Data Quality and the Customer Experience

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Businesses face a multitude of challenges in today’s environment. The overall speed of business is constantly increasing. Decisions are made within minutes and channels are diversifying rapidly. Perhaps most importantly, face-to-face interaction has started to become a luxury, rather than a necessity or consequence of everyday behavior.

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Data Quality and the Customer Experience

  1. 1. Data Quality and the Customer ExperienceToday’s consumer and how contact data affects relationshipsAn Experian QAS white paperJanuary 2013
  2. 2. Contents Page1 Executive summary 32 Introduction 4 Research overview 4 Research methodology 43 findings 5 Key Motivation 5 Current accuracy levels 5 Affects of inaccurate data 6 Practices in maintaining data 7 The omnichannel environment 74 Improving the customer experience through accurate data 8 Preventing human error 8 Alleviating duplicate data 9 Using intelligence to create relevant messages 105 Conclusion 112. Data quality and the customer experience
  3. 3. 1. Executive summaryBusinesses face a multitude of challenges in today’s environment. Theoverall speed of business is constantly increasing. Decisions are madewithin minutes and channels are diversifying rapidly. Perhaps mostimportantly, face-to-face interaction has started to become a luxury,rather than a necessity or consequence of everyday behavior.With all of these challenges, or behavioral intelligence.businesses need to ensure that However, businesses needevery interaction, regardless of to ensure accuracy beforethe channel, creates a positive depending on data for corecustomer experience. Achieving business functions. Withoutthis goal will improve loyalty and completely correct information,ultimately increase revenue. businesses will operate on inaccurate information,But to truly deliver a positive potentially wasting resourcescustomer experience, and damaging the customercompanies must increasingly experience they are working sorely on data to communicate hard to improve.with consumers and providebusiness intelligence. Despite the overall advances in analytics and businessData is a major area of focus intelligence, most businessesfor most businesses in 2013. struggle with data accuracy.Terms like big data, master data According to the survey, 94management, data governance percent of businesses believeand predictive analytics are there is some level of inaccuracytossed around as organizations within their system.try to use analytics andmodeling based on consumer To ensure positive, personalintelligence to get ahead in the consumer interactions,marketplace. businesses need to have a firm understanding of theirOrganizations are analyzing customers and accurate data tothe information in their internal help drive business decisionssystems, but a majority of and strategies.companies also leverage thirdparty information to gain Thomas Schutzinsight. In fact, according to the SVP, General Manager ofstudy, 63 percent of businesses North American Operationsappend additional demographic Experian QAS 3. Data quality and the customer experience
  4. 4. 2. Introduction2.1 Research overviewIn December 2012, Experian QAS commissioneda global research study to look at currentapproaches to contact data. This report, ‘DataQuality and the Customer Experience,’ explorescurrent contact data quality perceptions andpractices. It also includes insight into how dataquality affects the customer experience in amultichannel environment.2.2 Research methodology804 respondents from three countries took partin the research, produced by Dynamic Marketsfor Experian QAS. Industry sectors included inthe sample were education, finance, government,manufacturing, retail and utilities. Respondentsconsisted of C-level executives, vice presidents,directors, managers, and administrative staffconnected to data management, across a varietyof functions. Seniority Level in Survey Industry Breakdown 35 30 Manufacturing 25 Travel Percentage Retail 20 Financial Services 15 Utilities Telecommunications 10 Education Public Sector 5 Other 0 Admin Level Junior Middle Senior Director Manager Manager Manager Level or Level Level Level Above4. Data quality and the customer experience
  5. 5. 3. Key findings3.1 Motivation Both of these motivations are aBusinesses are driven to strive for accurate data.Almost all organizations have a data quality direct reflection of businessesstrategy in place; in fact, less than one percent of utilizing analytics and consumerbusinesses surveyed lacked such a strategy. Themain reasons cited for maintaining data are to intelligence to inform decisionincrease efficiency, enhance customer satisfaction making that will improve theand enable more effective business decisions. customer experience.Over the past few years, motivation for data qualityhas shifted. The percentage of organizationsciting efficiency, company reputation, customer in marketing and sales suspect a greater proportionsatisfaction, and compliance has decreased by of their data might be wrong, most likely due to thevarying levels when compared to responses from the fact that these departments experience data qualitypast two years. The response that has become more challenges first-hand.popular is enabling business decisions – up fivepercent over the 2011 study. But the level of inaccuracy is improving. The average percentage