As digital media spend increases, digital media teams are reevaluating the attributed impact on the relative share of e-commerce revenue. In the new normal, with great success come even greater volumes of data. To maintain the momentum of success, manual data analysis in particular represents a critical barrier to success. A survey from Anaconda revealed that data scientists spend 45% of their time loading and cleansing data and an additional 21% on data visualization, leaving just the remaining third of the time to focus on model analysis, selection, and deployment. These findings are consistent with a study from the CMO council, indicating the top area of focus for 53% of marketing leaders is marketing analytics software.
The bigger the data crunch, the harder the performance crush
It doesn’t take a data scientist to realize that data wrangling sucks. Those leaders who rose to the challenge as the global pandemic disrupted the markets are feeling the pressure to deliver on performance. The stark reality is that most will fail. Without the resources to load, cleanse, and visualize the growing volume of data points, digital leaders will be flying blind. To avoid hitting that incoming wall and crushing the emerging growth opportunity, digital media teams need access to a new class of automation tools and knowhow. The innovators are already pioneering AI automation that will empower media traders to reduce time to insights so they can focus on what they do best.
Finance doesn’t care about “attributed revenue”
The complexity of the attribution challenge is not new. John Wannamaker is famously quoted lamenting that he would never know which half of his ad spend is wasted. Although we have more data to work with today, piecing it together is still tricky. It’s easy to paste the attributed revenue from a digital media campaign into a monthly marketing report. Cross referencing spend data with actual revenue is a tough challenge in and of itself. It’s virtually impossible if you read “attributed revenue” at face value.
Consider the data crunch digital media traders are up against :
Leaders are relying on inefficient (and sometimes even misleading) data analysis with limited granularity across business lines, agency partners, and regions to deliver on visibility and actionable business insights
Executive leaders take for granted the lack of centralized visibility into up-to-the-minute trends. That kind of unacceptable sacrifice prevents marketing leaders from responding to consumer demand and pivoting in real time. Let’s liken the standard monthly report presented to the CMO as a smoothie mix she will receive for the next month. She has no control or visibility into the contents and origins of that smoothie. By accessing an automated analysis layer, she could get a customized daily special smoothie and even tweak the contents by asking questions no one dared raise before. This new paradigm represents a quantum leap in the way digital media is measured and managed, delivering qualitative improvements that transcend incremental efficiency.
Instead of getting that one smoothie formula that is intended to keep the CMO satiated for weeks, consider offering up a daily special of data driven insights. With a steady flow of insights, the critical questions will trigger a qualitatively more insightful conversation
Discover the true cost of manual data analysis
On average up to 50% of time spent on insight generation is spent on manual data analysis. With proper automation of a dedicated analysis layer, your media team can not only deliver twice the productivity, but deliver a measurable uplift in ROAS. On average, Paragone customers have delivered a 5% uplift in ROAS.
As an example, let’s examine a small retail operation with $10 million in ad spend ($833K per month) and 2 full time media traders with an attributed ROAS of 3X delivering $30M in ecommerce revenue. At an hourly rate of $50 per hour, the total cost of lost productivity is $108,000 annually.
While that figure is far from negligible, it doesn’t begin to approach the scale of unrealized opportunity cost.
It quickly becomes clear why there’s a significant gap in the way organizations approach data analysis – from measurement down to model deployment procedures. At the very least, the imperative is to move from manual to automated data analysis so everyone can see the bigger picture as it emerges. Over time organizations will learn to adapt to new data driven workflows, which will enable the operationalization of cutting edge technologies such as AI to drive digital growth in the modern enterprise.
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