04 Nov 21 Practical Ways To Implement Data Science In Marketing

Because of the World Wide Web’s low cost, online information consumption has skyrocketed over the last decade. It is estimated that over 6 billion devices are currently connected to the internet. Every day, approximately 2.5 million terabytes of data are generated. By 2020, every single person will generate 1.7 MB of data every second.

This massive amount of data is a gold mine for marketers. If this data is properly processed and analysed, it can provide valuable insights for marketers to target customers. However, decoding massive amounts of data is a monumental task. This is where Data Science can come in handy.

Data Science is a field that extracts meaningful information from data and assists marketers in determining the appropriate insights. These insights can be on various marketing aspects such as customer intent, experience, behaviour, and so on, which will help them optimise their marketing strategies and maximise revenue.

Let’s Look At 20 Real-World Examples Of How Data Science Can Be Used In Marketing

1. Marketing Budget Allocation – Marketers are always working with a limited budget. Every marketer’s primary goal is to maximise ROI from their allotted budgets. This is always difficult and time-consuming. Things do not always go as planned, and efficient budget utilisation is not always achieved. A data scientist can build a spending model that can help a marketer better utilise their budget by analysing their spend and acquisition data. The model can assist marketers in allocating their budget across locations, channels, mediums, and campaigns in order to optimise for key metrics.

2. Marketing to the Right People – In general, marketing campaigns are widely distributed, regardless of location or audience. As a result, marketers are more likely to exceed their budget. They may also fail to meet any of their revenue and goal targets. However, if they use data science to properly analyse their data, they will be able to understand which locations and demographics provide the best ROI.

3. Choosing the Correct Channels – Data science can be used to determine which channels are providing the marketer with an adequate lift. A data scientist can compare and identify the types of lift seen in various channels using a time series model. This can be very useful because it tells the marketer which channels and mediums are delivering the best results.

4. Customer-Matching Marketing Strategies – Marketers must match their marketing strategies with the right customer to get the most out of them. Data scientists can accomplish this by developing a customer lifetime value model that can segment customers based on their behaviour. Marketers can apply this model to a variety of scenarios. They have the ability to send referral codes and cashback offers to their most valuable customers. They can use retention strategies to keep users who are likely to leave their customer base.

5. Lead Segmentation – Marketers can use data science to target leads more precisely and learn everything they can about their online behaviour and intent. Marketers can determine their business requirements and the types of brands they’ve been associated with in the past year by looking at historical data.

6. Advanced Lead Scoring – Every lead obtained by a marketer does not result in a customer. If the marketer can accurately segment customers based on their interests, the sales department’s performance and, ultimately, revenue will improve. Marketers can use data science to create a predictive lead scoring system. This system is an algorithm capable of calculating conversion probability and segmenting your lead list. The list is divided into three categories: eager customers, curious prospects, and uninterested customers.

7. Personas And Profiling Of Customers – Marketers consider creating customer personas when marketing a product or service. They are constantly compiling lists of prospects to target. They can use data science to determine which personas should be targeted. They can determine the number of personas and the characteristics required to build their customer base.

8. Development of a Content Strategy – Marketers must always provide relevant and valuable content in order to attract customers. Data science can assist them in gathering audience data, which will allow them to create the best content for each customer. For example, if a customer found the marketer through Google by searching for a specific keyword, the marketer will know to include that keyword more frequently in their content.

9. Emotional Analysis – Marketers can conduct sentiment analysis using data science. This means they will have a better understanding of their customers’ beliefs, opinions, and attitudes. They can also track how customers react to marketing campaigns and whether or not they interact with their company.

10. Product Development – Data science can assist marketers in gathering, aggregating, and synthesising data on their products for a variety of demographics. They can develop products and create highly targeted marketing campaigns to their intended demographic based on the insights provided by this data.

11. Pricing Strategy – Data science can assist marketers in improving their pricing strategy. Marketers can identify what drives prices and customer buying intent for each product segment by focusing on factors such as individual customer preferences, past purchase history, and the economic situation.

12. Customer Interaction – Marketers can determine the best time to communicate with their prospects and customers by properly analysing data. For example, they may recognise that a customer reads and responds to emails but is not as responsive to SMS. Such insights can assist marketers in determining the best time and channel for communication.

13. Marketing Through Real-Time Interaction – Data science can generate information about real-time events that marketers can use to target customers. For example, hotel marketers can use data science in real-time to identify travellers whose flights were delayed. They can then target them by directly sending ad campaigns to their mobile devices.

14. Using Data to Improve Customer Experience – Providing a rich customer experience has always been a critical component of marketing success. Marketers can use data science to collect user behaviour patterns that predict who might want or need specific products. This enables them to market more effectively and provide customers with more enriching experiences.

15. Customer Loyalty – Customers who are loyal to a company help it to thrive. They are less expensive than acquiring new customers. Data science can assist marketers in improving their marketing to existing customers and thus increasing their loyalty. Target, for example, used data science to create a profile of pregnant women based on their pre-pregnancy purchases. During their pregnancies, the company then targeted these customers with product offers. This marketing strategy proved to be a huge success for the company in terms of purchases and loyalty.

16. Marketing on Social Media – Customers are increasingly active on social media platforms such as Facebook, LinkedIn, and Twitter. Marketers can use data science to see which leads are visiting their social media pages, what content they are clicking on, and other information. They can develop a proper social media engagement strategy with these insights.

17. Community Organizations – Data science can be used to access customer feedback by targeting specific social media groups. This is accomplished by assisting marketers in determining the most frequently discussed topics based on keyword frequency.

18. Expanding on Word Clouds – Marketers have long used word clouds to analyse social conversations. Word clouds, on the other hand, were useful when there was a high level of social activity. When there is less social activity, marketers frequently use irrelevant keywords. They can go beyond word clouds by contextualising word usage and delivering meaningful insights using data science and natural language processing algorithms.

19. Advertisement Offers – Marketers can use data science to specifically target ads to customers and track clicks and campaign results. It can ensure that the right people see the banner ads and increase the likelihood of them being clicked.

20. Email Marketing Campaigns – Data science can be used to determine which emails are most appealing to which customers. How frequently are these emails read, when should they be sent, what type of content resonates with the customer, and so on. These insights allow marketers to send contextualised email campaigns and target customers with the appropriate offers.

21. Platforms for Digital Marketing – Data is the lifeblood of digital marketing platforms. Marketers can gain better insights by providing refined data to these platforms. Data science can improve digital marketing platforms by providing the right data, allowing marketers to determine what they need to do to meet their marketing objectives.

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