Why Machine Learning is so important in customer analytics

The huge data volumes that are present in most industries, call for better structuring and analysis in order to serve companies' decision-making processes better. Significant help on that matter comes from machine learning, usually viewed as part of artificial intelligence. When computer algorithms, by experience and as they use certain data, can automatically improve their performance, we refer to them as "machine learning". Those algorithms are able to build specific models based on sample data, and with time and this learning process, they can make predictions or certain decisions without being told to do so. Such a machine learning process helps businesses to analyze various aspects of their performance as well as present predictive analytics for future outcomes and planning.

The advantage of those algorithms "learning by themselves", replaces the need for people to develop similar data analytics computer methods on their own. One of the many areas where such machine learning comes in handy is customer behavior analysis, as many companies try to turn such big data into actionable insights when it comes to serving their customers better.

As more and more companies go through their digital transformation phase, the need to have deep learning of their customer experience becomes clearer. Customer analysis for business leaders is now a necessity, and machine learning in customer data becomes key for improving products and services. Data analysts, with the help of machine learning and artificial intelligence, can now invest their time in interpreting current and historical data, in order to serve their companies with actionable insights for improved business decisions for all customer segments.

The customer's journey just entered a new phase, as the dialogue between companies and their millions of customers continues. Once customer conversations from all over the open web are collected and analyzed, companies can make data-driven decisions about their brands. When there is a better understanding of the true needs and wants of millions of customers, companies can maintain their competitive advantage and use such data models to improve their brands for both new and existing customers.

Companies nowadays have no choice but to perform as customer-centric entities. Analyzing data of millions of customer behaviors and consumer conversations all at once, while trying to treat such a large customer base as individuals, calls for careful descriptive analytics. This means using current and historical data to identify business trends. It also signifies the need to quickly define patterns of behavior and customer experience in many areas, so that better and more accurate solutions would be offered to any business problems.

Machine learning in customer analytics, therefore, holds many important data science benefits, intended for the improvement of the customer journey. Behavioral patterns of customers, based also on their feedback data, are easier to understand using machine learning applications. Knowledge gained from such advanced analytics is what companies need in order to improve customer engagement with their brands.

Why machine learning is so important for businesses

Machine learning answers questions, challenges, and digital experiences companies face when dealing with large volumes of customers. Machine learning techniques help identify patterns of consumer behavior. Applications of machine learning in customer analytics prove themselves time and again key for many businesses, as they consider the following factors.

• The dramatic increase in the levels of data that needs to be processed

The need for companies to collect data and draw useful insights from it keeps increasing a lot. This is due to the need to analyze the ever-increasing large data volumes from thousands, and sometimes millions of customers. In order to find patterns of customer experience, business leaders must use machine learning and artificial intelligence for structuring such endless pieces of information.

• The need for quick understanding of such unstructured data

Customer analytics of millions of data points must happen quickly since business markets are very dynamic and competitors also move fast. Machine learning enables the quick processing of huge volumes of big data, in order for data analytics to take place. Business leaders can work with such customer analytics information and decide quickly on their next strategic moves. Big data can be analyzed in minutes, once machine learning is employed. Business analytics and decision-making can then be carried out quickly in order for companies to stay ahead of their competition.

• The improvements in computing power needed to run the algorithms

As algorithms hold the key for computer power, it is apparent that as long as such algorithms keep improving, they require less computing power. That equation helps in saving costs and time for many businesses and contributes to the quickness of the entire process. Since customer analytics happen quicker and with more time efficiency, so does the ability of companies to enact machine learning data conclusions in a much faster way.

• The growth of inexpensive massively parallel processing techniques

The ability of machine learning to operate several parallel processes simultaneously is also a key factor in supplying businesses with faster and more accurate customer analytics. The algorithms carry parallel operations on the same data since they need to analyze it from different angles. If they had to do such operations separately and wait for each task to finish before the next task can begin, this would have been a much slower and more expensive process. Thanks to today's technology and constant computing improvements, customer data, and customer experience patterns are being analyzed with massively parallel processing techniques and therefore result in quick insights and conclusions.

The benefits of using machine learning vs. traditional data analysis methods

While the benefits of using AI-powered consumer insights serve for the ultimate deep-learning customer experience data that companies evaluate, it is the machine learning operations that make up an important part of that reality. When digital insights in customer analytics are employed, the following benefits help companies keep pace with their markets and competitors. And as expected, customer analytics take a whole different turn for the better when compared with traditional methods of analyzing customer experience.

• Handling massive amounts of data simultaneously and quickly

It simply became impossible to draw conclusions from millions of customers' needs and wants based on small sample surveys and focus groups. Data analytics replaces the traditional methods of collecting and analyzing customer behavior, as it supplies actionable insights for businesses more fully and quickly. In addition, customers may change their sentiments for brands very quickly, as companies need to react to such turns. Companies also need to worry about their competitors' actions, as they also look for their customer data for newer actions.

