Data Quality and the Customer Experience

Data Quality and the Customer Experience

Today’s consumer and how contact data affects relationships

An Experian QAS white paper

January 2013

Contents

1 Executive summary 3

2 Introduction 4 Research overview 4 Research methodology 4

3 Key findings 5 Motivation 5 Current accuracy levels 5 Affects of inaccurate data 6 Practices in maintaining data 7 The omnichannel environment 7

4 Improving the customer experience through accurate data 8 Preventing human error 8 Alleviating duplicate data 9 Using intelligence to create relevant messages 10

5 Conclusion 11

Page

2. Data quality and the customer experience


1. Executive summary

With all of these challenges, businesses need to ensure that every interaction, regardless of the channel, creates a positive customer experience. Achieving this goal will improve loyalty and ultimately increase revenue.

But to truly deliver a positive customer experience, companies must increasingly rely on data to communicate with consumers and provide business intelligence. Data is a major area of focus for most businesses in 2013. Terms like big data, master data management, data governance and predictive analytics are tossed around as organizations try to use analytics and modeling based on consumer intelligence to get ahead in the marketplace.

Organizations are analyzing the information in their internal systems, but a majority of companies also leverage third party information to gain insight. In fact, according to the study, 63 percent of businesses append additional demographic

or behavioral intelligence.However, businesses need to ensure accuracy before depending on data for core business functions. Without completely correct information, businesses will operate on inaccurate information, potentially wasting resources and damaging the customer experience they are working so hard to improve.

Despite the overall advances in analytics and business intelligence, most businesses struggle with data accuracy. According to the survey, 94 percent of businesses believe there is some level of inaccuracy within their system.

To ensure positive, personal consumer interactions, businesses need to have a firm understanding of their customers and accurate data to help drive business decisions and strategies.

Thomas SchutzSVP, General Manager of North American OperationsExperian QAS

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.

2. Introduction

2.1 Research overview

In December 2012, Experian QAS commissioned a global research study to look at current approaches to contact data. This report, ‘Data Quality and the Customer Experience,’ explores current contact data quality perceptions and practices. It also includes insight into how data quality affects the customer experience in a multichannel environment.

2.2 Research methodology

804 respondents from three countries took part in the research, produced by Dynamic Markets for Experian QAS. Industry sectors included in the sample were education, finance, government, manufacturing, retail and utilities. Respondents consisted of C-level executives, vice presidents, directors, managers, and administrative staff connected to data management, across a variety of functions.


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ManufacturingTravelRetailFinancial ServicesUtilitiesTelecommunicationsEducationPublic SectorOther

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3.1 Motivation

Businesses are driven to strive for accurate data. Almost all organizations have a data quality strategy in place; in fact, less than one percent of businesses surveyed lacked such a strategy. The main reasons cited for maintaining data are to increase efficiency, enhance customer satisfaction and enable more effective business decisions.

Over the past few years, motivation for data quality has shifted. The percentage of organizations citing efficiency, company reputation, customer satisfaction, and compliance has decreased by varying levels when compared to responses from the past two years. The response that has become more popular is enabling business decisions – up five percent over the 2011 study.

Another trend lending urgency to data quality strategies is achieving a single customer view. 37 percent of organizations have a contact data quality strategy in order to support a single customer view. This concern was especially important to data management and IT professionals.

Both of these motivations are a direct reflection of businesses utilizing analytics and consumer intelligence to inform decision making that will improve the customer experience.

3.2 Current accuracy levels

While most organizations have a data quality strategy in place, 94 percent suspect their customer and prospect data might be inaccurate in some way. On average, respondents think that as much as 17 percent of their data might be inaccurate. Individuals

in marketing and sales suspect a greater proportion of their data might be wrong, most likely due to the fact that these departments experience data quality challenges first-hand.

But the level of inaccuracy is improving. The average percentage of inaccurate data is down eight percent over last year. However, 27 percent of respondents are unsure how much data is inaccurate, which could suggest that accuracy levels have not improved as much as respondents seem to think.

The most common types of errors are incomplete or missing data, outdated information and duplicate data. 92 percent of organizations admit that they have duplicate data within their system.

The main cause of these data problems is human error, which was cited by 65 percent of organizations. While other causes clearly lag behind this frontrunner, other responses included a lack of internal manual resources, an inadequate data strategy and insufficient budget. Only 14 percent of those surveyed cited inadequate senior management support, illustrating that data quality is an important issue for the C-suite.


