Contrary to advanced analytic techniques that look for causality first, machine learning techniques look for opportunities to improve decision making depended on the predictive value of more significant data sets. Datasets comprise of both structured, completely organized data like databases, and unstructured, less organized data, for example, text in a sales contract or web traffic. The global success of social networks is increasing the growth in unstructured data type, making it extremely beneficial for enterprises to efficiently leverage unstructured data.
In enterprise businesses, machine learning is used heavily in managing predictive and prescriptive tasks, allowing companies to describe which behaviors have the potential to drive required results. Now, various businesses are successfully applying machine learning principles to marketing and sales challenges and thereby improving their customer base. These companies are even making lemonade out of lemons by ensuring their employees are taking an online machine learning course to get trained in the principles of machine learning. In this way, they don’t have to struggle to find people with these skill sets.
Customer segmentation
Customer segmentation, churn prediction and customer lifetime value prediction are some of the problems that most marketers face at least once in their lifetime. Enterprises often receive a bulk of marketing data from different sources like email campaigns, website visitors, and lead data. The right prediction for individual marketing can be done using data mining and machine learning. Now, marketers can eliminate the guesswork involved in marketing like a user-provided pattern of behavior during a trial period. With this model, past behaviors of users, identifying chances of conversion to paid version can be predicted with zero investment in expensive hardware. An efficient model for this decision problem would allow a program to accelerate customer interventions to prompt the customer for early conversion or for better engagement during the trial period.
Core Data Filtering to Eliminate Setbacks
According to your business and platform, you might want to implement machine learning for data filtering and fraud detection objectives. These problems come under the machine learning bucket called classification problems. For online operating businesses, machine learning is valuable. Businesses can track whether a transaction is real or fake using algorithms implemented for fraud detection.
Spam filtering for mail is an important example of machine learning to data filtering. Now, ransomware attacks can be prevented as machine learning algorithms can decide whether a mail is legitimate or not depending on various pieces of content within a mail (and some data not visible to the email reader)
Moreover, these algorithms can be implemented to other problems like whether a user-specific content posted being posted is relevant to a potential audience which has people of different age groups.
Right Product Recommendations
Machine learning recommender systems are in great demand nowadays. Amazon’s website is a perfect example of a machine learning recommender system. Amazon can retain their large customer base using purchase histories and by predicting browsing and buying habits to recommend specific products customers are likely to purchase.
Not only can bigger companies use recommender engines like Amazon, but businesses of any size, big or small, with an established online or mobile presence can use these systems.
There are many other examples of recommender engines – like Netflix uses one to recommend movies you would like to watch, LinkedIn uses recommender engines to people you would like to know, and Best Buy has started to use recommender systems to improve its online sales.
If your business is also part of an online landscape, you can use recommender engines to improve your business sale.
However, it must be noted that these systems are complicated systems that consume time and money to build. Skilled data scientists are required for this; those who are adept at R, SAS or other data science-related skills.
Understanding behavior of customers
Machine learning is also helpful in sentiment analysis. Though public opinion may sound squishy to non-marketing folks, it really helps in making big decisions.
Consider a movie studio that has recently put out a trailer for a summer blockbuster. They can evaluate social chatter to check what tickles their target audience, then adjust their ads instantly to surface what people are actually looking for to get their audiences to flock to theaters.
Another example:
Recently a game studio integrated a new title in a video game line that lacks a game mode that fans were looking for. The gamers used social media to oppose this move. By studying the online chatter, the studio understood their conversation and rescheduled their release to integrate the feature, thus turning critics into users.
Now, the question is how could companies pull faint signals out of a series of tweets? This is where machine learning comes in. This type of social media listening using machine learning has become a norm for businesses.
The major problem for businesses today is to know how and when their strategy will be achieved. They need to be able to run with a pace of change that predictive analytics can offer. The advanced era of business decisions will put those companies on top of the corporate food chain which knows what makes a good ML project.