Elliot Holding
Elliot Holding 14 August 2020

Why Marketeers Can no Longer Rely on Their Own Data

Elliot Holding, cloud account manager at Cloud Technology Solutions, looks at why collaboration is key for a truly data-driven marketing strategy.

The marketing industry is constantly looking for new innovations and ways to drive strategies and influence consumer behaviour. 

The adoption of technologies like machine learning (ML) and artificial intelligence (AI), which is expected to completely shape the market, holds one of the biggest opportunities for the sector. These technologies are already helping brands uncover new information about their existing and prospective customers, shaping how organisations are targeting consumers. 

Predictive analytics is a prime example of this. The tool allows brands to analyse and interrogate datasets on customers and, using technologies like AI, make intelligent predictions or identify hidden behavioural traits that marketeers can leverage. The predictive analytics market has grown considerably over the last few years and is expected to be worth a staggering $11bn by 2022. 

Despite the growing use of predictive analytics tools, marketeers are arguably still failing to harness the true benefits of ML and AI when compared to other professions. The main reason for this is the data that marketeers rely on, which – in many instances – fails to include organisation-wide information. This siloed approach to data gathering has hamstrung the sector’s ability to utilise ML and AI as effectively as other sectors.  

The Power of Data

Healthcare, for example, has become one of the leading industries in the digital transformation thanks to its tightly controlled approach to gathering huge datasets from across organisations. As a result, machine learning is now regularly helping with early detection of diseases like cancer. And elsewhere in the financial services sector, while initial adoption of ML and AI was slower, those in the industry are now speeding ahead with their digital strategies, with high street banks using ML to tackle fraud by identifying abnormal behaviour in real-time, for instance.  

For marketeers to mirror the successes of other industries and professions, they must start to utilise data from every corner of their organisation – from finance through to R&D. The sector can no longer rely on its own data gathered from past campaigns or via owned channels. Datasets from across every department within an organisation must be stored in one central location, allowing marketeers (as well as other departments) to interrogate the data. 

Even with tools like predictive analytics, if the data that’s fed into the technology fails to reflect what’s truly happening across an entire organisation, a brand could end up with a ‘reality gap’ between what it thinks it knows about its customers and what is actually knows, risking false predictions being made that will fail to deliver any results. 

Once this collective way of storing data is achieved, however, the opportunities for brands are limitless. For instance, predicting trends can be done almost automatically, as ML technology combs through the huge amounts of internal and external data available to make connections between data points in real-time. This information can help brands in retail, for example, predict how a product might sell, who might buy it and even the quantity needed to avoid unnecessary and costly waste. The speed at which the ML can pinpoint these trends means marketeers can adjust their strategies accordingly. 

From a measurement perspective, the technology can also help evaluate campaigns. For example, if a retailer was to host an online sale, the technology can comb through datasets from across the business – including stock management to take into consideration any customer returns – to judge the success of that sale and create new benchmarks for such campaigns moving forward.

And looking beyond the marketing departments remit, if the technology collects data on customer complaints to call centres, brands can build a vivid picture of every aspect of their business before and after campaigns begin and adjust their messaging and approach in real-time.

There is a clear business case for organisations to get their data sets in order. While it will undoubtedly benefit the marketing department, the entire business will also see a return on their efforts and collaboration in this way cannot be underestimated.

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