3. Key findings

Both of these motivations are a direct reflection of businesses utilizing analytics and consumer intelligence to inform decision making that will improve the customer experience.

3.3 Affects of inaccurate data

Given the level of inaccurate contact data, businesses are facing several consequences. First, the bottom line is suffering. 91 percent of organizations think that at least some of their departmental budget was wasted in the past 12 months as a result of contact data inaccuracies. On average, 12 percent of departmental budget was wasted. It is worth noting the correlation between number of distinct databases within an organization and amount of budget thought to be wasted – more databases directly tie to more wasted dollars.

There are other consequences facing companies. 93 percent of organizations say they have been negatively impacted in some way over the past three years as a result of contact data accuracy issues.

The most common problem is sending mailings to the wrong address. This is followed by sending mailings to the same customer multiple times and staff inefficiencies. 32 percent of respondents said that customer perception is negatively influenced by inaccurate contact data. Additionally, 29 percent stated that they had lost a customer because of inaccurate data input.

All of these problems ultimately hurt the customer experience and the company’s goal of driving loyalty. Unfortunately, these problems also appear to be on the rise. In this year’s study, respondents identified with more of these issues than respondents in the previous survey.


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3.4 Practices in maintaining data

Most organizations have processes in place to manage contact data. In fact, 98 percent of respondents manage the accuracy of contact data. There are a variety of different tools used by organizations. 62 percent use some sort of automated method, whether that is a dedicated point-of-capture verification tool or a back-office software product.

Manual methods are also utilized, with 66 percent stating that they use at least one manual process to manage data accuracy. Analysis in Excel and use of response rates from campaigns are the most common manual efforts used by respondents. About 23 percent of organizations only use manual processes to measure data accuracy.

Software-as-a-service (SaaS) is also a growing data quality deployment model. About 60 percent of organizations are using SaaS tools for data quality and 19 percent only use SaaS technology to manage their contact data.

There are regional differences in SaaS usage. SaaS technology is more prevalent in the US than in the UK and France.

Interestingly, organizations that manage data accuracy solely through automated methods are more likely to be utilizing SaaS technology to manage data quality, compared to those that use only manual methods for data accuracy management. This shows that those using SaaS technology may be more advanced in their data management practices and have chosen to upgrade their systems when modernizing their CRM.

3.5 The omnichannel environment

The diversification of channels has gathered speed as companies have attempted to reach consumers through their preferred outlets. Large organizations

included our survey operate across an average of four different channels. Overall, organizations in manufacturing and retail interact with consumers in more channels than organizations in education and the public sector.

The most common channel for interacting with consumers is online through an organization’s website, with 72 percent of respondents citing this channel. Other popular channels include call center, mobile, and face-to-face interaction with a sales team.

Mobile channels continue to be a point of interest for organizations as consumers utilize them for a growing number of transactions. About 50 percent of organizations are capturing customer contact data through mobile applications. About 85 percent of businesses either have, or are considering or implementing mobile data capture.

About 40 percent of respondents interact with consumers via social media, a relatively new channel for organizations. The importance of the catalog channel has declined, with only 23 percent of businesses stating that they interact with individuals via catalogs.

Marketing channels are also important. Email is the most important marketing communication channel for 2013. This is followed by social media and landline phone.



4. Improving the customer experience through accurate data 4.1 Preventing human error

To operate effectively in the omnichannel environment, businesses need to do more than just exist in each channel; they must create a seamless customer experience that crosses all channels. Even though organizations may operate each channel in a silo, consumers view the brand as one entity.

To conduct business effectively across channels, organizations need data and analytics. Business intelligence is only as accurate as the information that supplies it, and as mentioned previously, managing that information is challenging for many businesses.

In order to improve data accuracy, businesses need to eliminate human error, the main cause of poor data quality. There are several steps businesses can take to combat this issue.

First, identify data entry points. Businesses need to understand how information enters their system and through what means. Consider all channels and data entry points so a full data workflow can be created.

Then, prioritize projects based on high volume channels or excessive data quality errors.

Second, train staff. Staff education can go a long way toward improving data quality as a lot of information is still manually entered by employees. Explain the importance of accurate data to employees and educate them about how information is used throughout the business.

Next, businesses should utilize automated verification processes. Software solutions can be implemented in various channels to help prevent inaccurate information, like poor address and email contact details. Figure out what data is most important to the business and evaluate and prioritize available solutions.

