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How to write an RFP for data analytics

Requirements, questions, and evaluation criteria specific to data analytics procurement

7 min read

Data analytics in customer experience (CX) has evolved from basic reporting to AI-driven conversation intelligence, making RFP creation complex. Choosing the right solution impacts revenue, compliance, and customer loyalty, demanding a thorough and well-structured RFP. A detailed RFP is critical to navigate the transition from legacy systems to cloud-native solutions and to address the risks associated with AI implementation and regulatory compliance.

What makes data analytics RFPs different

RFPs for data analytics in CX are unique due to the rapid technological advancements and the critical need to analyze vast amounts of customer interaction data. The shift from "dark data" to real-time, AI-driven insights requires careful consideration of data ingestion, processing, and security. Regulatory compliance, especially with laws like the EU AI Act and PCI-DSS, adds another layer of complexity.

Procurement teams must also evaluate the trade-offs between all-in-one platforms and best-of-breed solutions, each with its own integration challenges and cost structures.

  • Data security and compliance with industry regulations (e.g., PCI-DSS, GDPR, EU AI Act)
  • Integration with existing CCaaS, CRM, and UCaaS platforms
  • Scalability and flexibility to handle growing data volumes and evolving business needs
  • Real-time analytics capabilities for agent guidance and proactive issue resolution

RFP vs RFI vs RFQ

Here's when to use each document type when procuring data analytics software.

RFI

Request for Information

Use early in your search to understand what vendors offer and narrow your list. Gather general capabilities, company background, and high-level pricing ranges.

RFP

Request for Proposal

Use when you know your requirements and want detailed vendor solutions and pricing. This is your main evaluation document for shortlisted vendors.

RFQ

Request for Quote

Use when requirements are fixed and you just need final pricing. Often used after RFP when you're ready to negotiate with finalists.

For data analytics in CX, an RFI is useful for initial market research to understand available technologies and vendor capabilities. An RFP is essential for a detailed evaluation of specific solutions, their technical architecture, and commercial terms. An RFQ is less applicable due to the complexity and customization required for these solutions.

Technical requirements checklist

Use this checklist when defining your RFP scope.

Data Ingestion & Processing

  • Omnichannel data ingestion (voice, email, chat, SMS, social media)
  • Data normalization and cleansing capabilities
  • Support for various audio codecs and transcription formats
  • Scalable data processing infrastructure

Analytics & Reporting

  • Real-time sentiment analysis
  • Automated topic discovery and categorization
  • Customizable dashboards and reporting
  • Predictive analytics and churn prediction
  • Root cause analysis tools

Security & Compliance

  • Automated PCI/PII redaction
  • Data encryption and access controls
  • Compliance with GDPR, HIPAA, and other relevant regulations
  • Data residency and sovereignty options

Integration Requirements

  • CRM integration (Salesforce, Zendesk, ServiceNow)
  • CCaaS integration (NICE, Genesys, Five9)
  • UCaaS integration (Microsoft Teams, Zoom)
  • Data lake integration (Snowflake, AWS S3)

AI & Automation

  • Generative AI summarization capabilities
  • Real-time agent guidance and next-best-action recommendations
  • Automated quality assurance and compliance monitoring
  • Predictive behavioral routing

Questions to include in your RFP

Data Ingestion & Processing

  • Describe your system's ability to ingest data from various channels (voice, email, chat, SMS, social media) in a unified format.
    Ensures a holistic view of the customer journey across all touchpoints.
  • Explain your data normalization and cleansing processes to ensure data quality and accuracy.
    Accurate analytics depend on clean and consistent data.
  • What audio codecs and transcription formats are supported by your system?
    Compatibility with existing systems is crucial for a smooth data migration.
  • Detail your system's scalability to handle increasing data volumes as our business grows.
    Future-proofs the investment against increasing data loads.

Analytics & Reporting

  • Describe your real-time sentiment analysis capabilities and how they are used to improve customer interactions.
    Real-time sentiment analysis enables proactive agent intervention and issue resolution.
  • Explain your automated topic discovery and categorization features for identifying emerging trends.
    Helps identify previously unknown issues and opportunities.
  • Provide examples of customizable dashboards and reports that can be tailored to different user roles.
    Ensures that relevant insights are accessible to different teams and stakeholders.
  • Detail your predictive analytics capabilities for churn prediction and proactive retention offers.
    Helps reduce customer churn by identifying at-risk customers.
  • Describe your root cause analysis tools and how they help identify underlying issues affecting customer experience.
    Enables data-driven process improvements and problem resolution.

Security & Compliance

  • Explain your automated PCI/PII redaction capabilities and provide third-party certification of its accuracy.
    Ensures compliance with PCI-DSS and other data privacy regulations.
  • Describe your data encryption and access control mechanisms to protect sensitive customer data.
    Protects customer data from unauthorized access and breaches.
  • Detail your compliance with GDPR, HIPAA, and other relevant regulations, including data residency options.
    Ensures adherence to all applicable legal and regulatory requirements.
  • Describe your approach to data sovereignty and how you can guarantee that data collected in specific regions stays within those regions.
    Critical for complying with data localization laws.

