How SMEs in e-commerce can drive value from machine learning

Despite the promise of major benefits and positive return on investment, adopting technologies such as machine learning or artificial intelligence is often seen as a daunting and disruptive process – particularly to smaller businesses. In contrast, e-commerce big hitters such as Amazon and streaming giants such as Netflix have used machine learning to hugely positive effect, helping to improve their customer experience and stand head and shoulders above the competition.

Although many smaller businesses might see machine learning as financially prohibitive, implementing the technology need not be as big an undertaking as many think. While making the most of machine learning does indeed require significant effort, there are ways of approaching it that make it accessible to all e-commerce companies.

Where machine learning makes a difference

The applications for machine learning technology are becoming more widespread every day. At present, machine learning 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.

Implementing this technology and integrating all of it into an e-commerce platform is never going to be a straightforward process if you’re not a ‘money is no object’ behemoth like Amazon. However, below we explore how this can be done, even in the wake of the financial impact of Covid-19.

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Dig for victory

It’s important to realise that implementing machine learning in processes like customer segmentation means digging deeper into data than ever before, and ensuring the algorithms your business uses are underpinned by a thorough understanding of this data. Simply taking superficially similar customers and grouping them together when recommending products won’t go far enough for it to work successfully.

The next step is to ensure the business is compatible with machine learning in the long run. For example, business problems where machine learning could be useful should be identified early on, and companies should get into the habit of preparing their data so that machine learning can be integrated without too much difficulty and disruption. Crucially, organisations should also identify relevant machine learning experts who can drive such projects forward, either internally or through outsourcing via external consultants.

Finally, one of the most pressing concerns in the minds of many business leaders reluctant to implement machine learning is the threat the technology could pose to human staff. Some level of natural scepticism is understandable, but once the ins and outs of machine learning are fully known, it becomes clear that it poses little danger to people’s roles.

With machine learning innovation in place, the more time-consuming or mundane tasks which had previously been overseen by humans are now carried out automatically. This leaves staff with more time to address the most pressing issues, and quicker access to data and insights that can help them transform the customer experience.

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The bold approach works best

When armed with the right knowledge and expertise, smaller businesses need not be daunted by the challenges of making machine learning innovation a reality. As long as companies are willing to be bold and meticulous in their approach, there is no need for the technology to be reserved strictly to the largest corporations.

To help motivate teams in this drive to integrate machine learning, it is important to consider the end goal and the major benefits the technology promises. Once the initial transition has occurred, your business will have successfully implemented a customer-first approach to its offerings, of a quality similar to that of the big players in ecommerce. In an era where customer service can make or break a business, making full use of machine learning will be fruitful in the long run.

Written by Łukasz Grzybowski, head of machine learning & data engineering at STX Next

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