People are so consumed with data these days, they are struggling to absorb it let alone make sense of it.
But do we really need to analyse every data point to ascertain where impact is being delivered? Or shouldn’t communicators instead figure out how best to examine audiences, competitors and key messages?
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Executive summary
- Rather than leaving the task of making sense of insights to automated tools, communicators must learn to contextualise the masses data at their fingertips.
- At the outset, having defined goals for your analysis is vital: there should be a direct connection from the business goal to the analysis objective.
- Consumer brands and public affairs both benefit from a narrowed focus on measurement goals: whether tracking car showroom footfall or targeting the rightpolicymaker, measurement programmes should carefully define the coverage to assess, the attributes to track and the business data to marry it with.
Today, the communications industry finds itself awash in data. The binders of clips that landed with a thud on clients’ desks decades ago have been replaced by 0’s and 1’s in the “cloud.”
With traditional media now joined by social media, citizen journalists, podcasts and a proliferation of new media outlets, the potential to build a brand in a day has never been so great, but the opportunity to ruin a brand with a single misguided Tweet or off-handed remark caught on video, meets or exceeds that potential.
"Today, the communications industry finds itself awash in data."
The concept of public relations measurement used to be defined by metrics aligned with the advertising industry. Rather than linking press coverage to business outcomes or gauging the impact on corporate reputation, the focus was on perceived return on investment measures like advertising value equivalency. Advertising value equivalency provided neither any real value in terms of strategic data, nor made any correlation to actual business impact. It was designed to justify public relations budgets and give the communications department a pat on the back for a job well done.
As much as measurement gurus disparage advertising value equivalency, it is understandable that it took root. After all, it is a simple measure that executives can easily understand. It uses what they already know and attempts to translate something that – to many – is much more opaque and uncontrollable. That approach follows the path of least resistance, eliminating difficult conversations at the expense of accuracy and insight.
Nowadays, with so much at stake – and so much data available to analyse – communicators must figure out how best to examine audiences, competitors and key messages.
Finding purpose behind analysis
The biggest problem we see is people hunting for data and not for insights - this leads to over-investment in automated tools, platforms and data sets – and under-investment in the hard work needed to put this data into context and to extract meaning that helps keep organisations well-informed and empowered to make good decisions.
Appreciating the role of humans in the data analysis process will lead to better outcomes. The appeal of technology is that it can accelerate insights and improve affordability, but it is vital to assess the value of the analysis provided. Through a prudent combination of technology and human expertise, organisations can achieve better results.
"Appreciating the role of humans in the data analysis process will lead to better outcomes."
It’s easy to become enamoured with the idea that big data can help improve your communications. After all, the media sells big data as a virtual cure-all for all that ails business and society. It’s easy to see the 0’s and 1’s flying around in one’s own mind at the thought – and that’s exactly how the media portrays big data! But too much of a good thing can simply be overwhelming rather than insightful.
Defining your universe is key. There should be purpose behind every piece of data that is collected, annotated and analysed. If a company doesn’t know why it is touching a piece of data, and it can’t tie it back to a business outcome, then it is wasting time and money. More importantly, it will end up with bad analysis.
I consider best practice measurement should be focused on finding insights that drive business impact. And to do this a company only needs the data that supports that process. Not everything that is mentioned about a company, for example, it should be of critical importance. Without defining your data universe, you end up investing enormous amounts of time and money capturing, storing, processing and analysing billions of pieces of data every year. By focusing on the people and issues that count, you eliminate all the noise and focus on insights that make a difference.
With the launch of a new car from BMW or Tesla, the noise on social media from Instagram to Facebook, Twitter and Snapchat would be deafening. Many organisations rely on automated tools to make sense of the sheer volume of content, resulting in quantitative data reports on metrics such as mentions, reach, sentiment and engagement levels. However, accurate qualitative insights on reputation, perceptions and brand advocacy are rarely derived from a computer algorithm alone.
