Steven Ledgerwood
Steven Ledgerwood 14 October 2016

Machine Learning – Stop Guessing and Start Predicting with Accuracy

For the longest time, retailers have tried to pre-empt what their customers want to buy, even before they know it themselves. Luckily for marketers, we are creatures of habit. Over a sustained period of time, we wear the same clothes, watch the same TV programmes and shop with the same brands.

In our modern era, this repetitiveness is starting to filter through to the data each of us generates every day. As a result, predictive marketing is becoming less of a mysterious dark art and more of an informed scientific process.

This is welcome news to retailers everywhere. However, even if your business has a vast customer database, capturing this behavioural information and analysing patterns is not easy. Filtering and creating a coherent strategy out of actions that appear to be random is even more daunting.

For these reasons, many forward-thinking companies have started to use technology to help. Smarter analytics tools and new machine learning software now offer the opportunity to process vast volumes of diverse behaviour to identify actionable trends.

With this insight to hand, retailers can create relevant, personalised messages for each customer, garnering their loyalty and boosting their sales.

Reducing the guesswork                 

Most retailers know that success equates to keeping customers engaged across every channel and device. Data has become crucial in this battle to engage and retain, especially in today’s omni-channel, mobile-centric environment.

Analysing granular information like page views, check outs, add-to-cart events and search queries across thousands of products should be the first step.

Predictive marketing and machine learning algorithms then build on that intelligence. The technology learns from the historical data pulled and displays relevant product choices to customers, all of which are built around their personal preferences and shopping habits.

Essentially, this lets marketing teams tailor, evolve and grow their campaigns alongside each customer demographic. This effort builds a unified customer profile across every channel. Marketers can create more engaging content that encourage shoppers to take action. For example, reminding customers of an abandoned product in their cart, style recommendations or personalised discounts.

Preventing marketing fatigue

Another benefit is reducing ‘communication irrelevance’. Shoppers are continuously bombarded with empty marketing messages everywhere they go, albeit online or offline. With predictive marketing, retailers can meaningfully communicate with their customers without overwhelmed by messages they do not relate to.

At the same time, retailers can plan ahead with campaigns that will run automatically. This enables better preparation for sales peaks through the year. The results include greater agility and new growth opportunities that would have otherwise remained hidden beneath day-to-day activities.

For example, a retailer might want to know if customers are buying items for themselves or as presents for their loved ones. Once they have gathered the relevant data, predictive marketing can generate specific product recommendations for each category ahead of sales peaks such as Black Friday, Christmas or Valentine’s Day.

Wouldn’t it be nice if your favourite brand created a special offer for your birthday or for someone else you care about? Of course! Would such campaigns increase your loyalty and spending with the brand? Yes, they would.

Backed by smart analytics, predictive marketing and machine learning, marketers can deliver these outcomes in a non-intrusive way. Scheduling and automating a series of personalised emails, discounts and special offers becomes much simpler.

Customer intelligence, analytics and machine learning might seem complicated and intimidating, but what they actually do is decipher basic human behaviour and triggers— improving retailers’ strategies, expanding their conceptual thinking and increasing the success of their marketing programmes.

Consumers are who benefit the most from this technology as they should feel more connected to the brands they love, receive relevant offers and stay in touch with the latest trends.  


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