Convergence & Fragmentation: Video Planning By Example In 2019
A brand’s video stories can go in countless directions now. But what strategies and channels are working now? We open the TV and Video Insider Summit with practice rather than platitudes. We ask buyers and planners to pop the hoods on some of their most recent cross-screen campaigns to share what did and didn’t work, How did they course correct and optimize? What were the biggest surprises and lessons learned? Let’s start with what is possible now.
Robert: So, where do things sit?
Sean: Challenge is a lot of traditional clients want linear TV. Uncomfortable with data targeting. Try them to look at it a a video plan, not a TV plan. Look at it holistically. Internally, we’re siloed. Who handles what. How to divvy up video vs. TV. Example: when people run TV linear plan have specific day parts. Want to know where money is going. But programmatic digital is all over the place and they don’t care where. It’s funny.
Jesse: From OTT perspective, clients are, Oh, what? We see other side of story. Advertisers obsessive about digital buys and safety.
Larene: Audience first strategies, flipping the script. making foundation about audience and then putting in traditional media on top of that to build reach. With linear, clients are used to seeing a schedule. That’s not how data driven strategy works. Trying to get them to understand platform-agnostic as long as you reach the right person, it doesn’t matter.
Robert: Pop hood on specific plans. Any specific cross-screen plans done recently?
Larene: We have a CPG client didn’t have traditional TV plan, specific audience, how do we build media mix? Created a strategic target, adding filters, then understanding where those audience consume media. One hiccup: addressable, OTT on different currency, had to talk to client about effective CPM. Some clients have understanding but others we educate to think differently. Moving with industry the way it’s going.
Jesse: As a performance digital agency, measurement should be digital. I care about scale but not as much about reach, frequency measurement. I care about outcomes. That’s how we’re measuring. Some things we can measure, other are more tricky. Taking a geo-saturation approach. Mix becomes vehicle for driving the most lift you can get. It’s become a data-planning game. Digital to us is through a programmatic lens because it helps with data, measurement, keep costs down.
Sean: Attribution, you have your DR, but what about top of funnel? Can it not be both? Awareness tactic or response drive or both at the same time?
Robert: Can you talk specifics?
Sean: Retail client mostly in Walmart. Din’t Have budget for national, competitor spends 10x as much on national. Let’s take your money and be smarter. Targeted smart buy. Pulled MRI, created targeting segment, hit spots that we think your competition isn’t. CPM will be a little higher. But hitting the right people, it was successful. Ongoing now and for past two years. Paired with network buy recently, bigger budget. ”We’re not seeing our spot.” That’s not the point. If Walmart buyer wants to see it during an NCAA game, the we have to crter to client. Delivered 100MM impressions. Ultimate goal. More about optics. Felt we hit the right, not necessarily the most, eyeballs.
Robert: How to allocate for screens?
Jesse: It’s data planning for us. Deal directly with publishers where possible. The line between investing in upper funnel and performance programs is more blurred than ever. Look at best campaigns of the year, the end of the case study ends in revenue increased X%. There are no good tools for everything. Between YouTube, online video and connected TV, there is no good way to plan. Where to start? I don’t know if that’s answerable today. Happy to let my buys run across TV screens, desktop and mobile.
Sean: It starts at MRI and comScore, if client has a certain CRM, we use that, try to find a target and go from that.
Larene: We have developed our own proprietary data stack. We’re taking it all in with third-party data to inform client. Outcome driven, how it affects business goal for that client. Creating algorithms and how does it deliver on sales goal.
Robert: One of the biggest complains is that we are poor at looking back and learning from last ad campaign.
Sean: It’s hard to match up with sales data sometimes.
Larene: Feedback is not in real time. How to optimize for the next campaign. Hard because results comes back six or eight weeks later.
Jesse: It’s important not to look back at false negatives or false positives. Learnings have to be about channel and vehicle.
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