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Big data is messy. It’s scattered across platforms, it’s diverse, and in its raw form, it’s practically unusable. We know, it’s a painful truth.
The fact of the matter is that having a lot of data doesn’t necessarily mean that you have the answers to your most pressing questions. Looking for the most relevant bits in your pile of big data is like looking for a needle in a haystack.
A digital, not-particularly-cozy haystack.
Nowadays, customers are online on a number of different devices, interacting with brands on social media and beyond, and generating their own unique trace when it comes to what they want, seek and purchase on the internet. Every one of them is different in their expectations, and companies have learned to understand that.
One-size-fits-all solutions are not cutting it anymore. Instead, brands are increasingly expected to offer customers a personalized experience and 1-on-1 communication.
So far so good.
The challenge, though, begins when brands and their marketers – or hired marketing agencies – attempt to sift through all the data generated by their customers and make sense of it. As data is growing in size by the minute, professionals are increasingly looking for ways to create certain logics and structures within their data to help them make it “smart” and understand it in its entirety. If you want to make good use of your Marketing analytics, it starts with your data.
For many, turning data into smart data, and doing so as quickly as possible, has proven crucial in recent years. Dealing with large amounts of information, marketers have embraced data analytics tools to help them clean and harmonize their data, and transform it into insights.
But how significant really is the difference between your raw, big data and this thing called smart data? Well, quite significant.
It is the difference between seeing a long list of numbers for your weekly or monthly sales and a graph that visualizes the ups and downs in those sales over time. Not only does the latter look nicer, it’s a lot more useful, too. Overlay that with an additional set of criteria and you know who is buying your products, when, and why they do it. Data is your friend and you really should invest enough time to gather all those smart insights.
And mind you, if not too long ago marketers were focused on analyzing historical data to understand their customers’ wants and needs, they now do so using current data on an ongoing basis.
Having relevant, real-time insights at hand is one of the most crucial assets a business could have nowadays.
Collecting and analyzing data in real time is important not only to keep track of what customers are responding to, but also to identify patterns and help predict purchasing behavior.
Say, a car manufacturer wants to know if there are any seasonal fluctuations to the amount and type of sales it makes. With the help of data analytics, it is possible – and easily so – to go back years and check if there is a correlation between the weather outside at the time and the type of cars sold.
Research has shown, in fact, that many manufacturers experience a peak in the sales of convertibles during late spring and the summer, and of 4×4 vehicles during the winter. With this knowledge, companies can not only anticipate such trends, but follow them in real time, too, to make sure they are providing their customers with the most relevant product and information. Of course, this seems a bit trivial at first, but keeping in mind that many stakeholders may need to be convinced of a marketing plan, these smart data insights do the work.
At the end of the day, smart data has immense potential in predicting customer behavior, and perhaps even more so, in giving time-sensitive incentives to current and potential customers.Having relevant, real-time insights for your Marketing analytics at hand is one of the most crucial assets a business could have nowadays.
What is your take on the role of smart data for businesses? Let us know in the comments!