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CRM systems have long served as the primary hub for customer data. Platforms like Salesforce and HubSpot excel at tracking customer interactions, managing sales pipelines, and providing basic reporting. These functions have made CRMs indispensable for sales and marketing teams seeking a centralized view of customer relationships.
Go-To-Market teams typically use 10 to 20 different marketing technology tools. Each tool produces unique datasets, often in proprietary formats. Data volume has increased exponentially, encompassing product usage metrics, customer behavior patterns, and third-party information. Decision-making increasingly relies on integrating and analyzing these diverse data sources. While Customer Relationship Management (CRM) systems are powerful tools, they often fall short as a true single source of truth.
Let's explore how a data warehouse first approach can provide a competitive edge for your business.
Modern GTM teams use 10 to 20 marketing technology tools, each of them regularly producing data that need to be integrated and organized. Managing this integration is chaotic if done in a CRM system. Modern data transformation approaches enable code-based transformation and aggregation of data where the transformation sequence and lineage is transparent and under control. (i.e. not a mess!)
Example: Imagine a company heavily reliant on Salesforce for managing sales activities. The sales team uses Salesforce to track leads, opportunities, and customer interactions. Meanwhile, the marketing team runs campaigns through HubSpot, capturing valuable engagement data like email opens, clicks, and form submissions. The finance team, on the other hand, uses Net suite for invoicing and payment tracking.
However, these systems don't always communicate seamlessly. In our experience, there are gaps in the “point-to-point” integration, resulting in gaps in information. For example, a lead who has interacted with multiple marketing emails in HubSpot may still appear "cold" to the sales team in Salesforce. Or the sales team might not have visibility into recent financial transactions or overdue invoices.
The result? Disjointed customer insights where the sales team operates with incomplete information, leading to missed opportunities for timely follow-ups, cross-selling, or upselling. This fragmented view can cause frustration among teams and negatively impact the customer experience, as interactions are not informed by the most up-to-date information from marketing and finance.
CRM platforms like Salesforce and HubSpot are rapidly advancing their AI capabilities, transforming how businesses manage customer relationships and drive growth. Salesforce's Einstein AI brings powerful features like predictive lead scoring, opportunity insights, AI-powered forecasting. Similarly, HubSpot leverages AI to enhance marketing and sales automation, with tools like content strategy optimization, lead scoring, and conversation intelligence that analyze interactions and provide actionable insights.
Remember: As these CRM systems continue to evolve, they will only be able to use the data that is available to them. If some of the relevant data is not in the CRM system, the AI-based capability will not be able to leverage it for better decisions.
Example: A tech company wants to use AI to predict which customers are most likely to churn based on a combination of factors including product usage data, support ticket history, and recent engagement with marketing campaigns. While their CRM has some built-in AI capabilities, it can't incorporate the product usage data which is stored separately. A data warehouse, on the other hand, can integrate all these data sources, allowing for more comprehensive and accurate AI-driven predictions. And then the output can be fed back to the CRM system, where the AI based workflow can take advantage of the holistic view!
CRMs like Salesforce are great for managing customer data, but they struggle with large volumes. Transforming and processing massive data set sin a CRM is difficult, limited, and impractical due to storage and computational constraints.
That’s where a data warehouse comes in. Designed to handle bigdata, a data warehouse can transform and store vast amounts of information efficiently, giving you the power to unlock valuable insights that CRMs alone can't provide.
Example: A rapidly growing SaaS company uses HubSpot to manage customer interactions and sales data effectively. However, as the company expands, they begin to collect extensive product usage data, such as in-app behaviors, feature adoption rates, and user activity logs. This raw product data is rich with insights, but it needs to be transformed—aggregated, cleaned, and analyzed—to be useful for the sales and marketing teams.
The problem? HubSpot, while robust for managing interaction data, isn't equipped to handle the complex data transformations required for product data. The CRM can store and display simple data, but it lacks the capability to perform the intricate processing needed to convert raw product data into actionable insights. As a result, the company struggles to create accurate reports and dashboards that combine product data with customer interaction data, leading to incomplete views and missed opportunities.
