In the digital world, data is invaluable. However not all data is good data; it is believed that as much as 35% of data submitted into online lead forms, such as names, email addresses and telephone numbers are inaccurate in some way, rendering it completely useless. What’s more, around 65% of leads from CPL campaigns turn out to be false because of inaccurate data, meaning that your actual CPL could be much higher than you think.
In order to counter such an issue, a form of quality control needs to be in place. If you have ever filled out an online form yourself and noticed that some fields are marked with an asterisk and the word “required”, or you are unable to submit your form due to mistakes in your phone number, your inputs are likely being subject to a data validation system.
Data Validation tools accomplish quality control by ensuring that data inputs are accurate, complete and suitable for purpose by checking them against much larger databases. Furthermore, data validation tools corroborate the inputs at the point of capture, rather than afterwards, eliminating the need for a data cleanse and ensuring that all data you receive is clean and correct before it is submitted. Without a data validation system in place, there is no way of knowing what data is correct and what isn’t, meaning that the true value of your lead generation activity is difficult to quantify. Invalid or flawed data can impact the entire sales process, from wasted sales time to a loss of leads as well as a potentially damaged reputation and all of the associated costs. Therefore, data validation is essential for maintaining the integrity of your database and for facilitating a valuable analytics process.
There are many different ways in which data can be validated, including (but not limited to):
- Required field validation – this prevents users from submitting a form until all of the fields marked as required have been completed.
- Email Validation – verifies that email addresses are real, active and will receive emails from you.
- Duplicates – ensures that you are not getting leads that already exist in your database.
- PAF (Postcode Address File) – checks postcodes against a national database containing all known ‘delivery points’ and postcodes in the United Kingdom.
- HLR Validation – ensures that mobile numbers are live and correctly formatted by checking with all international network providers.
- Landline Validation – ensures that landline numbers exist or are in use. This is used less and less these days and is only available in the UK.
- IP Validation – corroborates the IP address of online customers. A users IP address indicates the locality of the user, which is essential for location-based campaigns.
- Custom Filters and Lists – some data validation providers allow you to create your own filters such as where profanities have been used.
- Telephone Preference Service (TPS) – the official central opt-out register in the UK on which customers will record their preference to not receive sales or marketing (“cold”) calls. All organisations are obliged by law to not call TPS registered numbers unless they have consent to do so.
- Range validation – ensures that the data inputs match the expected range limitations such as character limits or a value between two specified numbers (eg.1-10).
- Pattern matching validation – checks that the data entered into a certain field matches or is compatible with the format required e.g. email addresses must be formatted as “xxx@[domainname].com/.co.uk” or they will be rejected.
- Numeric validation – checks that the data input is either numeric or alphabetical depending on the requirements of the individual field.
This list is by no means exhaustive and there are myriad ways in which data can be validated which itself indicates the countless ways in which data can become invalid and useless. When your sales strategy depends on the accurate data of hundreds or even thousands of customer lead forms, then data validation is an essential part of mitigating the associated risks and the costs to your business.