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According to researchers from MIT, “companies in the top third of their industry in the use of data-driven decision making were, on average, 5 percent more productive and 6 percent more profitable than their competitors.”

But how does the knowledge of Data Science help Digital Marketers in their everyday duties? Why should you learn data science? Let’s take a look at the specifics of the two roles, and try to understand how they can complement each other to improve a business’s bottom line.

What is Data Science and what do Data Scientists do?

Data Science

Simply put, Data Science is the use of algorithms, techniques, and systems to extract knowledge and actionable insights from huge amounts of structural and unstructured data.

A professional Data Scientist’s tasks can differ depending on the company they work for and the industry they are working in. Though, usually, they can expect some or all of the following daily tasks and responsibilities:

  • Defining, extracting, and cleaning data sets
  • Creating machine algorithms to implement automation tools
  • Analyzing data to understand patterns and trends.
  • Creating visualizations or dashboards to reflect data visually
  • Researching to identify opportunities for increasing efficiency
  • Predict future trends
  • Presenting findings to the company to help them in decision making

 

Generally, these tasks can be separated into four fundamental components:

  • Building and managing databases
  • Analyzing data
  • Understanding data
  • Communicating data

Data Scientists go through large amounts of data that is collected and assemble key sets based around the goals of the company or the desired metrics required by their organization. They will try to understand what the data says and then communicate it to the decision-makers of their company.

 

Optimising Digital Marketing with Data Science

Optimising Digital Marketing with Data Science

While the two roles might seem different, the skill-sets can complement each other to improve a brand’s bottom line. Data is revolutionizing the way marketing is done and sales are achieved.

Today, as an increasing number of people go online, the amount of data that they generate is immense. All information that gets collected helps you to tailor campaigns to be consistent with every customer’s behaviour and spending habits.

Here are a few ways in which how skilled Digital Marketers can reap the advantages of Data Science by implementing it for their campaigns:

 

1. Optimization of Marketing Budget

Optimising Marketing Budget
Marketers always have a strict budget. Every marketer aims to derive maximum ROI from their allotted budgets. Achieving this is often tricky. Things don’t always go according to the plan and efficient budget utilization isn’t accomplished.

By analyzing a marketer’s spend and acquisition data, a Data scientist can build a spending model which will help utilize the budget better. The model can help marketers distribute their budget across locations, channels, mediums, and campaigns to optimize for key metrics.

 

2. Identifying the appropriate channels

Identifying the appropriate channels

Data science can be used to determine which channels are giving an adequate return for the marketer. Using a time series model, a data scientist can compare, analyse and identify the kinds of lift seen in various channels. This can be very advantageous as it tells the marketer exactly which channel and medium are delivering proper returns.

 

3. Matching Marketing Strategies with Customers

Matching Marketing Strategies with Customers

To derive maximum value out of all their online marketing campaigns, marketers need to match them with the right customer. For this, data scientists can create a customer lifetime value model that can segment customers by their behaviour. Marketers can use this model for a variety of use cases. For example, they can send discount offers and referral codes to their highest-value customers while they can apply retention strategies to users who are likely to leave their customer base.

 

4. Advanced Lead Scoring

 Advanced Lead Scoring

Not every lead that a marketer procures converts into a purchasing customer. If the marketer can accurately segment customers as per their interest, it’ll increase the sales department’s performance, and ultimately, revenue.

Data Science enables marketers to make a predictive lead rating system. This technique is an algorithm that’s capable of calculating the probability of conversion and segmenting your lead list accordingly.

 

5. Sentiment Analysis

Sentiment Analysis
Digital Marketers can take help of Data Science to do sentiment analysis. This will give them better insights into their customer beliefs, opinions, and attitudes. They can also monitor how customers react to marketing campaigns and whether or not they’re engaging with their business.

 

6. Product Development

Product Development
Data science can help marketers collate and synthesize data on the user requirement for products and services for several different demographics. Based on the insights provided by this data, they can make a decision to create such a product. For example, Netflix takes into account what the users like based on thousands of data points. It then analysis what kind of shows to produce next.

 

7. Pricing Strategy

Product Development and Data Science

Data science can help marketers when it comes to improving their pricing strategy. By creating algorithms that take into account factors such individual customer preferences, their past purchase history, and the economic situation, marketers can identify exactly what drives the prices and the customer’s buying intent for each product segment.

For example, Uber keeps a track of all the data on every single trip the users take. This is then used to predict the demand for cabs, set the fares and allocate sufficient resources. Data science team at Uber also undertakes an in-depth analysis of the public transport networks across different cities so that they can focus on places that have poor transportation and make the best use of the data to improve travelling experience.

In fact, uber drivers continue to generate data even when they are not ferrying passengers because they transmit data back to the central platform at Uber which is used to analyse the traffic patterns. The data is stored in the database for supply and demand algorithm analysis. One of the purposes that driver data is used for is surge pricing. Algorithms can predict what the right pricing would be for a particular are at a particular time depending on all the big data it collects. This pricing strategy helps uber earn it’s profits.

 

8. Going Beyond Word Clouds

Word Cloud
For analyzing social conversations, marketers usually relied on word clouds. However, word clouds were useful when there was a high level of social activity. If the level of social activity was less, marketers often ended up using irrelevant keywords. With data science and natural language processing algorithms, they can go beyond word clouds by contextualizing word usage and delivering proper content to attract customers.

There are a lot of ways in which Digital Marketers can benefit from Data Science knowledge. If you are a Digital Marketer yourself, the question you should be asking is not whether you should learn Data Science, but how soon should you start learning it?

 

How to get skilled in Data Science?

How to get skilled in Data Science

One of the most popular applications of data science today is in digital marketing. The existence of great Digital Marketing strategies would not be possible without the existence of data science. That is why the knowledge of Data Science will help you earn the best professional opportunities and a better lifestyle in this digital era.

Prepare for a career as a Data Scientist with our brand new 11 month Full-time Postgraduate Programme in Data Science. We are training a select 30 students this year to become professional Data Scientists. This programme will equip you with the knowledge that is required to compliment your Digital Marketing skills. The compulsory internship along with the classroom sessions will give you first-hand experience of how Data Science is used in the industry.