When big data as a marketing concept burst onto the scene a few years ago, the potential benefits seemed almost too good to be true for marketers. The backlash came quickly, as companies struggled to really get to grips with what it actually was – it became another buzzword to learn about/ignore, another seminar season to sign-up to, and the blogs, oh the endless blogs to read on the subject; was big data too much hassle to be valuable? Surely this will be just another fad that will flare up and fade away as quickly… just like social media?
Like social media, big data has refused to budge from our everyday marketing consciousness and is now even more important than ever. Big data has been around for decades but the velocity, variety and volume of data in recent years merits this refreshed term.
Much like social media, big data has evolved into a different beast; an intelligent beast; a beast with purpose. Let me rewind a bit to explain my point…
Big data is data. All of the data. Everything. Every phone recording, digitally stored health record, social media interactions, government record, genetic sequence, online purchase, web visit and any other digital trace we make in our day-to-day lives. We produce 12.5 exabytes – that’s 12.5 billion gigabytes (GB) – of data every day! I’m sure you can see the problem here: how on earth is all this unfiltered data going to be analysed and made sense of? Small chunks, that’s how. One of the most important factors to consider here is big data is relative – Tesco’s data pool will be bigger than a small accountancy firm for instance. Carefully choosing what data you want to capture is the first step in building a successful big data strategy.
Big data provides the raw information but turning this information into actionable insight is what everyone is talking about now. Social media is one such strand of data that can help.
We can spend all day pondering the amount of data available to us as marketers, but what really counts is the insight you can gather from this data and knowing where to look for this is one of the biggest battles.
A few years ago, social media reporting and analysis was fuelled by the number of followers your page had, the number of interactions your content received and how many page clicks from social sources your profiles generated back to your website. Essentially, the more you were getting each month, the better your social presence was performing. This isn’t completely untrue now but understanding why certain content performs better than others and how you can replicate this success by predicting trends is a far juicier proposition.
Trial and error and gut instinct used to play a big part in how a social campaign gained momentum but with more and more social analytics and data capture technology being released, marketers can be far more confident that their decisions are the right ones to make.
The first step is to always research and monitor your target audience, so what data can be seen in this regard? Demographics are a huge part of social media marketing (given the global size of its user base) but data captured is much more specific than merely tagging a tweet’s country of origin. With more and more people using mobile technology, we can now pin-point a user who, using an Apple iPhone on iOS9 in the NYC Newark Airport Arrivals lounge, has tweeted a demand for a steak >
Now, take this profile as an example. What data can be ascertained from the one tweet sent? Firstly, the name and Twitter handle, you can also see the Twitter bio which gives more indication of who this person is, what they like and what they do for a living. From here you can see where in the world he lives and what time this specific tweet was posted. A quick scroll through his public Twitter feed you can start to see what topics he commonly talks about. All of this information helps you build a profile of whether this person is in your target market.
This is just the first glance, human view of a single profile. From a wider stand-point you can check out the type of people who follow him and also the people he follows. Already you can start to see the amount of data that can potentially be collected across all platforms and from all sources. Ok, knowing when this guy brushes his teeth might not be of too much interest to your business (unless you sell tooth brushes), but if you take this information out of individual context and start to view the data from a wider perspective, things get interesting.
Choosing what data you collect from channels is imperative to cut through the noise on social channels to really get the intelligence your company needs.
For example, launching a new product into the market will always be a challenge so many companies are utilising the data gathered from search, bloggers and social media to judge market place interest prior to launch. Using social listening tactics to filter specific keyword mentions from Twitter helps reduce risk factors and enable you to calculate whether your audience is ready for your new innovation.
Questions that need to be asked include:
Luckily, there are tools available to help you capture this information and I covered these in my recent article on how to target local customer using social media.
Having access to every shred of information about your customers and their journey to purchase is obviously a benefit to any business but what you do with it all is far more important. Consider what you want to achieve with your social media and marketing strategy before wading into the data ocean. Insight won’t reveal itself without focus and intelligence but always remember, the data doesn’t lie. Monitoring the growth of a social following alone doesn’t tell you much about your customers but analysing social and digital touch points throughout the sales funnel can lead you to some exciting places.
If you want to base your social media on data as opposed to gut feel, call our team of experts today on 023 9283 0281 . In the meantime, you might find these articles useful:
A beginner’s guide to social media lead tracking
Having fun with your brand on social media
Targeting local customers using social media
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Categories: Data Science
Categories: Social Media
Categories: Data Science, Social Media