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

  Ensuring that data remains consistent across different systems and locations.

• Up-to-dateness

  Ensuring that data is current and free from outdated information.

• Reliability

  Ensuring that data is obtained from trustworthy sources.

• Availability

  Ensuring that data can be utilized whenever it is needed.

By fulfilling these elements, companies can make faster, data-driven decisions.

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

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 prospects with a high probability of purchase from among identified potential customers (leads). By contacting customers who have a stronger interest in your products or services, you can conduct sales activities more efficiently. Lead qualification has transitioned from an era where sales teams relied on intuition and experience to identify focus accounts, to an era where AI is utilized to dedicate more time to customer engagement. Assigning scores to high-potential leads and prioritizing prospects with higher scores is one of the primary functions that AI provides in the B2B market.
Specifically, by integrating and maintaining the following data items internally, it becomes possible to identify prospects with high purchasing intent more accurately.

• Lead's past interaction history
• Access Information on Websites
• Search Trends on Comparison Sites
• Data Stored in CRM/SFA

Data Quality and AI Utilization in Marketing Processes

Personalized marketing is a strategy that delivers optimal messages and content based on specific customer needs and behaviors. By identifying the characteristics of customers who are likely to generate higher profitability through data and AI, companies can clarify the needs and pain points of specific customer segments and gain high-precision insights into the information and solutions they seek. Establishing a workflow that efficiently executes appropriate value propositions within account-based journey maps (also known as Account-Based Experience or ABX) and bringing high productivity to marketing and sales teams is also a key benefit 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, it is possible to achieve higher-precision personalization by utilizing data with the following characteristics:

• Data Reflecting Recent Customer Behavior
• Data Indicating Needs and Interests (Intent Data)
• Data Regarding Buying Groups

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 the future purchasing potential of accounts, rather than just identifying targets on an account-based level through lead scoring. To predict future outcomes based on behavioral patterns, accurate and high-precision data regarding customers is essential.

Particularly in the B2B market, purchasing decisions are made by multiple stakeholders (buying groups), making it critical to manage customer data by units of 'companies' or 'buying groups' rather than 'individuals.' It is important to understand the entire purchasing process by optimizing AI-provided insights 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 the fragmented state of data existing 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 implemented technologies and competitor contact status are all different, information regarding a single account has been scattered, creating many barriers to integrating all this information and keeping it up to date.

The customer data integration solution 'uSonar' that we provide is equipped with 'LBC,' one of the largest corporate information databases in Japan. The integration and data cleansing functions of the customer information platform, which uses high-quality data provided by LBC, are used by many companies to realize more efficient sales and marketing activities.
By matching your internal data with LBC data to identify and correct duplicate or inaccurate information, we enable you to maintain the latest data for your target accounts.
Furthermore, based on the integrated customer information platform, we also provide functions such as predictive analytics and AI-driven list generation.
For more details, please feel free to contact us from this page.

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