Interviews, insight & analysis on digital media & marketing

Data needn’t be a minefield if analysed correctly

By Thomas Linley at Cloud Technology Solutions, looks at how technologies like Machine Learning (ML) and Artificial Intelligence (AI) can help marketers make the most of their data. 

The future direction of marketing departments has been significantly influenced by digital transformation. The ability to gather large quantities of data on consumers and their behaviours has unearthed new ways of personalising marketing collateral and engaging prospective customers.

As a result, marketing strategies are increasingly being driven by data, which continues to garner results. Indeed, a report from PwC found that data-driven organisations are three times more likely to report significant improvement in decision-making. 

Traditionally, marketing teams have relied on their own data. They’ve been able to monitor their customer’s journey online, use information from newsletter sign-ups and rely on campaign and social media engagement figures to see how consumers respond to their brand.

But this siloed approach to holding and analysing data means marketers could be missing crucial information about their customers from other departments within their organisation. After all, every department will be collecting their own sets of data. 

Breaking down these siloes within organisations so that data can be stored in one central location, and available to everyone across a business, ensures information can be compared and interrogated so brands have an entirely accurate view of their customers.

It also means technologies like machine learning (ML) and artificial intelligence (AI) can be utilised to uncover a whole treasure trove of information, allowing brands to shape their model and marketing strategies accordingly.

Making use of nascent technologies

The marketing industry has already started to embrace ML and AI, but there is still so much that can be done once the foundations have been laid with gathering data.

For example, marketeers and sales teams are using ML to help with online chatbots in some cases. However, the technology can be used to analyse more complex conversations – for instance an interaction between a call centre employee and a customer.

Using audio interpretation, ML can predict how a conversation between a team member and a customer is going, by analysing the conversation and instructing the team member how best to deal with the customer. Not only can this help marketing teams boost sales, but it will also improve efficiencies across the business. 

It’s instances like these where having complete data sets from each department really comes into play. Information from the finance teams, marketing teams and IT teams will all be utilised by the ML technology to inform the employee on how to respond to the customer. 

By breaking down any siloes in an organisation, any learning made by ML can feed to all departments seamlessly. This is particularly beneficial when ML and AI are used to forecast trends. Being able to respond and adapt quickly to trends can have a huge impact on sales.

But trends – particularly those born on social media – are becoming increasingly short-lived, meaning speed really is of the essence if brands want to capitalise on them.

To identify trends, ML will comb through huge amounts of external and internal data to make connections between data points. The technology can identify how a product might sell, who might buy it as well as the quantity needed to avoid unnecessary and costly waste – all at speed.

This means the marketing teams will be able to quickly amend their marketing strategies, buying teams can ensure they have the right levels of stock, while the IT department can focus on ensuring the technology is prepared for a potential increase in website visits. ML technology can ensure the entire business is prepared. 

Recently we have seen an increasing number of marketeers looking to utilise ML and AI in their reporting and evaluation, pulling data from across the organisation to analyse whether campaigns have delivered value and to measure its true success.

The technology can also identify areas that performed well so marketing departments can build on those in the future. 

The possibilities of ML and AI in the marketing industry are endless. But it all relies on good quality data that underpins it all. In order to access that data, departments need to be working together and sharing their information so that the entire organisation can benefit, and individual teams can deliver value for their business.