Keeping data clean and devoid of unnecessary details is required for businesses today. Businesses are accordingly investing in data cleansing services to keep their marketing lists clean and accurate.
The quality of marketing databases that businesses use for communication, not only affects customer insights, but also business performance and market reputation. In the UK, Europe and USA for instance, it is required by law, to maintain a clean and healthy customer database. Compliances like the Data Protection Act and GDPA (enforceable from May 2018) are necessitating the use of clean customer data.
However, data cleaning or cleaning is not that simple. There’s a way to carry it out, and that’s what we shall be looking at here.
7 steps for cleaning customer data
Appropriate planning is necessary for the success of any act – and data cleaning is no different. Like in a jumble sale you would have to plan when, where and how much to offer on sale, similarly for data cleaning, a plan-of-action needs to be defined.
For instance, your plan may include the manual and automation for methods of data cleansing. It may also involve the inclusion of a standardized validation rule for arrangement of data.
For eg: Name Order: (Title) (First Name) (Middle Name) (Last Name)
This may require the the assistance of IT, HR, Finance and other related teams to simplify the process. A coordination process should be in place.
2. Audit Data Field/Category
The next step involves auditing data and identifying the priority data fields/categories. In an organization, you may either choose to go for complete data cleansing or select specific fields based on marketing plans.
In either case, you will need to start by auditing your customer database, i.e. identifying through statistical and manual methods data that is missing, inaccurate, duplicate or need update. Some of the usual categories that businesses cleanse include – Company name, job title, contact details, personal name, mailing address, renewal dates, subscription types, income etc.
It must be kept in mind that small steps taken can go a long way in simplifying the data cleansing process. For instead, the validation of data during an inbound call, the update of contact details from a form fill etc. can help in saving time during the actual process.
3. Identify Methods of Data Cleansing
Once the structure of and the gaps in the database has been identified, you need to select the right method for cleaning it. Database cleansing at this point may involve buying external data from vendors as well. Of course, this does not come with the guarantee that the data is accurate, but it provides a reference point for comparing internal and external data. It is also recommended to invest in pre-screened data in this respect.
Purchasing external data is beneficial especially when businesses lack in-house bandwidth and resources to carry out the process. In times like these, seeking a data cleansing service provider is a good place to begin from.
4. Select a Process
The establishment of a consistent system/procedure of cleaning is needed to ensure that the end goal of the process is achieved, resulting in a single database with complete customer view.
The document should also enlist the rules of the process and its goals for every stakeholder to be aligned and working towards a single goal.
For instance, if the goal is to update customer databank to identify new channels of communication, then the process should not only include removal of inaccurate data but also identify additional sources for compilation of new customer data.
5. Cleanse Data
This is where data normalization comes into place, i.e. standardization of fields. A standard format must be maintained for consistency and comprehension. A combination of manual and automated tools for data validation is permitted. The use of tools can help with identifying errors/missing data, append data, fill in blank categories and even trigger an alert for manual updates.
It must also be remembered that, when using third party data sets, data validation of the source file should also be included.
6. Monitor & Feedback
Once the cleansing part is over, it is required to systematically monitor data and collect feedback whenever necessary. Since the process will most probably promote accessibility of data among different business units, a clear workflow should be established previously. In order to maintain accuracy and consistency, a central team should monitor data-access and update changes as per feedback.
It is also recommended to have a periodic review process in place so as to identify and fix any anomalies. This may involve keeping a check on data inaccuracies, customer response rate etc.
The final step is the review the existing process, identify scope for improvement and repeat.
In time, organization goals and business strategies might change, and data requirements might change. For instance, the need for social profiles in a customer database might not have been necessary a decade ago; but current market needs are different.
It must be remembered that having access to “clean” data is not a one-time process. It needs to be carried out periodically. With an estimated 22-25% business email addresses losing validity every year, businesses will certainly miss out on a lot without maintaining an up-to-date database.
If you were organizing a ‘jumble sale’ you would not want to come home with an unsold item and have it piled up at your place. Your database is no different. Old data is not needed, and you will have to take the right steps for cleaning it.