At present times, marketers are experiencing a huge inflow of data about consumer preferences. Theoretically, This information eases down the process of grouping users and creating relevant content. But that’s not always the scene.
Generally, more data added to a marketer’s workflow equally increases the time required to generate some meaning of the information and take action.
Machine learning is a subset of artificial intelligence. The technology installs computers with the capacity to analyze and interpret data to present accurate predictions without the requirement for explicit programming. When more data is registered into the algorithm, the algorithm learns more and in theory, it becomes more accurate and performs better.
If marketers expect to produce or generate more meaningful campaigns targeting the audiences and encouraging further engagement. Integrating machine learning can become a handy tool to uncover hidden patterns and reasonable tactics tucked away in those heaping amounts of big data.
An article at the Entrepreneur mentions a few ways brands are using machine learning to boost their campaigns.
The article explores the 2017 case of the ice cream giant Ben & Jerry’s which launched a range of breakfast-flavored ice cream: Fruit Loot, Frozen Flakes and Cocoa Loco, all using “cereal milk.” It has been considered as a step to uncover new trends since the new line was the result of using machine learning to mine unstructured data.
The company realized that artificial intelligence and machine learning allowed the insight division to listen to what was being talked about in the public sphere. At least 50 songs within the public domain had mentioned “ice cream for breakfast” at one point and discovering the relative popularity of this we learned how machine learning could uncover emerging trends.
Machine learning also facilitates Targeting the right influencers. The article mentioned the Japanese automobile brand Mazda employed IBM Watson which chose influencers to work with for its launch of the new CX-5 at the SXSW 2017 festival in Austin, Texas.
Searching for various social media posts for indicators that attuned to the brand values, involving artistic interests, extraversion, and excitement, the machine learning tool recommended the influencers who could best connect with festival fans. Those brand ambassadors rode around the city in the vehicle and posted about their experiences on Instagram, Twitter, and Facebook.
The report highlights that
“A targeted campaign, #MazdaSXSW, fused artificial intelligence with influencer marketing to reach and engage with a niche audience, as well as promote brand credibility”.
The machine learning can also Analyze campaigns as a part of important marketing requirement. For the past few years, cosmetics retail giant Sephora has boasted a formidable email marketing strategy, embracing predictive modeling to
“send customized streams of email with product recommendations based on purchase patterns from this ‘inner circle [of loyal consumers].’”
Predictive modeling is an effective process of creating, testing, and validating a model to best predict an outcome in the sphere of probabilities. The data-centric tactic led to 70 percent increases in productivity for Sephora and also brought about a fivefold reduction in campaign analysis time — alongside no measurable increase in spending.
Overall it can be noted that Machine Learning has been emerging as a cost-effective and relevant to time measure of addressing the audience’s preferences and taking quick action to improve productivity.