Top 8 Data Science Use Cases in Marketing
In this article, we will discuss some notable data science use cases in marketing. As far as the primary goal of data science is to extract actionable insights from data, the marketing sphere cannot exclude the application of these insights for its advantage. Big data in marketing offers a chance to understand the target audience a lot better.?
Data science is mostly employed in marketing areas of search engine optimization (SEO), profiling, customer engagement, responsiveness, and real-time marketing campaigns. Furthermore, new ways to employ data science and analytics for marketing emerge every day. Among these, some of the new use cases are micro-targeting, advertising, micro-segmentation, and many others.?
Let’s focus on several examples that present specific interests and manage to prove their efficiency in the course of time.?
Customer segmentation
Every customer is an individual. Therefore, a one-size-fits-all approach is inefficient. Customer segmentation is the solution in this case. Implementation of the statistical analysis enables marketers to slice the data and group customers.?
Customer segmentation involves grouping customers into segments based on the coincidences of specific criteria in their characteristics.?
There are 3 significant segmentation types that are most often employed. These are:?
Implementation of micro-segmentation seems to be an increasing trend in marketing. Micro-segmentation is a lot more advanced. It assists in segmenting people into more precise categories especially concerning behavioral intentions. Therefore, you can tailor marketing actions to the preferences even of the least numerous customer segments.??
Real-time analytics
Real-time analytics instantly brings marketing insights into campaigns. The real-time marketing opportunities are a result of the recently increased popularity of social media and communication technologies.?
Efficient real-time analysis of data brings a notable boost in revenues for companies. Real-time algorithms work with two groups of data: operational data and customer data.?
Customer data offers insight into customers’ preferences, wants, and needs. Operational data reflect different actions, transactions, and decisions made by the customers. Implementation of real-time data analysis results in speed, efficiency, and high-performance rates to marketing campaigns.?
Real-time analytics in marketing offers an opportunity to:?
Predictive analytics
Currently, the data is available and easily accessible for middle-sized companies. This is the reason for the wide application of predictive analytics in marketing.?
Predictive analytics is about implementing statistical and machine learning (ML) algorithms to predict the future with high probability. There are many opportunities to implement predictive analytics in marketing. Let's explore the ones that proved to be the most efficient.?
Predictive analytics fur customers’ behavior?
Cluster models, collaborative filtering, predictions, and regression analysis are all implemented to identify correlation patterns in the customers’ behavior to predict future tendencies in purchasing.?
Predictive analytics to quality and prioritize leads?
Here belongs identification models, predictive scoring, and automated segmentation. They are associated with qualifying and prioritizing leads to increase the effectiveness of your marketing efforts. By implementing these models, you can ensure that the most effective ready-to-purchase leads will get your call-to-action correctly.?
Predictive analytics to bring the right product to the market
In this case, data visualization assists the marketing team in making the right decision about what product or service should be delivered to the market.?
Predictive analytics for targeting
This is associated with the entire bunch of predictive analytics models like response modeling, affinity analysis, and churn analysis. These models are employed to spot the highest value customers and address them with the best offer at the most appropriate time.?
领英推荐
Recommendation engines
Recommendation engines are powerful tools that are employed to offer a personalized experience and boost your customers’ satisfaction. Marketers should pay specific attention to implementing recommendation engines.?
The primary goal of recommendation engines is to match a customer’s preferences with product features they might like. For this purpose, recommendation engines often employ the following models and algorithms: decision tree, regression, K-nearest neighbor, neural networks, support vector machines, etc.?
Recommendation engines are a valuable targeted marketing tool for email and online marketing campaigns.?
Market basket analysis
Market basket analysis refers to the unsupervised learning data mining techniques that are meant for learning the purchase patterns to discover the co-occurrence relationships between purchases. Implementation of these techniques enable you to predict future purchase decisions.?
In addition, market basket analysis can substantially enhance the efficiency of the marketing message. Apart from the type of the marketing message, whether it is direct offer, social media, email, phone call, or newsletter you can offer the next best product suitable for a specific customer.?
Optimization of marketing campaigns
The primary goal of the marketing team is to create customer-oriented, efficient, targeted marketing campaigns aimed at delivering the right message to the right people at the perfect time.?
Optimization of marketing campaigns includes the implementation of smart algorithms and models that enable higher efficiency. Modern technologies automate data collection and analysis process, decrease time spent on them, offer real-time results, and identify the slightest changes in patterns. Smart data algorithms examine each customer individually. Therefore, a high personalization level is accomplished.?
The optimization process consists of several steps that are equally essential and require attention. The steps are as follows:?
Invest in those tools that efficiently collect and analyze data. Make sure that your selected tools can work together for your campaign’s benefit. Integrate the tools with current systems and data.
2. Measure the metrics
Measuring metrics enables you to spot processes and strategies that require improvement. Measure the parameters and compare them to your marketing goals.?
3. ?Draw conclusions
Make the right decisions according to the data to maximize the success of your marketing campaign.?
Lead scoring
Customers’ path through the sales funnel is staffed with different options, opportunities, and choices. Lead scoring is implemented to spot those prospective customers who would go through the funnel and make their selection to the benefit of your product or service. What is the trick?
Lead scoring involves ranking the prospect based on a scale representing each lead’s value. Each lead’s value may be labeled differently, but often they are referred to as hot, warm, or cold ones.?
Lead scoring includes data gathering concerning customers’ responsiveness, demographics, preferences, purchase history, visits, web page views, shares, likes, and even the type of e-mails they often react to.?
The result of lead scoring is that salespersons get qualified prospects regarding who is most likely to make a purchase. Therefore, when products are offered to the right people, the sales increase.
Conclusion
The use cases we have discussed in this article prove that use of data science brings many benefits to marketing campaigns of different brands. Considering the volume of data available today, it is important not just to freeze it but to leverage it for the benefit of your business.
Transformation of data into meaningful insights is essential for decision-making. The list we have made above of top data science use cases in marketing uncovers particular characteristics of data application in this area and a real positive impact that it can create.?
Data science autopilot is an amazing tool that Inqline offers for those business owners who want to take their first step with the use of data science in marketing. To find out more, visit our website.?