High Data Quality Is Essential for Streamlining Corporate Activities Using AI

What Is Data Quality?

Data quality refers to how well data is suited for its intended purpose. Generally, data quality is composed of the following six elements.

• Accuracy

  The extent to which data accurately reflects the actual state of reality.

• Completeness

  Whether all necessary data is present and no information is missing.

• Consistency

  Whether data is maintained without contradiction across different systems and locations.

• Up-to-dateness

  Whether the data is current and free of outdated information.

• Reliability

  Whether the data is obtained from a trustworthy source.

• Availability

  Whether the data can be accessed and utilized when needed.

By ensuring these elements are met, companies can make faster, data-driven decisions.

For a more detailed explanation of data quality, please also refer to this article.

What Is Improving Data Quality, Essential for B2B Data Utilization? | Blog | uSonar ▶

Data Quality and AI Utilization in Lead Qualification

Lead qualification is the process of identifying high-potential prospects from the pool of identified leads. By prioritizing contact with customers who have a stronger interest in your products or services, you can conduct sales activities more efficiently. Lead qualification has shifted from an era where sales teams relied on intuition and experience to identify focus accounts, to an era where AI is leveraged to allow more time for direct customer engagement. Assigning scores to high-potential leads and prioritizing those with higher scores is one of the primary functions that AI provides in the B2B market.
Specifically, by integrating and maintaining the following data points internally, it becomes possible to more accurately identify prospects with high purchasing intent.

• Past Lead Interaction History
• Website Access Information
• Search Content Trends on Comparison Sites
• Data Held in CRM/SFA

Data Quality and AI Utilization in Marketing Processes

Personalized marketing is a strategy that delivers optimal messages and content based on a customer's specific needs and behaviors. By using data and AI to identify the characteristics of customers who are likely to generate higher profitability, the needs and pain points of specific customer segments become clear, providing high-precision insights into what information or solutions they are seeking. Establishing a flow that efficiently executes appropriate value propositions within the journey map for each account (also known as Account-Based Experience or ABX) and bringing high productivity to marketing and sales teams is also a key point of leveraging AI. By promoting AI-driven personalization using high-quality data, companies can deliver the right information to the right accounts at the right time.

Specifically, by leveraging data with the following characteristics, it is possible to achieve higher-precision personalization.

• Data reflecting recent customer behavior
• Data indicating needs and interests (Intent Data)
• Buying Group data

Data Quality and AI Utilization in Customer and Behavioral Analysis

With the advent of predictive analytics, it has become possible to dramatically improve the ability to predict an account's future purchasing potential, going beyond simple lead scoring for account-based targeting. To predict future outcomes based on behavioral patterns, accurate and high-precision data regarding customers is essential.

In the B2B market, in particular, purchase decisions are made by multiple stakeholders (buying groups); therefore, it is critical to manage customer data in units of "companies" or "buying groups" rather than "individuals." It is important to understand the entire purchasing process by optimizing the insights provided by AI based on the behavior and relationships of the entire group, rather than on an individual basis.

For example, multiple individuals with different roles, such as marketing managers, technical staff, and financial officers within a company, are involved in the purchasing process. By integrating and managing data for all these members, AI can provide personalized messaging and content tailored to their respective interests and needs. This promotes an improved customer experience and facilitates the closing of transactions.

Conclusion

In the B2B market, the greatest challenge in effectively utilizing data across an entire organization is that data exists in a fragmented state across multiple tools and vendors. Because the sources for viewing account and contact information, the sources for viewing corporate attributes and intent, and the sources for viewing installed technologies and competitor engagement are all different, information regarding a single account has been scattered. Consequently, there have been many barriers to integrating all this information and keeping it up to date.

Our customer data integration solution, uSonar, is equipped with LBC, one of the largest corporate information databases in Japan. Many companies utilize our customer information platform integration and data cleansing features to achieve more efficient sales and marketing activities.
By matching your internal data with LBC data, you can identify and correct duplicate or inaccurate information, enabling you to maintain up-to-date data for your target accounts.
Furthermore, we provide features such as predictive analytics and AI-driven list generation based on the integrated customer information platform.
For more details, please feel free to contact us via this page.

View the Reality of Data Utilization in B2B Companies