AI/Watson

How to improve social influencer marketing with AI technology

Share this post:

If you were a marketer, imagine the thrill of creating a commercial so compelling that your audience would skip through regular programming to get to it.

That’s our goal at Influential. We’re in the business of helping brands, publishers and ad agencies create social media #ad and #sponsored posts that outperform organic content. We’ve achieved this level of engagement because the influencers we choose help brands resonate with consumers.

Choosing the right influencers

Although it’s known that influencer marketing can help drive brand sales, concerns about fake likes and comments on social platforms cause some marketers to mistrust social media. And they’re often unsure if a particular influencer will support a brand’s message and whether the demographics are cost effective. Influential’s Social Intelligence platform can help overcome these objections by matching brands with ideal influencers.

We do this by leveraging augmented intelligence and machine learning to inform influencer selection. To give you an idea of our platform’s scope, we work with 25,000 influencers reaching billions of followers. We also apply AI to large blocks of unstructured data to help brands identify their audience, profile and personality as perceived on social media. The analyses help to predict the outcome of influencer campaigns before they happen, transforming influencer marketing into a credible media channel.

Three IBM Watson APIs power our platform: Personality Insights, Natural Language Understanding and Language Translator. Here are some examples of how Watson helps create value for marketers.

Analyzing social postings

To match brands with effective influencers, Watson APIs running on the IBM Cloud analyze an influencer’s last 22,000 words posted on Twitter, Instagram, Facebook, YouTube and elsewhere. The psychographic analysis identifies 47 personality traits, from altruistic to adventurous, and pinpoints the sentiments within those words. If they mention a brand or product, was it in positive, negative or neutral terms?

Such insights help to predict whether an influencer will authentically resonate with the voice of a brand, event or campaign. Even if such resonance exists, there’s a need for brand safety. Natural language analysis can identify profane content and scandals that might damage a brand’s image.

Applying Personality Insights to a brand itself brings up interesting results. As an example, marketers at a major car manufacturer thought its persona was of the most alpha, aggressive person in the room. In fact, our analysis showed the personality to be of a very meek and modest person.

Another car manufacturer reported excellent results from a social campaign where the creative was informed by Watson. The campaign generated 100-percent positive sentiment—a rare occurrence, given the negativity often seen in posted comments. But a well-matched brand and influencer lifts sentiment and overall engagement.

We also help marketers through our Share of Voice analysis. Watson’s analysis of all the tweets, posts, engagements and video views across social platforms helps assess a brand’s share of voice versus the competition. Winning share of voice is second only to increasing sales.

Fulfilling the promise

One of my favorite lines is that social media is the largest crowdsourcing of public opinion in history. With Watson’s assistance, we can look at a brand and learn who the followers are, as well as their interests and affinities. Then we can answer questions about which influencers to choose, which messaging to create and which media to use. This makes it possible to take the guesswork out of social influencer marketing.

  

Hear Ryan Detert discuss AI-powered social intelligence:

Founder and CEO, Influential

More AI/Watson stories

How to make campus life better with AI technology

In many organizations, business intelligence is the exclusive province of IT specialists and business analysts. However, those of us at Michigan State University (MSU) have a different approach. At MSU’s Division of Residential and Hospitality Services (RHS), we put IBM Watson Analytics into the hands of our operations managers. Its ease of use gives them […]

Continue reading

How to build trust in AI with blockchain technology

Machine learning and AI are all about data. We give AI-powered systems massive amounts of data to ingest and analyze, and then we trust the results. But I want to know how we can ensure the integrity of those systems, from the algorithms to the data sources. Solving the AI data-integrity issue I believe the […]

Continue reading

How AI technology can help make city life easier for all

My company, Shimizu Corp., has long followed the management principles of Shibusawa Eiichi, who held that economic activity and morality are inseparable. This harmony can be seen in the experimental system we developed with IBM Research to help people, including those who are disabled, enjoy barrier-free urban exploration through voice navigation on their iPhones. We […]

Continue reading