• Self-learning algorithms fine-tune themselves for better results

The fact that machines can self-learn, in real-time, and without constant human help, improve companies' decision-making processes. The ability to self-learn doesn't end when machines encounter new data for the first time, as they continue to fine-tune themselves and actually perform better with time. Analyzing data is a continuous process that, therefore, improves over time. Such a process was never really achievable with the old analysis methods.

• Algorithmic details may sometimes be difficult to decipher but can lead to new thinking paths

Differing greatly from past methods is also the ability of data science to create new paths of thinking. Oftentimes, besides the customer analytics data results, machine learning algorithms may create new data variations with no business meaning. Data scientists and business managers may encounter strange features from machine learning algorithms that combine several regular aspects into a newly-created and difficult-to-figure-out item. For example, a song has a genre, a singer, lyrics, music, and language. A machine-learning algorithm may create a new item featuring all five aspects in a complex combination that humans may not be able to comprehend. Such an item would not have any business relevance too. But in a strange way, it sometimes can help business managers to predict something or lead them to new ways of looking at business situations.

• Creating correlations that can sometimes predict ahead of time

As data analytics shows, machine learning is more concerned with the future outcomes and predictive results it supplies, and less about the process which led it to achieve such data. Such deep learning data analysis helps to identify patterns of customer experience, but it is usually producing patterns that are more correlations than causations. Certain geographical disease-related reports, for example, may collectively and with machine learning data analysis, predict where the next cases of such diseases may erupt. Such data analytics can assist the Centers for disease control to prepare for outbreaks of sickness. Such predictions were not possible when using traditional data analysis methods.

• Dealing with unstructured data and having to filter spam and bots

Finally, customer data with millions of items to deal with can't stay unstructured, or otherwise, businesses may not be able to figure out what their customers are expecting from their brands. It is important for machine learning to filter out spam and bots when structuring customer data in order to achieve clean results. Traditionally, working with smaller samples, such as in surveys and focus groups, it was much easier to delete unrelated bits of information. Currently, with such massive data volumes, it is much harder to achieve, and data-driven rules and instructions must be given to machine learning algorithms in order for them to clean out any unwanted spam.

Cross-organizational uses of machine learning as offered by Affogata

Companies looking to improve their customer engagement and to offer better brands, must not only constantly evaluate their business results but also learn what their customers are saying about their products and services. Customer satisfaction can increase as a result of a better decision-making process, which relies on customer feedback analysis done by machine learning and artificial intelligence.

Affogata offers a variety of customer feedback analyses for the various uses of different teams within an organization:

• Product: how analysis of customer feedback leads to improving products’ design

Product teams are interested in receiving customer reviews of products they developed, on new features added to existing products, and on products currently in development. But as the collective opinions of millions of customers are being analyzed, Affogata offers product-related categories and clusters of customer topics so that product teams can prioritize through the vast volumes of data.

• Customer service: measurement of customer satisfaction based on feedback analysis

Artificial intelligence and machine learning contribute lots of valuable insights to customer service teams as well. Customer satisfaction is an important factor to measure, but it usually contains several different aspects, such as response time to complaints, the types of responses to customers, and more. Data-driven decisions can take place once the overall customer feedback is being tracked and analyzed, and companies are also able to measure the changes in customer service sentiment over time.

• Marketing: customer segmentation and also feedback analysis of campaigns

Marketing teams are concerned with such issues as the performance of the company's campaigns, customer segmentation operations, advertising issues, public relations materials and processes, content pieces for blogs and social media, and much more. All marketing efforts are measured not only by statistical analysis but also by understanding the customers' sentiments towards such actions. Marketing managers can receive specific reports and analyses of customer reactions to their efforts and campaigns, and analyze the results for future conclusions.

• Sales: customer churn predictions and other sales operations analysis

Sales teams are measured by how many units they sold and by how much revenue their efforts generated. But customer feedback data analysis may come in handy with respect to areas of concern, such as the ability to find out customers who would like to churn and their reasoning for wanting to do so. In addition, salespeople may learn what customers are saying about their pitches and attitudes in selling products and services. They may be able to build models of sales operations that would yield better results in the future, as they learn what customers did not like about how they sell the company's products to them.

• Management: brand sentiment analysis

Finally, CEOs and management in general, are concerned with the overall company's performance alongside the efforts of each team. Daily executive customer feedback reports may help keep managers with their pulse close to their clients. A review of their brand sentiment score, coupled with analysis of the top topics their customers are conversing about the company's brand is also key, as they can use such data for further business decisions. A statistical analysis, their sales reports, and their customer feedback intel would present them with as complete a picture as possible with regards to their company's performance. Machine learning plays a crucial part in such a process.