Hello, this is Matsubara, CRM Evangelist.

As the use of AI expands, more and more companies are thinking, "We want to analyze our customer data using AI too."
However, the fact that the state of the underlying data itself is crucial for effectively utilizing AI is often overlooked.
This time, we will explain how to organize and utilize customer data in the age of AI.

table of contents

1. The changing value of customer data in the age of AI
2. Why "quality over quantity" is becoming more important than ever.
3. Structuring data as a prerequisite for AI utilization
4. The potential of AI applications for structured and unstructured data.
5. The biggest obstacle: data silos
6. Poor data quality leads to poor quality AI decisions.
7. Five key points for creating an "AI-ready" data infrastructure
8. Changes in a company resulting from data organization
9. A roadmap for phased data preparation
10. Reasons why you should prepare now
11. Balancing Privacy and Data Use
12. Common misconceptions about AI utilization
13. Practical Application with EMOROCO CRM Lite
14. Frequently Asked Questions (FAQ)
15. Summary

1. The changing value of customer data in the age of AI

Until now, customer data has primarily been treated as "historical records."
Who purchased what, when, and what inquiries were made—this information has been accumulated as data for reporting and review.

As the use of AI expands, the role of customer data is changing dramatically.
It's no longer just a record; its value as "raw material" for AI to analyze, summarize, and extract patterns is rapidly increasing.
Even with the same data, the availability of a new tool like AI is changing the magnitude of the value that can be extracted from it.

2. Why "quality over quantity" is becoming more important than ever.

Many people expect that "AI can automatically extract valuable insights from large amounts of data," but in reality, it's not that simple.
The quality of AI's output is heavily influenced by the quality of the data it is given.

- If the notation is inconsistent and there is a lot of duplicate data, the AI ​​analysis results will also be inaccurate.
- AI cannot correctly understand the context from activity history that is only recorded in fragments.
- If outdated information remains, the AI ​​may analyze it based on incorrect assumptions.

In other words, especially in the age of AI, having "accurate and well-organized data" will be a key factor in making a competitive difference.
Rather than the idea of ​​"simply collecting a large amount of data," it's more important to think in terms of "accurately and structurally recording the necessary information."

3. Structuring data as a prerequisite for AI utilization

For AI to effectively handle customer data, the data needs to be structured.

- Each customer's information is linked to a unique ID (1 customer = 1 ID).
- Standardized data (basic customer information) and non-standardized data (activity records) are clearly distinguished and recorded.
• Data fields are entered according to consistent rules.

This unstructured data is "difficult to interpret" for AI, and no matter how advanced the AI ​​technology, the expected results cannot be obtained.

4. The potential of AI applications for structured and unstructured data.

The two types of data handled by CRM 4.0 each have different potential from the perspective of AI utilization.

・Standardized data (customer information)Structured information such as company name, contact information, and contract details is suitable for aggregation, classification, and trend analysis by AI.
・Non-structured data (activity data)Text data such as business meeting notes and inquiry details are suitable for AI-powered summarization, sentiment analysis, and pattern extraction.

Previously, large amounts of sales meeting notes and inquiry histories had to be processed manually, but with AI, it may be possible to efficiently extract trends and patterns from them.
However, it is essential that this data is recorded as a prerequisite.
Data that is not recorded is the same as data that does not exist for AI.

5. The biggest obstacle: data silos

For many companies, the biggest obstacle to adopting AI is not a lack of technical expertise, but rather the "siloed" state in which data is scattered across multiple systems and Excel files.

• Sales data, marketing data, and customer support data are managed in separate systems.
- Information about the same customer exists in different formats across different departments, resulting in duplicate entries.
- When attempting to integrate data, resolving inconsistencies and duplications in notation requires an enormous amount of effort.

The "one customer = one ID principle," which I explained in a previous article addressing common misconceptions about CRM , is a fundamental and crucial prerequisite in the context of AI utilization.

6. Poor data quality leads to poor quality AI decisions.

In the world of computer science, there is an old principle called "Garbage In, Garbage Out."
This means that if you input low-quality data, you will only get low-quality results.
This principle is becoming even more important in the age of AI, or rather, precisely because of the age of AI.

- If you have AI analyze data with many variations in notation, there is a possibility that it will treat the same customer as a different customer.
- If you use data with many inaccurate inputs to have AI make predictions, it will produce incorrect prediction results.
- If you let AI make suggestions using outdated data that hasn't been updated, the suggestions will be out of touch with the current situation.

As AI becomes more sophisticated, the quality of the data underlying its decisions will have a greater impact on the quality of the results.

7. Five key points for creating an "AI-ready" data infrastructure

Here are five key points to consider for setting up your data infrastructure now, with an eye towards future AI applications.

Point 1: Strict adherence to 1 customer = 1 ID
Information will be managed under a unified customer ID that is free from duplication and variations in spelling.

Point 2: Standardize the input rules for standardized data.
Use validation settings to prevent inconsistencies in input formats.

Point 3: Establishing a habit of recording non-standard data
Make it a habit to keep a record of business negotiations and inquiries, even if it's just a brief one.

Point 4: Data freshness management
Create a system to regularly review and update outdated information instead of leaving it as is.

Point 5: Centralization of data across departments
Sales, marketing, and customer support data will be integrated into a common platform called CRM.

These are not the result of flashy technological advancements, but rather the accumulation of steady data preparation.
However, whether or not this foundation is in place will greatly influence how effectively we can utilize AI in the future.

