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Using Machine Learning In Business: 5 Best Ways

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Machine learning has imperceptibly infiltrated every aspect of our daily lives: our cars, our healthcare, and the food we eat. Major market players have been leaders in cutting-edge technology so far, but a promising new generation of machine learning apps and tools are entering the game. They will transform the machine learning hype from fiction into tangible, socially useful things.

For business, machine learning is already an indispensable lifesaver. And here’s how founders can use it.

5 Samples Of A Machine Learning Appliance

1. Data And Behavior Patterns

How is the modern world different from the world that existed 25 years ago? With the computerization of all processes, as a result of which huge amounts of data are continuously generated and stored.

Machine learning technology allows you to take all this complex and immense data and describe it using a relatively simple model that is available for implementation in modern business systems.

For example, the audience of a retail chain that sells through supermarkets and the Internet is millions of shoppers who purchase thousands of products every day while creating huge transaction bases. Machine learning theory assumes that a finite number of patterns of buying behavior can be found in this data. A typical weekly shopping trip is one standard, recognizable pattern, planning a friendly party is another pattern, having a baby is another pattern, and they can all be explained by some underlying factors and their interactions. It remains to become data-driven organization and the prospects of such a business can only be envied.

2. Improving Advertising Campaigns

The algorithm of work of marketers is as follows: they create hypotheses, test them, evaluate, analyze. It is long, laborious, and sometimes incorrect because the information changes every second.

For example, it will take a marketer about 4 hours to evaluate 20 ad campaigns with 10 behavioral parameters for 5 different segments. If such an analysis is carried out every day, then the specialist will spend exactly half of the work time assessing the quality of campaigns. When using ML, the assessment takes place in a matter of minutes, and the number of segments and behavior parameters is unlimited.

This allows you to react more quickly to changes in the quality of traffic generated by advertising campaigns. As a result, the specialist spends more time creating hypotheses, rather than routine actions.

3. Website Design And Ux Optimization

Machine learning can help marketers optimize their website designs. It is also used to conduct A/B tests and improve user experience and measure UX metrics such as bounce rate, session time, etc. In no time, these metrics will help you understand user behavior, especially when working on new ideas.

4. Customer Support Using Chatbots

Not all chatbots are machine learning-based. Job-oriented chatbots tend to use ML less often, but their capabilities are also limited to answering simple questions. It is difficult, although possible, to communicate with such bots in a dialogue mode.

Digital assistants use machine learning and other technologies like natural language understanding principles to provide enhanced support.

The use of chatbots really depends on the specifics of the application. In addition to the obvious customer support, chatbots can help generate leads and make appointments to customers, announce new products and discounts.

5. New Products And Services Generation

For example, voice search is based on AI and has already become a part of the daily life of many users. Voice assistants help brands to hyper-personalize their requests, and voice analytics help them find new solutions to improve UX design and user support.

Wrap Up 

There is now every reason to be optimistic about the benefits that machine learning can bring. Smart apps and tools will reshape old industries and increase their productivity in the nearest possible future. They will reduce the barriers to building intelligent applications by driving millions of new ideas into production every day.

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