QuanticMind announced its Summer 2019 product release, giving performance marketers new levels of visibility into how technology solutions actually make optimization decisions. In an era of artificial intelligence, oversimplification, and black boxes, QuanticMind breaks open a window into the decision engine that powers international advertisers at scale. The new visibility, coupled with granular optimization capabilities, provides confidence and functionality for digital marketers focused on peak performance in paid search channels.
The Silicon Valley-based Martech company leads the industry in techniques to make the most granular optimization decisions on Google’s and Microsoft’s vast advertising networks. Granularity in advertising can’t be understated, for the amount of data generated in the modern digital environment represents a mammoth opportunity for any business aiming to do better audience segmentation, targeting, and messaging.
“While part of our current release addresses the changes required from Google’s business decisions,” said QuanticMind’s CEO, Chaitanya Chandrasekar, “other elements take a completely different direction. While the search engine ad publishers like Google and Microsoft continue to make product updates to incorporate artificial intelligence into their advertising offering, they do so without giving marketers sufficient visibility into how decisions are being made.” Chandrasekar goes on, “In an era of increased focus on marketing performance and technical optimization specialists, this black-box approach to paid search optimization doesn’t fulfill what a majority of advertisers want and need. QuanticMind is making the machine learning decisions more digestible for advertisers looking for recursively increasing performance.”
QuanticMind has kept its commitment to regular product updates that improve digital marketers’ performance. This Summer release introduces a compelling twist by not only improving marketers’ ability to grow their bottom line but also see a window into how the optimization calculations are made.
The key standout of the Summer 2019 release is new Bid Calculation Insights. Machine learning can feel like a black box, making it hard to trust its decisions. To cultivate trust, technology providers must give users more insight into the inner workings of the machine and the decisions it makes. This is important in the context of shared responsibilities between experienced people and data-crunching algorithms. On a granular, per-keyword basis, marketers will have the power to view the different steps that occur to generate a PPC bid–including the inputs and outputs of the calculations. Elements visible through this “window” include historical values, graphical visualizations, bid landscapes from publishers, and the values, constraints & weightings that affect the final cost-per-click bid. The result: a much clearer understanding of why artificial intelligence has made each keyword-level decision. Plus, more trust in our computer-counterparts.
The next major functionality rolled out is the Impression Share Bidding Optimization. While position-based bidding was the chosen strategy for many brand campaigns in the past, Google plans to sunset support for the key metrics used to drive these strategies in September. Despite some resistance to change from position-loyalists, the new impression share metrics are proving powerful for QuanticMind clients. In fact, QuanticMind’s strategy for bidding towards impression share is even more effective when the goal is a share of the competitive landscape.
The third major feature to highlight in this release is Dynamic Search Ads Bidding Optimization. Using the same machine learning algorithms that drive major upside in other key optimization areas, this functionality allows marketers to automatically set optimal bids at the Dynamic Ad Target level for DSA campaigns, rather than the Ad Group level. This advancement is notable for many search marketers using Dynamic Search Ads (DSA) on Google. A DSA campaign uses a company’s website to dynamically create ads and landing pages while capturing more traffic that may be missed by a normal campaign build. By optimizing PPC bids at the Dynamic Ad Target-level, the increased granularity above Ad Groups results in consistently better performance.
