How to organize customer data in the overseas loan industry? Guide to Number Cleaning to Improve Marketing Efficiency

Preface

In the overseas lending and finance industry, acquiring customer leads is not difficult.

What's really difficult is:
👉How to reach these customers efficiently and drive subsequent conversions

Many teams will encounter similar problems:

• Advertising to obtain a large amount of user data
• Client list keeps growing
• Sales team continues to follow up

But the actual effect is not ideal:

• Low contact efficiency
• Data chaos
• Follow-up costs continue to rise

Such problems are often attributed to channels or sales capabilities, but the underlying reasons are often:

👉Customer data itself lacks organization, resulting in inability to be effectively utilized

Why is the loan industry more prone to “data chaos”?

Structural issues brought about by acquiring customers through multiple channels

The loan industry usually has many customer acquisition channels:

• Advertising (Facebook/Google)
• Third party platform clues
• Channel cooperation data
• Historical customer accumulation

These data come from different sources and have completely different structures:

• Some include country codes
• Some only have local numbers
• Some formats are irregular
• Some are missing or wrong

When this data is aggregated together, it easily becomes:

👉 A customer pool that “seems like a lot but is difficult to use”

The larger the clue size, the more obvious the problem

When the amount of data is small, it can still be processed manually.

But when the scale of the clues increases, it will appear:

• There are a lot of repeat customers
• Data cannot be filtered quickly
• Different teams use different versions of data

Eventually leading to a result:

👉 The more data there is, the harder it is to convert

Low reach efficiency often stems from data problems

Sales time consumed by “invalid data”

In actual business, sales teams often need to face:

• Malformed number
• Data that cannot be used directly
• Duplicate customer information

This will have a direct impact:

👉 A lot of time is wasted on data processing instead of customer communication

Data chaos magnifies operational costs

When the data is not sorted:

• The same customer is contacted multiple times
• Repeated follow-up for different sales
• Statistics are inaccurate

These issues further impact:

👉 Judgment of marketing decisions

Many “low conversions” are actually illusions

Some teams will find:

👉 The delivery effect is getting worse and worse

But in fact, the problem may not be with delivery, but with:

👉The data is not organized correctly, resulting in the effect being "diluted"

The true value of number cleaning in the financial industry

In the loan industry, number cleaning is not used to determine whether a customer is "high-quality", but to:

👉Make customer data basically usable

Unify data structure and improve usability

Numbering rules in different countries are complicated:

• Country code difference
• Different digits
• Input format is not uniform

If no sorting is done:
👉 Data cannot be used stably

Number cleaning can help achieve:

• Data normalization
• Uniform format
• Improve system compatibility

Reduce the interference caused by duplicate data

Duplicate data is very common in the lending industry:

• User applied multiple times
• Repeat investment through multiple channels
• Data is imported multiple times

If not processed:

👉 Will seriously affect sales efficiency and user experience

Make data “manageable”

After the data has been sorted, you can then:

• Channel effect comparison
• Customer source analysis
• Basic group management

Otherwise, all analysis will be based on inaccurate data.

Typical scenarios in overseas loan business

Scenario 1: Advertising leads accumulate in large quantities

Many teams will encounter:

👉 Data is growing rapidly, but usage efficiency is very low

The reasons are often:

• Data is not organized
• Confusing format
• Can't be put into use quickly

Scenario 2: Multi-country market operations

Loan business usually covers multiple regions:

• Southeast Asia
• Latin America
• Africa

Numbering rules vary significantly from country to country.

If not handled uniformly:

👉 Data management costs will rise rapidly

Scenario 3: Historical customers cannot be reused

Many financial teams have actually accumulated a large number of historical customers:

• Applied but failed
• Consulted but not converted
• Users accumulated in the past

However, due to lack of data organization, these resources are often not reused.

👉 The essential problem is: data is not available, not that the customer does not exist

The positioning of Dingdang Assistant in this link

In the loan industry, Dingdang Assistant is not:
❌ Risk Control Tools ❌ Customer Rating Tools

Instead:

👉Data organization and cleaning tools

It solves the "data foundation problem"

The core value of Dingdang Assistant is:

• Unified number format
• Clean up duplicate data
• Improve data structure clarity

This allows teams to:

👉 Carry out follow-up contact and operations more efficiently

Why would this step affect overall performance?

In the loan business:

• Data quality → determines reach efficiency
• Reach efficiency → affects the number of conversions
• Number of conversions → directly affects revenue

If there is a problem with the first step:
👉 All subsequent links will be amplified and affected

Overlooked growth opportunity: data curation capabilities

When optimizing for growth, many teams prioritize:

• Increase delivery budget
• Optimize creatives
• Improve sales capabilities

But a more stable growth point is often overlooked:

👉Improve data quality

Although number cleaning is basic, its impact is long-term:

• Data can be reused
• Improved operational efficiency
• Decisions are more accurate

Summarize

In the overseas loan industry, competition is not just about traffic, but also about:

👉Competition for data processing capabilities

When the data is not sorted:

• Reach efficiency will decrease
• Marketing costs will rise
• The conversion results will be unstable

The significance of number cleaning is:

👉 Turn data from "messy resources" into "usable assets"

The role of Dingdang Assistant is to help the team complete this step of infrastructure construction.

FAQ

Q1: Can number cleaning determine whether a customer is of high quality?

cannot.
Number cleaning only deals with data structure issues and does not involve customer quality judgment.

Q2: Why is the data in the loan industry more likely to be confusing?

Because there are many sources, large scale, and complex countries.

Q3: Is it suitable for small financial teams?

Suitable, small teams need to improve data processing efficiency.

Q4: Will data collation really affect conversions?

It will affect the reach efficiency and thus indirectly affect the conversion results.

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