8. Changes resulting from data organization at a certain company

To give you a concrete image, let me introduce a change that occurred at a certain company.

In this company, the sales and marketing departments each recorded customer information in separate Excel files.
The same customer was registered under slightly different names in each department, requiring manual verification every time we tried to consolidate the data.
When we tried using an external AI analysis tool in this state, the same person was treated as a different customer, significantly impairing the accuracy of the analysis results.

Subsequently, by centralizing data on the CRM and strictly adhering to the principle of one customer = one ID, the accuracy when using the same AI analysis tool improved significantly.
A representative from this company reflected, "The time we spent organizing the data ultimately yielded a greater benefit than the AI ​​tools themselves."

9. A roadmap for phased data preparation

You don't need to aim for perfection in data preparation all at once.
We recommend proceeding in the following steps:

・1st stage: Inventory existing customer data and identify duplicates and inconsistencies in terminology.
・2st stageBased on the principle of 1 customer = 1 ID, data will be integrated.
・3st stage: Establish input rules (validation) and create a system to maintain data quality in the future.
・4st stage: Instill a culture of recording non-standard data (activity history) within the organization.
・5st stageBased on the compiled data, we will consider integrating with external AI tools as needed.

Following this sequence makes it easier to avoid the failure of "implementing AI but not achieving the expected results."

10. Reasons why you should prepare now

Some companies believe that AI utilization is still a long way off, but there are good reasons why you should start building your data infrastructure right now.

Data preparation takes a considerable amount of time. Starting only when it becomes necessary may not be completed in time.
Unrecorded past data cannot be recreated retrospectively. Starting to record data today will become an asset for the future.
If the data is well organized, it will immediately prove effective in analysis and reporting tasks other than AI.

Data preparation is a worthwhile initiative to start now, not only for the sake of AI, but also for improving the efficiency of daily operations.

11. Balancing Privacy and Data Use

When utilizing customer data, consideration for privacy is essential.

- Clearly define the purpose of acquiring and using customer information.
- We will explain to customers and obtain their consent as needed regarding how we will use the data, including the use of AI.
- Design access permissions appropriately so that only authorized personnel can handle the data.

The more we utilize data, the more important it becomes to have a proper management system in place.
BeforeCustomer information of Japanese companies is being targeted from all over the world.As explained earlier, the more data you aggregate, the greater your responsibility becomes to protect that data.

12. Common misconceptions about AI utilization

Misconception #1: "With AI, data preparation is unnecessary."
In reality, the opposite is true: data preparation is crucial precisely because it allows AI to be effective.

Misconception #2: "The more data, the better."
Accuracy and structure are more important than quantity.
Having a large amount of low-quality data won't lead to effective use of it.

Misconception #3: "AI utilization is only for large corporations."
Even small and medium-sized enterprises can lay the foundation for future AI utilization by properly organizing their daily customer data.

13. Practical Application with EMOROCO CRM Lite

EMOROCO CRM Lite supports the creation of a data infrastructure that anticipates future AI utilization with the following functions:

・Relationship management functionStructured data management based on the principle of 1 customer = 1 ID.
・Validation settings: Standardize input rules and maintain data quality
・Record of activity historyA system that allows non-structured data to be naturally accumulated during daily work.
・Security Role: Permission design that restricts access to data to an appropriate scope.

While EMOROCO CRM Lite itself doesn't currently have a built-in AI generation function, this organized data provides a solid foundation that will be easier to use when integrating with external AI tools in the future.

14. Frequently Asked Questions (FAQ)

Q. Where should I start with data preparation?
First, we recommend starting by eliminating duplicates and inconsistencies in customer information, creating a system where each customer has a unique ID.

Q. How detailed should non-routine data be when recording it?
You don't need to aim for a perfect record.
First and foremost, it's important to develop the habit of keeping records, even if they are brief.

Q. Is data preparation necessary for small and medium-sized enterprises in the age of AI?
Yes.
Regardless of company size, properly organizing daily data expands future options.

Q. Can AI be used immediately once the data is prepared?
Data preparation is merely the foundation for utilizing AI.
For practical application, selecting and integrating AI tools according to the specific purpose will also be necessary.

Q. Won't concerns about privacy hinder data utilization?
Appropriate consideration will, in fact, enhance customer trust and stabilize the foundation for long-term data utilization.

15. Summary

In the age of AI, the value of customer data is shifting from mere records to crucial material for AI analysis and utilization.
That's why "quality over quantity"—steadily implementing one ID per customer, standardizing input rules, establishing a habit of recording non-standard data, managing data freshness, and centralizing data across departments—has become more important than ever.
For companies that feel that AI adoption is still a long way off, setting up a data infrastructure now will make a huge difference in the future.

EMOROCO CRM Lite costs ¥1,500 per user per month (minimum 3 users) and has no initial setup fee, with a 30-day free trial.https://www.emoroco.com/You can start from there.
IT implementation subsidy eligible tool number: DL07-0022934.

We also offer a monthly newsletter, "ARCUS NEWS," which provides updates on EMOROCO CRM Lite and other useful information for CRM operations.
If you likeCLICK HEREPlease register here.

Additionally, we publish useful articles about CRM 4.0 on note ( https://note.com/arcuss_crm ), so please check them out as well.

Related article

・Debunking common misconceptions about CRM
・Customer information of Japanese companies is being targeted from all over the world.
・Validation Configuration Function Guide
・Security Role and Form Permission Management Guide