CRM data management is critical for engaging with prospects and customers throughout the customer lifecycle.
Maybe you’ve tried to inject personalization into your marketing automation campaigns, only to find that your CRM software was filled with data errors and standardization issues that reflect poorly on your company.
Or maybe you have sales team associates that have missed critical context when interacting with prospects due to the existence of duplicate contact records in your CRM software.
There are many ways in which low customer data quality and inconsistent customer relationship management system data can harm your marketing, sales, and revenue operations:
Source: eConsultancy
According to IBM, bad data costs US businesses 3.1 trillion dollars a year. Another study found that companies lose 12% of their potential revenue, on average, due to bad data.
In this article, we’ll cover some simple tips that any company can take to improve their data management and avoid the problems listed above.
You can’t know what you need to fix or where your data collection problems lie until you audit your data. According to SiriusDecisions, 10-25% of the average B2B company’s customer relationship management system contacts have critical errors at any given time.
This means a full examination of the data that you collect to identify common errors, find opportunities to improve standardization, and find out why low-quality data is hitting your CRM in the first place.
Some of the different types of customer relationship management data errors that you should be looking for in this audit include:
Without identifying these types of issues, it is impossible to create processes and plans for managing your CRM data and seeing data quality improvements across the board.
The best way to improve the quality of your customer relationship management data is to stop low-quality data from hitting it in the first place.
There are a few main places where low-quality CRM data comes from.
First, is forms and manual human data entry. Human data entry has been shown to have an average of 1% error rate — meaning one error in every hundred keystrokes. Across all of your CRM data, that would lead to many errors.
Additionally, errors for critical fields like email — like inputting “jane@gmal.com” instead of “jane@gmail.com” — are common CRM system data errors that can have a profound negative effect across your database. Those emails will bounce or be picked up by verification checks. Most companies will remove the records from their database rather than make small adjustment to
Those forms can be customer-facing or employee-facing. While you might see slightly lower error rates from data entered by your internal teams, the error rate of any manually entered data will still be significant.
One way to shrink that error rate is to add proper validation to your forms. Validation ensures that data entered into these forms meet certain standards — that an email follows the correct format, a zip code is the correct length, or that names use appropriate capitalization.
Additionally, providing picklists and pre-set options can help to limit error rates. For instance, providing a dropdown picklist when a customer enters their “State” can help cut back on standardization issues from customers entering terms like “Wa,” “Washington,” or “Washington State” that will ultimately need to be cleaned up at a later date.
We covered the different types of validation extensively in our Guide to Data Cleaning.
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When you are just getting started with CRM system data management and audit your data, many are shocked to find just how error-filled their CRM database actually is.
In fact, it can be so overwhelming that some companies end up throwing it on the backburner, expecting that fixing all of their CRM data would be a huge time sink for their team — and they would be right, too. Trying to fix a CRM database full of errors, inconsistencies, and general data issues can be time-consuming and aggravating. That’s especially true if you are using complicated Excel formulas and VLOOKUP to handle your CRM system data cleanup by hand.
But you can make the task seem a whole lot less daunting by breaking it down into chunks.
One great place to start is by identifying the fields that mean the most to your marketing and sales operations. These are the fields that you’ll use most in automated personalization. They often include fields like names, phone numbers, addresses, and emails. Those present a good baseline.
Other fields may affect critical systems within your business such as lead scoring. For instance, ensuring that your B2B prospects have a standardized and correct job title will play a critical role in their overall lead score and prioritization among your marketing and sales teams. These present another strong consideration for fields that you need to prioritize as you get started with CRM software data management.
Focus on cleansing and standardizing one field at a time. Then, make sure that you have proper validation and data collection processes in place to ensure that any new data that enters your system is cleansed and standardized as well.
By breaking the task of data cleansing down into small chunks and handling one field at a time, you can keep your team from becoming overwhelmed while pushing your data quality upward.
Most companies handle their data cleansing and standardization in Microsoft Excel. This can be complicated, error-prone, and requires advanced Excel knowledge. When you edit data in Excel, there is no way to automate, either. The same processes have to be completely repeatedly on a regular basis.
While using Excel is typically fine for companies with smaller CRM databases, problems can arise when you are working with large sets of data. Excel can be prone to crashing as the number of records and fields increases. It can also be difficult to find all of the errors that exist within a dataset using Excel functions and VLOOKUP alone. Many of your data problems will go undetected.
Even when you have a well defined process in Excel, there will always be problems and mistakes that arise when you need to export the data and import it back constantly. Companies will find that there are all sorts of unintended consequences with importing, and those issues may differ across the different systems and platforms that they use, leading to a whole host of new data tasks that must be completed before your data changes are usable.
Automate where you can. Processes like creating new contacts, companies, or deals can reliably be automated with the right data validation processes in place. Other tasks like standardizing specific fields, merging duplicates, or bulk updating existing datasets can also be automated with careful planning, saving your company a substantial amount of time on CRM data management.
With your data processes and plan in place, you’ll need to educate your team on how implementing these processes will change things for them moving forward.
Remember, when you haven’t had a full CRM system data management plan in place, your team has likely developed their own ways of rectifying the dozens of different issues that they are likely to come across when working with the data. These could be good habits or bad habits but are often workarounds for a more standardized way of doing things. Either way, you’ll need to educate your team on the importance of proper CRM system data management and make your new processes easy to understand and install.
Start by evaluating the technical experience of your team. Anyone that will be expected to work with the data will need training or retraining as you begin to move up through the phases of data management.
Some simple steps that you can take to help train your teams and ensure that your new CRM system data management plan is effectively launched include:
A data management plan requires buy-in especially from the rank-and-file in order to be successful. As the ones entering data most regularly, any effort to clean up CRM data depends on them.
Having clearly defined and detailed CRM system data management processes is important for improving your data quality. A report from Harvard Business Review found that one of the top complaints of employees was a lack of clear direction — particularly with administrative management tasks.
Standardized processes give employees easy step-by-step directions to follow that are not up to interpretation. It takes the guesswork out of fixing specific issues and helps to make those processes seem more approachable to those that worry about dealing with data on larger scales.
Document these processes well. Provide defined workflows, screenshots, and examples that help to illustrate not only how the processes work, but why they are important. Make these documents available to your teams in both physical and digital versions to cater to all employees.
Your internal team training sessions can help you to identify what information you need to include in your process documentation and what data management misconceptions might exist among your teams.
Insycle is a complete solution for CRM data management and cleansing. By signing up for Insycle and connecting your HubSpot, Salesforce, Intercom, or other popular CRM system, you can:
If you want to make the management of your CRM system data a priority within your organization and free yourself from the tedious Excel tasks associated with managing that data — fill out the form below to start your free 7-day trial.