Improving Call Center Performance with Better Customer Data
By Greg Brown, Vice President, Marketing, Melissa
Call centers can generate more data than any other function in an enterprise – adding value and creating challenges at the same time. As new information flows in, it feeds master data systems that provide the engine for all types of business operations, call center modeling, and analysis. Yet as much as 25 percent of an average company’s data is likely inaccurate, according to insight from industry leaders such as Gartner Group. Bad data comes at a price, and can result in increased call handling times, undeliverable shipments, low customer retention, and inefficient CRM initiatives. It gets worse too – the longer poor contact data remains in the system, the more difficult and costly it is to correct. Budget just $1 to verify the accuracy of a record at point-of-entry, versus $10 to clean the data in batch form, and an even more costly $100 per record if no action is taken for an extended period of time.
With the right tools and consistent focus on data, call centers can reduce costs and frustration in operations ranging from handling customer service, inbound inquiries, or outbound sales. Four key steps will improve call center operations dramatically – improving lead generation, increasing customer satisfaction, and realizing a greater return on investment for CRM efforts.
Capturing accurate data at the point-of-entry is the ideal, reducing costs and ensuring the most accurate information enters the system from the start. This initial line of defense creates a “data quality ﬁrewall,” capitalizing on tools that both auto-complete and verify data in real time. By immediately verifying the accuracy of information as it comes into the call center, data managers gain a tangible advantage in verifying, standardizing (for faster processing), and consolidating data into its cleanest form possible. With auto-completion/suggestion functionality, the same solution can also reduce keystrokes and simplify data entry. When users type, only valid address suggestions populate data fields, ensuring only accurate, correctly formatted info enters the system.
Add Missing Data to Get a Better View of the Customer
As CRM-focused environments, most call centers employ validations that check certain mandatory data fields; even so, it may be a challenge to ensure a value for every field at the time a record is generated. For example, a contact source such as a list of tradeshow attendees may include only contact names, emails and addresses. Without a verified phone number, this contact source has limited call center value and cannot easily be used as part of a telemarketing or omnichannel campaign.
Lead generation – and resulting revenue – is optimized with a holistic view of the customer. The data append process adds invaluable missing information to such records and should be scheduled routinely. Every record can become as complete as possible, with the addition of verified email and street addresses, phone numbers, names, and selected demographic and lifestyle attributes that can help round out a customer profile in meaningful ways. Smart data enhancements will help call center operators identify purchasing trends, and capitalize on this information to strengthen and improve engagements and develop more targeted services.
No More Duplicate Records
Duplicate records in an average call center may be as high as 10 percent of its total contact database. It’s critical that these records are identified, merged, and purged – however without the right tools, this may prove difficult. “Beth Smith” may be noted as “Smith, Elizabeth” in another resource database, even though they are one and the same person. Considering options for software or technology-based de-duplication services may be a wise investment for call centers. Duplicates can be weeded out, and the best data in each record is then merged into a single “Golden Record.” Call centers benefit from better insight into customers, as well as reduced call handling. Working in real-time adds further value, as it is typically faster and more cost-effective to match extraneous records using incremental de-duping operations; this occurs as data is first entered into the call center system.
Establish a Data Quality Routine
Consistency is important, cleaning data routinely to correct issues that arise from never-ending customer changes or just plain human error. Is the customer address current? Phone number still callable? Annually, nearly 11 percent of individuals, families, and businesses relocate in the U.S. alone. Unless managed in an ongoing fashion, data’s constant pace of change will create a raft of costly business problems based on bad information. A proactive approach is ideal, ensuring that call centers continuously update customer records with validated change-of-address information. These efforts are optimized through a data quality service provider with global capabilities and operations based on multisourced change-of-address records. To maximize value, ‘move updates’ should be applied to customer data quarterly and at minimum twice each year. This may vary depending on the role the call center plays in an enterprise; for instance, if mailing services are driven by call center data, move updates must be applied within 95 days prior to each mailing to qualify for USPS® postal discounts.
Customer Data is a Long-Term Asset
Bad data is expensive – wasting immediate resources such as mail, printing, and calling efforts, as well as losing opportunities to provide great long-term customer service. These issues can be reversed with a data quality solution that verifies, cleanses, and guarantees valid customer contact information from point-of-entry all the way through updating. By catching data entry errors immediately, bad information never even enters the system. Standardized data can also be processed faster, quickly adding enhancements that improve customer handling. Focusing on data quality keeps call centers operating at peak performance – with stronger lead generation, high customer satisfaction, and better overall value from CRM investments.
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About the Author
Greg Brown, Vice President, Melissa
Greg Brown is vice president of Melissa, provider of global contact data quality and identity verification solutions that unlock accurate data for the most compelling customer view. Contact him at firstname.lastname@example.org.
IMAGE: Data quality firewall
CAPTION: Call centers must avoid “garbage in, garbage out” in terms of customer data. By verifying and correcting contact data at the point-of-entry, operators create a data quality firewall as a first line of defense in saving time and money. For example, fraud is prevented with processes that ensure address data is both correct and matches the customer name before credit cards are processed. Data assets are protected for the long term, improving response rates, enhancing analytics, and improving customer satisfaction.
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