3 Ways Marketers Can Use Machine Learning Effectively

3 Ways Marketers Can Use Machine Learning

Najung Gim

November 29, 2018

Segmentation

Gain insights on audience segmentation

Social media matrix is not something that is foreign to digital marketers. However, with Machine Learning, it is a twist to how marketers could gain insights through audience segmentation.

By analyzing social media conversations, brand marketers could detect similar patterns and cluster audience based on psycho-graphic and characteristics such as interest, lifestyle, and attitudes. What this means is that marketers could easily identify which audiences would best match with products and services, deep dive into their interest areas, and determine which social platforms they use frequently.

“Machine Learning enables brand marketers discover their target audience in order to customize their future messages and select communication channels wisely.”

Trend Spotting

Uncover upcoming trend

Machine Learning could also illuminate opportunities that are not otherwise obvious. For example, Isentia uses Machine Learning to find out the future trend related to cashless payments. By finding out the similarities from the latest social media conversation, NETS QR code (NetsPay) emerged as a key theme along with hawker centres. This could hint that, in the near future, hawker centres with QR code capabilities would be a major trend.

“Machine Learning enables marketers to spot future opportunities and, thus, engage their potential customers effectively.”

Influencer Identification

Identify top influencers

Last but not least, brands can also tap into Machine Learning to gain insights for their Influencer Marketing. Isentia leverages Machine Learning to narrow down an influencer list depending on industries, campaign goals and even target audiences. Taking into account a comprehensive range of variables such as resonance, reach, reaction, Machine Learning helps discover the right influencers.

For instance, Machine Learning can effectively combine the total number of comments in a given time frame (resonance), the total number of unique netizens who commented on the influencer’s post (reach) and the degree of agreement with influencer’s postings (reaction) to cherry-pick the top influencer to engage. Such a data-driven approach helps brands make an informed and strategic decision and, thus, get high ROI on their Influencer Marketing.

“Machine Learning enables marketers to obtain high ROI for Influencer Marketing by making an informed decision.”

Conclusion

Najung Gim

Najung is a Senior Insights Analyst at Isentia. With more than 6 years of experience as media intelligence analyst, Najung has covered diverse media landscapes such as traditional, social and digital media to help clients drive better ROI by making data actionable. With her strong industry expertise, she is passionate about serving internal teams and clients with insightful thought leadership pieces.