Buying a prospect file or generating one yourself doesn't automatically guarantee satisfactory commercial results. The difference between successful prospecting campaign and crushing failure often comes down to a single factor: the quality of data used.

Many companies discover too late that their prospect file contains obsolete, incorrect or unsuitable information for their target. Result: wasted hours, squandered budgets and sales teams demotivated by disappointing conversion rates.

This situation isn't inevitable. Proven methods exist to evaluate prospect file quality before using it. These preliminary checks enable considerably optimising your prospecting campaigns and avoiding disappointments.

A quality prospect file is recognisable by several distinctive signs. These indicators, when correctly analysed, enable predicting with good reliability the success of your commercial actions. They constitute safeguards against bad surprises and unproductive investments.

This article's objective is to give you keys to precisely evaluate your prospect file quality. We'll detail the seven most important criteria to systematically verify, whether it's a file purchased from a supplier or generated internally from sources like Google Maps.

1. Data Freshness: Collection Date

Data freshness constitutes the first quality indicator of a prospect file. Business information evolves rapidly: address changes, contact detail modifications, management team evolution, business cessation. A file over six months old begins to significantly lose value.

Why is Freshness So Important?

Statistics are clear: approximately 20% of business data changes annually. This means a year-old file potentially contains 20% obsolete information. This loss accelerates in certain sectors, notably digital, start-ups or consumer services.

A manager who changed position, a company that relocated, a modified telephone number: all elements that render your prospecting efforts ineffective. Worse, contacting obsolete information can harm your brand image by giving an impression of negligence.

How to Verify Freshness?

Ask for collection date: any serious supplier must be able to tell you precisely when data was collected. Beware of evasive responses or undated files.

Test a sample: before using the entire file, contact twenty companies to verify information is correct. This preliminary test gives reliable estimation of obsolescence rate.

Favour updated sources: Google Maps, for example, is permanently updated by companies themselves. Files generated from this source are generally fresher than those from static directories.

What Freshness to Require?

For optimal prospecting, favour data less than three months old. Beyond six months, consider the file needs thorough verification. Past one year, obsolescence risks become too important to justify investment.

2. Geographical and Sectoral Targeting Precision

A quality prospect file must correspond exactly to your commercial target. This evidence hides numerous traps: poorly defined geographical areas, approximate business sectors, mix of different company types.

Importance of Geographical Targeting

Your catchment area largely determines your commercial possibilities. A poorly targeted geographical file will waste time on unreachable or unprofitable prospects.

Verify geographical precision: should the file cover a city, county, region? Ensure boundaries correspond exactly to your needs. Beware files that overflow into areas you don't cover.

Control density: in certain rural areas, a file may appear complete with only a few dozen companies. In metropolises, several thousand companies may match your criteria. Density must be consistent with your target area's economic reality.

Sectoral Targeting: More Subtle Than It Appears

Business sectors are often poorly defined in prospect files. The same company can be classified differently according to sources (SIC codes, Google Maps categories, proprietary classifications).

Test sectoral consistency: browse a file sample to verify companies correspond well to your target. A consulting company classified in "IT services" or a restaurant categorised "retail" reveals classification problems.

Beware overly broad sectors: a file targeted at "business services" risks mixing very different activities. Prefer more precise targeting, even if it means crossing several sectoral files.

Targeting Warning Signs

• Suspicious round numbers: a file of exactly 5,000 or 10,000 prospects may indicate artificial completion with off-target data
• Inconsistent geographical distribution: beware files where certain areas are over-represented without economic justification
• Incompatible sector mix: industrial companies mixed with local businesses often reveal composite file of doubtful quality

3. Essential Information Completeness

An incomplete prospect file seriously handicaps your campaigns. Each missing piece of information reduces your prospecting options and complicates your sales teams' work.

Indispensable Information

Company name: this seems obvious, but some files contain incomplete company names, incomprehensible abbreviations or duplicates under different appellations.

Complete address: street, postcode, city. An incomplete address complicates localisation and may reveal approximate data. Beware addresses limited to city without street precision.

Telephone: company main number, preferably landline. Beware files with too many mobile numbers, often less reliable for contacting companies.

Contact email: generic address (contact@, info@) or nominative. Total absence of emails in a modern file should alert you.

