top of page
  • Writer's pictureYogesh Jain

4 Key Applications: The Role Of Data Science In Marketing

Updated: Mar 24

In the modern world, nothing can work without data. All your business decisions, marketing decisions, decor, cuisine, all these need data. People even read product reviews in depth before they buy a product, using the data to determine if the product is worth the effort and money. This is because data helps you decide what is best for you and it supports your understanding of the situation. Marketing is much the same. Target demographics need data, and so does budgeting. You require data before you cement any marketing strategies to support your objectives. There is still a world of data that we have not scratched the surface of and data science can help you deep-dive into it to make the most of your marketing buck.


What is data science?

Role of Data Science in Marketing


Data science is an interdisciplinary field with a mash of business, statistics, and programming. It uses large volumes of data to provide actionable insight by processing and translating data. Hence, it gives actionable intelligence to inform business decisions and marketing strategies. Data scientists provide a refined look into raw data and design models to predict the future using historical data. Data science leverages data in an innovative method and provides results that boost ROI. One would think that data science is a part of engineering. However, that is not the case. While data science includes tools such as machine learning and mathematical concepts (clustering and regression), it has little to do with engineering. Data scientists do not develop softwares or manage infrastructure like engineers.  Many people use data science and data analysis interchangeably. However, they are not the same. Their similarities end with the data they use. Data science helps predict the future through model creation using R, Python, and SAS. Whereas data analytics provide insight into the past using descriptive statistics to analyze past patterns.  Read More: What is Data Storytelling and How to Master It?


How does data science work?




Data Science in Marketing Analytics

Understanding how data science processes work helps in understanding how they can be applied to different segments of marketing. Hence, we will begin with the data science workflow before diving into its marketing applications.


When it comes to data science, it is part research, part programming, and part math. You use data to draw a conclusions that will aid in your marketing efforts. Therefore, you should always begin with an objective. This objective will help you frame questions that will help you generate how to conduct your study and what it requires. Once you have defined your objective, you move on to gather all related data. Then, data scientists will use exploratory data analysis to find the right model. Once the model is chosen, it is run on half of the data. The other half of the data is used to test its accuracy. Finally, a little fine-tuning is done, and the model is ready to run.


An average person would take decades to manually do what data scientists can do in a day using modern tools. The employment of statistics and programming makes it a thousand times easier to spot trends and patterns in data that a human eye can not because of the vastness of the data. Moreover, data scientists can extract more than just the base parameters of a demographic, such as age, location, sex, etc. With the help of affinity analysis, data scientists can analyze customers' behavior, purchases, likes, and dislikes, providing an in-depth understanding of a person. Using this affinity analysis, they can predict what a customer might buy in the future by analyzing their past purchases.


For example, a reader is also likely to spend money on bookshelves or bookmarks. With data science, you can analyze their behaviors and make an unassuming connection between various aspects of a person. For instance, a suspense reader likes binge-watching supernatural shows, loves chocolate, and prefers ordering out. With the data, you can enter your marketing in places your customers frequent that have directly nothing to do with suspense novels. Read More: What is Lean Software Management? Why Does it Work?

How does data science figure into marketing?



1. Marketing budget optimization


Optimizing the marketing budget is the most important task a marketing department has. But what does this mean? You would think that the ultimate goal is to lower the marketing spend in any way possible. When in fact the task is not to spend the lowest budget. On the contrary, the objective is to get the biggest bang for your buck. In simpler words, increasing the ROI for a single marketing dollar. The higher the ROI value, the more optimized your marketing budget is.


A data scientist can link marketing dollars to an action taken by a customer through their customer journey. This way, they can build a spending pattern or the cause and effect of each purchase. Since each campaign has different results, scientists can't figure out what works and what does not. They can even tell you which channel needs optimization and how you should go about it. Therefore, data scientists evaluate the performance of channels, campaigns, and every marketing budget to give you the best parameters. Hence, increasing your ROI for a set budget by reducing elements that do not drive results. Read More: What is Product Lifecycle Management? What are Its Implications in SaaS?



2. Advanced lead scoring and customer segmentation


Lead scoring refers to allocating a score to a potential lead based on their likeliness to convert to a paying customer. It is an incredibly critical metric in marketing because you do not want to spend a lot of time on low lead scores when you can divert the same resources to convert a high lead score client. Using data, marketers can create an intuitive algorithm to predict lead scores for leads. 


No two companies' products are ever the same. Similarly, no two customers are ever the same. Therefore, no two customers will have the same needs, no matter how similar they are. This means that your customer personas are not 100% accurate. However, you can group customers based on their pain points, desires, etc. Doing so manually would take forever, time that no marketing department has. Thankfully, you don't have to. Data science can segment your customers for you and tell you exactly what they want. Read More: What Is A BCG Matrix & How is it Relevant for SaaS Businesses?



3. SEO and Content Strategy


For a long time, SEO had been a guessing game. As algorithms become more accurate, they also become more complex. Making it hard to understand how your content can affect your ranking. Data science is data-backed and therefore it eliminates the guesswork from the equation. With the data model, you can figure out what you need to do to get certain results. It also provides you with a way to track this progress for future applications. 


Content strategy is one of the best ways to bring in new clients and reach new demographics. Most of the time, it feels like walking in the dark because you assume what your customers want. Moreover, assumptions hardly ever work. While quality testing is still very complex, many tools in data science can help you understand what your customers enjoy. With the information, you can create content that your customers respond the most to and drop ideas that are not enticing. Read More: 5 SEO Tips & Strategies for SaaS Businesses (New Websites)



4. Recommendation engines


Recommendations are one of the best things that came out of the data science-marketing integration. A recommendation system provides ‘similar’ or ‘most likely to pick’ items based on previous customer data. Excellent examples are Netflix’s “recommended for you” and Spotifys’ “daily mix.” When you log into Netflix or Spotify, they ask for your preferences. They then compare your watches/listens with others who have similar interests. They even study your search queries and compare them against their content database. Over time, they can predict what you like and make recommendations based on that.

5 views0 comments

Comentários


bottom of page