The Role of Data Science in Improving Customer Satisfaction

The world’s most successful companies set their focus on customer satisfaction. The reason being that customers leave organizations where they are not satisfied with the service.

New products with unique and improved features will continue to pop up in the market. Still, the customer would rather continue doing business with companies that serviced them well over time. This is why companies have to pay apt attention to customer loyalty and advocacy.

Data Science in Improving Customer Satisfaction

The advent of new technologies and the utilization of data science methods on huge amounts of data makes it easier for companies to place laser focus on the factors that cement customer loyalty for their products.

Companies across the world now invest time and money in data science, analytics, and statistical testing. Data scientists help businesses navigate their way through the vast ocean of data available to them in a bid to make the right, timely business decisions.

How B2C & B2B Companies Use Data Differently

Data analytics is a source of valuable insights that can inform how both B2c and B2B companies make decisions about products, marketing, and sales. Though they each have a unique set of challenges, B2c and B2B businesses both collect, visualize, and analyze their most valuable asset – customer data.

Both B2B and B2C companies use data analytics to unlock new pathways to increase customers, more profits, and better decision-making. But they access these pathways in totally different ways. So let’s go over the differences between how B2B and B2C companies use data.

Sales Data

B2C businesses often have shorter sales cycles, with a large part of their revenue coming from advertisements. This implies that the customers need to be engaged for longer and the sales cycle optimized. Leveraging data on the customer’s experience in making a purchase can help point decision-makers in the right direction.

B2B companies, on the other hand, have much longer sales cycles. Here, the goal is to minimize the amount of time the customer spends making a purchase. Using data science, the company can improve efficiency and shorten the sales cycle. Data scientists can analyze sales data for insight into improvements in customer experience.

Customer Data

Since B2C companies typically have more customers than their B2B counterparts, there is usually no shortage of data to analyze. This allows data scientists to analyze several different customer data points related to their experience with the business. Data scientists can use customer data to segment customers accurately and outline better user personas to guide product and marketing initiatives.

B2B’s fewer customers present both an advantage and disadvantage. Fewer customers equal fewer customer data for analysis. Conversely, fewer customers make it easy for B2B companies to develop meaningful relationships with their customers. Data scientists can leverage data from real-world customer feedback to develop and inform their product and marketing strategies.

Data Science: Transforming Data into Value

A 2018 study by MIT Sloan found that 59% of businesses use data analytics to gain a competitive advantage, and this figure is up from previous years. This shows a shift towards a more data-based approach to customer service, and more companies are utilizing analytics to get closer to customers.

The enormous value in data science and analysis is becoming more evident by the day. Which begs the question, what exactly are the benefits of data science to a business?

1. Mitigating Risk and Fraud

Data scientists typically have advanced training in statistics, math, and computer science. This training helps them identify data that stands out. Then, they create statistical processes that can predict the propensity of fraud and alert the data scientist in a timely fashion when any unusual data is found.

2. Helping Management make Better Decisions

Competent upper management setups like to have an experienced data scientist as an advisor to maximize their analytics capabilities. A data scientist analyzes, communicates, and demonstrates the real-world implication of company data, thus facilitating better decision-making across the entire company.

Data scientists track and record key performance metrics then recommend actions that will help the business level up its performance, improve customer engagement and increase revenue.

3. Defining Target Audiences

Most companies have some sort of data collected, from customer surveys to Google Analytics, but if this data cannot be used to identify demographics, its purpose is defeated. Data science is all about being able to take existing data that, on its own, is practically useless and combine it with other information to reveal insights about customers.

Data scientists, by thoroughly analyzing separate sources of data, can precisely identify key groups of customers. The company can then use this in-depth knowledge to tailor its products and services to customer groups.

4. Recruiting Talent

Data science provides a major solution to one of the most monotonous tasks in the life of a recruiter– having to go through resumes. Data science experts can leverage the vast amount of information available on potential employees; social media, recruitment websites, corporate databases, and so on.

Using this information, they can sort out which candidates best fit the company’s requirements. As a result, data science can help your company make faster and more accurate hires.

 5. Case Studies of How Bad Data can affect your business

Bad data refers to inaccurate data. Bad data could be missing key elements, fraudulent, irrelevant for the purposes it is intended for, duplicated, poorly compiled, and so on.

Research shows that bad data costs businesses large swathes of their revenue. For example, Gartner found that the average cost of poor data quality on businesses is between $9.7 million and $14.2 million annually.

