Are Your Creatives and Data Analysts Working in Harmony? 4 Helpful Tips

A key narrative to come out of this year’s Cannes Lions Innovation festival was the need for collaboration between creativity and data. So how does a business get the most out of these two vital skills?
Creativity is what grabs the consumer. It’s what makes the brand’s message connect emotionally and is what encourages the heart of the consumer to say “yes” before the head can say “no”.
But a marketer must always remember to put the interests of the consumer first and leverage all the information on the consumer to build a campaign around. This is where data comes in and it is now widely accepted that a unison of data and creativity will reap optimal results.
With that said, the analysis of data is still being massively under utilised in the business decision making process. In this year’s biannual CMO Survey from Duke University’s Fuqua School of Business, CMOs said that, on average, just 29% of marketing projects use analytics – down from 39% in 2012.
Why then, when there is so much valuable data available to companies about their target audience, are decision makers opting to neglect analytics from their projects?
Not everyone in your team will be analytical, just as not everyone has that creative flair, but the companies that will succeed will be the ones that create environments to allow both creatives and analysts to work collaboratively together. Harnessing the swathes of data to hone in on that audience most likely to greatly accept the sprinkling of creative dust will drive optimal business results.
Here at Crimson Hexagon we train businesses to glean insights from social data, working with both creative and analytical teams. Here are 4 crucial steps to get both teams working in harmony.
1. Don’t start with the data, start with a SMART question!
This is the trap that so many businesses and analysts fall into. The business wants the analyst to start with the data, the analyst dives straight into the data and ends up not finding any of the glorious insights we all read about. There is an over reliance on the data for having a good hypothesis for the data to validate.
By aligning both teams on a common SMART question which will ultimately lead to taking an action, the business prevents the analyst from getting lost in the sea of data, without direction and never arriving at a valuable decision for the business.
What are the drivers of sentiment in the UK around the adoption of in-car-telematics?
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2.“I believe that this is going on…”
As a stakeholder in your business, you should have an interest in it and it’s natural that you form opinions about how your product or service is faring with clients and prospects. Whether it be something confusing on your website, a glitch in your mobile app or a feature in your latest product release that you’ve heard is even more popular than previously thought.
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Again, getting any worker in the business to provide these opinions and building a hypothesis with the analyst beforehand will allow for a much more focused analysis which can be proved out by the data. For the first time ever, actions can be taken by a business based on insights gleaned from the social data; never before have we been able to gather, store and slice through so much social data like we can do today.
3.Building that SMART question
The previous two steps have centred around this all important hypothesis that needs to be formed before even tackling the data.
As important as it is, it would be wrong to conclude that in order to glean great insights, the question needs to be straight out of a NASA lab. In this respect the world has not changed much from the days when an apple fell on Isaac Newton’s head. It led him to make an assumption about what had happened, he formed a hypothesis and then tested it to prove gravity. The same can be done by anyone in any business.
Blog pic 3Visualisation mapping the interests of consumers, based on Twitter data, that index strongly towards your brand (left of image) and how they compare to the rest of Twitter/competitors/area of research.
There will be members of your team who have no interest in the data whatsoever but they will still have a whole host of great ideas and beliefs. Getting these communicated clearly to an analyst will provide them with a useful foundation to go and quantify these beliefs in the data.

  1. Speaking a language we can all understand

The most important outcome of this collaboration process is that insights are found in the data and actioned upon by the business. Steps 1-3 have dealt with finding those insights but where the process often breaks down is in transferring those insights into the all important business action.
Analysts must appreciate that as important their job is at finding these insights – with the help of the rest of the team – they mean nothing if no action is taken from them by the business. What’s the point? This requires the analyst to communicate the insights in a language that all can understand.
The approach that works is not a number of big review sessions where lots of work can be showcased, but a constantly iterative process, where project approach can be tuned based on findings as they emerge. Daily or bi-weekly informal review sessions can reduce the number of missteps and improve the speed to market for results.
The world is becoming more and more data driven. Companies recognise that they cannot survive without being analytical. There is absolutely no reason, however, for this to trigger replacing creativity for analytics, in fact they both need to coincide.
 
 

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