By Elliot Holding, cloud account manager at Cloud Technology Solutions

Data has become a key tool in a marketeer or creative’s arsenal. It provides the industry with a constantly updated stream of information about existing and potential customers, and entire marketing strategies are being based on the data being gathered.

But with an ever-growing volume of data being collected, making sense of it without the help of technologies like machine learning (ML) and artificial intelligence (AI) is becoming a challenge.

The potential that ML and AI offer marketing is huge. The tech can help brands pinpoint exactly who their audience is, what their interests are and even what they will like in the future. If there’s a solid set of data available, it’s likely ML or AI can be applied to uncover patterns and insights within it.

It’s this widespread use that has spearheaded AI and ML to the top of almost every sector’s digital transformation agenda. There’s hardly an industry untouched by the technology. For example, the healthcare sector is increasingly using ML to help with early detection of diseases like cancer, while the banking industry is using the same technology to identify and block fraudulent activity in real-time.

ML and AI’s ability to be used in almost any scenario is, however, holding some brands back. Individual teams within organisations are increasingly gathering and analysing their own data, rather than storing and evaluating it centrally. For marketers, this siloed way of gathering data means they are missing out on crucial bits of information that allows them to build up a universal understanding of customers, their market and their own business.

Data-driven strategies

With marketing strategies being increasingly data-driven, it’s vital that the data itself is robust. Insights on customer behaviour and changes in the market can only be derived when every piece of data collected by an organisation is stored centrally, and not by individual departments.

Let’s take the example of a marketing team hosting a 20% off sale online and an individual customer transaction with a customer, who later returns their items. If the stock management team’s returns data isn’t available centrally, the marketing team could be incorrectly judging the success of the campaign. However, with both sets of data, marketing teams can create new benchmarks on the number of sales that have resulted in returns within a campaign, and how this can be improved as the campaign is live by, for instance, including a broader range of products in the sale.

This is a very simplified example, of course. But if you think beyond returns data to data on things like inbound customer complaints to call centres or footfall trends in store too, suddenly marketeers can start to build a vivid picture of every aspect of their business before and after campaigns begin. Coupling this with data gathered in the marketing team itself, such as positive and negative engagement on social media, data from focus groups or the success of in-store POS offers, it then becomes particularly powerful.

Trend forecasting is another prime example of ways the creative industry can derive insight from data.

Machine learning technology can comb through huge amounts of external and internal data to make connections between data points. Using this information, the technology can identify how a product might sell, who might buy it as well as the quantity needed to avoid unnecessary cost – all at speed. This means the industry can make campaigns more personal to their audience while ensuring products are highly targeted – and adjust campaign tactics like social media targeting in real-time based on demand.

There’s a strong business case for the creative industry to champion a centralised approach to data storage and analysis already. If every department has its own data strategy, the benefits achieved by gathering more and more data will become increasingly marginal. Collaboration is key for the industry to benefit.