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Financial Services CRM Success: How to Clean Your Data

Financial CRM Data Management Concept: Businessman Managing People Icons

Expected to reach $1.5 trillion by 2022, the global financial services industry is forecasted to grow at a compound annual growth rate of 5.9%. While the industry is growing, so is the competitive landscape. The growth in the financial services market is largely due to increasing demand for insurance and loans, which is a highly saturated marketplace.

If this is the industry you call home, you’re well aware of the over-saturation, resultantly sky-high customer expectations, and unique challenges financial firms face, including stringent industry regulations and legal restrictions. Attention to customer care has never been more important.

That’s likely why you have invested in or plan to purchase a customer relationship management (CRM) system. It’s a dynamic software tool that can give professionals and firms as a whole greater control of their client data and business development efforts. The first item on your CRM priority list should be, without a doubt, focusing on how to clean your CRM data to make sure it’s accurate, complete and detailed.

But if you’re not sure how to best clean your CRM data, don’t despair. First, you’re not alone: 66 percent of companies across all industries don’t have a coherent, centralized approach to data quality.

Read on to learn how to clean your CRM data, and ensure that it stays that way.

Step 1: Analyze Your Existing Contact Data

Before you can clean, you need to know what to clean. Some data in CRM is new but some data can be years old. That can further complicate data relevancy as data privacy laws have become more strict. You can download a guide that addresses best practices for data privacy and security by clicking here.

If you haven’t yet, it’s time to run an audit on any data quality issues (i.e., missing info, entry errors and duplicate records) and make some tough decisions. What types of data and how many years of history will you keep or cleanse?

To make this process easier, consider using a dashboard. A carefully designed data quality dashboard can help you spot duplicate records, review data completeness, conformity to defined rules, integrity, and accuracy. Your CRM administrator or analysts can help start this process. One you have insight into the state of your current data set, you have some options.

 

  1. You can task teams to manually scrub the data. This will be the most time consuming and likely resource heavy option.
  2. You can outsource your data clean up project to a 3rd party. This will also take a few weeks and will be a manual process.
  3. You can invest in data enrichment technology that leverages AI pattern mapping to track changes in your data (new emails, titles, job changes, etc), notifying both users and admins of data inconsistencies that maybe hurting accuracy.

Step 2: Standardize Your Data Requirements and Train Your Teams

It’s no surprise that human factors are among the leading causes of bad data in CRM. People make mistakes. But did you know most of your faulty data likely isn’t due to an accident, but a conscious choice?

It’s true. Nearly 90 percent of CRM users admit to entering incomplete contact info, and the majority also report they don’t log all their activities.

Once you import your list, it’s time to take steps to get your data back on track. Outside of investing in new tech that eliminates manual data entry, developing, introducing and training CRM users on defined data entry standards is one way you can lessen the human-factor data fallout.

Next up, it’s time to review your database, flag inaccuracies, incomplete records, or errors, and direct the necessary parties to update their records.

Often firms can fill in the blanks using a little deduction and placeholders. Here are a couple of my favorite quick-fix tricks:

Missing Email

For example, if you know John Doe at Widget Co.’s address is [email protected], the odds are very good his manager, Guy Everyman’s address will be [email protected].

Missing Phone Number

And, in a pinch, you can always use the main company number to complete the phone field until you’re able to fill in a direct line.

For more advanced segmentation of your database, requiring users to input company size, revenue, industry and job title roles will help marketing and business development teams to create a more personalized and effective campaign down the road.

Step 3: Get Rid of Duplicate Leads, Contacts, and Accounts

If your firm is in line with typical data statistics, somewhere between 5 to 10 percent (but sometimes as much as 30 percent!) of your database is just duplicate information.

This might not sound like a big problem, but redundant data can tank campaign effectiveness and negatively impact your firm’s growth potential. Prospects and customers today want personalized marketing experiences from trusted advisors.

If your marketing teams are relying on CRM data to drip value added communication to your network, the data better be fresh and accurate in CRM. You don’t want your client getting sent 3 copies of the same email because their in your system under three different records.

Removing duplicate contacts or accounts has a formal, tongue-twister of a name: data deduplication.

While your in-house IT team may be able to handle this process, due to its tedious nature, deduplication is often farmed out third parties. Many firms use vendors to consolidate their CRM data once or twice a year.

Or you could elect to skip both of those projects and instead use data enrichment software for on-going, automated data maintenance.

Step 4: Perform Data Validation

Data validation helps stop incorrect contact records before they start. Most current CRM systems have built-in validation tools. Some even perform on-the-fly data verification as users enter contact information, suggesting mail-ready postal addresses, spotting email address typos, and even validating phone numbers.

But unless your firm’s data receives ongoing attention, it won’t be long until your data is more useless than useful. After just 12 months, as much as 70 percent of all CRM data has errors and inaccuracies.

Data validation doesn’t have to be heavy lifting for your firm. Learn how Frazier Deeter mapped over 31,000 relationships across their key accounts, enriched their CRM data and grew their marketable database by 7,900 contacts, by downloading this free case study.

Implement a Fresh Approach to Data Quality Management

Ensuring clean data prior to implementing a financial services CRM system can be a thankless job, but it doesn’t have to be—if you have the right tools.

Advances in machine learning, automation and artificial intelligence (AI) can take data-clean-up dirty work off your hands, so you can focus on growing revenue and differentiating your firm in a crowded market.

Plus, with a platform like Introhive, you have access to an automatic data enrichment process and more authentic insights into the relationships that exist across your business.

To learn more about how to clean your CRM data with Introhive, and gain more time building the financial services relationships that grow your business, contact me or request a demo today.

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