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Machine learning (ML) and artificial intelligence have become prominent amongst platforms like Microsoft, Azure, Amazon and Google. These organisations are so invested in ML that they have come up with their own cloud learning platforms. For most of us, ML seems like a new phenomenon, but it’s been around for a while – just without a name. The most common use of ML is image recognition – like when Facebook automatically tags uploaded photos or speech recognition – think Siri and converting audio into texts.
Here are some more ways ML makes everyday life easier:
When we think of product recommendations, online retailers like eBay and so many others may immediately come to mind. The way ML is integrated into these online stores is that it is based on the customer’s purchase history and the bigger the store, the more they can recommend. The algorithm identifies specific patterns among items and hones in on grouping products into clusters. Other companies that do this include Booking.com (upselling – listing several options) and ASOS (cross-selling by recommending other items that are like the one being searched).
Eliminate Manual Data Entry
Accurate manual data entry is probably one of the most significant issues for organisations. ML makes everything more manageable by using algorithms and predictive modelling to improve how data is organised. These programs use discovered data to improve the process in the long run through mimicking data entry tasks. As a result, employees are more productive and can focus on other high-value tasks rather than data-entry.
Customer Segmentation and Lifetime Value Prediction (LVP)
Customer segmentation and understanding the value of that customer is imperative to businesses and their survival. Even though this concept is so important, many businesses struggle when they have lots of data coming in from different mediums such as email campaigns or website forms. Data mining and ML is the answer to this issue because these powerful tools use an accurate prediction for the marketing professional to achieve their goals. Marketers are now able to eliminate all the intensive hours that go to organising data because ML is data driven. ML allows businesses to discover response patterns, for example, when a customer buys something from a business and then they randomly switch to another business for the same product; the first business can reach out to that customer and offer them incentives like a discount or coupon to potentially make the customer return.
ML helps businesses to forecast any potential unplanned outcomes. In ML, sales and inventory are the two main vital concepts to determine if a company will fall short in the future. This can be used to assist in determining specific prices and maximise sales.
It is widely known and recognised that when it comes to customer service, it does not end after the customer has purchased something, rather the post-sales relationship is essential for customer retention. This is a common problem and most of the time it relates to the after-sales support. The introduction of chatbots is a primary example of how ML helps businesses to assist customers with enquiries after hours.
ML is also an excellent tool for businesses to personalise the customer experience. There are so many companies that have mastered personalisation such as Spotify or Netflix where they utilise their algorithms to customise, create and manage what their viewers are interested in. According to Gartner, organisations that invest in individualisation will outsell their competitors by more than 30%. Integrating a personalisation strategy across all related channels should be the goal for businesses.
Data is the forefront accelerator for businesses today, and without it, businesses would not know what to improve. ML enhances the way this data is captivated and improves everyday operations. Become a data subject matter expert (SME) today by specialising in machine learning with a Master of Data Science at University of New South Wales online!
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