Digital Marketers Play With Machine Learning

Machine Learning is already in use on search engines -RankBrain, which is part of Google, is an AI system that is used quite prominently in ranking websites for search engine results.
Natural Language Processing (NLP) applications attempt to understand natural human communication, either written or spoken, and communicate in return with us using similar, natural language. ML is used here to help machines understand the vast nuances in human language, and to learn to respond in a way that a particular audience is likely to comprehend.So, let’s see below the different areas at which machine learning is making waves in digital marketing.
- Search Engine Optimization (SEO)
AI is letting search engines learn more about the queries themselves as opposed to the keywords being used. This means that Google’s RankBrain takes the information of the user’s past queries as a significant part of what they are searching for now. From an SEO point of view, keywords could become less important as more non-traditional factors are given greater consideration which results in a richer, more fulfilling experience for the user.
Hence, the algorithm they use needs to be more focused on providing each user with content that will serve a specific purpose, rather than be packed with the right keyword density.
2. Content Marketing
ML is used to match the right content to the right user at the right time in the right context (i.e., Context-based Marketing). This means that ML will be applied to customer interaction data in order to find and exploit patterns in different types of user interactions that occur at different times and locations: purchase histories, emails, call center interactions, social media, website searches, previous marketing campaigns, and even location data and/or “emotion data” from wearable sensors while the customer is shopping
ML could eventually dictate not just the subject matter of content, but also the right medium, the tone, and other critical facets of how brand stories are told.
3. Link Building
After Google released the Penguin algorithm, many thought that link building was dead because Penguin penalized any business it caught buying links. But, instead of dying out, link building has evolved. As a marketer you need to look for brand recognition content, social signals that mention your brand, and content research for guest posts instead of simply buying your links.
4. Pay Per Click (PPC) Campaigns
With Google launching new “smart” features such as Google Smart Bidding, Smart Display Campaigns, and In-Market Audience to help businesses maximize conversions, it is clear that the future of PPC lies in machine learning.
To become more strategic and take your PPC campaigns to the next level you need to:
- Get to grips with the metrics that are most valuable to your business
- Understand obstacles that could get in the way of meeting your goals
- Know the underlying performance drivers to make more strategic decisions
5. Influencer Marketing
Influencer marketing is being seeing a steady rise among several marketing plans.ML (including social network analysis and social graph mining) is now helping marketers identify the key influencers within their market domain. Consequently, by engaging directly with those influential individuals to help spread their message, a marketer can expect to see the impact of their campaigns amplified to even wider, yet more targeted audiences than could otherwise be reached.
Using ML to identify those influential marketing partners is powerful social data science that should definitely be considered as a high-ROI component in all online campaigns. Using machine learning for marketing.
Remember, in order to fully capitalize on the above opportunities, you must also:
- Make Your Website Responsive
- Optimize for Local Search
- Be Ready for Conversational Search Terms
Where Do We Go from Here?
Machine learning, data science, and predictive analytics are the new, increasingly crucial complements to traditional marketing best practices. Having said all, they may require some new talent on the team, including people with specialized knowledge of analytics and experience working with engineers who have built machine learning models. In addition, bringing science into your marketing process might require a mindset shift among digital marketers. An important measure would be fully embrace testing and experimentation with new technologies.