Creating and Pricing Data & Analytics Products

If your organization collects industry data and analytics, you may be sitting on a gold mine of untapped value. But without a clear strategy to bring this value to market, its potential remains unrealized.

Interest in data democratization and monetization is on a continual trend upwards, and for good reason; it’s fueling organizational growth through igniting new revenue streams, creating competitive advantage, accelerating innovation, and much more.

In our Data and Analytics Monetization series, we explored the differences and similarities between monetization and democratization approaches, highlighting how to maximize value based on your company’s unique needs.

With this foundation in place, your organization is ready to implement your data democratization or monetization strategy and activate some initial go-to-market (GTM) motions.

In this article, we’ll dive deeper into creating and decommissioning data and analytics products, including setting up stock-keeping units (SKUs), and how to guide you in determining your costs and setting up your pricing. 

Creating Data and Analytics Products

As noted in our second article of the series, before you create your SKUs, you’ll want to make sure to:

1. Thoroughly research your customers and market

2. Know the costs associated with your products

3. Establish value-based pricing

Once you’ve completed these steps, you’ll want to decide on the best pricing model for your data and analytics products.

Determining your Pricing Models

When choosing a pricing model, numerous factors can influence your decision. Does this model align with our product offerings and target market? Does it reflect our customers’ preferences? Understanding various pricing models and asking these essential questions can help you make the best choice for your offering.

Types of Pricing Models

One-time Purchases

Ideal candidates for one-time purchases (OTPs) include conference attendee or member lists, market research reports (e.g., by Nielsen, Gartner, or Forrester), specialized data visualizations, industry benchmarks, geospatial data, patent databases, and economic forecasts from financial institutions or consulting firms.

Subscription-based Offers

If you are collecting and analyzing on an ongoing basis, a subscription-based offer is your best bet. Examples of companies using a subscription based offer: 

• MasterCard’s anonymized credit card transactions, which gives insight into consumer spending patterns and trends
• Nielsen’s consumer behavior data and market trends analysis on media consumption
• INRIX’s real-time traffic data
• SproutSocial’s analytics on social media performance and audience engagement

Usage-Based Pricing

Usage-based pricing is common across cloud data warehouses, like Snowflake and Salesforce, where users pay for the computation and storage they use, only. Data enrichment services, such as Clearbit, charge per record enriched, while satellite imagery is often priced by area covered or images accessed. IoT data platforms typically charge based on connected devices or data volume, and market research firms offer pay-as-you-go access to consumer panels, charging per survey response or data point.

Tiered or Package Pricing

Pricing tiers are typically structured to address varying user needs and budgets. Higher tiers generally offer more robust data, advanced features, increased analytics options, and higher usage limits. In the Business Intelligence field, Dun & Bradstreet provide tiered access to business information and credit data; the higher the tier, the higher number of comprehensive profiles become available to you. Similarly, data brokers price access based on the level of detail in customer profiles and the volume of behavioral data included. Firms like Bloomberg and Reuters structure their tiered pricing around the depth and frequency of data access while research databases, such as Web of Science and Scopus, base theirs off on the extent of access and the number of users supported.

Offering multiple pricing tiers or packages can help cater to a wide range of customer needs, with each level providing distinct functionality, data access, support, or additional services. This approach allows customers to select the option that best matches their requirements and budgets, while also creating opportunities for bundling, upselling, and cross-selling.

The ideal number of tiers depends on your target market segments, product complexity, competitor offerings, and customer needs and willingness to pay. Most organizations offer 3-5 tiers, providing sufficient options for different customer profiles while keeping choices straightforward. Starting with a select number of tiers allows you to refine offerings based on customer feedback and sales data over time.

Custom Pricing

Custom pricing is often reserved for enterprise-level customers or clients with specific data and analytics needs. This approach typically involves a comprehensive discovery and sales process, resulting in individualized quotes that reflect the unique requirements of each customer. 

Focus on Premium Offers

Regardless of your pricing model, consider implementing a premium-tier offering for advanced or specialized data products. By offering enhanced features or exclusive insights at a premium price while providing basic or limited versions to a wider audience, you can monetize higher-value elements without fully democratizing access. 

Additional Pricing Considerations

Test and Iterate

Pricing is a continuous process. Regularly gather customer and supplier feedback through pilots, surveys, focus groups, and interviews to gauge perceptions of value and pricing adequacy. Monitor market dynamics, conduct competitive pricing research, and proactively refine your strategy based on these insights. 

Be Transparent

Build trust by clearly communicating your pricing structure and any associated terms. Avoid hidden fees or complex models that  may confuse customers.

