High data quality in CRM should be among banks' top priorities

High data quality in CRM should be among banks’ top priorities

The customer relationship management (CRM) software market demonstrates its steady annual growth, reflecting the increasing appetite of businesses for data.

For example, a typical bank has over 500 million data records per $1 billion in assets. Merging all contact and personal data in a bank CRM database, financial institutions look to create an in-depth view of their customers to make relevant offers of financial products and services. But over time this database not only grows in size but soon becomes clogged up by outdated and inaccurate customer profiles.

When neglected or left to fester, CRM data can turn into a ticking time bomb, threatening a bank’s reputation and revenue. Essentially, bad data causes undesirable interruptions in the normal flow of business activities, and no financial institution is immune to possible difficulties. Recently, Bank of America faced a service issue resulting from storing bad data in CRM. Because of a large number of incomplete and incorrect customer profiles, the bank had a failure in the authentication process and as a result had to assign 100 employees to solve the problem.

Data quality is a problem

Even the best CRM cannot prevent data on banking clients from decaying. Indeed, according to the Ringlead study, the annual decay rate of CRM data is about 36%. Still, this can’t be attributed to CRM alone; it’s in the nature of data itself.

Since customers grow older and change with time, so do their needs, interests, tastes, expectations and preferences. For example, to satisfy a 20-year-old student, a bank should try to cover demands related this stage of life (e.g. clothing, car, travel, entertainment, university studies, etc). But in five years, the same person will probably change priorities to furnishing an apartment, a family vacation and children. Thus, if a bank keeps track of its customer records and takes active actions to both fix and prevent problems, it can timely offer short-term loans, good car financing and an attractive mortgage, to name a few products.

Since the insights from a banking CRM system are based on historical behavioral data, the more outdated and shabby customer profiles get, the more inaccurate offers a bank makes. After all, sending promo messages that rely on data updated a decade ago makes a bank rather blear-eyed. That is why the majority of financial institutions say to have worked out a data management project for the next 12 months, and 74% of them believe that data quality impacts customer trust and perception (according to the 2017 global data management benchmark report).

Where bad data comes from

Generally, a silent killer of most CRM databases is the inability to keep accurate customer profiles. As ScienceSoft‘s CRM consulting practice shows, a CRM database may contain the following categories of bad data:

  • missing data – when fields that are supposed to contain data are empty, e.g. missing e-mails or phone numbers;
  • wrong or inaccurate information – false or incorrect facts about leads;
  • inappropriate data – when data appears in the wrong field, e.g. when e-mail is written in a field for a phone number;
  • non-conforming data – data that doesn’t follow a bank’s naming rules, e.g. using various types of data format;
  • duplicate data – two or more identical profiles in a CRM database;
  • poor data entry – misspelling, typos, etc.

How does it get in CRM?

The reasons are many.

First, any incorrect third-party data can automatically seep into a unified database after mergers and acquisitions, which are frequent in the banking and financial services industry.

Second, bad data is often a result of call centers’ and sales’ work, since they often fill in customer profiles in a hurry leaving out details or making errors.

Third, demographic, behavioral or contextual customer data often change but the changes are not always reflected in CRM.

What are the consequences?

If a bank does not succeed in achieving and maintaining a high data quality in the system, the potential and actual benefits of CRM will remain just theory. When left unchecked, poor data can lead to:

  • Inconsistent analysis and inaccurate planning

For example, analysing old data, a bank can make a mistake in determining the target customer segment, which can later mislead the staff and diminish the value of their efforts.

  • Lack of visibility

When employees lack fresh and high-quality data or certain facts in customer profiles, they cannot make informed business decisions and foresee possible bottlenecks.

  • Lack of focus

In the absence of up-to-date information about customers, marketing and sales representatives may have trouble in launching efficient promotional campaigns and prioritising the opportunities that otherwise would be most likely to succeed.

  • Time losses

DiscoverOrg found that sales and marketing employees lose about 550 hours (or 27% of selling time) and as much as $32,000 per sales representative as a result of relying on poor CRM customer records.

  • Lost revenue

According to Experian, an average company loses 12% of revenuedue to using bad data.

  • Reputation damage

A bank that relies on faulty CRM data can become infamous for sending irrelevant or annoying messages.

Make data scrubbing a priority

CRM in banking serves to help building long-term relationships with customers, thereby increasing cross-selling effectiveness through the entire customer lifecycle. It implies that a bank’s promotional offers should always stay relevant in customers’ eyes and keep up with clients’ ever-changing needs and interests.

Taking into account the long-term nature of financial partnerships, making prompt and relevant offers on the eve of significant customers’ lifecycle events may become a promising opportunity for a bank.

If not allowed to degrade, data in CRM profiles can turn into a quantifiable and valuable commodity for any bank.

@banking
techno