Death of the ‘creative’ CMO: time to embrace maths in media
The job of the CMO has shifted from largely a creative one to applying data to solve a multitude of challenges, writes Yieldmo’s GM of Analytics.
Marketing is thought of, first and foremost, as a creative industry. Graduates with creative degrees jump into marketing roles, looking to use their problem solving and blue-sky thinking to make campaigns soar and connect with customers. While these are important parts of marketing, many would-be CMOs are surprised when they find out they’ve also entered a world of numbers and stats.
The job of the CMO has shifted from largely a creative one to becoming a driver of company growth, disruptive external developments and internal relationships. Recently, Unilever changed the role of the CMO to Chief Digital and Commercial Officer recognising the need to bridge online marketing and sales. Gone are the days of creativity and problem solving being the key attributes of any talented marketer – to truly take their skills to the next level, marketers will need to feel confident applying data to solve a multitude of challenges.
Understanding what’s impressive against what’s important
The latest ‘Media KPIs That Matter’ study from the Association of National Advertisers found that CPM, unique reach, and ROI/ROAS rank among the most used KPIs for media leaders.
In theory, this plethora of performance watermarks should make judging the success of a campaign, why it went well, and how these lessons can be applied elsewhere easier for CMOs and their marketing teams.
But often the most common KPIs that a partner shares will dazzle CMOs without truly revealing any insights. If a CMO is unable to tell the difference between an important metric and an impressive, but ultimately meaningless number, or derive insights that tell him or her what to do differently or better in the future, campaigns will not be fully optimised.
For example, the ‘Media KPIs That Matter’ report found that CPMs are the most used KPI indicator. Despite this wide usage, it was ranked a low 22nd in terms of importance. A savvy CMO will understand that cost (efficiency) needs to be balanced against performance (effectiveness) and media quality. By looking at a metric in isolation and using it to optimise, many CMOs miss the nuances that better express overall media quality and contribution.
Keeping a clear view of a campaign’s aims and combining their natural problem-solving skills with an understanding of maths allows CMOs to better judge the success of a campaign.
Embracing a quantitative test and learn mindset
As well as understanding what is being bought and where, CMOs need to understand its effectiveness. For this, access to historical data, as well as adopting an agile test-and-learn process in their team, is imperative.
Many CMOs embrace tools such as media mix modelling and multi-touch attribution to understand performance but, not only are some of these tools unworkable in a cookieless world, they lack insight into why something happened and whether what happened is likely to happen again in the future. Especially in times of huge consumer and marketplace shifts, the need to get to why something is producing a certain result matters more than ever.
To get to the “why”, marketers need to embrace an agile test and learn mindset. The idea of running constant tests can seem an overwhelming task to many marketers, but it is here that technology – and specifically machine learning-enabled software – can step in and provide some of the heavy lifting for CMOs and their teams. Machine learning massively reduces testing time and increases the number of variables that can be simultaneously analysed. Artificial intelligence can quickly spot unexpected patterns in data and use those insights to optimise current campaigns and enhance strategies for the future.
Businesses are investing $1tn in marketing globally, but budgets are being more closely watched than ever – CMOs need to know more than simply what is working and what is not. A combination of both a solid understanding of the numbers and powerful machine-learning enabled algorithms can help find the true insights hidden in the stats, and allow CMOs to optimise towards the campaign KPIs that matter.
Ultimately, CMOs don’t have to become Chief Mathematicians, but embracing a quantitative mindset – constantly iterating, testing, and learning – can lead to a greater chance of campaign success. A good CMO doesn’t need to be a statistician but he or she should have an eye out for partners who employ them, have robust data sciences practices, and can help lead them through the maths to sustainable business performance.
Ultimately it is the role of the CMO to orchestrate brilliant campaigns that creatively solve real problems using quantitative and qualitative insights. With an analytical eye that judges creative success based on numerical performance and a mindset oriented toward continuous learning and improvement, CMOs will be able to maximise the potential of every advertising campaign.
Lisa Bradner is GM of Analytics, Yieldmo