Business sector: clear and precise activity description. Too vague sectors ("services", "commerce") are poorly exploitable.

Calculating Completeness Rate

To evaluate a file, calculate completeness percentage for each field:

• Telephone completeness rate: number of lines with telephone / total number of lines
• Email completeness rate: number of lines with email / total number of lines
• Address completeness rate: number of complete addresses / total number of lines

A quality file must display:

• 95% minimum for basic information (name, address, telephone)
• 70% minimum for emails (according to sector)
• 90% minimum for sectoral classification

Managing Missing Information

Rather than accepting incomplete file, negotiate with your supplier:

• Information supplement: ask if they can enrich file with missing data
• Price reduction: incomplete file should be charged less
• Completeness guarantee: require commitment on minimum completeness rates

4. Data Format and Structure

A prospect file's structure directly impacts your productivity. A poorly organised file wastes time, generates handling errors and complicates import into your commercial tools.

Formats to Favour

Excel (.xlsx): universally compatible format, easy to manipulate, allows sorting and filtering. It's the standard for prospect files.

CSV (.csv): simple and light format, ideal for import into most CRM software. Beware delimiter used (comma, semicolon) and special character encoding.

Avoid proprietary formats: .pdf, .doc files or specific formats that complicate data exploitation.

Column Structure

A well-structured file respects several principles:

One piece of information per column: avoid columns mixing several pieces of information (first and last name in same cell, complete address on one line).

Explicit column names: "Tel" rather than "T", "Email" rather than "Mail", "Sector" rather than "Act".

Logical order: group information by theme (company identification, coordinates, commercial information).

Standardised columns: use consistent formats for telephones (with or without spaces), postcodes, business sectors.

Detecting Structure Problems

Duplicates: verify same company doesn't appear multiple times with variants (Ltd/PLC, with/without accents).

Empty columns: entirely empty columns reveal export or structure problem.

Inconsistent formats: telephones with different formats, incomplete postcodes, non-standardised business sectors.

Parasite characters: spaces at beginning/end of cell, undesirable special characters, deficient encoding.

Tools to Clean a File

If you receive poorly structured file:

Advanced Excel: use search/replace functions, duplicate removal, conditional formatting to identify problems.

OpenRefine: free and powerful tool for cleaning and structuring data.

Custom scripts: for large volumes, a developer can create automatic cleaning scripts.

5. Absence of Duplicates and Erroneous Data

Duplicates and errors in a prospect file harm your campaign efficiency and can tarnish your professional image. A company contacted multiple times or manifestly false information reveals insufficient preparation work.

Duplicates' Impact on Your Campaigns

Prospect irritation: receiving same commercial message multiple times annoys recipients and may lead them to blacklist your company.

Resource waste: lost commercial time, multiplied routing costs, distorted statistics.

Degraded image: duplicates give impression of negligence and lack of professionalism.

Types of Duplicates to Detect

Exact duplicates: same company with exactly same information, often linked to export errors.

Approximate duplicates: same company with variants (Ltd/PLC, with/without accents, different abbreviations).

Address duplicates: multiple companies at same address that may actually be same entity under different denominations.

Contact duplicates: same telephone number or email associated with different companies.

Detection Methods

Sort by company name: classify alphabetically to visually identify similar companies.

Search by telephone: same number associated with multiple companies often indicates duplicate.

Address comparison: group by postcode and street to detect identical addresses.

Automated tools: Excel offers duplicate removal function, but it remains basic. Specialised tools like Duplicate Cleaner are more effective.

Common Erroneous Data

Invalid telephone numbers: too short, too long, with non-existent prefixes. A valid UK number counts 11 digits and starts with 01, 02, 03, 07, 08, or 09.

Inconsistent postcodes: codes that don't correspond to indicated city, foreign codes in UK file.

Manifestly false emails: addresses with fantastical domains, incorrect formats (@@ instead of @).

Aberrant business sectors: descriptions that don't correspond to company name or actual activity.

Cleaning Process

1. Backup: always keep copy of original file before cleaning
2. Automated detection: use tools to identify obvious duplicates
3. Manual verification: check doubtful cases identified by tools
4. Reasoned deletion: delete real duplicates keeping most complete version
5. Validation: test sample of cleaned file to verify consistency

6. Regulatory Compliance (GDPR, Opt-out)

A prospect file non-compliant with regulation exposes your company to important legal and financial risks. GDPR compliance and opposition list respect constitute major stakes to verify before any use.

