Unlocking the Value of Partner Data in Business Analytics

Revelate

Table Of Contents

In business analytics, what you know is just half the game. The other half is what your partners know. Combine your data with theirs, and you unlock opportunities you never saw coming. It’s like 1 plus 1 suddenly equals 3. Yet, despite this winning formula, the untapped potential of partner data remains a significant blind spot for many organizations. Addressing this oversight could not only redefine business strategies but also create unparalleled opportunities for growth and success.

The value proposition

We can’t overstate the value of partner data—it improves marketing, makes sales easier, and builds stronger ties with customers. It accomplishes these outcomes by providing a fuller picture of customer habits and preferences, letting you tailor your strategies to your audience. Before diving in, however, make sure you have a solid data-sharing agreement that covers governance and compliance. No one wants to step on legal landmines while mining for valuable data.

Types of partner data

Understanding different types of partner data is key to leveraging their potential. The three main types are demographic, behavioral, and transactional data. In a B2B context, demographic data might include company size, industry, and location, rather than age or income. Behavioral data shows how businesses interact with each other, or even third parties—think website visits or webinar attendance. Transactional data refers to your sales and financial exchanges, helping you see what’s driving profits or losses.

These types of data don’t exist in a vacuum, however; they often intersect to offer a 360-degree view of your partnerships. For instance, demographic data can shape the behavioral trends you notice, which in turn could influence transactional behavior. By piecing these data points together, you can create a comprehensive understanding that drives smarter strategies and better results.

Data integration challenges

Merging partner data can be tricky. To begin with, you have to deal with data quality, ensuring it is compatible and remains private. To get past these hurdles, there are essential steps and challenges that need careful attention. Making sure your data is reliable, knowing who owns what, and having clear rules for handling it sets the stage for successful data integration.

Ignoring these challenges isn’t an option; it can lead to messy data sets, flawed analysis, and even legal headaches. For example, poor data quality can result in misleading insights, while compatibility issues can slow down data processing times. You also have to take into account the risks of mishandling private information—think fines and a tarnished reputation. It’s not just about making your data work better; it’s also about avoiding pitfalls that could hurt both of your businesses.

Combining internal and partner data

Combining your data with your partner’s offers a 360-degree view of your business performance. It’s like piecing together a puzzle; you see how everything from sales to customer habits to daily ops connect. The clearer view makes it easy to identify trends and solve issues, guiding better decision-making.

Merging and integrating data creates a single, trustworthy source for accurate analysis. Keeping that data tidy and spot-on ensures you can count on it. Plus, a culture and system focused on data-sharing helps sync up different sectors of your business, making collaboration smooth and effective. 

Having one system for managing partnerships is crucial, especially when you’re sharing data. It helps everyone stay on the same page, in turn making everything run smoother. Here are some key strategies that make the most of combining internal and partner data:

  • Business Intelligence (BI) Implementation: Using BI tools to analyze and visualize both internal and partner data for better decision-making
  • Agile Business Integration: Quickly and efficiently syncing up internal processes and partner data for more adaptive operations
  • Harnessing External Data Sources: Pulling in data from outside your organization, like social media or market trends, to complement your internal and partner data
  • Collaboration and Coordination: Building strong communication channels and workflows between you and your partners to keep data and objectives aligned

Tackling these strategies head-on can supercharge your efforts to blend internal and partner data, setting the stage for more informed and effective business decisions.

Analytical tools & platforms

In the complex world of partner data, analytical tools and platforms are your best buddies. They cut through the noise and turn raw numbers into actionable insights. While well-known names like Microsoft Power BI, Tableau, and Domo often come to mind, the real magic happens in the types of analyses these platforms can perform.

  • Descriptive Analysis: Tells you what happened. It’s the basic “show me the numbers” approach
  • Exploratory Analysis: Scans your data to identify trends and patterns without a specific goal in mind. It’s like casting a wide net to see what you catch
  • Diagnostic Analysis: Digs into specific issues to find out their root causes. It’s targeted, looking to answer ‘why’ for a particular problem
  • Predictive Analysis: Forecasts future trends based on past data. Think of this analysis as like having a crystal ball but backed by algorithms
  • Prescriptive Analysis: Not only predicts but also suggests what steps to take. Think of this analysis as your built-in business advisor 

Diving into these different analyses can make a world of difference. They allow for easier data sharing, boost your visualization game, and can analyze large chunks of data with ease. Regardless of the tool you choose, focus on what types of analysis best serve your goals, and you’ll set the foundation for smarter decisions.

