The Future of Brand Marketing with Data Science

The use of data science in marketing is not new. We have seen already how data analytics and AI ML capabilities are used in marketing to deliver personalized content feeds, chatbot communications, and product recommendations. But, has brand marketing been able to catch up with the pace at which data science teams are influencing marketing principles? According to the latest market intelligence studies, brand marketing is still lagging in the field. That’s why, if you are pursuing data science training in Bangalore, you have a great chance to work with teams that can turn this lag into a winning proposition.

How? Let’s understand here.

In the age of Social media and video interactions, brand marketing has become the center point of action for almost every customer-facing company. Traditionally, brand marketing teams have been at the forefront of using the most cutting edge technologies and platforms available to them to promote their brands and products. But, with the rise of new age concepts like social media influencers like Instagrammers, Twitterati, and YouTubians, we are seeing a rampant convergence of branding principles with user generated content. Brand marketers, therefore, have much lesser control over the brand promotion tactics that have suddenly moved to a more personalized sphere.

How Brand Marketing Teams can Utilize Data Science?

Data Science Course in Gurgaon can add powerful tools and analytics to a traditional marketer’s inventory. With advanced analytics, embedded automation, and CRM reporting tools, a brand marketer can unlock hidden insights into an otherwise dark side of its market and branding targets.

For example, brand marketers can use AI and data science models to answer these questions;

  • Who is my most promising customer?
  • What are the unique behavioral patterns and traits of our biggest customer base?
  • What kind of features and benefits are these customers most likely to pay more compared to competitors?
  • What are the biggest pain points of our marketing strategy in branding and communications?
  • Which communication medium and channels are most likely to engage more customers?
  • How do people feel about our brands in these times of pandemic? What are the biggest emotionally strong points of our branding? How can we improve these?

There are countless other questions that brand marketing teams have to keep in mind to further solidify their belief in their marketing strategy with respect to traditional marketing research, statistics, and business expertise, some of which may need a strong backing from the data science capabilities.

Therefore, we are seeing a large number of brand marketing teams going after Amazon and Google, to strengthen their knowledge of brand position and pricing, centered around customer experience, communications, social media listening, and sentiment analysis.

By leveraging the ability of data science capabilities and tools that are now commercially available in the market, brand marketing teams can eliminate errors and noise from their campaigns, targeting a much larger population of customers who are most likely to engage, interact and fulfill marketing demands in a meaningful manner.

Brand Marketing Example

Netflix is a very popular on demand video streaming platform with a subscriber base of over 200 million globally. The platform added more subscribers in 2020 through the pandemic lockdowns compared to its previous years! Reason: Netflix brand marketing teams kept a track of the pulse of the subscriber base and provided the most engaging content across its channel. Not only did it leverage its own VOD platform to promote content but also aggressively published and promoted content on YouTube, Instagram, and other channels. But, did you know what worked most for Netflix — its ability to predict the role of TV advertising on its own video subscriber base. Despite its positioning as an alternative to TV watching, Netflix reversed the trend on itself and invested heavily in TV advertising to engage the audience. And, it worked!

Netflix hired the highest number of data scientists and engineers for its research and product development teams of which research applications spanned multiple projects in areas of customer experience, hyper personalization, content visualization, customer service management, subscriber loyalty, and streaming optimization. One of the most highlighted advancements within Netflix Data Science was its ability to improve the streaming quality using machine learning! Seen as an immense technical challenge, the Netflix team gathered full steam with its Cloud Computing and Data Analytics outcomes to design the most optimized content streaming algorithm that improves viewing experience in a meaningful manner.

So, what’s stopping you to explore data science for branding!

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