Four Steps to Improve Data Quality in the Call Center

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Four Steps to Improve Data Quality in the Call Center

By Special Guest
Greg Brown, vice president of marketing at Melissa Data
  |  May 23, 2016

New information flows constantly into the call center, feeding master data systems that run all sorts of business operations, call center modeling and analysis. The call center itself may even generate more data than any other corporate function, creating valuable information assets for the company. Yet according to industry analyst Gartner (News - Alert), the surprising truth is that at least 25 percent of an average company’s data is likely inaccurate. This high level of bad data comes at a significant cost, resulting in increased call handling times, undeliverable shipments, low customer retention, and inefficient CRM initiatives. And the longer poor contact data remains in the system, the more difficult – and expensive – it is to correct.

It is estimated that firms dish out approximately $1 to verify the accuracy of a record at point of entry vs. $10 to clean it in batch form, and an even more costly $100 per record if nothing is done for an extended period of time.

While many call center managers express frustration at the difficulty in maintaining accurate data for use in day-to-day operations, there are four essential steps that will improve call center performance – whether it’s handling customer service, inbound or outbound sales.

Check Data as it Enters the System

Costs can be reduced by capturing the most accurate data at the point of entry, using tools that both auto-complete and verify data in real time. This initial line of defense creates a data quality ?rewall, immediately verifying the accuracy of information as it comes into the call center. This type of real-time data verification can go a long way to verify, standardize (for faster processing), and consolidate data into its cleanest form possible. The same solution can also reduce keystrokes, simplifying data entry with auto-completion/suggestion functionality. For instance, as users type, they are shown only valid address suggestions, ensuring only accurate, correctly formatted information enters the system.

Fill in the Gaps by Adding Missing Data

Gaps in data can adversely affect lead generation and ultimately, revenue potential – preventing a holistic view of your customer. Even though most CRM environments such as call centers have validations to check for mandatory data fields, it’s not a simple task to ensure a value for every field at the time a record is generated. For example, if your contact source is a tradeshow list of attendees with only contact names, emails, and addresses, it has limited call center value without a phone number that can be used as part of a telemarketing or omnichannel campaign.

Scheduling a periodic data append can add invaluable missing information to records. The goal is to make every record as complete as possible, adding data that might include verified street addresses, email addresses, phone numbers, names, and other key demographics and lifestyle attributes. With the right data enhancements, operators can more easily identify purchasing trends, using the information to improve engagement and guide more targeted services.

Eliminate Duplicate Records

An estimated 10 percent of contact records in an average call center database are duplicates. Identifying these and merging/purging them are critical operations to improve customer data – but again they are not easy processes. For example, Beth Smith can be recorded as Smith, Elizabeth in another database, even though the two entities are actually the same person. It’s worthwhile to take a look at options for software or technology-based deduplication services to handle the challenge. Ideally, duplicates are weeded out and merged into a single golden record using the best data in one record for each customer. This not only enables better insight into your customers, but also reduces call handling. It’s also important to note that deduplication is faster and more affordable when extraneous records are matched using incremental de-duping operations, handled in real time as data is entered.

Keep Records Up to Date

Most importantly, data must be cleaned routinely, fixing issues that arise from constant customer changes or plain old human error. Are addresses current? Are phone numbers still callable? Every year, around 11 percent of individuals, families, and businesses relocate. Left unchecked, this ever-changing nature of data triggers a cascade of costly business problems based on bad information. To combat this, call centers must continuously update customer records with validated change of address information. Look for a data quality service provider with global capabilities here, including access to multisourced change of address records. Ideally, move updates should be applied to customer data quarterly and at minimum twice annually. However 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 (News - Alert) postal discounts.

Maintaining Data as an Asset

Bad data is expensive but costs much more without a data quality solution in place to verify, cleanse, and guarantee valid customer contact information from point of entry all the way through updating. Catching data entry errors immediately prevents bad information from permeating the system. Standardized data can be processed more quickly, and further enhanced for improved customer handling. With the right tools and consistent focus on data, call centers can reduce costs and frustration – improving lead generation, increasing customer satisfaction, and realizing a greater return on investment for CRM efforts.




Edited by Maurice Nagle
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