4 Unexpected Customer Insights Uncovered Through Analytics

Analytics can uncover valuable information about your customers, allowing you to connect on a deeper, more personal level.

A customer insight is a piece of information or metric that helps a business better understand how their customers think or behave. Unlike just a few years ago, businesses don’t need to rely on a stodgy market research firm to gain these insights. Today’s most successful companies are digging deep into their own datasets — past superficial metrics like gender, age, and location — to uncover valuable knowledge that was unattainable until very recently.

Here are a few key examples.

Eloquii Discovers New E-Commerce Revenue Streams

Because e-commerce companies exist in such a data-rich world, it makes sense that they’d be ahead of the curve in terms of using analytics to gain new insights. That’s exactly how Eloquii, a fast-fashion house catering to plus-size women, has solved several of its marketing problems.

After noticing that customers were returning white dresses at a higher proportion than other products, Eloquii’s marketing department dug into its data and discovered that many of those customers had actually bought multiple dresses, with the intention of using one of them as a wedding dress. That unexpected insight enabled Eloquii to have a more effective conversation with its customers around those products and better serve their needs.

According to Eloquii VP of Marketing, Kelly Goldston, the company also relies on analytics to anticipate customer behavior and tailor their marketing efforts to proactively engage each of the brand’s customer profiles, such as customers who indicate a potential high lifetime value and those who have started to shop less frequently at the site.

DirecTV Uses Customer Insight to Create Double-Digit Conversion Boost

Satellite media provider DirectTV used data to uncover an underserved portion of its customer base – those who had recently moved. The company discovered that statistically, people who have recently moved are more likely to try new products and services, especially those who’ve moved within seven days.

Armed with this information, and change of address data from the U.S. Postal Service, DirecTV created a special version of their homepage that would appear only for people who had recently moved. Not only did the targeted campaign result in a double-digit conversion improvement of the homepage, it did so with a reduced offer compared to the one on the standard website.

Whirlpool Uses Customer Insight to Drive Positive Social Change

While analyzing customer data, Whirlpool discovered that 1 in 5 children in the U.S. lack access to clean clothes, and that not having clean laundry directly contributes to school absenteeism and increases the risk of dropping out. This further predisposes these children to a variety of negative outcomes as an adult, including a 70% increased risk of unemployment.

To help stop this vicious cycle, Whirlpool created the Care Counts Laundry Program, which installs washers and dryers in schools with high numbers of low-income students. The machines are outfitted with data collection devices, enabling the Whirlpool team to record laundry usage data for each student and correlate their usage with their attendance and performance records.

The program has yielded dramatic results, including a 90% increase in student attendance, an 89% improvement in class participation, and a 95% increase in extracurricular activity participation among target students. As a result of its success, the program has attracted interest from over 1000 schools. It’s also drawn support from other organizations like Teach for America, which partnered with Whirlpool on the initiative for the 2017/2018 school year.

Prudential Better Serves its Customers with Data-Driven Insight

Financial services firms are a leading adopter of data analytics technology and Prudential has established itself as one of the forward-thinkers in the field. In August of this year, the company announced the launch of a completely new marketing model built on the customer insights gleaned from analytics and machine learning.

A central part of that initiative is the Prudential LINK platform, a direct-to-consumer investing service that allows customers to create a detailed profile, set and track personal financial goals, and get on-demand human assistance through a video chat. The LINK platform not only provides a more convenient customer experience, it also gives the Prudential team access to customer data they can use to make optimizations to other areas, such as the new PruFast Track system, which uses data to streamline the normally tedious insurance underwriting process.

Quality Customer Insights Have Become Vital to Business Success

As customers grow used to data-driven marketing, businesses will be forced to approach prospects with customized messages, or run the risk of losing competitive advantage. Research from Salesforce shows that 52% of customers are either extremely likely or likely to switch brands if a company doesn’t personalize communication with them.

2nd Watch helps organizations uncover high value insights from their data. If you’re looking to get more insights from your data or just want to ask one our analytics experts a question, send us a message. We’re happy to help.

A High-Level Overview of Amazon Redshift

Modern data warehouses, like Amazon Redshift, can improve the way you access your organization’s data and dramatically improve your analytics. Paired with a BI tool, like Tableau, or a data science platform, like Dataiku, your organization can increase speed-to-insight, fuel innovation, and drive business decisions throughout your organization.

In this post, we’ll provide a high-level overview of Amazon Redshift, including a description of the tool, why you should use it, pros and cons, and complementary tools and technologies.

Overview of Amazon Redshift

Amazon’s flagship data warehouse service, acquired from ParAccel originally, is a columnar database forked from Postgres. Similar to AWS RDS databases, pricing for Amazon Redshift is charged by size of the instance, along with how long it’s up and running.

Value Prop:

  • Increased performance of queries and reports with automatic indexing and sort keys
  • Easy integration with other AWS products
  • Most established data warehouse


  • Flexibility to pay for compute independently of storage by specifying the number of instances needed
  • With Amazon Redshift Serverless, automatic and intelligent scaling of data warehouse capacity


  • Instances maximize speed for performance-intensive workloads that require large amounts of compute capacity.
  • Distribution and sort keys are more intuitive than traditional RDBMS indexes, allowing for more user-friendly performance tuning of queries.


  • Easy to spin up and integrate with other AWS services for a seamless cloud experience
  • Native integration with the AWS analytics ecosystem makes it easier to handle end-to-end analytics workflows with minimal issues


  • Can be set up to use SSL to secure data in transit and hardware-accelerated AES-256 encryption for data at rest

Why Use Amazon Redshift

It’s easy to spin up as an AWS customer, without needing to sign any additional contracts. This is ideal for more predictable pricing and starting out. Amazon Redshift Serverless automatically scales data warehouse capacity while only charging for what you use. This enables any user to run analytics without having to manage the data warehouse infrastructure.

Pros of Amazon Redshift

  • It easily spins up and integrates with other AWS services for a seamless cloud experience.
  • The distribution and sort keys are more intuitive than traditional RDBMS indexes, allowing for more user-friendly performance tuning of queries.
  • Materialized views support functionality and options not yet available in other cloud data warehouses, helping improve reporting performance.

Cons of Amazon Redshift

  • It lacks some of the modern features and data types available in other cloud-based data warehouses such as support for separation of compute and storage spending, and automatic partitioning and distribution of data.
  • It requires traditional database administration overhead tasks such as vacuuming and managing of distribution of sort keys to maintain performance and data storage.
  • As data needs grow, it can be difficult to manage costs and scale.

Select Complementary Tools and Technologies for Amazon Redshift

  • AWS Glue
  • AWS QuickSight
  • AWS SageMaker
  • Tableau
  • Dataiku

We hope you found this high-level overview of Amazon Redshift helpful. If you’re interested in learning more about Amazon Redshift or other modern data warehouse tools like Google BigQuery, Azure Synapse, and Snowflake, contact us to learn more.

The content of this blog is an excerpt of our Modern Data Warehouse Comparison Guide. Click here to download a copy of that guide.