Throughout the month of March, G2 Crowd’s research department will share our insights around the topic of big data. Check back here, on the blog and on our social channels to read the latest.
Digital advertising software professionals are always looking to establish, or maintain, brand identity. Whether a brand is determined and tweaked through hyper-personalization platforms or by a lead scoring model using marketing automation software, gathered data and machine learning software methods are used. This “big data,” which is gathered internally, externally and everywhere in between, is helping brands meet the buyer with the right message at the right time.
When algorithms and data utilization are mentioned in the marketing world, real-time bidding and DSP software come to mind. Using variables, DSPs help advertisers determine the value of ads, guiding them on the path to successful campaigns. For example, Rocket Fuel uses something it calls, “moment scoring” that uses historical and exchange data—such as domain, geographical location, and frequency—to estimate the value of an impression and bid on it in milliseconds.
These platforms are already impressive, with users reporting an average satisfaction rating of 87 percent for overall DSP functionality (according to G2 Crowd’s Winter 2017 Demand Side Platform Grid℠ Report). And, as security continues to sail into the sunset and predictive analytics and machine learning tools become more sophisticated, heaps of new data sets are accessible and utilizable.
Branding with Big Data
Data in itself allows businesses to analyze, plan and improve. For marketers, new streams of data means deeper insights into customers’ needs, preferences and desires. Successful sales through branding hinges on the ability to identify prospects and determine whether they want what you’re selling.
Using big data through a marketing automation or DSP tool can help uncover the branding opportunities you actually want: micro-targeted ads that present consumers with exactly what they want to buy using the message they are most likely to connect with.
Amazon and Netflix are good examples of the power of big data and predictive analytics. Amazon has, and is continuing to, explore user preferences to create the optimal buying experience. Netflix, in the midst of rolling out more original offerings, is discovering new niches of viewership. This is all done using the power of new data-driven technologies, but it starts with the vastness and quality of the data itself.
The Bigger the Data, the Bigger the Challenge
The increase in quantity and accuracy of data available has certainly given businesses the ability to know their customers. However, there are challenges that come with this new level of intimacy.
After data scientists gather first-, second- and third-party data, which can be conveniently tossed in a data cloud, potential prospects begin to take shape. Neural networks and deep-learning pods begin to comb through the data to zero in on the habits of ideal customers. Marketing teams then set goals based directly on the results of these processes.
While this helps define your brand identity, success rests not only on marketing prowess, but also on the quality of data. If the data gathered by a company is flawed or irrelevant, the result is a massive time and resource loss.
Also, ads that are hyper-personalized are great and all, but can they be too personalized? Have you ever been shown an ad that seems, for lack of a better word, “creepy?” Ads should be relevant to the user, but should remain distant enough to maintain a certain level of comfort.