10 Best Practices to Manage & Clean Your Phone Number Database

Keeping a clean and accurate phone number database is one of the most effective ways to improve campaign performance, reduce wasted spend and stay compliant.

The core principles are simple: capture correctly, standardise, validate and regularly clean your data.

Bulk phone number cleaning removes dead, invalid and risky numbers from your existing datasets. By removing these, you will maximise the efficiency of your marketing and transactional messaging, save money by not calling or sending SMS to inactive numbers and reduce the risk of complaints, fraud and delivery issues

67% of businesses rely on CRM data for marketing and sales, yet 94% of B2B companies suspect inaccuracy in their database. Are you confident in the quality of your phone data?

Maintaining phone number data manually is slow, repetitive and prone to human error. The good news: with the right process and tools, you can keep your database in good shape with much less effort.

Below are 10 best practices to help you manage and clean your phone number database effectively.

1. Verify the phone number at point of capture

The best time to validate a phone number is when you first collect it.

If you’re capturing numbers via web forms, apps or sign-up flows, you can:

1. Run a real-time HLR lookup as the number is entered to confirm it’s real and active.

2. Send a one-time code via SMS or voice and ask the user to enter it back into your form or app.

This approach will allow your business to prevent “fat finger” mistakes and fake sign-ups (it happens to the best of us), prove that the customer had control of that number then, and save headaches later when your data officer asks why you messaged the wrong person

If you’d like help integrating real-time checks into your capture flow, our team can support you with implementation and API examples.

2. Standardise phone number formatting

Raw phone data can arrive in all kinds of formats. Standardisation is essential if you want to search, deduplicate and validate easily.

Aim to store numbers in e164 format wherever possible (e.g. +441133910781).

Strip out spaces, brackets, commas and any non-digits before storing.

Check that the country code exists, the prefix is valid, and the total length makes sense for that country.

You can still display numbers to users in a friendly local format while storing them in E.164 behind the scenes.

If you already have a large, messy dataset, consider programmatically converting numbers to e164 where you know the country and use an API (like HLR Lookup) that accepts non-standard inputs plus a country and returns a normalised, validated number

Correct formatting makes it much easier to spot duplicates, detect errors and keep your database consistent.

3. Regularly check that existing phone data is still valid

Even if numbers are correct when you collect them, they don’t stay that way forever.

Around 55% of people change their mobile number within 2 years, and numbers are frequently recycled, which means you could be messaging someone completely different from the original customer.

Use automated checks (for example, via API) to:

• See whether numbers are still active

• Detect when a number has become unreachable or has changed status

• Trigger workflows to ask customers for updated details

Some businesses clean their entire phone database monthly; others do it quarterly or annually. Your ideal frequency depends on your use case, but as a rule of thumb, every customer number should be revalidated at least once per year.

4. Verify purchased phone data for quality

Purchased, compiled or crowdsourced data can be unreliable. Some providers “oversupply” records (e.g. 1100 records for the price of 1000) to compensate for poor quality, but that doesn’t help when 30% of numbers turn out to be dead.

Before using purchased data:

• Run HLR checks on the list to confirm which numbers are real and active

• Flag and remove inactive, dead, invalid or suspicious numbers (e.g. premium rate, pagers, fake/stage numbers)

• Challenge suppliers on quality and insist on validated data where possible

Remember, the cost of validating up front is usually far lower than the cost of calling or messaging thousands of dead numbers.

5. Do a one-off clean

If you’ve never cleaned your phone number database properly, start with a one-off deep clean to establish a baseline.

Here's our simple approach:

1. Export your current dataset from your CRM, including a unique reference for each record.

2. Upload the telephone numbers (plus those references) for bulk validation.

3. Append the validation results (active/inactive/invalid/type) back into your CRM.

From there, you can quickly identify customers with inactive numbers. You can then reach out via alternative channels (email, post, app) to request updates, and give sales, marketing and customer service teams confidence they’re working from a known-good dataset.

Once the deep clean is done, regular incremental checks become much easier.

6. Scrub your data regularly

Data scrubbing is the process of removing or correcting data that is:

• Incorrect

• Incomplete

• Improperly formatted

• Duplicated

Industries like banking, insurance, retail, telecoms and transport rely heavily on scrubbing tools to maintain quality at scale.

To get the best results, we recommend you standardise your phone number format before scrubbing, and use tools that can identify common error patterns and duplicates. It helps to set up a regular schedule (monthly/quarterly) instead of one-off, reactive clean-ups.

Well-scrubbed data reduces errors, speeds up reporting and improves campaign performance.

7. Manage blacklisted and “Do Not Call” numbers

Calling or texting people who’ve opted out can be costly – both in terms of budget and brand reputation.

This is when we recommend two key steps:

1. Screen against national “Do Not Call” lists, where they exist (e.g. the Telephone Preference Service in the UK, if you don’t have an existing relationship with the customer). You can check against TPS Unlimited quickly and easily.

2. Maintain your own internal suppression / DNC list for people who have explicitly told you not to contact them.

Regularly cleaning your phone data against these lists will help you to avoid wasted spend, reduce complaints and stay aligned with local regulations and best practices.

8. Control data access and permissions

A good phone number database is a valuable asset. Too many hands in the data will quickly lead to inconsistent formatting, accidental overwrites, and crucially, security and privacy risks

Take a look at some of our best practice tips to manage data access and permissions:

• Limiting who can edit and export core datasets

• Providing read-only interfaces where possible for users who only need to view data

• Appointing a data owner or data steward who is responsible for oversight and standards

Under GDPR (and similar regulations), protecting access to personal data isn’t just good practice, it’s essential. 

9. Understand number types: landline, mobile, premium, pager

Not all telephone numbers behave the same way. Knowing what type of number you’re dealing with is key:

• Landline – usually voice only

• Mobile – suitable for SMS, voice and certain app-based journeys

• Premium rate – may incur high charges

• Pager / special services / fake (stage & screen) numbers – may indicate risk or be unusable

When you run a number through HLR Lookup, we detect the type so you can decide the right channel and tone of contact, avoid accidentally calling premium-rate or fake numbers and tailor workflows depending on the destination type.

Storing this information alongside the number in your database gives better control and insight.

10. Keep GDPR and privacy in front of mind

GDPR (and other regional privacy regulations) places strict requirements on how you store, use and protect personal data – including phone numbers.

Cleaning your phone number database helps you to remove outdated, inaccurate or unnecessary records. You'll also be able to demonstrate that you’re taking data quality and minimisation seriously, and reduce the risk of contacting the wrong person or misusing their details.

While it requires effort, revamping your data cleansing and management processes almost always leads to better campaign performance, higher trust and stronger long-term growth.

Clean data, better results

To manage and clean your phone number database effectively:

1. Capture accurately – validate numbers at the point of entry.

2. Standardise – use consistent formats (ideally e164).

3. Validate and revalidate – check that numbers are real, active and correctly typed.

4. Scrub and deduplicate – remove duplicates, dead and risky numbers regularly.

Automated tools like HLR Lookup make it easier to keep data complete, up to date and compliant, whether you’re cleaning existing databases in bulk or validating in real time via API.

Clean, accurate phone numbers will increase the performance of your marketing and operational messaging, as well as reduce wasted spend on invalid or outdated numbers and ultimately, free your teams to focus on real customers, not bad data

The bottom line? In following these steps, your data is clean, and every message has a better chance of reaching the right person.