2 min

Unlocking Your GTM Potential: Why Investing in a Data Warehouse is the Next Step

A Go-To-Market (GTM) organization may find it beneficial to invest in a Data Warehouse (DWH) alongside their Salesforce CRM when they encounter specific indications that their data and analytics requirements have surpassed the analytics and reporting capabilities of Salesforce.

Applied AI

Data Analytics

Data Engineering

Author

Hasan Caliskan

Managing Partner

Table of contents

Integrating Diverse Data Sources:

Customized Analytics Requirements:

Collaboration Across Teams:

External Reporting:

Future Growth and Innovation:

Conclusion

Conclusion

The following signs serve as indicators that the GTM team should consider the adoption of a data warehouse:

Integrating Diverse Data Sources:

If your organization relies on data from various sources beyond Salesforce, such as product data, marketing platforms, external databases, APIs, or custom applications, and you need to consolidate and analyze these diverse data sets together. A data warehouse offers more flexibility if you blend data from different sources.

Customized Analytics Requirements:

If your analytics needs go beyond standard reporting and require complex calculations and custom metrics, possibly utilizing predictive analytics (machine learning). Here are some 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: There are various approaches to segmentation. In essence, segmentation is about value and needs. And to approach these parameters, past purchases, usage behavior, demographical, and other variables can be used.
  • Lead scoring: Data and sophisticated modeling can be used outside of Salesforce and then fed back into Salesforce for data activation.
  • Churn analysis: Churn analysis can take various shapes depending on the industry and sophistication of the company. It can have different definitions (activeness, value, usage of products, etc.), and different KPIs. All this customization and depth can be managed in a data warehouse.
  • Development of custom KPIs and signals: Custom KPIs and signals can be calculated or scored via machine learning models. CLV (Customer Lifetime Value) and Account health score are some examples.
  • Historical Analysis: If you need to perform historical trend analysis, cohort analyses, track changes over time, or analyze data across extended periods beyond what Salesforce can efficiently manage.

Collaboration Across Teams:

If multiple teams within your organization (e.g., sales, marketing, product, finance) need to collaborate on complex analyses involving different data types. Some examples:

  • Product team analyzing usage and churn for insight on user experience.
  • The product team assesses the addressable market based on third-party market data that the sales team procured.
  • The finance team performs profitability analysis with the “then current” territory mapping over time, enabled by point-in-time data snapshots.

External Reporting:

If your organization needs to generate external reports for regulatory compliance, industry standards, or customer demands, an automated data warehouse provides more control over the formatting, aggregation, and delivery of data.

Future Growth and Innovation:

If your GTM organization has ambitious plans for future growth, innovation, and expansion of data-driven initiatives, a data warehouse can offer the scalability and flexibility needed to support these endeavors.

Striving for More?

If any of the scenarios above sound familiar, and your organization grapples with temporary manual solutions to fulfill these objectives, it's a clear sign that you're outgrowing Salesforce's capabilities. At 205 Data Lab, we recognize that your GTM journey may eventually surpass Salesforce's capabilities. When that pivotal moment arrives, we stand ready to assist you in seamlessly transitioning to a data warehouse solution that aligns with your evolving needs.

Data Analytics

#Data Warehouse

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