Personalization has taken over from redefining marketing. What could be better than understanding customer preferences and providing specific products/services that meet their needs. Let’s understand the power play of machine learning and AI right now.
Nowadays, customization has reached the pinnacle of success in the products or services marketed by any company. The machinery of traditional media began to fade a decade ago as more and more smartphones began to enter the market with profitable internet services and all against the backdrop of the increase in personal disposable income in India. The intensive use of cash and the absence of a results-oriented approach have led the traditional marketing technique to take a back seat.
Driven by the demographic approach, primarily through traditional, usually physical, marketing techniques, companies could only influence a particular consumer base where they sought to distribute their flyers, place billboards or broadcast radio commercials on a particular local radio channel.
But with the advent of the Internet, a new world of marketing has opened up. This is due to the widespread penetration of digital media. It has allowed the consumer to choose from a wide array of choices whatever they want to enjoy, whether it is the type of shirt that suits them perfectly based on their previous search or hailing their favorite SUV for hire from a driverless car. aggregator for their upcoming stay. This personalization did not happen out of the blue but is based on timeless data. This data is nothing more than a repository of past customer preferences and predicts their future behavior in using a product or service.
Therefore, nowadays, the main objective of a business while designing a marketing campaign is to collect essential relevant data about the consumers they plan to tap into.
This solves the majority of the hurdle, as a consumer usually makes a decision considering various aspects. This is a good thing from the point of view of understanding the consumer and predicting their approach to a product. But every consumer has had a totally contrasting experience in their life to date and their perspective may be totally different upon seeing the product. So does that mean you would prefer the first consumer to the second? No. These are both crucial avenues for a business to generate cash flow. Also, these are just two people we’ve quoted, but you have hundreds of thousands of people you want to tap into who have had different experiences and have a unique consumption pattern. Does this mean that your unique marketing approach will inspire them to become your consumers and, in turn, your brand ambassadors? No. Personalization is the answer.
This is where the new era of marketing comes in. Machine learning and artificial intelligence (AI) have come a long way in improving the operations of all the traditional practices that remain the building blocks of commerce to this day. Be it production, sales, marketing, feedback, etc., these technologies have brought forth careful application which has enhanced the functionality of several businesses. Let’s delve deeper into the application of AI and machine learning in marketing and how it improves the consumer experience.
Now comes the question of how to target each individual from the huge database with an approach that only influences them in their decision to take a step and purchase a product or service while believing that the transaction will benefit according to his understanding? This is where machine learning comes in.
For example, suppose you buy a particular size of shirt from a particular brand in a particular pattern and often in selected color shades from a fashion website/app of which you are a member. Suppose one day you were looking for new shirt designs but you couldn’t find anything that matched your expectations. In a few hours, do you get a notification from the app reading something like “(Your name), Did you find a United Colors pink shirt on Benetton? We’re sorry they’re out of stock, but here are some similar shirts you will look just as stunning in! You might want to check them out, so you give in and click the notification and you may even end up buying an alternative. Which you did in turn, is that you have entered several keywords in the search tab of the app while searching for your favorite shirts, such as pink shirts, United Colors of Benetton, etc. The app has saved and learned your preferences through to AI. It then filtered and displayed only related products from different brands based on your other search preferences. This is how AI made clothing store marketing easier by online while providing you as a consumer with a personalized experience.
A classic example of machine learning can also be mentioned using the same online clothing application. Suppose you bought size 34 pants from a popular brand like Raymond through this online fashion marketplace app about a year ago. Now, if you’re looking for a similar shade pant and like one of the many listed, however, it’s not from Raymond, but from its sister brand Parx. Suppose you select size 34 again, but you know that each brand, even if they belong to the same company, has a lot of different fit criteria. In this case, when you select size 34, it will offer you a statement below: “Based on your previous purchase, size 32 will fit you perfectly at Parx.” What happened here is that it learned your preference from the past and kept it recorded and the AI on the back informed you about the variation in clothing size between two brands of the same company that could suit you ideally. It also makes your shopping experience seamless so you can rely more on the recommended size on the app to go ahead when shopping without being aware of size/fit.
This is just citing an example of the application of machine learning and artificial intelligence in one case. There are hundreds and thousands of permutations and combinations where AI and machine learning can be incorporated into marketing to improve the customer experience by personalizing it at every touchpoint.
Disclaimer
The opinions expressed above are those of the author.
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