Customer Data and Data Science: How Are They Related?


The ability to understand past behaviors and create predictions with successful outcomes: this is the promise of data science. As pressure mounts on companies to account for every dollar spent, data science cannot only measure and understand current business performance but also predict, given similar inputs and scenarios, what might occur in the future. 

Data science can be applied to a company’s overall marketing efforts, as it can better track the performance of campaigns as they relate to converting prospects to customers. Essentially, if it’s digitized, it can be tracked and measured. Such areas where data science has made inroads include search engine optimization, paid search, email/demand generation, and social media.

Additionally, customer data can be used for either supervised or unsupervised learning to gain insights into customer behavior. Supervised data have the filled-in y variable, or an attribute given a value for x. For example, the sale price for a product, or whether a tweet was shared, are supervised data. Data scientists can run a supervised model for the marketing team by predicting the likelihood of a purchase given other factors in place.

Data scientists can also use unsupervised learning for the marketing effort, studying the elements common to a group of objects instead of making predictions. For example, groups of prospective customers can be clustered based on when they open an email or when they make a purchase.

Let’s have a look at how data science works with customer data to improve an organization’s business performance.

Customer Segmentation

Marketers understand that no two customers are alike, and treating them as such will lead to confusion or even abandonment of a company’s products and services. 

Further, companies have found ways to tailor their products and services for different segments, and each requires its mix of marketing tactics.

Customer segmentation helps marketers group customers into categories based on coinciding criteria or characteristics. Such characteristics can be based on the customer’s identity (i.e., age, location) or past buying patterns. 

Data science interprets datasets of different customer segments, along with associated behaviors (i.e., clicks, email opens, purchases) to “train” an algorithm. Then, given data on new prospective customers having similar characteristics, the algorithm will go to work and apply a set of automated activities with the expectation that those new prospective customers will behave similarly.

This process is repeated over and over again, with the algorithm getting trained more intuitively, enabling marketers to predict and realize more and more successful outcomes.

As customers convert from prospects to leads, marketers also understand the need to limit the losses from potential fraud. This is especially true when such leads begin the onboarding process with a bank or lender online. Financial institutions can outsource their onboarding to Instnt, which uses advanced data science to ensure that the right customer is signing up while stopping fraud in its tracks.

Marketing Campaign Optimization

Again, marketers focus on efficiency: delivering the right message to the right customer at the right time — to obtain the right outcome.

However, it’s more than simply the right message, the right customer, and the right time: it’s also the right channel.

Data science not only helps marketers with customer segmentation; it also helps them with selecting the right channels. This is important because each marketing channel carries its own cost, and usage of a channel that is not yielding sufficient results leads to inefficiency and loss. 

Even Google uses data science to predict the likelihood of clicks and conversions in its ad platform, Google Ads. Marketers rely on this data to allocate their budgets and further integrate this performance data into their analytics for a more robust analysis.

Lead Scoring and Sales Enablement

Customer data and data science help sales teams, too. Lead scoring is a ranking system that assigns values to different customer segments based on previous responsiveness to marketing campaigns. 

Leads can be scored based on such factors as website visits, social media likes, and shares, and email opens.

Lead scoring is important to sales teams because the value of a prospective customer can help sales teams be more efficient. Rather than wasting time on prospects with little to no likelihood of converting, salespeople can instead focus on higher-quality leads. 

Rather than “hand-scoring” leads, data science can take over and automatically assign scores based on similar behaviors for similar customer segments. This adds efficiency to the lead delivery process and ensures that higher-converting leads are delivered to the sales team faster.

Customer Data Privacy

Data science applies algorithms to various datasets, but it’s important to note that there are measures in place to strip away any personally identifiable information (PII) to protect consumers’ information.

General Data Protection Regulation (GDPR) rules have been in place in the EU since May 2018 and have since then been adopted by many U.S.-based firms. These rules regulate how companies use consumer data and empower consumers with the right to control the data that companies maintain. Similarly, the U.S. enforces the U.S. Privacy Act to further protect individuals.

Customer Data and Data Science

The use cases mentioned above prove the statement that the application of data science brings numerous benefits to marketing campaigns regardless of company size or industry. 

The transformation of data into meaningful insights is crucial for decision-making in marketing. Marketers can limit waste on underperforming channels and increase the speed of delivering leads — and revenue — to the organization.

Instnt is the first fully managed digital customer onboarding service for businesses with up to $100MM annually in fraud loss insurance. With a codeless integration on websites or apps, Instnt can reduce rejection rates by 50% without friction or fraud, grow top-line revenue, and lower operational costs by 30%. Try a demo today!


About the Author

Instnt's fraud loss indemnification technology provides coverage of up to $100M for fraud losses stemming from synthetic, third-party, and first-party fraud. With Instnt's comprehensive fraud loss protection, businesses can confidently extend their services to a wider customer base, enabling them to embrace more opportunities and enhance revenue streams while maintaining a secure, fraud-free environment.