5 Ways Artificial Intelligence is Helping Marketers do a Better Job
Note: This is a guest post authored by Aditya Khanduri, from Niki.ai. Niki is your digital chat-based and Artificial Intelligence powered shopping assistant.
Marketing, in its raw form, is being practiced for centuries. From the traders in the bronze age trying to barter the best deal for their items to the Growth Architects in the information age trying to boost their app installs, the art and science of marketing have gone through multiple transformations. Now, thanks to the quick advent of Artificial Intelligence in this data age, we are at the cusp of another revolution in the world of marketing. And, it might be the biggest one yet.
Artificial Intelligence has already made inroads in digital marketing, as major brands are trying to one-up on their competition. Here are five ways in which AI and ML are helping marketers do their job more effectively:
An AI engine lets you track and gather various data points of individual users. Using Machine Learning algorithms, it can slowly begin to understand their behavior, their interests and preferences. This allows you to recommend and suggest items or content that is relevant to each user. The chances of conversions increase if you push personalized action items.
You might have experienced it with YouTube, as it recommends you music similar to your taste. Amazon, too, has become so good at using AI that a third of its business comes from the machine learning-powered function: recommended purchases.
In a similar way, the Niki App also offers relevant recommendations to users to help them make the right purchase for each domain. For example, the app can analyze the last 4-5 bus bookings of a user and observe that whenever a person has to travel within 400km on weekends, the person has booked an overnight Sleeper bus. This way, the app recommends similar sleeper bus travel deals whenever the person tries a new booking.
2. Ad Targeting
Any marketer will tell you that the art of targeting is a process of trial & error; tweak and repeat. You create ads based on a certain hypothesis about your target audience, run the ads, analyze the conversions, and retarget again. Machine learning can help in this regard by ‘learning’ the user behavior with all ads. It can help you define your demographics with better targeting. Hopefully, in the near future, machines will be intelligent enough that we won’t even need to tweak the targeting ourselves! There will be ‘master algorithms’, that will do the targeting, experimenting, analyzing and retargeting all by itself.
Like for a lot of other AI applications on marketing, Deep Learning is behind how to do the right ad targeting. Deep Learning is the science to ‘teach’ the machines, recognize patterns and then apply these ‘learnings’ to solve various complex queries. Andrew Ng, Chief Scientist at Baidu Research, recently told the Wired magazine, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”
3. Cross Selling
Acquiring new users is tough, time-consuming and expensive. But, selling more to existing customers and gaining the maximum revenue from your mutually beneficial relationship with them, is one of the main drivers of growth for any firm.
The analysis of past transactions, views and actions can help you classify users into various “similar” groups of customers. You can then use the behavior of these groups as data points, and use predictive analytics to push immediate, real-time suggestions about other products in the catalog effectively to the customer. For example, the Niki app makes informed and cautious decisions of who, what, and when to sell, backed by past data and user behavior.
4. Marketing Automation
AI not only impacts how we practice marketing externally, but also internally. If AI is the rocket to propel us towards the future, then data is the fuel.
Marketing is equally about data and analysis as it is about strategy and creativity. With AI, we are able to automate and delegate the boring mechanical work to the machines, so marketers can focus on the work which requires creativity, understanding the customer and figuring out what the overall positioning is going to be.
The number crunching will be done using AI to provide data-backed analysis to the marketers such as how well the ad is performing, who to target, whether the recent social media campaign is being perceived as positive or negative, etc. An example of such assistance is the firm Narrative Science, which has ‘robot writers’ who produce written stories within seconds based on analytics.
In the future, the bots will be working in the background as virtual assistants to aid the marketers in amplifying the effect of their marketing efforts and provide a level of personalization that cannot be achieved manually.
5. Sentiment Analysis
Sentiment analysis, at its core, is the process of determining the emotional tone conveyed by sentences of natural English. It can be used to gain an understanding of the attitudes, opinions (positive, negative or neutral) and emotions expressed by customers in their online reviews, social media posts and other places where they voice their opinion. The underlying concept working its magic behind the scene is Natural Language processing. (You can try a live demo here.)
There are multiple ways to make use of this technology with the ultimate aim of gauging your user’s emotion and mood at various points in time. In the near future, you would be able to push the right product at the right time based on the emotions of a particular user. Thousands of product reviews on Amazon or other such sites can be used to judge the sentiment of people about that product. The Niki app tries to use sentiment analysis to gain valuable user feedback: it analyzes chat messages sent by users and gauge if people find the app features helpful or not.
While we have seen many advancements in AI till now, the potential that the future holds is immense! Imagine as a marketeer walking into your office and finding a detailed analytical and predictive report already at your desk, prepared by a bot using last day’s data. You study it and instruct the bot to target certain users and personas with a ‘call to action’. The bot then drafts the personalized messages and engages the target audience. This may all seem fancy, but the future is really not that far away!