of inaccurate data is down eight percentAnother trend lending urgency to data quality over last year. However, 27 percent of respondentsstrategies is achieving a single customer view. 37 are unsure how much data is inaccurate, which couldpercent of organizations have a contact data quality suggest that accuracy levels have not improved asstrategy in order to support a single customer view. much as respondents seem to think.This concern was especially important to datamanagement and IT professionals. The most common types of errors are incomplete or missing data, outdated information and duplicateBoth of these motivations are a direct reflection data. 92 percent of organizations admit that theyof businesses utilizing analytics and consumer have duplicate data within their system.intelligence to inform decision making that willimprove the customer experience. The main cause of these data problems is human error, which was cited by 65 percent of organizations.3.2 Current accuracy levels While other causes clearly lag behind this frontrunner, other responses included a lack ofWhile most organizations have a data quality internal manual resources, an inadequate datastrategy in place, 94 percent suspect their customer strategy and insufficient budget. Only 14 percent ofand prospect data might be inaccurate in some way. those surveyed cited inadequate senior managementOn average, respondents think that as much as 17 support, illustrating that data quality is an importantpercent of their data might be inaccurate. Individuals issue for the C-suite.5. Data quality and the customer experience
  6. 6. 3.3 Affects of inaccurate dataGiven the level of inaccurate contact data, Methods for Managing Contact Databusinesses are facing several consequences.First, the bottom line is suffering. 91 percent of Do Not Measure Data Accuracyorganizations think that at least some of their Otherdepartmental budget was wasted in the past 12 Use Third Party Consultantsmonths as a result of contact data inaccuracies. Manually Examine DataOn average, 12 percent of departmental budget was Analysis in Excelwasted. It is worth noting the correlation between Dedicated Back-Office Softwarenumber of distinct databases within an organization Dedicated Point-of-Capture Softwareand amount of budget thought to be wasted – moredatabases directly tie to more wasted dollars. Measure Response Rates 0 5 10 15 20 25 30 35 40There are other consequences facing companies.93 percent of organizations say they have beennegatively impacted in some way over the past threeyears as a result of contact data accuracy issues. Channels UsedThe most common problem is sending mailingsto the wrong address. This is followed by sending Physical Locationmailings to the same customer multiple times and Sales Team Websitestaff inefficiencies. 32 percent of respondents said Mobilethat customer perception is negatively influenced Catalogby inaccurate contact data. Additionally, 29 percent Call Centerstated that they had lost a customer because of Social Mediainaccurate data input.All of these problems ultimately hurt the customerexperience and the company’s goal of driving loyalty.Unfortunately, these problems also appear to be onthe rise. In this year’s study, respondents identifiedwith more of these issues than respondents in theprevious survey.6. Data quality and the customer experience
  7. 7. 3.4 Practices in maintaining data included our survey operate across an average of four different channels. Overall, organizations inMost organizations have processes in place manufacturing and retail interact with consumers into manage contact data. In fact, 98 percent of more channels than organizations in education andrespondents manage the accuracy of contact the public sector.data. There are a variety of different tools usedby organizations. 62 percent use some sort of The most common channel for interacting withautomated method, whether that is a dedicated consumers is online through an organization’spoint-of-capture verification tool or a back-office website, with 72 percent of respondents citing thissoftware product. channel. Other popular channels include call center, mobile, and face-to-face interaction with a salesManual methods are also utilized, with 66 percent team.stating that they use at least one manual processto manage data accuracy. Analysis in Excel and Mobile channels continue to be a point of interestuse of response rates from campaigns are the for organizations as consumers utilize them for amost common manual efforts used by respondents. growing number of transactions. About 50 percentAbout 23 percent of organizations only use manual of organizations are capturing customer contactprocesses to measure data accuracy. data through mobile applications. About 85 percent of businesses either have, or are considering orSoftware-as-a-service (SaaS) is also a growing implementing mobile data capture.data quality deployment model. About 60 percent oforganizations are using SaaS tools for data quality About 40 percent of respondents interact withand 19 percent only use SaaS technology to manage consumers via social media, a relatively newtheir contact data. channel for organizations. The importance of the catalog channel has declined, with only 23 percentThere are regional differences in SaaS usage. SaaS of businesses stating that they interact withtechnology is more prevalent in the US than in the individuals via catalogs.UK and France. Marketing channels are also important. Email is theInterestingly, organizations that manage data most important marketing communication channelaccuracy solely through automated methods for 2013. This is followed by social media andare more likely to be utilizing SaaS technology landline phone.to manage data quality, compared to those thatuse only manual methods for data accuracymanagement. This shows that those using SaaStechnology may be more advanced in their datamanagement practices and have chosen to upgradetheir systems when modernizing their CRM.3.5 The omnichannel environmentThe diversification of channels has gathered speedas companies have attempted to reach consumersthrough their preferred outlets. Large organizations7. Data quality and the customer experience