Essential GDPR Verifications

Documented collection source: your supplier must be able to justify data provenance and their collection conditions. Beware files without traceability.

Identified legal basis: for B2B prospecting, legitimate interest is generally appropriate legal basis, but it must be documented and justified.

Person information: company managers present in file must have been informed of their data processing, even if this information can be provided during first contact.

Purpose limitation: a file collected for specific use cannot be used for other purposes without appropriate legal basis.

Opposition List Management

TPS (Telephone Preference Service): verify telephone numbers present in file have been confronted with telephone marketing opposition list.

Internal opposition lists: if you've already done prospecting, ensure people who opposed your solicitations don't appear in new file.

Opt-out mechanisms: file must enable easily managing future unsubscription requests.

Regulatory Warning Signs

Abnormally low prices: files sold at derisory rates may come from doubtful sources or non-compliant collections.

Documentation refusal: supplier who cannot document sources and collection methods presents high risk.

Unjustified sensitive data: presence of personal information not necessary for prospecting (age, family situation, income).

Suspicious international origin: files from countries with less strict regulations without European compliance guarantee.

Questions to Ask Your Supplier

1. What is exact source of this data?
2. On what legal basis was collection performed?
3. Have people been informed of processing?
4. Has file been confronted with opposition lists?
5. What guarantees do you offer in case of complaint or audit?

File Compliance Implementation

If you identify compliance problems:

Complete audit: precisely evaluate risks and non-compliances.

Compliance implementation: confrontation with opposition lists, legal basis verification, processing documentation.

Internal procedures: implementation of processes to manage people's rights (access, rectification, opposition).

Team training: awareness of your sales teams to GDPR issues and best practices.

7. Email and Telephone Deliverability Rate

A file's technical quality is also measured by its capacity to enable effective contact with prospects. Bouncing emails or unreachable telephone numbers considerably reduce your campaign efficiency.

Evaluating Email Deliverability

Validation test: use tools like MailTester or ZeroBounce to verify email address validity on representative sample.

Types of errors to detect:

• Non-existent addresses (hard bounce)
• Full mailboxes (temporary soft bounce)
• Unreachable mail servers
• Addresses with typos

Acceptable rate: good file must present less than 5% invalid emails. Beyond 10%, quality becomes problematic.

Beware generic emails: contact@, info@, sales@ are valid but often poorly consulted. Good file mixes nominative and generic emails.

Verifying Telephone Reachability

Number format: verify numbers respect national format (11 digits in UK) and use correct regional prefixes.

Sample test: call fifteen randomly selected numbers to evaluate valid number rate.

Warning signals:

• Too many numbers ringing into void
• Frequent error messages ("number not allocated")
• Personal answering machines instead of company switchboards

Technical Quality Indicators

Email bounce rate: percentage of emails that cannot be delivered

• Excellent: less than 2%
• Good: 2% to 5%
• Average: 5% to 10%
• Poor: more than 10%

Telephone reachability rate: percentage of numbers enabling company contact

• Excellent: more than 90%
• Good: 80% to 90%
• Average: 70% to 80%
• Poor: less than 70%

Improving Deliverability

Preliminary cleaning: remove manifestly invalid emails and telephones before use.

Automated validation: use validation services to massively clean your files.

Continuous updating: maintain your files updated by removing contacts that bounce.

Segmentation: isolate high deliverability contacts for priority campaigns.

Recommended Verification Tools

For emails:

• CleanMyList.email (professional verification tool with excellent accuracy rates)
• MailTester (free for small volumes)
• ZeroBounce (professional paid service)
• Hunter.io (verification + email search)

For telephones:

• Yell.com (manual verification)
• Professional validation services
• Direct sampling tests

Practical Quality Control Methods

Evaluating prospect file quality cannot be improvised. A rigorous method enables quickly identifying weak points and avoiding bad surprises. Here's how to proceed concretely.

Rapid Statistical Analysis

First impression: open file and browse quickly. Quality file immediately gives impression of consistency and completeness.

