3 Powerful Uses of Machine Learning in Marketing

We've entered an era in which marketeres are being bombarded by volumes of data about consumer preferences. In theory, all of this information should make grouping users and creating relevant content easier, but that's not always the case. Generally, the more data added to a marketer’s workflow, the more time required to make sense of the information and take action. 

Machine learning is a subset of artificial intelligence. The technology equips computers with the capacity to analyze and interpret data to proffer accurate predictions without the need for explicit programming. As more data is fed into the algorithm, the more the algorithm learns, in theory, to be more accurate and perform better.  If marketers expect to create more meaningful campaigns with target audiences and boost engagement, integrating machine learning can be the tool to unveil hidden patterns and actionable tactics tucked away in those heaping amounts of big data. 

Here are a few ways brands are using machine learning to boost their campaigns.

In 2017, ice cream giant Ben & Jerry’s launched a range of breakfast-flavored ice cream: Fruit Loot, Frozen Flakes and Cocoa Loco, all using “cereal milk.” The new line was the result of using machine learning to mine unstructured data. The company discovered that artificial intelligence and machine learning allowed the insight division to listen to what was being talked about in the public sphere. For example, at least 50 songs within the public domain had mentioned "ice cream for breakfast” at one point, and discovering the relative popularity of this phrase across various platforms revealed how machine learning could uncover emerging trends. Machine learning is capable of deciphering social and cultural chatter to inspire fresh product and content ideas that directly respond to consumers’ preferences. 

Ben & Jerry’s is far from the only brand leveraging the power of machine learning. Japanese automobile brand Mazda employed IBM Watson to choose influencers to work with for its launch of the new CX-5 at the SXSW 2017 festival in Austin, Texas. Searching various social media posts for indicators that aligned with brand values, such as artistic interests, extraversion and excitement, the machine learning tool recommended the influencers who would best connect with festival fans. Those brand ambassadors later rode around the city in the vehicle and posted about their experiences on Instagram, Twitter and Facebook. A targeted campaign, #MazdaSXSW, fused artificial intelligence with influencer marketing to reach and engage with a niche audience, as well as promote brand credibility. 

Of course, while the examples above show how machine learning taps into brands’ customer bases more effectively, it’s important not to overlook the real cost-efficiency of such intelligent marketing campaigns. 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 the process of creating, testing, and validating a model to best predict an outcome’s likelihood. The data-centric tactic led to a productivity increase of 70 percent for Sephora, as well as a fivefold reduction in campaign analysis time — alongside no measurable increase in spending. 

As the influx of data continues growing uncontrollably, the implementation of machine learning in marketing campaigns will become even more relevant when it comes to striking up engaging conversations with consumers. Indeed, it could be so integral that spending as a whole on cognitive and artificial intelligence systems could reach a whopping $77.6 billion by 2022, according to the International Data Corporation. Companies like Ben & Jerry’s, Mazda and Sephora have already recognized the positive impact that machine learning can have on their brands, including higher engagement rates and increased ROI. Other marketers will likely soon be following their lead. 

Source: Entrepreneur.com

Powered by NewsAPI.org

Keywords:

Machine learning • Marketing • Data • Consumer • Information • User (computing) • Relevance • Data • Marketing • Workflow • Time • Sense • Information • Action theory (philosophy) • Machine learning • Subset • Artificial intelligence • Technology • Computer • Data • Accuracy and precision • Prediction • Computer programming • Data • Algorithm • Algorithm • Accuracy and precision • Marketing • Marketing • Machine learning • Tool • Big data • Machine learning • Ice cream • Ben & Jerry's • Breakfast • Ice cream • Fruit • Frozen food • Cocoa bean • Cereal • Milk • Machine learning • Mining • Unstructured data • Corporation • Artificial intelligence • Machine learning • Public domain • Ice cream • Machine learning • Machine learning • Product (business) • Consumer • Ben & Jerry's • Brand • Machine learning • Car • Brand • Mazda • Watson (computer) • Mazda CX-5 • South by Southwest • Austin, Texas • Social media • Creativity • Extraversion and introversion • Machine learning • Instagram • Twitter • Facebook • Advertising campaign • Artificial intelligence • Influencer marketing • Niche market • Promotion (marketing) • Brand • Machine learning • Customer • Marketing • Cosmetics • Retail • Sephora • Email marketing • Marketing strategy • Predictive modelling • Personalization • Streaming media • Product (business) • Recommender system • Pattern recognition • Social network • Consumer • Predictive modelling • Scientific method • Probability • Productivity improving technologies • Sephora • Reductionism • Analysis • Time • Measurement • Data • Machine learning • Marketing • Marketing • Relevance • Conversation • Consumer • Holism • Cognition • Artificial intelligence • System • International Data Corporation • Ben & Jerry's • Mazda • Sephora • Machine learning • Marketing •