Consider Product Bundling

Bundling complementary products or partnering with other organizations can add value and differentiate your offerings.

Evolve Value-Added Services

Offer additional services and customization to enhance product value. Bundling these with your data and analytic products can generate added revenue while enhancing customer experience. Over time, feedback from these services can guide enhancements to core products, effectively making your value-added services a “voice of the customer.”

Monitor and Adjust Pricing Over Time

Regularly review your pricing strategy by tracking key metrics like customer acquisition, retention rates, profitability, and market feedback. Adjust pricing as needed to optimize revenue, adapt to market changes, or meet customer demands.

Once your pricing model is set, it’s time to create SKUs.

Create Sales Product Definitions (SKUs)

Building a product catalog with unique SKUs is the next step. Ensure that each SKU defines the features, functionalities, and pricing for your data and analytic products. Descriptions should be unique, easily understood, and aligned with established pricing models, clearly communicating the value and benefits.

Considerations for data & analytics product SKUs

Plan SKUs carefully to support effective analytics and decision-making.

Set Up SKUs

Start by deciding how much and what information needs to be encoded (e.g., type, features, pricing tier.) Choose a structure and an appropriate character length (typically 8 to 12 characters) that balances simplicity with precision. Avoid special characters and maintain a consistent format across products to keep SKUs simple and intuitive enough to be memorable.

Anticipate Analytics

Structure your SKUs to enable data analysis, product segmentation, and reporting. Ensure consistency across different sales channels and platforms.

Build for Scalability

Design your SKU system for future growth by planning out new product lines, updates, or regional  variations with localized options, if you plan to sell internationally.

Ensure Data Quality and Compatibility

Maintain accurate product data, and regularly update SKU information. Ensure SKUs work with inventory systems and analytic tools and are compatible with inventory management systems and barcode scanners (if applicable) as well as analytics tools.

Comply with regulations

Consider any industry-specific regulations around product identification.

Plan for the Product Life Cycle

Remember that all products have a finite lifespan, so it’s crucial to proactively plan for phasing out your data and analytic offerings to prevent them from becoming liabilities. 

Start by notifying customers well in advance about transition timelines and offering guidance on alternate solutions. Establish a sunset period during which the product is still available but no longer enhanced, providing minimal support. 

Capture and document technical knowledge, best practices, and lessons learned for future use, training, or integration into other products. 

Lastly, review and address all legal or contractual obligations related to phasing out the product to ensure a smooth transition and continued customer satisfaction.

Additional Sales and Go-To-Market Motions

By implementing Sales Enablement and Go-To-Market (GTM) motions, sales teams can more effectively promote and sell your monetized data and analytic products. Aligning their expertise with customer needs, providing training, and leveraging targeted sales collateral will enhance their ability to communicate value and build strong customer relationships.

GTM motions involve coordinated efforts across the entire customer journey, including collaboration between product, marketing, customer success, and support teams. 

Consider GTM motions like product-led growth (PLG), which uses your product to acquire and retain customers. You can also explore models like Freemium or free trials, allowing users to test limited features and upgrade to premium versions. Other approaches, such as event-led, community-led growth, can also be highly effective. 

Collaborate with Marketing Teams

Collaboration with marketing is crucial for aligning messaging, campaigns, and lead generation efforts. It’s important to ensure that all marketing materials, website content, and digital presence effectively support the sales process. Providing feedback to marketing teams will help refine and optimize lead generation strategies.

Continuously Refine Your Sales Processes

A fundamental Lean principle is the regular assessment and refinement of sales processes based on feedback and performance indicators. This can be achieved  by monitoring key metrics such as conversion rates and sales cycle length, while also gathering customer feedback. Implementing continuous training and equipping sales teams with the necessary tools and resources will enable them to adapt to changing market dynamics and optimize sales outcomes.

Build Go-To-Market Partnerships

Partnerships can play a crucial role in expanding your reach and accessing new customer segments. By enabling joint marketing and sales efforts, partnerships provide opportunities to tap into challenging markets, broadening the impact of your products. Leveraging these partnerships can enhance your credibility, increase visibility, and enable collaborative strategies that amplify your go-to-market success. 

Harness the Power of Data & Analytics Monetization for Your Organization

Creating and pricing data and analytic products is both an art and a science. It requires a deep understanding of customer needs, market dynamics, and the unique value your products provide. By addressing these key considerations and continually refining your pricing strategy, you can effectively monetize your data and analytic products, maximizing customer satisfaction and ensuring profitability.

Get Started Today

If you have any questions or are interested in learning more about how to start monetizing your organization’s data and analytics, reach out to our team today. We’re here to help guide you through the process and ensure you harness the full potential of your data.