Basic counts:

• Total number of lines
• Number of columns and their relevance
• Fill rate per column
• Geographical distribution of companies

Anomaly detection: use Excel sorting functions to identify aberrant values (foreign postcodes, too short telephones, suspect emails).

Sample Testing

Random selection: take 50 to 100 contacts distributed across entire file, not just beginning.

Cross-verification: for each sample contact, verify information on Google Maps, Yell.com or company website.

Error rate calculation:

• Percentage of non-existent or closed companies
• Percentage of obsolete information (relocation, management change)
• Percentage of sectoral classification errors

Quality Control Checklist

Format and structure: □ File in exploitable format (Excel, CSV) □ Clearly identified columns □ No entirely empty columns □ Correct special character encoding

Data completeness: □ Less than 5% missing company names □ Less than 10% incomplete addresses □ At least 70% contacts have telephone □ At least 50% contacts have email

Information consistency: □ Postcodes consistent with cities □ Telephone numbers in correct format □ Emails with valid formats □ Relevant business sectors

Targeting: □ Geographical area compliant with request □ Homogeneous and relevant business sectors □ Company size consistent with target

Automated Analysis Tools

Advanced Excel: use pivot tables to analyse distribution by sector, geographical area, field completeness.

Google Sheets: offers analysis functions similar to Excel with collaborative work advantage.

OpenRefine: free tool specialised in data analysis and cleaning. Particularly useful for detecting duplicates and inconsistencies.

Custom scripts: for large volumes, Python or R script can automate quality analysis.

Control Documentation

Keep trace of your verifications:

Control report: document your tests, detected anomalies, calculated quality rates.

Preserved samples: keep tested contacts with verification results to contest possible problems.

Supplier correspondence: preserve exchanges with your supplier about file quality.

This documentation will be useful for:

• Negotiating with your supplier
• Improving your next purchases
• Justifying your internal choices
• Building quality history

Negotiating with File Suppliers

Prospect file quality is negotiated from purchase. Serious supplier will accept your quality requirements and offer guarantees. This negotiation largely determines your future campaign success.

Criteria to Require Contractually

Freshness guarantees: maximum 3 months for contact data, maximum 6 months for company data.

Minimum completeness rates: 95% for basic information, 70% for emails, according to your sector.

Deliverability commitment: less than 5% email bounces, less than 10% unallocated numbers.

Regulatory compliance: confrontation with opposition lists, source documentation, GDPR respect.

Control Methods to Negotiate

Evaluation period: ask for 48h to 72h to test file before final validation or ask to test micro-file for few pounds.

Free sample: require representative sample (100 to 200 contacts) for preliminary evaluation.

Penalties and Compensations

Proportional discount: price reduction proportional to established error rate.

Free replacement: free provision of new contacts to compensate deficient ones.

Essential Questions to Ask

1. What is average age of your data?
2. Can you guarantee maximum error rate?
3. Are your files confronted with opposition lists?
4. Do you offer evaluation period?
5. What recourse for non-compliant file?
6. Can you provide client references?

Supplier Warning Signs

Guarantee refusal: supplier refusing any quality commitment often hides problems.

Abnormally low rates: derisory prices generally hide mediocre quality.

Excessive commercial pressure: beware salespeople pushing immediate signature.

Absence of references: serious supplier can cite satisfied clients.

Evasive information about sources: inability to explain data provenance is suspicious.

Conclusion

Prospect file quality directly determines your commercial campaign success. The seven criteria we've detailed constitute safeguards against disappointments and unproductive investments.

Data freshness remains most important factor: information less than three months old guarantees optimal efficiency of your prospecting actions.

Targeting precision geographical and sectoral avoids useless contacts and improves your conversion rates by concentrating on your real prospects.

Information completeness conditions your action possibilities: incomplete file limits your contact options and reduces your sales teams' efficiency.

These preliminary verifications represent time investment largely paid back by commercial performance improvement. One hour spent analysing file can save you weeks of ineffective prospecting.

Companies mastering these quality criteria maintain significant competitive advantage. They prospect better, faster and with better results than those neglecting these fundamental aspects.

Don't hesitate to be demanding with your suppliers and systematically test your files. This rigour in data selection will enable you to sustainably develop your commercial activity by maximising return on investment of your prospecting actions.