Security concerns

Security isn’t just a box to tick; it’s the bedrock of your entire operation, especially when dealing with partner data. Failure to take security seriously doesn’t just risk a slap on the wrist from GDPR or other regulatory bodies; you could be the next headline a la Equifax 2017. Nobody wants that kind of fame.

  • Compliance: Make sure you’re in line with all the rules, from GDPR to whatever comes next. This is your first layer of armor
  • Data Rights: Know who owns what and who can access what. Clarity here avoids data breaches or unauthorized access later
  • Proactive Measures: Regular software updates and active monitoring can stop a breach before it starts. Don’t wait for the house to catch fire to buy an extinguisher
  • Data Culture: Build a workplace where everyone gets that data is valuable and vulnerable. A team trained in best practices is your last line of defense

Taking security seriously is non-negotiable. It’s not just about dodging fines or bad press; it’s about keeping trust—both with your partners and your customers. Remember, in the data game, the best offense is a rock-solid defense.

Key metrics to track

Tracking metrics isn’t just about making fancy spreadsheets; it’s your roadmap to what’s working and what’s not. It’s about asking the right questions and knowing where to look for the answers. Here’s a breakdown of key metrics to track:

  • Return on Investment (ROI): Measures the efficiency of an investment. It’s the ratio of net profit to initial cost, helping you see if the money you put in is paying off
  • Partner-Sourced Revenue: Shows the revenue directly generated from your partnerships. It helps you gauge the value of teaming up with other companies
  • Average Deal Size & Cost of Partner-Sourced Customer Acquisition:  The first indicates the value of the average deal you’re closing, and the second shows the cost to acquire a customer through partnerships. Together, they let you balance the cost and value of each deal
  • Increased Revenue & Profitability: Revenue measures the total money made, while profitability looks at what’s left after expenses. This helps you understand both top-line and bottom-line growth
  • Leads & Conversion Rates: Leads are potential customers you’ve attracted, and the conversion rate shows the percentage that actually become customers. These metrics help you understand the effectiveness of your sales funnel
  • Gross Profit Margin: This metric tells you what percentage of your revenue is profit after accounting for the costs to produce your goods or services. It’s a measure of operational efficiency

Don’t just set these metrics and forget them. Regularly check in to adjust your game plan as needed. Remember, these aren’t just numbers; they’re a story about how well your partnership is working—or not.

Benefits of using Revelate to exploit partner data

Revelate is a data fulfillment platform that unlocks the value of partner data for businesses of all sizes. By providing a secure and compliant way to share data with partners, Revelate enables businesses to achieve a number of benefits, including: 

  • Increased revenue: Revelate helps businesses to increase their revenue by enabling them to monetize their data. Businesses can sell data access, charge for data usage, or license data to partners
  • Improved customer insights: Revelate helps businesses improve their customer insights by enabling them to share data with partners. They improve customer segmentation, targeting, and product development
  • Reduced costs: Revelate can help businesses to reduce their costs by making it easier to share data with partners. They reduce the need for businesses to invest in their own data infrastructure and resources
  • Improved risk management: Revelate helps businesses improve their risk management by enabling them to share data with partners. They identify and mitigate risks more effectively

Revelate also offers a variety of data monetization options, so that businesses can choose the method that best meets their needs. Contact us today to learn more about how Revelate can help you to achieve your business goals.

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Frequently asked questions

What is a data partner?

Data partnerships are collaborative arrangements between companies whereby data and resources are shared to achieve joint goals. They involve many types of agreements, from simple data-sharing to more complex joint ventures. Data partners are typically outside companies that team up with you to make the most out of your shared information. 

What is an example of a data partnership?

Walmart and JD.com (China’s largest online retailer) have entered into a data partnership where Walmart gained access to JD’s pool of consumer behavior data in China, allowing them to more effectively market and sell their products.

How can businesses effectively combine internal and partner data?

To mix your own data with a partner’s, first make sure both datasets are clean and compatible. Then use data integration tools to pull it all into one place. This unified data can give you better insights and help you make smarter decisions.

What are the key performance indicators for data-driven partnerships?

Key performance indicators for data-driven partnerships include Partner-sourced revenue, Average Deal Size, Cost of Partner-Sourced Customer Acquisition, Joint Sales Revenue, Cross-Sell Success Rate, and Partner Contribution Rate.