September 9, 2019
Now, more than ever, businesses need to know both their customers and market trends. As the Earth’s population increases, so does the amount of competition in the market place. This is why analytical programs, data mining and machine learning should be in every company’s business strategy.
The word analytics may bring Google Analytics to mind. The service is used to measure website statistics and digital campaigns. Though it's thorough, it’s not quite the machine learning that companies can use for general business needs.
Artificial intelligence (AI) and machine learning are often treated as interchangeable terms, but there are differences between them. An AI can be programmed to act like and mimic something else, but it can’t learn on its own. Machine learning, on the other hand, has the ability to learn and adapt from the data it assesses and consumes.
Standard analytical solutions typically look at and assess current data, and often over a broad spectrum. While this does lead to crucial information for companies, it still relies on human understanding and general data sets. By data mining this information, a specific set of rules can be analyzed and verified. If the desired results are achieved, machine learning is able to apply it to more and more sets in order to determine outcomes and ‘evolve’ as new data is received.
The integration of machine learning is the next logical step when it comes to analytics. Systems will be able to report on real-time events as well as predict future ones based on previous data.
One of the industries that will benefit from this is retail. As one of the largest sectors in the business world, B2C relies on pulling in the right customers and figuring out their buying habits. Unfortunately, it’s not always as easy as looking at raw analytical data.
By integrating a data mining and machine learning solution into a customer’s purchasing history, it’s possible to extract and categorize their buying habits. This information can be used to scour more customers with specific criteria and adapt the algorithm as it needs to. This enables retailers to stock the correct products and not be left with dead stock.
Another beneficiary of the marriage of AI and machine learning would be the supply chain. It is difficult trying to predict how much stock a customer will require, and how much should be produced overall. By using data mining and machine learning, stock problems in the supply chain can be negated.
Similar to retailers, manufacturers and distributors can use data mining to assess business customers and manufacturing. Now manufacturers and distributors can predict what customers will order and manage their warehouses and stocks accordingly.
Big banks and startups are already utilizing machine learning and data mining in order to boost profits. This ranges from general accounts to private wealth management. In the latter field, companies are building customer profiles through data mining and looking at which investments would be best for those customers. It is coupled with the mass consumption of relevant data, as well as digitization and analytics. This leads to an overall better business and experience for the customer.
Analytics can be used to further marketing and integrate into customer relationship management (CRM) solutions. Have a look at our article on the subject of analytics and business, which includes the customer’s journey and identifying opportunities.