How To Use Machine Learning In Your Business

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Machine learning is evolving the way that organizations work with data. The ability of computers to learn from past experiences is presenting new analytical opportunities for businesses of all shapes and sizes across the globe. As machine learning becomes increasingly prominent, learning to integrate the technology into your business processes is paramount to keeping up with competitors. However, to get started, it is possible to incorporate machine learning at a small scale and use ready-made solutions. The key is in doing your homework to ensure you allow machine learning to improve business performance. In this article, we’ll outline some things to consider before you get started with machine learning and ways you can leverage the new technology to improve your business.As with any new technology, it's well worth doing your research before you dive in. There are some fundamental considerations to take into account that will help you choose the best starting point and implement it effectively: 

  1. Outline your business challenges

The first step to the successful integration of machine learning is knowing what you want to improve. You need to be clear about how it will help, using it just because it is a new technology won’t do you any favours, it should solve a business challenge. Usually, the problem will be something that can’t be addressed using standard business logic, and it will rely heavily on a subjective opinion. This could be to help your customers make better decisions using predictive insights. This can work equally well for online shopping recommendations as for generating fraud scores for credit risks. You also need to know what you are going to do with the insights you obtain. We are able to gather increasing volumes of data, which is great, but analysis should always result in actionable insights. 

  1. Ensure your data is good enough

You will probably need to initially work with a data scientist to qualify your data. You need to eliminate unnecessary noise and conflicts and then clean and restructure it. Also, you should put in place a minimum degree of confidence for the problem you’re trying to solve. Once you’re up and running, ensure you have a diverse team of people to check your machine learning algorithms for biases. You should ensure that the output from the initiative doesn't, for example, favor one region or group over another due to an unseen bias in the original programming. 

  1. Strengthen your results with human insights

Be careful not to overdo it with machine learning. Using the technology to automate all of your processes without carefully considering the quality of output won't give you the results you desire. Think of machine learning as a way to allow you to scale and create more significant capabilities for analytics. Applying a layer of human intelligence on top of machine learning will enable you to fully use the expertise of your analytics team and gain the most actionable insight from your data. You’ll also need to ensure you have someone in your team who is able to reconcile business and technical vision to ensure you take the right actions based on the knowledge you gain. 

Ways to incorporate machine learning into your business

There are plenty of ready-made solutions already available to businesses to make machine learning accessible. The following areas are great places to start: 

  1. Improve productivity

You may have uncovered all sorts of business challenges that could be solved with machine learning. However, it’s best to start small with achievable goals. Third party appspowered by machine learning can help you vastly improve your productivity. You can use them across your business to do things such as help select the best images for use on your social media sites, take notes of your phone calls and even invest your money wisely. 

  1. Enhance customer experience

WIth customers growing expectations, improving their experience with your business is paramount. Machine learning enables you to create much more personalized communications. A great place to start with the technology is by creating welcome content for new subscribers and ongoing journeys based on individual engagement and interaction. Using the technology in this way can be a cost-effective solution for driving email campaigns and web content. 

  1. Invest in analytics

Analytics software is a low-cost route of entry to machine learning. What's more, the software enables you to gain business intelligence that can make a real difference. Many of the solutions available have user-friendly drag and drop interfaces, allowing you to deploy predictive analytics solutions in no time. 

  1. Implement chatbots

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Chatbots offer a way to gain immediate value. They are able to interact with customers, answer simple questions and direct issues in the right direction. The implementation of chatbots can particularly help smaller companies with a limited volume of staff and allows customer support to be provided around the clock. Your machine learning technology can hold the fort while your human workforce is sleeping.  

  1. Take advantage of existing platforms

Starting from scratch to build your own machine learning algorithms is quite an undertaking and a costly one at that. Luckily companies such as Google and Facebook have outsourced their efforts which makes it much easier to incorporate the technology into your own applications. Tensorflow is a great place to start; it can be used across a variety of platforms for countless applications. Companies have used it to develop smart replies to messages, to train neural networks to recognise images and even to improvise music. Getting started with machine learning shouldn’t be as difficult as it might seem. We are all interacting with machine learning in our day-to-day lives, even though we may be unaware of it. Implementing some simple solutions can help your company improve productivity and customer service and gain invaluable business insights.

If you’d like to know how we can help with your business then get in touch, we’d love to chat.