While lots of data can make your end product look impressive, you need to focus the conclusions in such a way that real insight is developed that can lead to meaningful changes in your communications programme – so that ultimately you get improved business results.
The power of planning
The key to defining a data universe is planning. Too many organisations are anxious to do any kind of monitoring and measurement, irrespective of its relevance, that they jump in without taking the time to think about clear objectives. Objectives that must clearly tie back to actual business goals.
By looking at everything you focus on nothing. What’s more, trying to be all things to all people will result in a meaningless dataset that doesn’t provide true insight. There should be a direct connection that runs from any given business goal to the analysis objective, the data universe, to data point collection, raw analysis, actionable insight and communications change.
"By looking at everything you focus on nothin."
With effective media measurement designed to drive decision-making by an organisation’s communications leaders and c-suite, time and effort needs to be devoted to making the link between the communications outcomes and business outcomes.
That takes planning and collaboration across the company, engaging people who may not work together on a daily basis but who have the ability to tie together disparate data points that can tell a real story. In fact, the teamwork required to develop solid insights will have additional benefits to the organisation as it seeks to better understand the drivers of success. Overcoming organisational inertia, then, becomes just as important as defining the data universe. In fact, the two go hand-in-hand as most companies don’t realise just how much useful information exists across various business and functional units.
Tying this data together often reveals insights that go well beyond basic communications evaluation. It can keep the c-suite focused on the bigger picture and not just what is going on right in front of them.
Making the most out of measurement
Major consumer brands face the challenge of taming data every day in their communications efforts. If you seek to sell your product to millions of people, you will inevitably have a wealth of media and business data points to analyse.
If you want to drive sales with your earned media coverage, you need to measure your media results against sales. It seems obvious when you say it out loud, but many companies aren’t taking advantage of the data at their disposal to make those connections between communications and business.
Consider the case of an automotive brand. Each model they produce has a very specific target market. Certainly, retirees may purchase minivans and city-dwellers might own a pickup truck, but the reality is that each one has been designed – and is marketed to – a narrower segment of the population.
"Many companies aren’t taking advantage of the data at their disposal to make those connections between communications and business."
When evaluating the effectiveness of public relations programmes around these car models, then, the measurement programme should carefully define the coverage to assess, the attributes to track within each article and the business data to marry it with. Connecting the media results with practical business activity like call centre traffic, showroom footfall, test drives and sales data will assist the automaker in their evaluation efforts. Suddenly the mountain of data surrounding the model launch has become something much more manageable – and much more likely to deliver meaningful insights that will improve business performance. It can help the brand decide how to invest future resources for maximum impact, or even how to adjust the ongoing campaign in some instances.
Similarly, a public affairs programme around something as controversial and widely covered as Brexit would be quite difficult to evaluate without narrowing the criteria. Understanding which policymakers or citizens will have the greatest impact on an issue outcome helps to design a more effective media evaluation effort. By combining the media data with electoral information, polling results and other resources, the smart organisation can improve messaging and targeting. Ultimately, that increases the odds of successfully achieving the desired policy decision.
Just as important, taming the big data beast will allow better benchmarking against competitors to understand why you are over- or under-performing sector peers. With large companies increasingly diversified, these comparisons need to be done on an apples-to-apples basis so that the flood of data doesn’t lead to misleading results.
Making those smart comparisons between similar companies often reveals insights about targeting and messaging that can provide new ideas for communicators to embrace in future campaigns.
Technological know-how, human expertise
While it can be tempting to buy in to the hype surrounding big data, machine learning, artificial intelligence and automated analysis, the reality is that a combination of technology and human expertise are much more likely to be effective.
The problems associated with big data can be overcome by investing in holistic research that balances automated data capture and the immense capabilities of the human brain – the world’s greatest computer. The expert mind adds critical insights, contextual analysis, emotional IQ metrics and more.
When it comes to media intelligence we strive to deliver what matters and for CARMA that means insights, insights and more insights – you can have more data than you need, but you can never have too many insights.
