2016 was an important year for Artificial Intelligence. The wonderful capabilities of AI were foreseen by many people. But AI made a stellar announcement of its arrival when Google’s AlphaGo beat 18-time world champion Lee Sedol in a five-game Go match. AI is becoming one of the most invested areas of recent times. All the tech giants (the likes of Google, Facebook, IBM, Apple, and Amazon) are pouring billions of dollars into developing and nurturing AI-based products.
AI is finding its roots in many industry verticals. In this post, I am going to talk about one such vertical, Marketing. With the availability of user data, marketing has become more personalized, targeted, and data-driven. Gone are the times where brands spend lavishly on mass communication channels. There are numerous spaces where AI is helping marketers. Ad optimization, sales, customer support, content curation, and analytics are all getting revolutionized by AI. Let’s explore more such applications of AI in marketing.
Application and Use Cases of AI in Marketing
Learning User Behavior
As I mentioned earlier, AI is helping brands understand user behavior and needs. We leave online footprints all the time when we use the internet (which is ~6 hours per day). Your search data and social media conversations can be analyzed and leveraged by marketers to understand your needs.
With the advent of Deep Learning algorithms, it is possible to predict your behavior. This lets brands to show you the ads you care for. According to this study, machine learning algorithms can predict your behavior better than your spouse by analyzing your mere 300 ‘Likes’ on Facebook. Imagine, with all the browsing data available to the marketers, the ads can be highly targeted. This leads to better optimization and ROI.
As an experiment, we would encourage you to install the browser extension of Data Selfie. This application shows how Facebook generates an understanding about a user from all the browsing and click data on the platform.
This is the era of personalized marketing. And personalization is not limited to targeting ads based on your needs. Brands are trying to establish an emotional connect with users to drive more engagement. Simon Sinek, in one of the most famous Ted Talks of all time, explained the power of making an emotional connection with the customers. He highlighted the fact that:
People don’t care what you do, they care why you do it.
Thus, analyzing sentiment and emotions from user conversations becomes important for brands.
AI can help detect sentiment and emotions from user data. This data can further be used to target specific user and personalize product/service recommendation. Also, it can be used for competitive research and comparison. We have talked extensively about the emerging role of emotion detection in marketing in a previous post.
AI can help optimize sales funnels as well. Brands hire a horde of sales professionals to identify, target and pitch customers. Identification of potential buyers and targeting them is a hectic task for a sales professionals. This task can be automated by using AI to predict user behavior. Filtering leads using intelligent algorithms can save plenty of time for the sales team and reduces human efforts.
AI can help manual work to identify prospects and contacts, filtering leads, and finalizing the clients based on user behavior.
Customer Support Optimization
AI is changing the way customer queries are handled and analyzed. The user chooses social media, e-mails, and forums to voice their opinions and reviews about a product/service. Using AI, marketers can classify the data into various categories and allow relevant sources to respond to such user queries.
Using intent analysis, marketers can automate the identification of underlying intent of the text. This helps marketers classify and manage numerous user queries surfacing online. This makes the overall process fast and efficient.
Content Curation and Recommendation Engines
AI can help content marketers curate content as well. Semantic analysis can help media houses, journalists, bloggers and news platform get ideas and reduce redundancies.
Also, many brands are using AI to power their recommendation engines. Recommendations have come out as a very efficient re-engaging and re-targeting strategy. Recommendation of the content based on user behavior increases engagement. Take Netflix’s recommendation engine for an example. By recommending relevant content to the recurring users, Netflix keeps them from canceling the subscription. Netflix reported that recommendation engine could save $ 1B per year and 80% of content discovery on Netflix happens through recommendations.
NLP-powered virtual assistants have made their mark as well. Apple’s Siri, Microsoft’s Cortana, Amazon Alexa, and Google Assistant are the most notable ones. If you wonder what assistants have to do with marketing, you should check out Hubspot’s Growthbot. GrowthBot is a Chatbot for marketing and sales. It connects to a variety of marketing systems (like HubSpot, Google Analytics and others) and databases and gives you quick, easy access to information and services.
Though not much has been done to integrate marketing and virtual assistants. I can imagine bots being used to automate most of the redundant work in marketing, perform A/B testing, optimizing funnels et cetera. The future looks good and full of possibilities for this vertical.
With the increased use of Chatbots and Virtual Assistants, marketers have begun to develop their strategies around this field. In future, Bot-to-bot marketing would become a norm. Where an intelligent bot will interact with other bots/virtual assistant to search, interact and execute a particular task. For example, you may ask your bot to book a flight for you. The bot of a hotel booking platform might push an ad to your bot recommending hotels to stay in that destination.
These are few emerging applications of AI in marketing. I am sure future holds the door for plethora of application of AI in marketing. In this era of highly personalized and targeted customer acquisition, AI has a vital role to play. Since marketing has moved from masses to individuals, where are brands now playing with Unit Economics and Customer Life Time Value (CLTV), AI can revolutionize reading user behavior to the grass root level.
I started this article citing Alpha Go’s triumph as the breakthrough in AI. In the same match, Alpha Go produced the famous “Move 37”, a move never seen in the more than three-millennium old history of the game. It was a step up from human intuition. With increased cognitive ability of machines, it’s hard to imagine what the future looks like for AI in marketing. Who knows if AI has a “Move 37” to predict the future too. I know a thing for sure, it is going to be really exciting.
ParallelDots is an Artificial Intelligence research and Deep Learning startup that provides AI solutions to clients in multiple domains. You can check out some of our text analysis APIs and reach out to us by filling this form here.