.
Usually, to work in this still emerging field, it would be necessary to study applied statistics or a similar subject matter. Certainly, if you are already a graduate, then you can get your masters in statistics online from Michigan Tech that would provide high-level training on how to collect data, analyze it and draw meaningful conclusions. These types of courses are studied in an accelerated format and entirely online making them accessible to busy employees.
Customer data is currency to many companies now as they try to better understand what people want and how best to satisfy them with new products and services. The sooner SMBs embrace this new reality, the better equipped they’ll be.