IBM estimated the yearly cost of poor quality data in the US alone was $3.1 trillion in 2016. In other words, bad data is bad for business. However, how much damage can bad data do to a business? Let’s examine real-life examples of the adverse effects of bad data.

The 2001 Enron Scandal

In the early 2000s, Enron Corporation reached dramatic heights only to face a dizzying fall. Wall Street giant’s fall from grace due to fraudulent financial data affected thousands of employees and shook Wall Street to its core. Unfortunately, the data that was being provided to shareholders was entirely made up.

So much so that towards the end, auditors began to shred documents to cover their tracks. But it was too late. Enron’s executives and their auditing firm delivered fictional data to stockholders and the Board of Directors in annual reports and financial statements for years.

Independent ethical auditing of the data Enron put forward could have prevented a financial fraud of this magnitude from occurring.

Lead (Tetraethyllead) in Gasoline in the 1920s.

In the 1920s, Tetraethyllead was commonly added to gasoline on the premise that it could control knocking in engines. This unfortunately contributed to over 5,000 deaths in the US alone from lead exposure.

The leaded gas industry and the US government at the time conducted and made use of data from inconclusive tests to ratify this decision.

The lead paint and leaded gas industries traded the blame for the sudden rise in fatalities from lead poisoning for decades, each claiming their products were safe for people. If an independent analysis of these industries’ bad data and the government had relied upon it was conducted, perhaps so many lives would not have been lost.

How Data Science can Improve Customer Experience

Organizations around the world now approach the issue of customer service and experience from a data-driven standpoint. Customer service has evolved to meet the needs of customers in the digital era.

Customers have unique needs and expectations for their customer service experience. They don’t want to be asked the same questions over and again. They don’t want to be kept on hold for hours, or talk to a bot, or be transferred from one person to another.

The more issues a customer has to face, the more their frustration builds up, and effective communication between the customer service agent and the customer is hindered.

Data science comes in handy at this juncture by providing deeper insight into what a customer wants. With data analytics, machine learning, and artificial intelligence, companies can now meet customer needs, resulting in a powerful improvement to the customer experience. Let us explore how businesses can use data analysis to improve customer service.

  • Collecting and Using Customer Data

Many businesses use multiple customer service platforms that allow customers to communicate via different mediums such as phone calls, emails, and live chat. This creates multiple streams of data that now need to be integrated. Without bringing these different sources together, you only get an incomplete picture of your customers.

Data science collects and integrates data across multiple communication channels, painting a complete image of the customer. For example, integrating your data can tell you what products a particular customer purchased in the past, what mode of communication they prefer when they are most likely to respond, and many other details that come together to improve the overall customer experience.

  • Improving Agent Productivity

Customer service agents that are productive create happier customers, and a happy customer is a buying customer. Data analysis and reporting can be used in-house to score agents’ performance and see which agents perform best and in which areas.

This allows your company to know the best agent to touch with your customer and measure the agent’s skill progression concerning their career goals and company requirements.

  • Acquiring and Retaining Customers

The probability of selling to an existing customer is between 60% and 70%, while the probability of selling to a new customer is 5-20%. Data science can help you audit your sales and marketing strategies by telling you which strategies are most successful with new customers and which work best for existing customers.

A competent data scientist on your team will enable you to reach for the top of both probabilities as you put the customer’s needs first. In addition, continually using data science to manage your customer service strategy will help you decide what should stay and what needs to be changed.

  • Setting Your Company Aside From The Competition

The majority of businesses want their customers to think first of them before the competition for several reasons. It could be that your products and services are cheaper, or they are of higher quality, or because you offer a superior customer experience.

Data science helps companies pinpoint what features customers love about their products and services so they can focus on them. In doing this, your company can outpace its competition and strengthen customer loyalty.

  • Improving Products and Services

Data science is one of the most powerful tools companies use to understand the position of their products and services in the market. This understanding is essential to staying relevant to both customers and competition. In addition, data science helps businesses find when and where their products and services sell best.

With data science in their corner, companies can deliver the right products that meet their customers’ needs at the right time.

Data analysis will show you how your products and services help people improve their lives and how they use these products to solve problems in their daily lives. Through this, your company can identify areas for improvement and birth ideas for new features.


We live in an age where data is every company’s greatest asset. Data science has the potential to bring tremendous value to your business, boost customer satisfaction, and in the long run, increase ROI.

All you have to do to unlock your data’s full potential is to take advantage of the several data analysis tools available and invest in the services of an experienced data scientist.

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