In our ultra fast-paced age of computer-connectivity, businesses produce massive amounts of data that can be challenging to keep up with. But when you learn to analyze this data with artificial intelligence, you can produce results far beyond what humans are capable of, both in terms of speed and accuracy.
Machine learning data analytics platforms can automatically process big data constantly, and in real time, so you won’t miss a single insight.
AI data analytics tools, like MonkeyLearn, can locate, clean, analyze, and visualize your data, with almost no need for human input. The results are fast, making even big data appear small, and the processes scalable to any needs. And, perhaps best of all, they are extremely easy to use.
AI-powered software can analyze data from any source – internal and external – and deliver valuable insights that help drive decisions. Customer feedback data analyzed with AI can be particularly revelatory for businesses and help influence product development, improve support team performance, and guide top-level business decisions.
Artificial intelligence (AI) is a data science field that uses advanced algorithms to allow computers to learn on their own, while data analysis is the process of turning raw data into clear, meaningful, and actionable insights. Using AI-guided systems in your data analysis allows you to automatically clean, analyze, explain, and ultimately visualize your data.
Traditional software requires constant human input. When a new process needs to be added or an existing function changed, it requires an engineer to physically manipulate the code.
AI software with machine learning, on the other hand, requires only initial human input – we call this training data.
Machine learning algorithms are fed labeled training data, or tagged samples of text, which they learn from to find patterns in subsequent data. Effectively they use human-tagged information to learn how to analyze data themselves.
Artificial intelligence and machine learning have made advancements in data analysis that were unimaginable even just a few years ago.
“We’re at the beginning of a golden age of AI. Recent advancements have already led to invention that previously lived in the realm of science fiction — and we’ve only scratched the surface of what’s possible.” – Jeff Bezos, Amazon CEO
Now businesses are realizing the benefits of AI and using it to analyze their data for fine-grained insights, automate processes and make data-based decisions.
A subfield of AI machine learning called natural language processing (NLP) allows machines to organize and “understand” human communication. Text analysis, or text mining, uses NLP to break down text (from documents, social media, internal communications, etc.) and uncover insights.
Because it reads open-ended, unstructured text data, text analysis goes beyond statistics and numerical values, into the qualitative results. Text analysis doesn’t just answer what is happening, but helps you find out why it’s happening.
More specifically you can use text analysis to detect sentiments and topics in your data and extract keywords, names, specifications, and more:
Sentiment analysis, or opinion mining, uses NLP to automatically categorize text by polarity of opinion (positive, negative, and neutral). It’s able to process huge amounts of text data from almost any source to understand the feeling and emotion of the writer.
Using sentiment analysis on customer surveys allows you to ask open-ended questions, so you can analyze responses deeper than a simple Yes/No or multiple choice. It can be used on customer service tickets and emails to read them for urgency or level of discontent and prioritize the most pressing issues.
Try out this pre-trained sentiment analyzer to see how it works:
Thanks to machine learning, AI data analysis tools allow you to train your own sentiment analyzer (often in just a few steps) to the language and criteria of your business for unsurpassed accuracy.
Imagine performing sentiment analysis on thousands of tweets about your brand, just as you release a new product, to find real-time customer sentiment, then follow the sentiment as it changes over time.
Text classification includes sentiment analysis, but offers many other advanced data analytics, like topic labeling – which reads a text for topic or theme, or separates texts into pre-assigned categories. Topic labeling works great on customer support tickets to automatically categorize them by topic (like Service, Shipping, Returns, etc.), then automatically route them to the correct department or employee, saving your employees countless hours of time-consuming, tedious tasks (which can put a strain on morale).
Try out the text classifier below that’s pre-trained to sort NPS responses into the categories, Customer Support, Ease of Use, Features, and Pricing.
You can use classifiers like the above on thousands of survey responses or social media comments in just minutes and train them to your own specifications to get exactly the information you need.
You can even combine topic labeling and sentiment analysis for aspect-based sentiment analysis, to categorize individual opinions, first by topic, then by sentiment. This way, you’ll find out which aspects of your business perform particularly well and which may need work.
Text extraction is a text analysis technique that extracts pieces of data from within text. It differs from text classification, in that, the data (words and phrases) already exist in the text.
Techniques like keyword extraction find the most used and most important words and phrases from within a text. The example below shows keyword extraction performed on a text:
This can be helpful to summarize whole texts (like business communications and news reports) or find what important words and phrases customers are using to describe your company and products. Keyword extraction can uncover emerging trends in your field, perform constant brand monitoring (all over the web), and help with competitive research. Try the above pre-trained keyword extractor to try it out.
Another useful text extraction technique is named entity recognition (NER) which finds “named entities” from within text: people, organizations, email address, prices, etc. These can be extracted to automatically populate spreadsheets for marketing (or other) purposes or used for further analysis.
When we think of automated bots, we usually think of chatbots, which help greatly with customer service interactions by handling many of the easy-to-answer queries with information they have gathered through machine learning. Analyzing a chatbot’s knowledge base can give insights into how they perform best for customers – are there, for example, certain specific words that are less helpful than others when aiding customers?
However, beyond this, bots will continue to expand into data analysis because they generate responses quickly, thanks to NLP, and are constantly learning from previous knowledge. AI bots perform analyses super fast because they are working with compounded stored data, meaning they get smarter with every passing second, able to respond to questions with relevant information that humans may have never thought could apply.
As mentioned earlier, AI data analysis is able to go beyond the simple diagnostic analyses of quantitative data, and tackle qualitative data for diagnostic, predictive, and prescriptive analyses. AI-powered business intelligence systems allow you to find out why something may or may not have worked because: 1) there are huge amounts of data available, 2) they have the processing power to handle it, 3) machine learning finds patterns and deviations in all manner of data – and constantly learns from them.
Imagine all that data working together – your personal CRM, survey, and customer interaction data; data about your company from social media, product reviews, and all over the internet; and data about your competition, what they do well and what they do poorly.
Machine learning and deep learning data analytics allows you to use multiple data analysis techniques simultaneously, so you can actually predict outcomes.
There are a number of powerful tools that make unstructured data analytics surprisingly simple and code-free. You don’t have to be a data scientist, or even particularly computer savvy, to harness the power of AI and machine learning to get the most from your data.
MonkeyLearn is a SaaS data analysis platform that allows you to custom-train machine learning models to your needs, often in just a few minutes. MonkeyLearn’s text analysis tools (like sentiment analysis, topic classification, intent classification, text extraction, and more) integrate with the tools you already use and require no code, whatsoever, to get started.
Furthermore, with MonkeyLearn Studio you can combine all of MonkeyLearn’s text analysis techniques – locate and upload data, analyze the data, and visualize it, all in a single interface.
Sign up for a MonkeyLearn demo to learn more about all the advanced text analysis techniques MonkeyLearn has to offer.
October 8th, 2020