  8. 8. 4. Improving the customer experiencethrough accurate data4.1 Preventing human error Then, prioritize projects based on high volume channels or excessive data quality errors.To operate effectively in the omnichannelenvironment, businesses need to do more than just Second, train staff. Staff education can go a longexist in each channel; they must create a seamless way toward improving data quality as a lot ofcustomer experience that crosses all channels. Even information is still manually entered by employees.though organizations may operate each channel in a Explain the importance of accurate data tosilo, consumers view the brand as one entity. employees and educate them about how information is used throughout the business.To conduct business effectively across channels,organizations need data and analytics. Business Next, businesses should utilize automatedintelligence is only as accurate as the information verification processes. Software solutions can bethat supplies it, and as mentioned previously, implemented in various channels to help preventmanaging that information is challenging for many inaccurate information, like poor address andbusinesses. email contact details. Figure out what data is most important to the business and evaluate and prioritizeIn order to improve data accuracy, businesses need available solutions.to eliminate human error, the main cause of poordata quality. There are several steps businesses can Finally, incorporate technology that continues totake to combat this issue. clean information over time. Even with software tools working at the point of capture, regularFirst, identify data entry points. Businesses need to database maintenance is required. Regularunderstand how information enters their system and cleansing allows organizations to review informationthrough what means. Consider all channels and data and make sure that installed tools are still effectiveentry points so a full data workflow can be created. in managing the data to the expected level of quality. Gaining corporate stakeholders tangible benefits to the organization. events or other initiatives that data Be sure a proposal includes financial quality can positively impact. To start a data quality project, it is models with a return on investment. important to gain other champions 4. Don’t underestimate time and sponsorship, particularly within 2. Demonstrate soft benefits – While requirements – to achieve the steps an organization’s senior management the bottom line is important, there above, stakeholders may need to put team. are other soft benefits that many in a significant time investment. Make senior managers look for. Link your sure to utilize other stakeholders There are several concepts data quality initiative to other soft within the business and software individuals should keep in mind when benefits the business cares about, like vendors when creating a data quality putting a business proposal together: customer satisfaction. proposal. With vendors, stakeholders should consider the vendor’s 1. Make the proposal credible – 3. Tie into strategic initiatives – underlying goals, but they can be a Stakeholders need to show that Stakeholders should know the good asset when making a project they have done their homework and company’s goals. Understand if there more credible and pulling financial the data quality project will provide are cost savings plans, compelling figures together.8. Data quality and the customer experience
  9. 9. 4.2 Alleviating duplicate data duplicates are identified according to the given definitions. Once records are identified, then theDuplicate data has become one of the most common golden record can be determined and the mergedata quality errors for organizations. 92 percent purge process can begin.of organizations admit to having duplicate data.Duplicate information spreads account history Once current duplicates have been removed, it isacross multiple records. This impedes intelligent important that organizations put processes in placedecision making and can harm the customer to reduce the possibility of duplicates being createdexperience. in the future. One way of reducing this trend is to implement fuzzy matching technology.Duplicate consumer records are created in a numberof different ways. The majority of respondents blame Fuzzy matching technology uses computer-assistedhuman error and multiple points of entry. Other translation to link records that may be less than onecommon responses include issues with multiple hundred percent exact. Most CRM systems requiredatabases and multiple business channels. US an exact match to find an existing record, whilerespondents also mentioned that customers provide fuzzy matching allows systems