If your analytics needs go beyond standard reporting and require complex calculations and custom metrics, possibly utilizing predictive analytics (machine learning), a CRM system may fall short.
Examples:
● Sales Funnel Analysis: Analyses performed for period-over-period comparisons, point-in-time analyses, and other similar purposes for sales performance optimization.
● Customer Segmentation: Various approaches to segmentation based on value and needs, using past purchases, usage behavior, demographic, and other variables.
● Lead scoring: Data and sophisticated modeling can be used outside of Salesforce and then fed back into Salesforce for data activation.
● Churn analysis: Can take various shapes depending on the industry and sophistication of the company, with different definitions and KPIs.
● Development of custom KPIs and signals: Custom KPIs and signals can be calculated or scored via machine learning models, such as Customer Lifetime Value (CLV) and Account health score.
● Historical Analysis: Performing historical trend analysis, cohort analyses, tracking changes over time, or analyzing data across extended periods beyond what Salesforce can efficiently manage.
When teams like sales, marketing, product, and finance need to collaborate on complex analyses, a CRM system like Salesforce may fall short. While Salesforce is great for managing customer interactions, a data warehouse centralizes multiple datasets, enabling deeper cross-functional analysis.
Examples:
● Product team analyzing usage and churn by combining product usage data with sales and support info.
● Product team assessing market size using external data purchased by the sales team, combined with internal product and revenue metrics.
● Finance team analyzing profitability with territory mapping over time, using historical data snapshots available in the data warehouse.
A data warehouse breaks down data silo sand enables a unified view for smarter decision-making across teams
A data warehouse offers several key benefits over relying solely on a CRM system:
A data warehouse provides all the key benefits mentioned above - plus one, unanticipated, and rarely discussed benefit of a Data Warehouse First approach in a GTM setting.
A Data Warehouse first approach gives the Go-To-Market leaders the freedom to use their preferred, best of breed marketing technology. They no longer have to worry about integration capabilities of their preferred technology with their other existing technologies.
As you reflect on your data strategy and capabilities, ask yourself:
● Are you still trying to solve complex business puzzles with incomplete data? Or are you ready to see the full picture?
● Would having real-time, integrated data empower you to make more accurate, value-adding decisions for your business?
● Are you spending too much time manually aggregating and quality-checking reports, rather than using that time to drive growth and innovation?
Stop trying to guess the facts—know them with confidence by integrating data from multiple sources into the Data Warehouse. Eliminate uncertainty where it can be eliminated, especially when it comes to what’s already happened. This way you can focus your organization's energy on the business decisions that truly require judgment.
A data warehouse-centric approach addresses these challenges by centralizing data from all business sources, including CRM. It enables advanced analytics, machine learning, and AI applications, scales efficiently for large-scale data processing and historical analysis, promotes cross-functional data access and collaboration, and offers flexibility to integrate best-of-breed tools without compatibility concerns.
It used to take a large team to provide the Data Warehouse capabilities up until recently. Current technologies applied correctly makes it possible for a smaller team to deliver this value at a fraction of the cost.
Additionally, transitioning to a data warehouse doesn't have to be an all-or-nothing proposition. You can start small, receive immediate benefits and expand to a more sophisticated implementation over time.
Every step towards a more integrated, flexible data strategy is a step towards greater competitive advantage in today's fast-paced business environment.
The data technology stack, uses of the data (think AI!), best practices have changed drastically over the last few years. Don't let outdated approaches hold your business back. It's time to explore what's possible for your unique situation. Hear our perspective on what is possible for your unique situation.
Take action now:
Contact us today to develop a tailored roadmap for your organization's data strategy and move beyond the limitations of a CRM-centric approach.
We’re excited to start our new blog series dedicated to conversations with data leaders. Each post will explore their challenges, innovations, and lessons learned in navigating the ever-evolving data landscape. We will be highlighting challenges they have overcome, pain points and the achievements they take the most pride in.
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