The last decade has seen a drastic increase in the consumption of online information. With more and more people able to access the Internet, colossal amounts of data are created every second; proper processing and analysis of this vast repertoire of data can provide priceless insights to marketers about customer needs and requirements. Decoding this enormous amount of data requires the tools of Data Science. In this article, everything regarding data science applications in marketing, has been discussed.
What is Data Science?
Data Science is an interdisciplinary field that provides the tools to process data for extracting meaningful information. It helps marketers gain invaluable insights on various aspects of marketing such as profiling, customer intent, engagement, and experience, search engine optimization, real-time marketing campaign, and more.
These insights, in turn, enable marketers to optimize their marketing strategies and ensure maximum returns. In other words, Data Science helps in projecting how a specific market behaves, as well as the various stages it goes through. To accomplish this, Data Science goes through a five-stage cycle – capturing of correct data from the source, storing the data, efficient data processing, data analysis, and finally, reporting the data that translates into marketing insights.
Data scientists are responsible for collecting data from different sources, processing them, analyzing the results, and communicating the solutions that, in turn, have a positive effect on business decisions.
Data Science in Marketing
When it comes to data science applications in marketing, Data Science has become crucial to marketing return on investment. The rapid boom in marketing technologies has had a two-fold effect – on one hand, the business environment has become more complex. On the other hand, businesses have abundant customer data to optimize business strategies. As a result, the demand for Data Science has skyrocketed in the marketing world in recent years.
Marketers need to quantify their marketing results to monitor budgets and strategies. The information collected from digital campaigns is used by marketers to improve their business tactics, and this is where Data Science has a role to play – it maps social networks to determine the persona of customers, identifies locations and demographics, and tracks the responses of the target audience. In this way, businesses can tackle marketing challenges while providing a more personalized shopping experience to their customers.
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Applications of Data Science in Marketing
In this section of the article, we will walk you through some top data science applications in marketing:
1. Customer profiling
Marketing a specific product or service requires a thorough understanding of the profile of customers and the attributes that set a particular customer apart from the thousand others shopping for the same product. Based on the characteristic of customers, data scientists consistently create specific lists of customer personas that need to be targeted. By leveraging the likes and interests of each persona, data science can help create a customer base for optimal delivery of results.
2. Budget optimization
Every marketer aims to achieve maximum returns on investment from their fixed budget. But it could be tricky and time-consuming since marketers are bound to stick to a strict budget. Hence, efficient utilization of the budget is often not accomplished. However, using Data Science, a spending model can be built based on the analysis of the marketer’s expenditure and acquisition data that will allow better utilization of the limited resources. Marketers can use the model for distributing the budget across campaigns, channels, and mediums.
3. Real-time analytics
Real-time analysis of data has been made possible with the growing popularity of communication technologies and social media platforms. Operational data and customer data are the two groups of data with which real-time algorithms work. Operational data shows the various decisions and transactions that the customers make.
Customer data reflect the wants, needs, and preferences of the customers. These real-time data boost the efficiency of marketing campaigns by gathering more customer details, running real-time tests, identifying efficient platforms, giving quick responses, and enhancing customer experience.
4. Targeting the specific market/audience
Marketing campaigns are often targeted across a general audience and location. This strategy may result in mediocre results, wastage of monetary resources, and failure to accomplish the revenue goals. However, these problems can be avoided if the targeting is made tighter and more directed towards an audience that is responsive and interested in the product. With the help of Data Science, it is possible to identify the specific locations, demographics, and interests that will ensure the success of the campaign and give maximum ROI.
5. Recommendation engines
A recommendation engine is a software that provides suggestions for products/services that might interest a website user. It is a powerful tool in marketing that helps to personalize the user experience with high customer satisfaction by matching the preferences of customers with the product features. Recommendation engines use tools and algorithms such as decision trees, regression, support vector machines, k-nearest neighbors, neural networks, etc. and are very useful for email and digital marketing campaigns.
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6. Lead Scoring
This is one of the most challenging tasks in marketing, but data science applications in marketing have simplified that as well. Every lead does not convert into a customer. But, if a marketer can accurately classify customers as per their interest, more and more leads will generate customers and provide examples for forecasting lead conversions.
Data Scientists helps marketers build a predictive system of lead scoring that will calculate the probability of lead conversions and help in categorizing the lead list. The lead scoring system will eventually reflect on the generated revenue.
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7. Analysis of pricing strategy
A marketer should have an intelligent pricing strategy that will be aligned with the expectations of the customers without compromising with the revenue generation. With the help of Data Science, marketers can take into consideration different factors like individual customer tastes, purchase history, market scenario, and the economic situation that dictate the price and buying intent of customers. Consequently, marketers can set a fair price for their products.
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As the demand for Data Science continues to grow, it is becoming more apparent how it has revolutionized the business world. With the ability to gather and analyze enormous amounts of information, data scientists are playing a pivotal role in driving businesses towards the path of success. The list of data science applications in marketing discussed in our article reveals the positive impact that data science is making on marketing.
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What should one keep in mind before starting a data science project?
The following points should be kept in mind before starting any Data Science project: Choose the programming language that you are comfortable with. However, the language chosen should be one of the in-demand languages such as Python, R, and Scala. Use datasets from trusted sources. You can use Kaggle datasets. Moreover, make sure that the dataset you are using does not contain errors. Find errors or outliers in your dataset and rectify them before training your model. You can use visualization tools to find the errors in your dataset.
What is the ideal architecture of a data science project?
The following components highlight the most general architecture of a Data Science project: Problem Statement is the fundamental component on which the whole project is based. It defines the problem that your model is going to solve and discusses the approach that your project will follow. Dataset is a very crucial component for your project and should be chosen carefully. Only large enough datasets from trusted sources should be used for the project. The algorithm you are using to analyze your data and predict the results. Popular algorithmic techniques include Regression Algorithms, Regression Trees, and Vector Quantization.
What are the characteristics of TensorFlow?
If you are a Machine Learning enthusiast, you must have heard about TensorFlow. It is the most popular Python library for Machine Learning. The following are the characteristics that contribute to its popularity: TensorFlow is an end-to-end open source ML platform that provides a flexible ecosystem of tools. It provides intuitive APIs like Keras to efficiently build and train machine learning models with easy debugging. It allows you to build ML models in the cloud, on-prem, in the browser, or on-device in any language you want. TensorFlow has a powerful architecture that helps you to take your ideas from concept to code and allows you to publish your models faster. Airbnb, Coca-Cola, Google, Intel, DeepMind, and Twitter are some of the companies that use TensorFlow. There is a vast TensorFlow community out there that constantly writes blogs and documentation to help beginners grow.