First-party data analysis can lead to effective customer intelligence, targeting, and engagement.
For most businesses, the success or failure of marketing and sales strategies depends heavily on the quality of their data foundation. Typically, the cornerstone of that data foundation is first-party data.
By analyzing and mining first-party data, companies gain valuable insights about customers – insights that can help them make smarter, data-driven decisions and strengthen relationships with prospective and existing clients.
Why is first-party data so valuable? Here’s a quick reminder of how first-party data differs from other common types of data that marketers and sellers use:
First-party data is the data your company collects mainly from customer and prospect interactions, and it often resides in an internal customer relationship management (CRM), enterprise resource planning (ERP), and/or master data management (MDM) system. This data is gathered digitally or input by employees during sales, marketing, and customer service interactions.
Second-party data is another company’s first-party data; to obtain it, a business typically needs a direct relationship with the second party. Programmatic advertising has helped make second-party data more widely available, and its rising popularity is due in part to its reputation as a compliant alternative to third-party cookies.
Third-party data is information that can be acquired from a trusted outside party, such as Dun & Bradstreet. Typically, companies select and purchase data from third parties based on their data gaps and specific business needs.
First-party data is generally the backbone of marketing and sales strategies. By merging it with second- and third-party data, you can build additional intelligence that helps improve engagement with customers, prospects, suppliers, vendors, and partners across data platforms. As a result, you can more easily and quickly identify growth opportunities; spot potential risk, fraud, and compliance issues; and build deeper relationships.
Three Ways to Begin Data Mining
Data mining – the process by which meaningful patterns and data insights are uncovered in large data sets, often from disparate sources – may sound like a complicated concept, but its benefits are easy to understand. The sooner you begin mining your first-party data, the sooner your marketing and sales efforts can leverage deeper, more compelling insights to help generate interest and opportunities with your target audiences. Here are three ways to get started.
1. Lookalike Modeling
Lookalike modeling involves creating an ideal profile based on customer or prospect first-party data. Your definition of “best customers” should be tied to key behaviors or characteristics, such as how often they order, how big the orders are, or how loyal they are. Then you can flow the information into a data management platform (DMP) or share it with an agency to help create advertising, develop a prospect list for your inside sales team, or mine your installed base for new opportunities.
2. Personalization
Personalization entails gathering specific information about customers, and then using it to tailor your message, offer, and approach. Because relationships with customers are likely spread across your company, focus on consolidating customer information, mining it, and sharing results with your marketing, sales, and customer success teams so they can:
Create targeted campaigns and send them directly to the appropriate contacts
Pinpoint high-value targets with the greatest likelihood to purchase
Deliver a warmer, more personalized brand experience for customers
3. Attribution
Attribution involves connecting a particular marketing or sales action to revenue. It’s one of the ways marketers gauge the impact of the tactics and channels that link a company to potential buyers. By analyzing the attribution data, companies can allocate their marketing budget more effectively and refine their lead scoring strategy by assigning appropriate scores based on the most influential actions.
To help you create a fuller picture of the multiple contacts associated with the companies in your customer base, consider adding third-party data so you can start to track engagement comprehensively at the account level. Then aggregate all that account engagement activity, which may be challenging; most tools do not have the “point and click” ability to see who, when, and how all contacts within an organization have interacted with your content. Try using a spreadsheet application to help cross-tabulate prioritized contacts in your accounts with your top performing assets — or consider a software solution to help you address attribution questions.
Accelerate the Benefits of Data Mining
Artificial intelligence (AI) can introduce significant efficiencies into the data mining process and help sales and marketing teams realize benefits more quickly. Replacing manual processes with AI-driven analytics and tools and enabling greater automation can result in more accurate, actionable customer intelligence — and facilitate digital transformation. The impact of AI may be particularly rewarding for companies already struggling with legacy technology systems; disconnected, large, and/or complex datasets; staffing shortages; and budget constraints.
Quality first- and third-party data and AI capabilities are only part of the solution. Companies need to set clear objectives for data mining efforts and establish strong data management practices to help protect and maintain data integrity. They also need experienced data managers on staff, who can play a key role in ensuring data mining tools and algorithms are performing effectively; their expertise is also vital for meaningful interpretation of analysis and results. Finally, companies should establish clear legal and regulatory compliance policies to help ensure teams are collecting, storing, and using data ethically and securely – essential steps for protecting their reputation and earning the trust of customers.
How Data Mining Pays Off
In today’s omnichannel, cross-platform world, there are more ways than ever for companies to connect with prospects and customers. However, it’s increasingly difficult to do so in a meaningful way. Not only do companies need engaging content that stands out from the competition, but they also need to ensure the right message gets to the right person at the right time and in the right context.
Data mining helps marketers and sellers find customer behavior patterns, and more importantly, begin to understand the story behind the data. That intelligence can help you develop more effective segmentation strategies, optimize the marketing mix, improve customer experience, and strengthen the sales pipeline.
| “Mining Diamonds from Data” presents more practical ideas on how to build vital customer intelligence you can leverage to construct unique and memorable buyer journeys. Download this Dun & Bradstreet resource for suggestions that can help you establish a reliable, enriched data foundation and develop focused, effective customer segmentation and targeting strategies. |
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The information provided in articles are suggestions only and based on best practices. Dun & Bradstreet is not liable for the outcome or results of specific programs or tactics undertaken based on your use of the information. Please contact an attorney or financial/tax professional if you are in need of legal or financial/tax advice.