“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Managing digital campaigns for the mid-market is hard. That’s because effective digital media management is all about connecting with audiences across platforms, ad groups, data layers and safety layers – yet this sector doesn’t always have the capacity to do it.
The workflow has to operate like a mechanical engine – one part synergistic to the next part’s efficacy. When you’re a smaller brand, it can be like running a V8 engine on a golf cart: just because you can do it, doesn’t mean you should.
With that in mind, mid-marketers need to consider speed, smarts, scale and spend. But pick three.
Speed for fast-moving campaigns
The mid-market often desires a nimble, speedy response because their ad dollars are hard-earned. In this environment, it’s not uncommon for brand managers to seek weekly (even daily) metrics, frequent optimizations and budget shifts.
This swift, attack-style campaign management works, but only if the campaign isn’t distributed across multiple platforms, and if there are enough hands to manage it all. In other words, speed works. But moving too quickly can come at the expense of thorough analytics, scale and cost.
Smarts to power analytics
Let’s say we’ve solved for speed with a nimble and well-managed campaign onboarding and optimization process. What about finding the data story that maps to business metrics? Or advanced attribution answers?
Data science expertise can be a challenging skill set for agencies that specialize in mid-market campaign performance. There exists a huge white space for digital mid-marketers in solving for advanced attribution, cross-channel budget allocations and integration with offline media.
Scale of available audiences
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Facebook, LinkedIn, Google, DSPs, Amazon. First-party audiences and third-party audiences. A-B creative testing. These are all ways marketers scale campaigns, working to deliver relevant messaging to receptive audiences.
Brands search for audiences wherever they exist on the web. But these audiences are increasingly living in walled gardens. With that knowledge, mid-marketers frequently deploy too many tactics against audiences that are too small.
Machine learning needs volume, but when the scale is spread across multiple walled gardens, each ad group ends up with miniscule budgets. The machine learning spins like a browser unable to connect to a server.
Spending for quality
Media agencies are unlucky with regard to compensation, since the traditional agency fee model is a percentage of ad spend. Even if agencies evolve to a fixed-rate or performance-based model, the ghost of “percent of ad spend” lingers.
It can be a really tough pill for brands to swallow when more than 20% of their media spend goes toward agency fees. But scale, speed and smarts necessitate dedicated workers, curated data layers and machine learning platforms. Those elements cost money.
And therein lies the rub. Media agencies representing the mid-market can’t possibly deliver on all four. Yet many agencies try to appease brands by overpromising and not having a frank conversation about realistic performance and goal setting.
Slowly, the industry is getting better about transparency. And having brands agree to an open dialogue is a great step in the right direction.
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