Data is everywhere. We collect so much of it that it’s become near impossible to make sense of the numbers without the help of technologies like machine learning (ML) and artificial intelligence (AI).
For marketeers, these technologies have allowed the industry to pinpoint exactly who their audience is, what they like and even what they will like in the future. As a result, entire strategies are being developed on the insights found in marketeers’ data.
Of course, mass data collection and using AI and ML to analyse it isn’t solely done by the marketing industry. The healthcare industry is using machine learning to help with early detection of cancer while the banking industry is using it when evaluating and reducing risk. There’s hardly any profession that hasn’t undertaken a digital transformation in this way.
The widespread use of collecting and analysing data means that within individual organisations, different teams and departments gather their own data and use it to meet their particular aims. But this siloed way of working can hinder businesses – particularly marketing departments trying to build up a universal understanding of customers, the market and their own businesses.
Data-driven strategies
Data that underpins overarching marketing strategies needs to accurately reflect consumer behaviour and be analysed with respects to every aspect of the business. This type of insight can only be sourced when every piece of data collected by an organisation is stored centrally, by departments themselves.
For example, a retail marketing team may want to host a 20% off sale online. A customer buys an item but later returns it because it was an impulse buy. 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 led to returns within a campaign, and how it can be adjusted for in future campaigns.
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, such as positive and negative engagement on social media or the success of in-store POS campaigns, then becomes particularly powerful.
For businesses that are already doing this, marketeers are able to unearth a whole treasure trove of insight and information on which to benefit from. Trend forecasting is another prime example of ways marketeers can derive insight from data.
For machine learning to identify upcoming trends, the technology will comb through huge amounts of external and internal data to make connections between data points. Using this information, the ML 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. Not only does this mean marketers can make campaigns more personal to their audience, it also means that they can ensure 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 marketeers to champion a centralised approach to data storage and analysis already. With the amount of data we collect every day only increasing, it will just become a larger, more complex beast. If every department has its own data strategy, the benefits achieved by gathering more and more data will become increasingly marginal.
We shouldn’t see data as a minefield. We just need to learn how to work with it.
Written by Elliot Holding, cloud account manager at Cloud Technology Solutions.
