Predictive analytics key for marketers, says professor
By Whitney Critten| Student reporter
Alexander Krasnikov, PhD, assistant professor of marketing, was a featured speaker on the "What's Next for Predictive Analytics After the Data Fails of 2016" panel at the annual Holmes Report In2 Innovation earlier this year.
The panel focused on the failure of predictive analytics to accurately forecast the winner of the 2016 presidential election has impacted the current media landscape. Krasnikov’s remarks focused on the value of social media and predictive analytics for brands in the current era of media convergence.
The panel also included Bob Pearson, president of W2O Group; Rebecca Haller, director of audience insights for Politico; and Mark Strouse, CEO of Proof.
According to SAS, predictive analytics works by using data, statistical algorithms, and machine learning techniques to ascertain the likelihood of future outcomes based on historical data. During the 2016 president election, voter sentiment or attitude towards a particular candidate was greatly impacted by the unprecedented rise of “fake news.”
Here, Krasnikov talks consumer trust in brands, how brands are using social media to track sentiment, and the value of predictive analytics for brands.
Declining consumer trust in brands
After the election, the effect that fake news had on consumer-servicing brands wasn’t felt immediately, as it took a few months for it to negatively impact consumer trust related to the content that brands were pushing through advertising and marketing initiatives on social media.
Prior to the election, consumer trust in brands on social media was already in decline as evidenced by these figures from a 2016 study by Censuswide for the Chartered Institute of Marketing.
- Facebook: 30% of consumers say they now have little or no trust in brand content on Facebook.
- Twitter: 25% of consumers say they now have little or no trust in brand content on Twitter.
- Instagram: 23% of consumers say they now have little or no trust in brand content on Instagram.
- Pinterest: 21% of consumers say they now have little or no trust in brand content on Pinterest.
- LinkedIn: 20% of consumers say they now have little or no trust in brand content on LinkedIn.
As more consumers use social media to interact with brands and ultimately influence purchase decisions, marketers should be aware of the impact that fake news has had on brand trust and work to find creative ways to gain needed credibility that helps to repair consumer trust in brands on social media platforms.
Social media and brand sentiment
Prior to the advent of social media, brands tracked sentiment through consumer panels, focus groups, and telephone outreach among other initiatives aimed at gaining honest consumer feedback. Now most brands are using social media, more specifically social listening platforms such as Brandwatch and Meltwater to track and measure online brand sentiment.
Social media and social listening tools provide good insights and metrics for brands on consumer sentiment related to an advertisement, new product, or service. However, social media and platforms that track sentiment have certain limits and restrictions because they only measure short-term impact, and in order to accurately determine true brand sentiment the long-term impact must also be included.
Value of predictive analytics for brands
Brands can use predictive analytics to determine consumer wants, preferences, and uncover early warning signs attributable to consumer unhappiness. The key to this is continuous audience segmentation in real-time on digital and social media platforms.
Brands that are continuously segmenting audiences online will in time be able to predict consumer behavior in different situations, whether it be online or in traditional brick and mortar locations. These insights will be very valuable for brands as it allows them to be prepared for diverse scenarios, while also being able to respond to consumer demand.