Machine learning is no longer the stuff of science fiction, nor is it all that new.

Its development dates back to the mid-20th century and was defined in 1959 by Arthur Samuel as a “field of study that gives computers the ability to learn without being explicitly programmed”.

As the modern world becomes increasingly dependent on data-driven technologies, machine learning, along with artificial intelligence (AI), has captured the human imagination.

It is clear that businesses are spending huge amounts of time and money scrambling to adopt the latest and greatest technologies in the hope of out-pacing and out-smarting their rivals.

However, without a customer-centric, business-relevant big data strategy that is embraced company-wide, all the technology in the world won’t sell a thing.

Essentially, machine learning is an automated process that enables software systems to analyse massive data sets and recognise patterns. Using these patterns, the software is able to reprogram and improve itself – without any human intervention.

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Artificial intelligence (AI) is different in that it produces predictable outcomes without digging deeper –  it doesn’t learn, it simply repeats. Both are powerful predictive analytics tools but they do, of course, have their limits.

For companies that are struggling to use the technologies they’ve invested in, or aren’t using them at all yet, here are some key areas of consideration.

Barriers to entry

Machine learning has been successfully adopted by huge global consumer brands such as Amazon, Netflix and Facebook. These tech industry giants use it to consistently improve their customer experience: suggesting relevant products, movies, or friends based on historic user or buyer behaviour.

B2B companies, by comparison, are having limited success.

They are often too quick to adopt software systems without a solid data strategy in place first – and then don’t guide employees on how to use the new tools effectively. This ultimately results in minimal take-up of advanced data-driven tools.

In fact, the Harvard Business Review Analytics Services recently reported that only 23% of global businesses use technology to manage their customer relationships.

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To successfully adopt in the style of Amazon et al, companies need to remember one fundamental fact: machine learning and AI technologies don’t solve problems, they deliver actionable insights to users based on the data they’ve been given. In other words, to get the most out of these technologies, they need a human touch.

Human technology

Modern marketing is about having a conversation with highly targeted audiences. It’s about creating engaging customer experiences by deploying relevant content and memorable contact.

Advanced marketers are so good at this that they can guide a customer further down the sales funnel than ever before, lining up a clean deal for the sales team to close.

Achieving this requires a clear strategy, a good relationship between sales and marketing, and to clinch it all, technology that is understood, integrated and used correctly.

Yes, machine learning and automated data analysis are useful tools for humans, but they’re not replacements for humans themselves.

Clean data

Data is a crucial ingredient in any machine learning tool. Take customer analytics as an example: using automated data capture, a company can continuously track and analyse customer and prospect engagement over time.

Based on this intelligence, the company can proactively identify current opportunities and future trends that are vital to both strengthening existing customer relationships and forging new ones. It sounds simple, but the insights are only as smart as the data they are based on.

Too many companies value the quantity of data they have on file, rather than its quality.

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Reams and reams of useless information is expensive to keep, clutters communication and ultimately does not generate leads. It also influences the patterns being processed by the software: when irrelevant data is added to the mix, it will naturally impact the outcome and deliver insights that may not be correct.

Businesses don’t need any and all data, they need the right data. This requires a careful approach to sourcing information first and foremost, followed by regular data health-checks to clear out old information that no longer holds any real business value.

Bad data will naturally lead to sub-par results, while the right data will help sales and marketing professionals build better ‘human’ relationships with their customers.

Greater transparency

In the modern business environment, customer relationships develop through multiple communications channels such email, social, and SMS.

Each interaction is valuable information that needs to be captured and turned into customer-centric insights, such as who buys what, when, where and why.

When implemented company-wide, new technologies can radically improve transparency across the company.

Customer relationship and communication silos are broken down and critical information is no longer sitting in one individual’s email inbox or on their mobile phone.

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This greater transparency is vital for inter-departmental collaboration, bringing teams together in pursuit of the same goal, with all the information they need at their finger-tips. For companies that are not just focused on – but obsessed with – offering the best customer experience possible, radical transparency is a bold and rewarding path to take.

There is no doubt that companies will have to increasingly rely on data-driven technologies to stay competitive.

Machine learning is still in the early stages though, and cannot replace the human element at the heart of long-term business relationships. It’s not about choosing between ‘man’ or ‘machine’, it’s about using technology to sell better, faster.

Sourced by Peter Linas, international managing director, Bullhorn