New Areas For Applying Data Science In Marketing

  • by Admin
  • 16th, Jun 2020

Data science is said to have brought a new direction in marketing management. As far as the key concept is concerned, data science helps marketers to fetch actionable insights from user data. These insights are later evaluated to find new opportunities in lead generation and traffic conversion. Google Marketing Company in India, Europe, America and elsewhere across the globe think and feel that data science is the new sun in the vast galaxy of digital marketing.

The significance this latest approach is largely visible in multiple niches of marketing like profiling, customer engagement, search engine optimization, responsiveness, real-time marketing campaigns, etc. However, the new ways of implementing data science in marketing management evolve every day. Let’s pinpoint the latest ones for you:

Also Read – How Paid Ad Campaigns Performed During The COVID-Crisis?

Customer Segmentation

All customers are different from the other. So, a uniform marketing approach is not efficient for each individual. That’s where the concept of customer segmentation comes in the rescue of digital marketers. Application of data science has made it easier for the marketers to slice the user data and segment them into smaller groups according to the correlations of specific criteria in their characteristics.

There’re two different approach of segmentation that marketers follow:

  • Segmentation on the basis of users’ purchase pattern
  • Segmentation on the basis of touchpoint engagement

 Another emerging trend in marketing is the application of micro-segmentation. This advanced technique is used to categorize people into more precise groups based on their behavioral intentions. As a result, marketing actions of any brand can thus be tailored to the preferences of all customers and potential buyers.

Predictive Analytics

At present, implementation of data science is increasing, which has made data easily available and accessible for all types of businesses. That’s the reason why predictive analytics is a rage in the field of digital marketing. It’s a technique of predicting the future with high probability by combining statistics with machine learning algorithms. There are a few effective opportunities of predictive analytics in marketing management. Here they are:

  • Predictive analytics based on customer behavior – Predictions, cluster model, regression analysis, collaborative filtering, etc. are used to identify collaborative patterns in consumer’s behavior to predict the tendencies of future purchasing.
  • Predictive analytics to qualify and prioritize leads – Identification model, predictive scoring, and automated segmentation are used to qualify leads and prioritize them to ensure that the most active ready-to-purchase leads correctly receive your call to action.
  • Predictive analytics for targeting highest-value customers – In this approach, predictive analytic models like churn analysis, affinity analysis, and response modelling is used to spot and address valuable customers with exclusive offers at the right time.
  • Predictive analytics for launching the right product – In this case, the digital marketers take the advantage of data visualization to perceptively and logically decide which and when a product or service should be launched in the market. 

Real-Time Analytics

Real-time analysis of customer data and operational data considerably increases the revenue of any company. Further, real-time analytics help the marketers to include marketing insights immediately into campaigns. With the help of customer data, it’s easier to figure out customers’ needs, preferences, and wants. On the other hand, operational data reveals different transactions, decisions, and actions of the customers. As a result, marketing campaigns prove to be more efficient, fast, and reflect high performance rates.

Here’re some opportunities that real-time analytics offer:

  • Offer more details about users and consumers
  • Help to find the most efficient platform
  • Provide unique experience to the customers
  • Help to run real-time analysis of data
  • React and respond immediately
  • Help to identity the best working practices

Market Basket Analytics

This analytic technique refers to the data mining technique that helps in unsupervised learning of purchase pattern. It also helps in disclosing the co-occurrence relation between different purchases. On implementing Market Basket Analytics, marketers predict the future of customers’ buying decisions. Moreover, this technique significantly recuperates the efficiency of marketing message. Besides the variety of marketing message, digital marketing experts can offer the best product suitable for individual customers no matter whether it is direct offer, social media, email, newsletter or phone call using Market Basket Analytics. 

Recommendation Engines

It is a powerful marketing tool that is used to provide personalized experience and high satisfaction to the consumers. Every brand and its concerned digital marketers should pay attention to the implementation of Recommendation Engines.

Its primary idea is to match preferences of customers with product features that they might like. To make this happen successfully, Recommendation Engine make use of algorithms and marketing models like regression, decision tree, neural networks support vector machines, and k-nearest neighbor. Typically, this data science model is mostly used for targeted marketing tools like online campaigns and emails.

Conclusion:

The use cases of data science, mentioned above, prove that its application is able to bring multiple strong benefits to the brand-specific marketing campaigns. As there are numerous data available in today’s date, it’s vital to freeze all the information and use them for company’s benefits.

Transformation of these data into valuable and meaningful insights plays a vital role in decision making. To be said precisely, the specific applications of data science brings real positive effects on marketing management. And, the benefits are only expected to increase day by day.

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