Integration Requirements

  • Describe your integration capabilities with our existing CRM (specify platform) and provide examples of bi-directional data flow.
    Seamless CRM integration is essential for a unified customer view.
  • Explain your integration with our CCaaS platform (specify platform) and how you handle telephony data.
    Ensures that voice data is properly captured and analyzed.
  • Detail your integration with our UCaaS platform (specify platform) and how you support collaboration between front-office and back-office teams.
    Facilitates efficient communication and problem resolution.
  • Describe your ability to integrate with our data lake (specify platform) for deeper insights and advanced analytics.
    Enables combining interaction data with other business data for comprehensive analysis.

AI & Automation

  • Describe your generative AI summarization capabilities and how they save agent time and improve efficiency.
    Reduces after-call work and frees up agents to focus on more complex tasks.
  • Explain your real-time agent guidance features and provide examples of next-best-action recommendations.
    Improves agent performance and customer satisfaction.
  • Detail your automated quality assurance and compliance monitoring capabilities, including how you handle the EU AI Act's restrictions on emotion recognition.
    Ensures consistent quality and compliance across all interactions.
  • Describe your predictive behavioral routing capabilities and how they improve customer outcomes.
    Connects customers with the most suitable agents for their needs.
  • How does your sentiment model differ from biometric emotion detection, ensuring compliance with regulations like the EU AI Act?
    Demonstrates an understanding of current regulatory constraints.

Pricing & Licensing

  • Detail your pricing model, including transcription costs (per minute, per user, or per concurrent port) and any "fair use" caps on unlimited plans.
    Transparency in pricing is crucial for budget predictability.
  • Describe your storage fees, including options for tiered storage and archiving of older data.
    Managing storage costs is essential for long-term cost control.
  • Explain your professional services offerings for implementation and tuning of the language models.
    Proper tuning is essential for accurate analytics.
  • Detail any additional costs for integration maintenance and API updates.
    Avoids unexpected costs associated with system upkeep.

Compliance and security requirements

Depending on your industry, you may need to require proof of these certifications and standards.

PCI-DSS

Required if handling payment card data. If applicable, request current PCI-DSS compliance certificate and Attestation of Compliance (AOC)

GDPR

Required if processing personal data of eu residents. If applicable, request documentation of GDPR compliance measures, including data processing agreements and data transfer mechanisms

HIPAA

Required for healthcare data. If applicable, request Business Associate Agreement (BAA) template and HIPAA compliance documentation

EU AI Act

Required if using ai systems that could impact fundamental rights or safety. If applicable, request documentation of compliance measures, including risk assessments and mitigation strategies, particularly regarding emotion recognition

SOC 2 Type II

Required for demonstrating security, availability, processing integrity, confidentiality, and privacy. If applicable, request the latest SOC 2 Type II report

Evaluation criteria

Here is the suggested weighting for data analytics RFPs.

Functionality Fit How well the solution meets stated requirements
25%
Total Cost of Ownership Implementation, licensing, and ongoing costs
20%
Integration Capabilities
15%
Security & Compliance Adherence to relevant security and compliance standards
15%
Scalability & Performance Ability to handle increasing data volumes and maintain performance
10%
Vendor Stability & Roadmap Financial stability and commitment to future development
10%
Usability & Training Ease of use and availability of training resources
5%

Some weights were adjusted based on your priorities.

  • Increase if replacing a highly customized legacy system
  • Increase if complex integration landscape exists

Red flags to watch

  • Vague pricing responses

    Vendors who can't provide clear pricing often have hidden costs or complex fee structures that inflate TCO

  • No customer references in your industry

    Lack of relevant references suggests limited experience with your specific requirements and use cases

  • "Black Box" AI

    If the vendor cannot explain why a call was scored a certain way or allow you to adjust the logic, you cannot defend the results to regulators or agents

  • Manual Redaction Dependence

    Any system that relies on agents pressing a "pause" button to stop recording during credit card entry is a compliance risk. Redaction must be automated.

  • Data Hostage

    If the vendor makes it difficult or expensive to export your raw transcripts and audio data (proprietary formats), avoid them. You own your data.

  • Lack of Data Sovereignty

    If they cannot guarantee data residency (e.g., keeping German data in Germany), they are not GDPR compliant.

Key metrics to request

Ask vendors to provide benchmarks from similar customers.

Implementation timeline for similar customers

Helps set realistic expectations and identify potential delays

Average time to first value

Indicates how quickly you'll see ROI from the investment

Transcription accuracy with custom vocabulary

Ensures the system can accurately transcribe industry-specific terms

Rate of false positives in PCI/PII redaction

Minimizes the risk of non-compliance and data breaches

Reduction in after-call work (ACW) due to auto-summarization

Quantifies the productivity gains from AI-powered automation