Online advertising is an integral part of modern-day marketing portfolios. In this new-age digital marketplace, advertising agencies play the pivotal role of middleman between sellers and potential buyers. Those advertisers able to best resonate with audiences will not only help their clients succeed; they will also distinguish themselves from competitors in a lucrative, white-hot market.
Optimizing web advertising ROI is a complex two-part game. First, advertisers have to be able to identify high-value content hosts. Second, they have to be able to accurately measure the effectiveness of different marketing campaigns. Both goals require the ability to efficiently collect and analyze massive volumes of disparate data, using a Big Data analytics platform for data management and processing.
But as the amount of digital ad data has continued to grow, so too have advertisers’ grumbles for a better way to manage information. Relational databases are expensive and do not scale well; companies with even tens of terabytes of data voice cost, performance, and reliability concerns. Hadoop has become a popular alternative because of its scalability, reliability, price/performance, and burgeoning ecosystem; unfortunately, its MapReduce framework is slow and cumbersome. Legacy DBMS venders have finally awoken to the realization that the future of Big Data analytics will involve a fusion of the relational database and Hadoop platforms. These venders’ response, though, is dumbfounding: pigeonhole Hadoop as a data landfill and, using a custom connector, ship a few select piles at a time to the DBMS to sift for gold. This two-system approach is slow, inefficient, and budget-blasting.
The Use Case
Hadapt has developed the industry’s only Big Data analytics platform natively integrating SQL with Apache Hadoop. The unification of these traditionally segregated platforms enables customers to analyze all of their data (structured, semi-structured, and unstructured) in a single platform—no connectors, complexities, or rigid structure.
Digital ad agencies continually ingest vast amounts of multi-structured data per day—ad name, provider URL, user ID, timestamp, actions taken, etc.—to decide which ads to place on which websites to maximize value for clients. Traditionally, they have evaluated “Return on Advertising” by examining relatively basic statistics such as ad cost and click-through rate. Hadapt opens up a new world of potential insights. Advertisers can perform attribution analysis to determine which advertisements should receive credit for successful sales driven through ads. For example, an agency might choose to reward the ad that attracted the first click from the customer, or it may credit the last ad viewed before the sale. With this insight, advertisers can gain a comprehensive understanding of the benefits provided by different online hosts. Taking into account ad costs and the “value add” of various hosts, advertisers can generate accurate predictive models for click-through rates and sales conversions. Then, leveraging these algorithms, they can optimize their clients’ budget allocations.
Importantly, advertisers can perform all of these analytics interactively, via SQL, with any standard or in-house BI tool. Hadapt features high-availability and scales limitlessly on commodity hardware. For game-changing insights, just “ad” Hadapt.