marketingtechoutlook

AI Casting a Spell on Marketing Analytics

By Marketing Tech Outlook | Tuesday, August 27, 2019

Marketing AnalyticsWith respect to marketing, AI has immense potential on three levels based on its functionalities: ML techniques, applied propensity models, and AI applications.

FREMONT, CA: Artificial intelligence (AI) is permeating the industries across the verticals. It offers a range of capabilities, such as image and voice recognition, semantic research, and machine learning (ML) techniques. AI is causing a tremendous stir in marketing too. However, marketers are still finding it a challenge to leverage the technology to achieve a significant ROI.

For the sake of simplicity, AI can be categorized under three categories based on its functionalities with respect to marketing: ML techniques, applied propensity models, and AI applications. ML techniques leverage algorithms to learn from past data sets, which can then develop propensity models. When these propensity models are engaged in predicting events such as scoring leads as per their likelihood to convert, such models are termed as Applied Propensity models. AI applications include tasks which are generally associated with a human operator such as writing new content or answering customer questions.

In general, some of the major capabilities of AI in the field of marketing analytics are stated below:

Content Generation

It's a relatively new domain but is already providing an edge to modern marketing techniques. Although AI can't create an opinion column or a blog post on a particular industry-specific topic, there are certain areas where AI-developed content can be effective in drawing the audience to a website. AI writers are also useful for and reporting on data-focused as well as regular events. For instance, AI can help with sports matches, quarterly earnings report, and market data. AI-generated content can also form a valuable part of content marketing strategy if a marketer plans to operate in a relevant niche such as financial services.

Content Curation

AI-enabled content curation helps in better engagement of the audience on the site by serving relevant content to them. The technique is especially effective in the recommendation of products similar to their already purchased ones. It is also effective for subscription businesses where the ML algorithm gets better as the users engage with contents, thereby providing them with increasingly better contents.

Voice Search

Voice search has immense potential in marketing. Marketers need to utilize the technology developed by the giants such as Amazon, Google, and Apple instead of developing their own capability. Voice search is bound to change the future SEO strategies, and brands must be ready to embrace and keep up with the transformations. Brands that are already working on to develop their voice search capabilities are likely to curb the organic traffic toward their pages.

Programmatic Media Buying

Programmatic Media buying will utilize propensity models developed via ML algorithms to target ads for the most relevant customers effectively. Programmatic ads are also expected to get smarter, especially after Google’s ad network appeared on terrorist’s websites. AI can be useful in recognizing questionable sites and eliminating them from the stack of sites ads can be placed on.

Propensity Modeling

As mentioned earlier, Propensity models are generated with the aid of ML algorithms. The ML algorithms are supplied massive amounts of historical data which is used by it to develop a propensity model. The propensity model can make accurate predictions about the real world and thus proves to be a vital component of current marketing strategies.

Predictive Analytics

Propensity model can be used across several areas such as predicting the price a customer is likely to convert at, predicting the chances of a given customer to convert, or which of the customers are expected to make a repeat purchase. The application is termed as predictive analytics as it uses analytics data to make predictions over customer behaviors. However, an essential aspect of such prediction models is that the models are only as good as the data fed into them.

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