Over the last decade or so, “data science” has become a de facto industry buzz term — and for good reason: The practice of data science, the use of data to extract knowledge or insight, is the driving force behind much of today’s most advanced technology. But when you boil it down, data science isn’t all that dissimilar from any other form of science; fundamentally, it’s about gathering information, forming and testing hypotheses, drawing conclusions, and putting those conclusions into practice. And here’s the thing about data: There’s a whole lot of it out there, just waiting to be analyzed.
At Simpli.fi, data is the crux of everything we do, and the foundation upon which we’re able to deliver exceptional results for our clients. Nowhere is this more apparent than our industry-leading combinatorial bidding process. If you missed our primer on the topic, combinatorial bidding is the weighing of individual data points, in real time, to determine the optimal impression and bid price in a programmatic ad exchange. Couple that with unstructured data, and our models have access to tens of thousands of data points to best inform the bidding process — and they’re constantly learning, updating, and getting better with every automated bid.
No two individuals are the same; we all have our own nuances, preferences, priorities, and worldviews that impact our receptivity to any given advertisement. What type of device is a prospective buyer using? What creative messaging do they connect with? What time of day will they see it? What part of the country are they in? Before bidding on a single impression, thousands of factors are weighed to determine their impact on the campaign’s CTR and CPA. The most influential factor has the most weight, and it’s then combined with the next most important factor, then the next, and so on. This process is continuous, progressive, and essential to identifying and refining the optimal factor combinations for a given campaign.
Simpli.fi’s combinatorial bidding process is unmatched in not only the sheer amount of data it weighs, but also its ability to crunch such large sets of data in real time, while making near-instantaneous calculations that learn and improve with each bid. Our advanced models also allow for task automation, which in turn enables us to manage large quantities of campaigns to scale — making our platform ideal for local media groups, multilocation brands, and agencies looking to localize their campaigns for optimal performance.
Of course, none of this would be possible without unstructured data. If we were limited to audience segments, the combinatorial bidding process would occur at the segment level rather than the individual impression level — ignoring the countless other factors that could potentially influence a user outside of pre-packaged segment. But because we store bid requests, impressions, and factors through unstructured data, we have the exclusive ability to target, bid, optimize, and report at the individual data element level. This wealth of data, in concert with data science, allows us to expose each campaign to thousands of factors before determining the ideal impression and price — all in less time than it takes to say the words “combinatorial bidding.”
Want to learn more about how we can put our data science and combinatorial bidding process to work for your business? Give us a shout at firstname.lastname@example.org.