Machine Learning Masterclass #5: The future of e-commerce with Machine Learning
Machine Learning has become an integral part of day to day life, particularly in the Digital World. Companies such as Google, Facebook, Netflix, and Amazon now use Machine Learning to continually update their services and algorithms. Amazon, for example, uses a Machine Learning algorithm to improve the accuracy of its product recommendations.
In our last blog, we looked at the general tools and applications for Machine Learning. But, more specifically, Machine Learning is going to have a dramatic effect on e-commerce.
Chatbots are the future
Companies are always trying to improve their customer service, especially e-commerce businesses. Websites or social media pages have some method of contacting the company, and a person usually responds to any messages or enquiries that come through these channels. This means that the customer can be left waiting for someone in the company’s customer service department to respond. This can cause delays and ultimately affect the customer’s impression of the company.
Machine Learning has a solution to this issue, and it is already slowly being implemented by some companies. Chatbots use Machine Learning algorithms to analyse customers’ queries to decide on an appropriate answer or direct the customer to the relevant department.
The obvious benefit of chatbots is that they can reduce the waiting time and potentially the overall time to find a solution to the customer’s problem. The future certainly looks to be one which includes chatbots, especially as Facebook has already opened up Messenger to chatbots. We will most likely encounter them when we have a query about our product or service but some reports have suggested that customers will place orders through a chatbot.
Improving product recommendations
We have already mentioned that Amazon uses Machine Learning in its product recommendations. This should prompt other companies to follow suit.
Instead of simply recording the keywords of users’ searches, a Machine Learning algorithm would allow other metrics such as click-through rates, previous ratings, or previously bought products to inform future product recommendations.
The more accurate a company’s recommendations are, the more likely it is that the recommendations will increase their conversion rates and pull in more sales.
Forecasting, logistics, and supply and demand analysis
As discussed previously, the application of a Machine Learning algorithm allows us to interpret a huge body of data and create an output that would take humans an indefinite length of time to compute. Using a real-time data input can allow these algorithms to perform some really clever functions.
A Machine Learning algorithm could allow for much more accurate forecasting and prediction. The algorithm could process data such as buyers’ behaviour, inventory and sales, seasonality, and much more. The output of this analysis could improve the overall efficiency of logistics, marketing and sales.
By analysing current demand and supply data, alongside extremely accurate forecasts, the Machine Learning algorithm could accurately predict the optimal time to order more stock or even when to start a promotional campaign to make the most of a spike in interest and demand.
If you are curious to know how we see the future of Machine Learning, its connection to digital marketing and its potential SEO applications, then keep an eye on this blog series where we’ll be exploring its practical applications. If you’re interested in how Machine Learning can affect your SEO rankings talk to one of our technical SEO experts today.