DQC for Sales Data
No more forgone revenue due to bad sales data
Stop manually checking and correcting customer master and sales transactional data in one-off projects. Fix and maintain your data with the DQC AI and human control.
DQC IMPACT
High-quality sales data is a must-have.
Daily challenge
Customer master data is generated by sales experts who prefer selling over maintaining data - but the customer data is important for many stakeholders.
Risks and losses
Unreliable customer master data hurts the business.
- Improve lead scoring based on comprehensive past information, contact persons at client and client behavior
- Get overview of similar projects in different regions / departments for cross- and up-selling opportunities
- Allow for smooth client handover in case of vacation replacement, illness or change of employer of colleagues
- Improve pipeline management to meet required Sales targets at end of reporting period
- Improve Sales forecasting based on comprehensive historic data
- Improve early detection of potential problems with clients and impact on overall pipeline
- Facilitate Sales content personalization based on comprehensive historic client information
- Get connected to Sales reps colleagues offering similar products to exchange best practices
- Get transparency on prices/budgets converted (benchmarking) at different customers for same products
- Set optimal prices and trigger next based action for long-term retention management
- Get transparency on meetings of different Sales teams with the same customer to get latest updates
- Get hints on potential product bundles successfully sold to other customers
Success with DQC Platform
- 100% data quality fit for purpose
- 35x+ faster issue remediation
- >1M€ cost saving in year 1
- >5M€ additional revenue potential
Calculate the cost of bad Sales Data
Works where you work
including CRM, SFA, and ERP
Built on 3 pillars
DQC Platform for 100% fit-for-purpose sales data
1) Find data issues with AI.
- Set up data quality rules with the help of DQC AI agent
- Import any rules, requirements, or issue descriptions in natural language or as code in seconds
- Find issues in the data and let the AI agent document everything for you
- Dummy and placeholder texts
- Incomplete contact data
- Invalid entries
- Inconsistent formats
- Information in incorrect fields
- Typos e.g., in names
- Inconsistencies between systems and tables
- Outdated or incomplete documentation
- Duplicates in contacts and accounts
- Incorrect account hierarchies or contacts matches to accounts
2) Fix data at source with AI + human experts.
- Generate AI suggestions for data corrections and enhancements
- Fix issues at source with subject matter experts in full control
- Track the change history for complete visibility
- Observe data quality improve over time
3) Prevent issues at source.
- Use all DQC data quality rules via API or SDKs
- Embed the DQC data quality rules directly in your product management system
- Prevent data issues in real-time in source systems Stop bad data from flowing through your data pipelines / ETL processes
Companies Can Generate Value by Improving Their Data
Dealing with data quality issues at the source lets businesses start with a strong foundation and make the most of GenAI, DQC’s Dr. Michael Spira explains.
Data quality made in Germany
Secure & responsible - DQC is headquartered in Munich, Germany. We prioritize data security and user control. Fully GDPR compliant. The DQC Platform is available as SaaS, or private cloud deployments and your data always remains in your systems. No copies are made. Also, the DQC Platform only needs reading rights. Finally, enterprises can bring their own LLMs.
Act now!
Start treating Sales data as an asset, today. Learn how AI agents can help you improve your Sales data.