ML innovation doesn’t need to be reserved for major players like Amazon and Netflix
Major players in areas including ecommerce and entertainment – such as Amazon and Netflix – are often lauded for their investment in machine learning (ML) technologies to improve the customer experience. While these global titans have the funding to support ambitious technology projects, such innovations also have huge potential for smaller businesses. According to software house STX Next, implementing ML need not be as big an undertaking as many think.
Some of the perceived barriers to implementing machine learning are that it requires huge investment in terms of time, money and manpower, and that it could threaten the role of existing human staff. While making the most of machine learning does indeed require significant effort, there are ways of approaching it that make it accessible to all ecommerce companies.
Łukasz Grzybowski, Head of Machine Learning & Data Engineering at STX Next, said: “The applications for machine learning technology are becoming more widespread every day. At present, ML is used in areas such as recommender systems employed by major retailers or streaming providers, alongside other customer-centric innovations like chatbots and digital body language tracking. Behind the scenes, it is also being used for tasks such as automated A/B testing and dynamic pricing. All of these applications are great in increasing customer conversion, decreasing bounce rate and improving customer satisfaction through better personalisation.
“Integrating all of this into an ecommerce platform might seem like an excessively painstaking task if you’re not a behemoth like Amazon, but there are ways of approaching it that make it viable.
“Firstly, it’s important to realise that implementing ML in processes like customer segmentation means digging more deeply into data than ever before, and ensuring the ML algorithms you use are underpinned by a comprehensive understanding of this data. Simply taking superficially similar customers and grouping them together when recommending products won’t go far enough.
“Secondly, it’s about going a step further and preparing the business to be compatible with machine learning in the long run. For example, business problems where ML could be useful should be identified early on, and companies should get into the habit of preparing their data so that ML can be integrated without too much difficulty. Crucially, organisations should also identify relevant machine learning experts who can drive such projects forward, either internally or through outsourcing or external recruitment.
“Finally, something that often comes up in discussions about ML innovation is that it threatens the role of human staff due to increased automation. In our experience, this isn’t the case. Instead, it frees employees from some of the more time-consuming, mundane tasks, and enhances their roles by giving them quicker access to data and insights that can help transform the customer experience.”
Grzybowski concluded: “There’s no need for smaller ecommerce companies to be left behind in the machine learning revolution. With the right approaches, there’s every reason to believe that any business can boast the innovations that have made the Amazons of this world such a success.”
About STX Next
STX Next is the largest software house in Europe specialising in designing and creating digital solutions in the Python programming language. The company has been operating since 2005 and cooperates with over 400 people through five offices in Poland. STX’s clients include leading international corporations, small and medium enterprises and the most innovative start-ups from around the world.
