What are the issues with your customer database?
If you don’t clean up your customer database, its quality will necessarily deteriorate. The customer database is a living matter, it has a life cycle and deteriorates over time. You cannot perform well with your marketing and CRM actions if you are working on a poor customer database: the quality of your campaigns, the actions of your salespeople, and your customer service depend on it. Cleaning up your customer database – let us specify: regularly – is, therefore, a major issue. The cleaning customer database is at the heart of any Data Quality Management strategy.
What is a poor-quality customer database? It is a base that contains the following elements:
A duplicate designates a customer or a contact who is found several times (at least two) in a customer/contact database. How to explain the existence of duplicates? There are several possible reasons:
- Syntactic – spelling differences, often related to human errors in data entry or a lack of standardization (FirstnameLast Vs LastNameFirstname Vs first name, etc.)
- The fact that the services/sources do not always use the same identifier. For customer service, it’s the account number, for marketing it’s email, etc. This makes it difficult to reconcile data (in the absence of a unique identifier acting as a reconciliation key).
- The fact that the same client can give different information from one collection source to another. For example, use an email A to fill out the form and give an email B to customer service. If email is the reconciliation key, duplicates are created.
In the presence of duplicates, it is a question of identifying the source contact (the reference data) and of merging the data (merging) taking care – the exercise is sometimes delicate – not to wrongly merge separate contacts.
This particularly concerns email. For example, the contact typed their email address incorrectly in the form or misspelled it to the customer service advisor: we are dealing with incorrect data. Name, first name, title, date of birth, city, telephone, etc. All data may be subject to data entry errors.
Data entry errors are the main reason for the existence of erroneous data. Another reason is linked to format incompatibility problems between the collection source and the database.
Databases often contain blank fields. Some databases look like Gruyère, a significant part (sometimes the majority) of the fields being empty.
Enter “bd. “And” boulevard “, you have to choose. Between “75” and “Paris”, you have to choose. Between Mr. and Mr., you have to choose. Given information (age, civility, postal address, etc.) must be presented in the same way for all contacts. In technical language: the data must be correctly formatted. Here again, it is the existence of a plurality of collection sources that is the main cause of non-standardized data.
As we said above, the customer database is a living matter. Some data which was true at time t becomes false at time t. People move, change their phone numbers, change jobs, etc. Most of the data is perishable, although there are a few exceptions: first name, title, etc.
How do you go about cleaning up your customer database?
Define an action plan
We do not go headlong into a customer database cleaning project. Before even getting started and carrying out the main steps of the action plan, you must identify the objectives of your database, its use cases. These objectives and use cases will serve as a point of reference when you decide to arbitrate. Agree on what you mean by the customer and choose a unique contact/customer identification key: email, customer account number, etc. You can use a key built on the combination of several fields, for example, email + name. Here are the different steps we recommend you take to clean up your customer database.
Step # 1 – Involve all the users of the customer database
Marketing, Commercial, Legal, Accounting, Customer service: involve all the people using or intended to use the customer database. This will allow :
- To make all the people who handle the customer database aware of the importance of maintaining the quality of the base.
- To listen to the expectations of those who use the database daily, to formulate target use cases.
- Identify the fields they need and, conversely, those which exist in the current database but which are of no use.
Step # 2 – Make a copy of the customer database
So that you can go back in case of a bad decision or any problem that arose during the cleanup, make a copy of the customer database. It’s a security measure.
Step # 3 – Rethink the fields of your customer database
You must remove the useless data fields, that have no use for your marketing team, your salespeople, customer service. There is absolutely no point in maintaining fields that will never be filled in by end-users.
Conversely, add the fields which are not currently used but which have real added value and will help you achieve your objectives or set up new use cases.
Step # 4 Delete inactive contacts
Customers or contacts who have been inactive for a long time should be considered as lost contacts and therefore removed from the database. You can try to relaunch them if you think they are recoverable contacts/customers, but regardless, a customer database should contain a minimum proportion of inactive customers.
It suffices to analyze the date of the last interaction (purchase, telephone contact, web visit, etc.) to identify the inactive.
Step # 5 – Deal with the erroneous data
Your customer database certainly contains incorrect data. We advise you to straighten data when possible, using Data Quality tools (we’ll show you a few in a moment). When erroneous data cannot be updated, delete it.
Step # 6 – Use the reconciliation key to identify and remove duplicates
Have you decided to use email as an identification key? Apply this key to your database to identify duplicates, i.e. fields that are filled in several times. Then define a prioritization rule to define, in the event of duplicates, which data to keep. For example, you may decide to prioritize data from source A over data from other sources when there is a data conflict. Once your prioritization rule is defined, merge the duplicate records.
Note that some CRMs offer duplicate identification systems and alert users when a duplicate is generated. All the tools that we are going to present to you later offer deduplication/deduplication modules.
Step # 7 Standardize the fields of the customer database
Postal code, civility, telephone, age: all fields must be standardized to ensure the uniformity of your database. This supposes setting up the right settings for your collection tools/sources (so that these tools/sources send data in the right form to the customer base) and writing the naming rules for users making manual entries.
Step # 8 Enrich your customer database
We recommend that you enrich your customer database by filling in empty fields as much as possible … so that your customer database no longer looks like Swiss cheese. How to proceed? There are many possibilities :
- Interview your customers by creating questionnaire campaigns.
- Import data from your other databases & tools.
- Use data enrichment tools/data providers.
Here are two bonus tips for cleaning up your customer base:
- If the cleanup is based on leads, delete crazy or at least non-serious email addresses, such as “ [email protected] ”, “ [email protected] ”, etc. These are fake leads (typically people who have created an address to download resources for free without having to give their real email address).
- Analyze the performance of your marketing campaigns (your emailing campaigns in particular) to identify erroneous email addresses as well as inactive individuals or no longer wishing to receive your communications (opt-out, spamming, etc.).
We are now going to present 4 tools/solutions/service providers to help you clean up your customer database.
What solutions/service providers to help me clean up my customer base?
We have selected 4 tools for you: Octolis, DQE Software, Winpure, and Amabis.
Octolis is a Lead Data Platform, specialized in the management of prospect databases: cleaning, enrichment, merging, scoring, segmentation. Octolis allows you to create a database of clean and enriched contacts from your existing contacts and new contacts from the Octolis database.
One of the great advantages of Octolis lies in its very wide functional coverage.
DQE Software is a software publisher specializing in Data Quality. The company, established in 2008, offers several tools to verify and standardize postal addresses, email addresses, phone numbers, gender, city, and more. DQE has also developed the ”DQE DEDUP” product to deduplicate and deduplicate your customer databases. All software is accessible in license mode or SaaS (cloud) mode. DQE is a player that is experiencing great growth in the Data Quality market.
Winpure is one, if not the most popular Data Cleaning tool on the market (worldwide). It automates a large part of the cleaning operations: deduplication/deduplication, correcting erroneous data, normalizing data, checking emails/telephone, etc.
Winpure can be used to clean up your CRM, Excel spreadsheets, or SLQ Server, Access, or Dbase databases. It also manages text files (.txt). Winpure also offers an API. This is a relatively affordable solution.
Launched in 1996, Amabis is a software provider and publisher specializing in Data Quality and CRM / Marketing database outsourcing. Postal standardization, deduplication, email/telephone validation: Amabis has several very good quality solutions to support you in cleaning up your customer base and, more broadly, in your Data Quality strategy. All Amabis tools are available in license mode or SaaS mode.
Amabis also offers operational support services. Note that Amabis offers a CRM (AmaCRM, targeting VSEs / SMEs) managing Data Quality natively.
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