Request a Demo

Uncovering Your Most Valuable Customer

Identifying opportunities for better business decision making


Customers are the lifeblood of any business, but it’s a reality for any organization that some customers contribute more to the bottom line than others. Organizations that understand this, and understand how to identify, attract, and retain their most valuable customers, have an edge.

Whether your criteria for most valuable customer, or MVC, is based on lifetime value, transaction value, repeat purchase intentions, willingness to recommend your business, or other metrics, having this level of data about your customers lets you take control of your customer experience, building new ways to foster ongoing relationships, accelerate purchasing, and even act as influencers for your brand.



But how do you know how to look at your data to find your most valuable customers and optimize your business to serve them well? After all, your customer data is full of possible connections, insights, and game-changing decision drivers, but typically, the way the data sets are organized can actually stand in your way. Most organizations’ data has evolved, over years or even decades, from a rich mix of structured and unstructured data sources, in various formats, so masses of valuable data exists…but using it well is another matter.

Data sources may not be adequate. Data capture is often inconsistent from one purchase point or platform to another. Some data sources may not contain all the types of information your analysts need to succeed. Data sources might come with gaps or ambiguities.

Data sources are often disconnected. Analysis is often based on subsets of your data or limited data sources. Sometimes this is an access problem. Your analysts find themselves only using easily accessed data, leaving valuable information on the table. Sometimes it’s a problem of joining disparate data sources in different formats.

Often, data analysis takes too long. Sometimes it’s simply an issue of size, that data sources are far too massive to be effectively combined without slowdowns. Sometimes it’s an issue of access, that critical data sets aren’t easy to get. This problem results in insights bubbling up too late; they would have been useful a week or a day ago, but now can’t be applied for best effect.



Overcoming these challenges begins with asking a set of questions.

  1. Do you have all the data you want to understand your customer better?
  2. Are there data sources that you can’t access?
  3. What data is missing?
  4. What columns couldn’t you live without?
  5. Are some columns typically incorrect?
  6. How often do you guess where the data might be?
  7. How many customer records have all of the data that you need?

By answering these questions, you begin to see that bringing all your data sources together to get the broadest picture is a critical foundation for success. It’s important to cast the widest net so as not to miss out on valuable insights.

The next critical stop is to address data quality by conducting data modeling to weed out inconsistencies, ambiguities, and gaps. You might find that columns are repeated across different source systems, and you have to decide which to use, whether to combine the two, or possibly to discard one. You might discover that one department uses a business term differently than another department. You might find gaps that can be filled with third-party data. In this process, you’re aiming to create a unified picture of customer data so that your analysts can ask questions, and more questions, to discover just how your most valuable customers are engaging with your organization.

Finally, you must also have the technologies and data accesses to accelerate analysis based on the latest data.. After all, with the accelerated rate of change we’re all seeing in our businesses, how you define your most valuable customers may rapidly change, and without agile access to data, your capacity for insight is left behind. Knowing how rapidly your data is updated, how quickly your data goes out of date, and how long it takes to get new data and fresh data is critical. You also need performant infrastructure and tools to ask additional questions and conduct drill-downs as quickly as possible.

Obviously, the right approach is to choose technologies that let you overcome these challenges. Is there a tool that helps organizations address the challenges by improving data access, data quality, and data performance?

That’s where AtScale can help.



AtScale offers a streamlined way for organizations to create a rich, self-service semantic model, across first-party and third-party data sources. It’s used by Global 2000 companies to help them discover the most valuable characteristics of their most valuable customers.

AtScale serves as an engine that accelerates and deepens analysis. By providing a connection to all disparate data sources, including data lakes and data warehouses, it gives analysts an agile way to build new data models based on a broader set of data where new data sets can be added quickly. And by providing a “virtual cube” for any and all business intelligence tools, AI/ML tools, and applications, everyone is able to use the same data with consistent definitions for improved consistency without concerns about added complexity or reduced performance.

AtScale combines all data sources to become a “single source of truth.” AtScale helps organizations avoid the necessity of conducting analysis on data subsets and extracts or limited data sources. Connecting different sources and worrying about access become problems of the past.

AtScale makes sure that data sources support your efforts. AtScale lets analysts identify and fix gaps or ambiguities in the data by giving everyone a consistent, single view of all data sources. New data capture methods or third-party gaps can fill in the gaps.

AtScale accelerates time to insight. Organizations that choose AtScale have a tool that powers live connections to the latest data sources with accelerated performance. Query times are improved anywhere by 5x to 100x. Analysts now have a way to access more data, from more sources, use more rows, more quickly, and deliver analysis to help identify MVCs right away for better business decision-making.

Powering an integration of data science and business intelligence to uncover new insights, organizations use AtScale to enhance their customer profiles with social media signals, integrate social influence data into MVC scoring, and combine direct sales and influence sales to gain a more complete understanding of customer value.


Organizations that use AtScale quickly migrate away from more complex, traditional approach- es, because the advantages are clear and immediate.

AtScale offers anywhere from 2.5-9x improvement of data and analytics ROI. This is a result of AtScale’s positive impact on query performance, user concurrency, query compute costs and SQL complexity across cloud data warehouses.

Based on cloud benchmarks, AtScale has an order of magnitude impact on the ROI of each major cloud data warehouse. Customers powering their analysis with AtScale control the costs and complexity of their cloud analytics, maintain a consistent and compliant view of data across the enterprise and force multiply the effectiveness of their data and analytics teams.

With AtScale, forecasts are performed faster and delivered sooner, accelerating operational decision making from weeks to days (and even minutes). They’re also higher quality, built on consistent data, even across multiple users using different BI tools over time. Businesses that use AtScale identify their MVCs more quickly and make better decisions to attract and retain more valuable customers, which gives them new opportunities to take advantage of evolving economic conditions.

Knowing who your most valuable customers (MVCs) are is one of the most effective and efficient ways to drive profitable business. Let AtScale make your business better.

AtScale Product Overview:

AtScale provides the premier platform for data architecture modernization. AtScale connects you to live data using one set of semantics without having to move any data. Leveraging AtScale’s Autonomous Data Engineering™, query performance is improved by order of magnitude. AtScale inherits native security and provides additional governance and security controls to enable self-service analytics with consistency, safety and control. AtScale’s Intelligent Data Virtualization™ and intuitive data modeling enables access to new data sources and platforms without ETL and or needing to call in data engineering.

More Great Content


AtScale powers the analysis used by the Global 2000 to make million dollar business decisions. The company’s Intelligent Data
Virtualization™ platform provides Cloud OLAP, Autonomous Data Engineering™ and a Universal Semantic Layer™ for fast, accurate data-driven business intelligence and machine learning analysis at scale. For more information, visit