How do you understand your customers in the digital age?

In today's digital-first world, there are seemingly no end of ways to capture your customers' data. From Google Analytics to signing them up to newsletters to simply asking for their personal details, the technology now exists to gain a 360-degree view of consumer habits. This was never the case for traditional bricks and mortar retailing a couple of decades ago but what do you do with the data when you have it?undersatnding customers

Data is ultimately just a series of numbers and letters. It’s not until you take time to put in place ways to analyse and understand it that it becomes useful and profitable. The term ‘big data’ was derived to bring a focus to this now crucial trend across all industries, worthy of its own department in any incumbent company hoping to survive against the competition.

Today’s startups and challengers make a point to focus on data before anything else. It drives their disruption, influences their decision-making, determines their product lines and develops their internal thinking and external marketing. As this TechCrunch piece argues, having a handle on the data is the only way to create sustainable growth and attract investment.

Larger businesses can learn much from startups’ use of data. Every element and every process is analysed in minute detail from products that were only looked at and not bought to purchasing decisions made, payment types used and feedback offered. This piece looks at the idea of A/B testing – a common practice used by startups and challengers to target the same audiences with different questions or propositions to see how each reacts.

Data collection is not purely about demographics in today's digital world. It is far more about habits. It's also not only confined to desktop use or mobile use. It should be viewed in the round, alongside any data sets collected from traditional bricks and mortar retail - all thrown into the mix and analysed as one to pick up on trends, issues or opportunities.

Look at some of the major UK retailers who are now not in business. From Comet to Woolworths and BHS to HMV, all failed – in part – because they did not adapt to changing tastes, buying habits and purchasing needs. Of course, just collecting data for the sake of it is pointless. In fact, this Forbes article argues that many companies are drowning in data, much of it useless and out-of-date. Like traditional retail collection of knowledge over the decades, it comes down not to what data you have, but the efforts you spend on what you draw from it.

On social media, brands spend fortunes tracking every move of potential and current customers to work out how to serve the most targeted adverts or suggest the most likely products someone will buy. They dive deeply into data sets looking at the differences on websites used via a desktop or on a mobile. They use this information to judge whether they can penetrate new markets or attack globalisation as a retailer. The right data can make the difference between wasting millions of pounds on a launch project or successfully transitioning into a new area, territory or product line.

Big data and analytics that helps the understanding of the single customer view is one key to success. But this needs to be a strategy across all footprint channels, whether in-store or online. It may be data gathered at various company touchpoints but also by using external and third party data. This customer intelligence is the only way to make predictions about the future, from potential purchases to possible disposable incomes and also which outlets are set to become less profitable.

Trends from data spotted in one region or one country can often be examined and extrapolated on a nationwide or international basis, using algorithms and computing power to create models years into the future. Retailers must look at the individual data as part of a much larger puzzle to understand how they deliver different experiences and different product sets to different users based on which interface they interact with and what demographic they are from.

This McKinsey article looks at how some big companies are using big data and analytics, but what’s clear is one data strategy does not fit all. There are so many ways to use customer data as this article testifies. However, as the author of this piece explains, failing to stay on top of data and update it regularly can become a huge detrimental problem.

In the digital-first world, consumer habits are constantly changing, not just daily but hourly. This presents a massive issue for companies trying to keep ahead of their competitors and keeping up with how their customers are using different devices and means of purchase. Customers in the digital age are becoming increasingly complicated to understand and that can prove fatal for businesses who get lost in the data rather than implementing a firm strategy to make the most of this information, especially from social media.

Customer Experience is now the phrase that retailers are trying to live by in 2017. Much of this is being driven by machine learning and artificial intelligence, both of which are driven themselves by the collection of data to offer a more personalised experience to the end user, one that can make them feel truly connected to a brand. Starbucks is one such company examining how this could work while machine learning technologies such as IBM Watson are being rolled out into the heart of companies in all sorts of sectors.

Using data to understand your customers though has one major flaw, its security. Well-documented hacks of all sorts of big companies from PlayStation to Yahoo are becoming more and more common. This piece believes 2017 could be a defining year for data security breaches. It is not good enough only to have a robust data strategy showing how you will use the information collected to boost profits and productivity. Before that work even begins, companies must be certain that the technological infrastructure they have in place is just as robust and secure. If that is not the case before beginning to collect the data that will drive their transformation and future success and sustainability, then the whole concept is flawed and fraught with likely long-term failure.

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