How AI is Transforming Marketing

artificial intelligenceArtificial Intelligence is revolutionizing the practice of marketing in many ways. Before we get into specific use cases for AI in marketing, let me provide a high-level view of some of the components of AI and how they work together to add intelligence  to marketing processes and results. Machine Learning is one such component that combines learning algorithms with increasing amounts of customer data (e.g., past purchases, click-streams, viewed content, channel preferences, survey data, third party data) and predictive modeling to to provide the right content to the right customer at the right time via each touch point in the customer journey over multiple communications channels  (e.g., web, social, search, email, video, chat, mobile, call centers, retail). As the customer continues to interact with the brand, machine learning leverages new data to continuously improve the precision businesses have to segment and target customers as well create and place the most relevant, personalized content in order to improve marketing effectiveness and ROI.

Another key component, Natural Language Processing, is needed to make sense of all of the words that customers use in their interactions with businesses and the entire digital ecosystem.  This is critical to understand customer intent at any time and ensure that the most relevant content is delivered to satisfy customer intentions.

The use of Big Data solutions have become increasing important as the amount of structured and unstructured data expands exponentially.  Traditional databases, even when integrated into larger customer data marts, are not equipped to handle the three Vs or the Volume, Velocity and Variety of data.  Instead, all of the relevant structured and unstructured data is pulled into computer memory in real-time for each decision made by the appropriate machine learning algorithm(s) or recommendation engine.  With this information behind us, let’s get into the various Use Cases for AI within marketing.

USE CASES

Programmatic Advertising

Programmatic advertising automates the decision-making process of where ads are placed, using artificial intelligence (AI) and real-time bidding (RTB) for online display, video, social and even addressable TV.  Machine-learning algorithms ensure that highly targeted users receive the most personalized content, leading to higher conversion rates for for advertisers and greater revenues for publishers at the fairest rate for both advertiser and publisher.

Content Creation, Curation, Matching and Placement

In a similar vein, AI leverages data models (and model scores for each user–including such  user behaviors as click-streams, downloads, purchases, demo and psychographics, survey responses, and social media  posts–to provide the content that is most likely to resonate with the customer at any given touchpoint and channel during the customer journey.  This content can provide anything from useful, shareable content of value to the customer (helping to establish the credibility of your brand) to personalized offers that increase conversion rates on e-commerce sites. Depending on user preferences and platform settings, it can appear among organic and/or paid search results, display ads, social media sites, mobile notifications, call and chat-center scripts, and retail POS systems.

Chatbots

Chatbot leverage large amounts of behavioral data to select and modify scripts that simulate human chat and call-center agents. Text based chatbots are typically used on websites to provide customer service and support sales on websites, while oral based chatbots s (sometimes referred to as conversational commerce) work on such intelligent voice platforms as (Amazon) Alexa, Google Assistant, (Apple) Siri, Microsoft (Cortana) and (Samsung) Bixby, as well as advanced IVR systems).  Chatbots are not yet as sophisticated as human agents, but are extremely cost effective, but perform more effectively over time and can typically and escalate chats to human experts.

Churn Prediction and Retention

Machine-learning algorithms use predictive modeling to identify disengaged customers who are likely to try a competitive product or service. These models often provide customized offers incentives to get them to re-engage with your brand and allow businesses to retain them as customers.

Conclusion

These are just a sample of the many use cases that demonstrate how AI is transforming the discipline of marketing. Moreover, many more are being developed over time. While AI’s role in marketing is currently limited by the relative infancy of machine-learning algorithms and the amount of data available, both the algorithms and results continuously improve over time as more data is collected.

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