Marketing a creative domain has started complementing the analytical domain. Many marketing teams are leaving a ton of money on the table by not leveraging the insights that can be delivered by Data Scientists.

Before diving into why Data Scientists are a catch, let us first clear the air over the confusion that most people have, Data scientists are different from Data Analysts. Data Analysts look at the past, they derive insights from the past which can then be used for future predictions whereas Data Scientists use Predictive analysis methods like Advanced statistical methods, machine learning, regression, etc.

What can a Data Scientist do for Marketing?

Marketing Segmentation

Customer segmentation is the process of categorizing customers into groups based on the overlap of specific criteria in their attributes.

The most commonly used segmentation types are:

• Purchasing pattern segmentation.
• Touchpoint engagement segmentation.

Read on types of marketing

Data scientists use predictive analysis to analyze past data which can be used to propose a different approach in the future. For example: When a shopping cart is abandoned by a group of customers, data scientists can analyze the past data to figure out which products are being abandoned by how many customers, to improve efficiency, and where to invest.

Analyse social media

Social Media platforms like Facebook, Instagram, Twitter, Linked In, etc offer their analytical tools to measure engagement, conversions, etc. But a data scientist can use sentimental analysis to figure out which content has reached the maximum number of people, which content, product, or service has received maximum attention and how do people react to that? This data can then be used to make precise decisions.

Campaigns

Marketing campaigns = burning cash. Let's say an organization runs an Ad and has a minimal engagement rate, but how to get the expected ROI? Leave that to a data scientist. A data scientist can analyze with a module called Market Basket Analysis. Which is nothing but finding the correlations between multiple entities. For example, if you run an ad for bedsheets, how likely are the customers to purchase a pillow cover as well? Finding this correlation is a job of a Data Scientist. It does not end here, finding correlations beyond the same category is also possible. For example, how likely is it that the customer that is interested in home decor is also interested in purchasing books on Home decor? This gives a marketer an edge, a new way to discover more customers.

Artifical Intelligence

Machine Learning and Artificial Intelligence can be used to understand metrics such as retention period, referral, and ROI. Otherwise known as Growth Marketing. This approach is data-driven, they are used to figure out patterns, for example, You refer a service that you have used to another organization by sending a link to their website. This can be tracked and recorded to analyze how many have referred, how many have converted, and so on. A data scientist (growth marketer) can understand the pain points by analyzing the data and predicting the reactions of those who will come across the product/service, which plays a vital role in Marketing.

Insights

Marketing messages that are sent to customers and potential customers are tracked, data scientists can analyze the retention period, cost per click, and conversion which then can be passed on to growth marketers who can come up with different effective campaigns which can reach customers. This opens up new roadways to engagement and conversions. Data scientists will come up with the right questions that can be asked to drive engagement by using sentimental analysis.

Below mentioned are the real cases where Data Scientists are real stars:

1. Channel Optimization
2. Persona Development
3. Ads Targeting
4. Lead Generation
5. Sentiment Analysis
6. Click through rate and PPC.
7. Insights.

Hiring data scientists for marketing can help increase efficiency, revenue, and ROI enormously as the process carried out by Data Scientists is purely data-driven. In the end, more profits to the organization.