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Why Sell Analytics Instead of Raw Data?

Higher margins, better control, happier customers

Selling raw data is a race to the bottom on price. Selling analytics as a service lets you capture the value of your insights, maintain control, and build sustainable recurring revenue.

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5-10x
Higher Margins
3x
More Customers
Full
Data Control
9 min read
For Sellers
Overview

The Fundamental Difference

When you sell raw data, you're competing on price with commodity providers. When you sell analytics, you're selling expertise, insights, and business value. This shift transforms your data from a commodity into a premium service.

Key Points

Analytics command 5-10x higher margins than raw data

Maintain complete control - data never leaves your systems

Reach 3x more customers with API-first delivery

Build recurring revenue vs. one-time sales

Differentiate on insights, not just data volume

Why It Matters

Why This Matters for Data Providers

Transform your data into a sustainable business

Higher Profit Margins

Raw data sells for pennies per record. Analytics sell for dollars per insight. Your expertise in processing and interpreting data is where the real value lies - capture it by selling the insights, not the raw materials.

Complete Data Control

Once you sell raw data, you lose control - buyers can resell it, combine it, or use it in ways you never intended. With AaaS, data stays in your systems and you control exactly what insights are exposed.

Lower Compliance Risk

Selling raw data means ensuring buyers handle it compliantly. With AaaS, you retain custody and control, dramatically reducing GDPR, CCPA, and industry-specific regulatory risks.

Recurring Revenue Model

Data sales are typically one-time transactions. AaaS creates usage-based recurring revenue as customers continuously query your analytics for fresh insights.

How It Works

How to Transition from Data Sales to AaaS

A proven three-phase approach

1
1

Identify High-Value Analytics

Start with the analytics your customers ask for most. These are proven to have market demand and you already know how to compute them.

Key Points:

Survey existing customers on their most-used queries
Identify 5-10 high-value analytics to start
Document the business value each provides
Determine pricing based on value, not cost
2
2

Build Analytics APIs

Create secure, well-documented APIs that expose these analytics without exposing raw data.

Key Points:

Connect APIs to your data warehouse
Implement query logic and business rules
Add security, rate limiting, and access controls
Write comprehensive API documentation
3
3

Launch and Scale

List your analytics in the marketplace and leverage the built-in audience to scale beyond your existing customer base.

Key Points:

Publish to Spartera Marketplace
Set pricing and usage limits
Monitor usage and customer feedback
Iterate and add new analytics based on demand
Comparison

Selling Analytics vs. Selling Data

Revenue Model

Selling Data
One-time sales, race to bottom on price
Selling Analytics
Recurring usage-based revenue, premium pricing

Profit Margins

Selling Data
Low margins (pennies per record)
Selling Analytics
High margins (5-10x higher)

Market Size

Selling Data
Limited to buyers with processing capability
Selling Analytics
3x larger market - anyone can use APIs

Data Control

Selling Data
Lost once sold - buyers can resell
Selling Analytics
Full control - data never leaves your systems

Compliance Risk

Selling Data
High - responsible for buyer's usage
Selling Analytics
Low - you maintain custody and control

Customer Success

Selling Data
Buyer must build infrastructure and analytics
Selling Analytics
Immediate value - insights ready to use

Time to Market

Selling Data
Months for buyers to process and integrate
Selling Analytics
Hours to integrate via API

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Key Benefits

Real Results from AaaS Providers

5-10x

Higher Revenue Per Customer

Analytics command premium pricing based on business value, not data volume

3x

Larger Addressable Market

API-first delivery reaches customers who lack data processing infrastructure

80%

Faster Customer Onboarding

Hours instead of months to go from purchase to production value

90%

Lower Support Costs

Well-documented APIs reduce integration support vs. raw data delivery

FAQs

Common Questions

Won't I lose customers who want raw data?

You can offer both - AaaS for the majority and raw data for sophisticated customers who need it. Most find that 80% of customers prefer ready-to-use analytics, while 20% still want raw data access.

How do I price analytics vs. data?

Price based on business value, not cost. An analytic that helps a customer make a $100K decision is worth much more than the computational cost. Start with value-based pricing and adjust based on usage patterns.

What if competitors copy my analytics?

Your competitive advantage is the underlying data and your expertise in deriving insights from it. Competitors can't replicate your API without your data source and domain knowledge.

Is the market ready for AaaS?

Absolutely. The shift from data to insights is already happening across industries. Companies increasingly want answers, not datasets to process. AaaS meets this demand directly.

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