Using Machine Learning to Build Audience-Led Analytics | QueryClick

17 July 2018 // Chris Liversidge

The proliferation of mobile use in our every day lives has seen the average person receive up to 4,000 marketing messages each day, from Facebook ads to direct mail, brands are fiercely competing for our attention. With consumer eyeballs taking in more content than ever, brands need to have a solid cross-channel marketing strategy in place to ensure their marketing messages are heard through the noise. Could machine learning be the answer?

The Data Challenge

Many brands admit that they find it challenging to gain a single view of their customer's buying journey. According to recent report published by multinational professional services network PwC, 35% of retailers admitted that they are currently struggling to implement a strategy that provides a single view of their customer, and 31% said that although they have achieved it, there is still room for improvement.

We believe that technology is the key to breaking down silos, successfully mastering a single view of the customer journey across all channels and determining which touch points are needed to turn the engagement into a conversion.

Forbes recently reported that by 2020 real-time personalised advertising across digital platforms and optimised message targeting for accuracy, context and precision will have accelerated. But we know the technology is here today: we've invented it!

Transforming Sessions to People with Machine Leaning

Historically, many brands using machine learning technology found that the tools on the market only delivered historic attribution insights and failed to map individual customers in legacy CRMs, preventing proactive spend adjustment to fit the marketing blend and deliver immediate spend performance benefit.

Our new machine learning tool Corvidae, is a unique in that it allows brands to unite data sources and obtain a complete picture of the customer journey across all channels, online and off. It de-duplicates online sessions to customers and maps them across data silos to offer brands a clear view of their customer behaviours and preferences.

Innovations like these allow CMOs to correctly measure and attribute the impact of their marketing spend and gain an understanding of which channels and campaigns are the most effective in driving revenue and key objectives such as increased brand awareness.

This also takes the guess work out of budgeting for a multi-channel marketing strategy. By adopting forecasted attribution, brands can adjust their marketing spend across channels in real-time to provide the greatest overall ROI for their current and future budgets. This allows us to put the emphasis on measuring performance and act as an Ombudsman for agency performance.

I shared my insights during a talk at BrightonSEO which focused on how brands can transform sessions to people with machine learning, build audience-led analytics to better target channel budgets and drive a multi-channel marketing strategy that will make a real impact with their target audience.

If you would like to view the content, or discuss the topic further with one of our experts please do get in touch.

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