ROI OPTIMIZATION & HY-OPS
- Ad-Targeting Optimization
- Tune-In Media Selection Insights
- Tune-In Content Selection Insights
RMT enables advertisers to optimize their advertising and tune-in campaign targeting through programmatic ad placement based on DriverTags™ revolutionary deep learning sciences. Because RMT knows both the underlying psychographic profile of TV shows and the the underlying psychographic profile of the Advertisement’s creative, RMT is uniquely positioned to optimize ad placement for your most important advertising campaigns.
Several opportunities and problems exist in the current advertising landscape that RMT has solutions to meet:
1. The percentage of networks using optimizers and ROI is still low, and the frequency with which such tools are used is still a fraction of total buys.
2. Many of the ROI studies being done are being done by Nielsen whose results tend to be flat (i.e. not to show compelling advantages) mostly due to the pricing of these studies which causes them to be done only on the small Nielsen panel almost all of the time.
3. Although the creative used accounts for 65-80% of the ROI for the brand, it is left entirely out of media optimization and out of ROI measurement. The focus of optimization and ROI measurement is the media reach of advanced targets such as specific types of product/brand purchasers most likely to yield highest lift in ROI. When the creative is taken into account, the lift in ROI is far greater because programs can be chosen not only for their efficiency against advanced targets but also their qualitative resonance with the specific ad creative.
4. ROI with valuable exclusive features and is priced for continuous massive use affordable and a must buy for all networks.
HYPER-LOCAL OPTIMIZATION (HY-OPS):
Hy-Ops is a Marketing Technology product that helps advertisers find above average opportunity to increase ROI without increasing ad spend, using accessible and scalable hyperlocal geographies. Hy-Ops has seen increased ROI by 15-30% by redirecting a portion of existing ad spend to scalable hyperlocal geographies. With data from industry leading partners, Hy-Ops finds high potential geographies down to the zip code and cable zone. Hy-Ops then executes these smaller geographic areas with top media partners. This allows advertisers more power and flexibility to quickly and accurately test various marketing mix models, and read results in record time. Our Hy-Ops AB Test systems allow an advertiser to easily see which reach, frequency, creative and geography respond more positively. Actual sales lift data is used to quickly and efficiently evaluate performance in a closed loop system. Benchmarks to top competitive brands is also provided a reference point to see the effect of share shifts. Hy-Ops is currently working with CPG, Automotive, and Entertainment Tune-In Advertisers.
TUNE-IN OPTIMIZATION FOR NETWORKS
Networks used to be able to get high awareness and trial of their new shows without more than using their own airtime to run tune-in ads. Today that has totally changed. Networks have to spend millions to buy airtime from each other in order to gain high reach, awareness, and trial of their new shows, and so far despite a collective annual spend of about $2 billion in the US for paid tune-in (growing over 20%/year), on top of the $14 billion worth of airtime networks use each year in the US to advertise their new shows, it’s estimated that the average new show tune-in no longer reaches 75% of viewers but fewer than 35% of them. Tune-in advertising can be optimized in terms of its ROI the same as brand advertising. In fact, it is easier to track the lift in ratings (the revenue driver in the ROI equation) than it is to track offline sales for most products, as this can be measured through the same set top box (STB) and related data forms used in optimization and brand ROI measurement. No network is currently even close to optimizing its tune-in. One far-seeing RMT client network is considering using RMT to optimize the level of spend for a new series, which is only one step toward the optimization of that spend. Even that is moving slowly due to the number of parties involved within the network viz. research, special audience promotion team, digital/interactive division, and the agency. The opportunity exists to solve these problems for the agency by means of a pre-testable proposition for automating full tune-in optimization with a closed feedback loop to self-improve over time. This can ultimately be one cloud based API accessed product used by all networks, with provision for self-customization on top allowing each network to be creative and try to gain an edge. Although initially aimed at networks, this version of the ROI Optimizer and the one aimed at brand ROI lift, will be all one system servicing all types of companies in the space.