To describe the negative impact of bad data, data analysts may use the phrase ‘garbage in, garbage out.’ This is shorthand, to describe the effect of bad data on analytics outcomes. But it’s a straightforward concept – if bad data is used in the algorithm, insights generated are unreliable, and any actions taken on the basis of that data can negatively impact the customer experience.
However, there are some surprising facts about bad data: its prevalence, and the amount that bad data costs companies. Research from Deloitte uncovered an average 71% of consumer data was erroneous, while Gartner estimates that poor quality data cost businesses $15 million on average in 2017.
Data is measured on a number of quality points, the most common of which are existence (does the organization have access to data?) and validity (are data values acceptable?). But beyond this, the data needs to be consistent, relevant, and have both accuracy and integrity. Data that fails on any of these measures can be considered bad data, and impact the methods by which enterprises create customer profiles – making those profiles at best, skewed and at worst, completely invalid.
How bad data impacts customer experience
Customer data is gathered by enterprises, or purchased from third-party providers, is used to create customer profiles, which can then be used to drive advertising, marketing, growth initiatives – even store locations, and in customer initiatives, to improve customer service or the overall customer experience.
Bad data impacts customer experience with brand interactions, including:
INBOUND CUSTOMER SERVICE
Bad data used for customer service may result in frustrating experiences for a customer, who may be asked to provide information more than once, or correct a call center employee that is attempting to provide an individualized, responsive customer experience. If the employee references erroneous demographic, personal, or prior purchase information in the course of a customer service interaction, it is unlikely that the customer will be satisfied at the end.
A recent survey of marketers showed that 21% of their budget was wasted on insights generated from bad data. This affects the customer experience, as brand interactions that are deemed irrelevant, or just plain wrong, erode the consumer-brand relationship. Personalization, when accurate and timely, can boost sales, engagement, and customer retention. Inaccurate personalization can negate those results just as easily.
Today, 90% of customers expect seamless interactions across all channels – but omnichannel strategies require accurate, timely, and integrated cross-channel data. If the data used in omnichannel customer interactions is of poor quality, the quality of the interaction will be poor as well.
How to ensure data quality
To ensure that data used in analytics is of good quality – and therefore the customer insights drawn from it are good as well – a company should create a regular, predictable, and manageable data hygiene policy. This will involve dedicating resources, including employee time and equipment, to ensure that data is accurate prior to being used in analytics.
PURCHASE CLEAN DATA
Data purchased from a third-party provider is subject to the same constraints and potential for error as data gathered by an enterprise for its own use. To ensure that the data purchased from an outside party is high-quality, you should know the following:
- Data methodology: How was the data collected?
- Data source(s): Where did it come from?
- Data scale: How large is the raw data set?
- Data parameters: What are guidelines for data verification and accuracy?
- Data freshness: What is the lag time between occurrence and reporting?
- Data type: Is it deterministic data – factual, or probabilistic data – inferred?
- Data privacy and security: Does the provider follow strict guidelines for protecting the security and privacy of personal information?
When this consumer data is inaccurate, inconsistent, or lacks integrity across data sets, the insights that are drawn from the data will be inaccurate as well. This negatively impacts business outcomes, including providing a positive, unified customer experience. To optimize the customer experience and ensure customer satisfaction, and strong brand relationships, consumer data must be high-quality, accurate, and cleansed. While third-party data can benefit an overall analytics strategy, it must be of the highest quality to ensure that it does not negatively impact outcomes.
Gravy Analytics provides companies with high-quality and verifiable data to improve and enhance their data analytics strategies. Learn about how Gravy’s AdmitOne processing engine filters and categorizes location data to eliminate problematic signals.
6 Benefits of Location Data
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