to identify that ‘Sueslightly different information, often causing new Smith’ could also be ‘Suzanne Smith’. By utilizingrecords to be created where an existing record could this software, staff members are more empowered tobe updated. find existing records rather than creating new ones each time they interact with a customer.Whatever the cause, it is important that businessesremove duplicates from their database in order toachieve efficiency and business intelligence goals.There are several techniques organizations can useto remove existing duplicate records within theirdatabase.First, organizations should standardize contact data.Since contact information is typically found in everyrecord, it can be used to help household informationand identify duplicate contacts.Next, administrators should define the level ofmatching they want to accomplish, as well as thetolerance level for what is considered a duplicaterecord. It is important to have an outline of whata single record means for the organization beforemerging records.Software should then be used to identify duplicatesbased on the defined criteria. While manual reviewis preferred by some organizations, it is importantfor larger organizations to utilize software to ensure9. Data quality and the customer experience
  10. 10. 4.3 Using intelligence to create relevant messages provides two business benefits. First, verifying contact data at the point of entry improvesThe omnichannel environment is changing the the accuracy of inbound information soway companies message to consumers. Today, organizations can get more from marketingconnections happen across various channels: efforts. Second, it ensures that a business canthrough telephone conversations, on websites, on get more accurate matches from third partymobile devices, and across a multitude of blogs data providers, who frequently use contactand social media sites in addition to in-person information to identify intelligence.interactions. 3. Enhance searching capabilities – MostTo create meaningful interactions and a positive databases require an exact match to identify ancustomer experience, organizations need to be existing record. Enhance capabilities to allowable to make real-time, dynamic offers. Marketers for matching, even with minor errors, to aid inneed consumer demographic and behavioral pulling and truly understanding internal data.details to better understand an individual’s needin order to achieve a personal approach. They need 4. Plan – Simply having data isn’t going to maketo combine buying patterns with purchase history, campaigns more effective. Marketers need tothird party demographic and behavioral intelligence. have a strategic plan for leveraging consumerWhile many talk about creating this repository and intelligence and be able to articulate which dataleveraging it in real time, few have actually achieved they need to achieve their goals. Businessesthe goal. should review what they want to accomplish by appending information and decide whichAppending third party information is actually attributes will help them achieve this goal.becoming more popular. 63 percent of businesses Organizations should use this step to buildappend third party demographic or behavioral a complete prospect profile that will enableintelligence. Those that are appending these details targeted offers and create models that willuse the information to enhance loyalty efforts, tailor actually allow them to execute on that plan.emails with specific offers and change websitedisplays to target prospects.There are four steps organizations can take in order Uses of Third Party Datato implement real-time consumer intelligence.1. Clean internal data – The key to real-time Adjust Website Displays consumer intelligence is being able to marry Tailor Emails lots of different information quickly to provide Target Advertising relevant offers. Accurate data allows businesses to more easily search information, combine Inform Business Decisions duplicate records and analyze data. Enhance Loyalty2. Clean incoming information – Ensuring the accuracy of data coming into the database10. Data quality and the customer experience
  11. 11. 5. ConclusionMaintaining a consistent, high-level customer business intelligence. Accurate analytics willexperience is a primary goal for many businesses allow businesses to make more informed businessin the year ahead. A positive experience can be decisions and operate more efficiently.challenging to deliver with the volume of channels,disparate data and inaccurate contact information Accurate data is the first step in creating ain the marketplace. However, businesses need to personalized customer experience. Stakeholdersprovide that unique experience that keeps customers should ensure the strategy they have in place forloyal and happy and driving additional revenue. data quality is producing the required results – and that customers agree.There are steps businesses can take to improvedata capture and aggregation in order to gainExperian QAS125 Summer St Ste 1910Boston, MA 02110-1615T: 1 888 322 6201info@qas.com ©2012 Experian Information Solutions. • All rights reserved. Experian and the Experian marks used herein are service markswww.qas.com or registered trademarks of Experian Information Solutions, Inc. Other product and company names mentioned herein are property of their respective owners.11. Data quality and the customer experience

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