Top 11 Use Cases of Data Science In Marketing

Even if a company or an individual has a fantastic product or service, getting customers will be tough without adequate and accurate marketing and advertising strategies. The concept of market research is not new. When compared to data collection in the early twentieth century, market researchers are currently swamped with information and to organize and analyze this vast information we need Data science. Due to the World Wide Web's low cost, online information consumption has increased dramatically over the last decade. Over more than 6 billion gadgets are currently connected to the internet, according to some recent estimates. Around 2.5 million terabytes of data are generated daily.

This massive volume of data is a gold mine for marketers and researchers. If this information is correctly processed and evaluated, it can provide marketers with important insights that they can use to target customers and can achieve the desired results. Decoding large amounts of data, on the other hand, is a monumental and a very hectic task. This is where Data Science can be quite beneficial and a very useful and accurate method for organizations to enhance customer acquisition.

Data Science is a field that focuses on extracting useful information from data and assisting marketers in identifying the correct and accurate insights in a particular industry. These insights might be on a variety of marketing topics, such as customer intent, experience, and behavior, and would aid them in effectively improving their marketing tactics and strategies and thus help the organization to make more money using data science for marketing.

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How to Implement Data Science in Marketing?

Now, let us understand how data science in marketing can be implemented and it can help marketers get outstanding returns on their marketing campaigns.

1. Budget Optimization for marketing

Marketers are usually working with a limited and very restricted budget. Every marketer's major goal is to get the most bang for their buck with their limited allocated funds. Getting there is usually a very difficult and time-consuming job. Things don't always go as planned. Data scientists can design a spending model that helps a marketer to better utilize the spending budget by examining the marketer's spending and acquisition data. To optimize for their key indicators, marketers can use the model to allocate their spending among various locations, channels, mediums, and events.

2. Target marketing to Potential Audience

In general, marketing initiatives are widely spread, regardless of considering the geography or target demographic of a particular area. As a result, there is a very high probability that marketers will go over budget and thus result in a loss for an organization. They may also be unable to meet any of their revenue targets and ambitions for that particular financial year.

However, if businesses employ a data scientist to properly evaluate their data, they will be very easily able to determine which areas and demographics provide the best return on investment on the particular spending for an organization. For more updates about Data Science Read This: Supervised and Unsupervised Learning

3. Selecting the Right Channels

Data science can be used to figure out which channels are providing the marketer with the best results. A data scientist can compare and detect different types of strategies through different channels by using a time series model. This is very useful because it informs the marketer as to which channels and strategies are yielding the best results for an organization.

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4. Marketing Strategies as per Customers' Behavior

Marketers must match their marketing techniques to the properly oriented customers to get the most output from them. Data scientists can help marketers to do this by developing a customer lifetime value model that can categorize consumers based on their behavior and purchasing habits. Marketers can use this concept in a wide range of scenarios. Their most valuable customers might receive referral coupons and cashback bonuses. They can utilize retention methods to keep customers who are about to depart, and how much to spend on the particular customer to retain them.

5. Advanced Lead Scoring

Every lead a marketer generates does not necessarily result in a customer. The sales department's effectiveness, accuracy, and eventually revenue, will improve if the marketer can effectively segment customers based on their interests and purchasing behavior.

Marketers can use data science to construct a predictive lead scoring system. Data scientists make use of algorithms to calculate the likelihood of conversion of a particular customer while also segmenting your lead list thus helping marketers to target the potential customer. The clients on the list can be divided into three groups: enthusiastic customers, potential prospects, and uninterested customers.

6. Strategy for Creation of Conte

ntTo attract potential customers, marketers must always provide relevant and valuable information. Data scientists can assist them in obtaining very accurate audience data, which will aid in the creation of the finest content for each particular consumer to optimize for the maximum profit for the organization.

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7. Sentiment Analysis

Sentiment analysis is a text categorization approach that allows you to learn about your consumers' or customer feelings about your company, product, brand, or a particular service. It helps in categorizing the attitude expressed in data such as social media posts, feedback, reviews, surveys, and customer service calls.

For understanding and analyzing the emotional tone behind these particular sentiments, as well as categorizing data at scale and also delivering real-time analysis, data science and machine learning algorithms used by data scientists are fantastic tools. Businesses can work more effectively, with greater and effective precision, and toward more helpful goals by utilizing these algorithms by data scientists to increase the profit of the organization.

8. Pricing Strategy and Development of Product

Marketers can use data science to obtain, aggregate, and synthesize data on their particular products for a variety of demographics. They can build the desired goods and establish highly targeted marketing efforts for their target audience based on the insights supplied by this data. Marketers may understand exactly what drives prices and the customer's buying intent for each product group by focusing on elements such as particular consumer preferences, the historical purchase history of the customers, and the economic condition of the particular consumers.

9. Real-Time Interaction Marketing strategy

Data scientists can generate knowledge about real-time events, allowing marketers to target customers based on those present circumstances and help them to target those particular segments. For example, hotel marketers can utilize real-time data analytics to identify travelers whose flights or trains have been delayed or by how much time. They can then deploy direct advertisement campaigns to their mobile devices to target them and convert them to customers by buying their products and services.

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10. Customer Loyalty and Using Data to Improve Customer Experience

Customers who are loyal to a particular company help it to survive for a longer duration. They are very less expensive than acquiring new customers. Marketers can use data science to better their marketing to existing loyal customers and so increase their loyalty. Target, for example, employed data scientists to help marketers to create a profile of pregnant women based on their pre-pregnancy purchases. During their pregnancies, the corporation targeted these clients with product offers during that interval.

This marketing technique made based on data analysis resulted in a significant increase in sales and customer loyalty for the organization and helped to increase the profitability of the organization. Providing a positive client experience has always been a key component of marketing success. Data scientists collect user activity patterns that can help marketers to forecast about the customers who would want or need specific products. This enables them to sell effectively and give enriching and fruitful experiences to their clients and helps to increase the loyalty of the customers towards the organization.

11. Word Clouds and Ad Offerings

Marketers have historically used word clouds to analyze social conversations and social media activities. Word clouds, on the other hand, were very useful when there was a lot of social media interaction. When there was less social interaction, marketers tended to use irrelevant keywords. They can go beyond word clouds by contextualizing word usage and giving relevant insights using data science and natural language processing technologies.

Marketers can use data science to tailor ads to specific customers and track campaign clicks and results. Data scientists also help them to ensure that the banner ads are seen by the proper individuals and increase the odds of them being clicked and purchased.

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Conclusion

We've seen that a marketing data scientist can help you improve the customer experience and thus plays a very important role for a team in any organization. It is very evident that a career in data science in today’s world is ferociously rewarding and if you want to start your career in data science then there couldn't be a better time than today. You can start your data science journey with our Data Science Certification in collaboration with IBM and kick-start your career with our 100% placement assistance.

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