Finally, incorporate technology that continues to clean information over time. Even with software tools working at the point of capture, regular database maintenance is required. Regular cleansing allows organizations to review information and make sure that installed tools are still effective in managing the data to the expected level of quality.

Gaining corporate stakeholders

To start a data quality project, it is important to gain other champions and sponsorship, particularly within an organization’s senior management team.

There are several concepts individuals should keep in mind when putting a business proposal together:

1. Make the proposal credible – Stakeholders need to show that they have done their homework and the data quality project will provide

tangible benefits to the organization. Be sure a proposal includes financial models with a return on investment.

2. Demonstrate soft benefits – While the bottom line is important, there are other soft benefits that many senior managers look for. Link your data quality initiative to other soft benefits the business cares about, like customer satisfaction.

3. Tie into strategic initiatives – Stakeholders should know the company’s goals. Understand if there are cost savings plans, compelling

events or other initiatives that data quality can positively impact.

4. Don’t underestimate time requirements – to achieve the steps above, stakeholders may need to put in a significant time investment. Make sure to utilize other stakeholders within the business and software vendors when creating a data quality proposal. With vendors, stakeholders should consider the vendor’s underlying goals, but they can be a good asset when making a project more credible and pulling financial figures together.

4.2 Alleviating duplicate data

Duplicate data has become one of the most common data quality errors for organizations. 92 percent of organizations admit to having duplicate data. Duplicate information spreads account history across multiple records. This impedes intelligent decision making and can harm the customer experience.

Duplicate consumer records are created in a number of different ways. The majority of respondents blame human error and multiple points of entry. Other common responses include issues with multiple databases and multiple business channels. US respondents also mentioned that customers provide slightly different information, often causing new records to be created where an existing record could be updated.

Whatever the cause, it is important that businesses remove duplicates from their database in order to achieve efficiency and business intelligence goals. There are several techniques organizations can use to remove existing duplicate records within their database.

First, organizations should standardize contact data. Since contact information is typically found in every record, it can be used to help household information and identify duplicate contacts.

Next, administrators should define the level of matching they want to accomplish, as well as the tolerance level for what is considered a duplicate record. It is important to have an outline of what a single record means for the organization before merging records.

Software should then be used to identify duplicates based on the defined criteria. While manual review is preferred by some organizations, it is important for larger organizations to utilize software to ensure

duplicates are identified according to the given definitions. Once records are identified, then the golden record can be determined and the merge purge process can begin.

Once current duplicates have been removed, it is important that organizations put processes in place to reduce the possibility of duplicates being created in the future. One way of reducing this trend is to implement fuzzy matching technology.

Fuzzy matching technology uses computer-assisted translation to link records that may be less than one hundred percent exact. Most CRM systems require an exact match to find an existing record, while fuzzy matching allows systems to identify that ‘Sue Smith’ could also be ‘Suzanne Smith’. By utilizing this software, staff members are more empowered to find existing records rather than creating new ones each time they interact with a customer.


4.3 Using intelligence to create relevant messages

The omnichannel environment is changing the way companies message to consumers. Today, connections happen across various channels: through telephone conversations, on websites, on mobile devices, and across a multitude of blogs and social media sites in addition to in-person interactions.

To create meaningful interactions and a positive customer experience, organizations need to be able to make real-time, dynamic offers. Marketers need consumer demographic and behavioral details to better understand an individual’s need in order to achieve a personal approach. They need to combine buying patterns with purchase history, third party demographic and behavioral intelligence. While many talk about creating this repository and leveraging it in real time, few have actually achieved the goal.

Appending third party information is actually becoming more popular. 63 percent of businesses append third party demographic or behavioral intelligence. Those that are appending these details use the information to enhance loyalty efforts, tailor emails with specific offers and change website displays to target prospects.

There are four steps organizations can take in order to implement real-time consumer intelligence.

1. Clean internal data – The key to real-time consumer intelligence is being able to marry lots of different information quickly to provide relevant offers. Accurate data allows businesses to more easily search information, combine duplicate records and analyze data.

2. Clean incoming information – Ensuring the accuracy of data coming into the database

provides two business benefits. First, verifying contact data at the point of entry improves the accuracy of inbound information so organizations can get more from marketing efforts. Second, it ensures that a business can get more accurate matches from third party data providers, who frequently use contact information to identify intelligence.

3. Enhance searching capabilities – Most

databases require an exact match to identify an existing record. Enhance capabilities to allow for matching, even with minor errors, to aid in pulling and truly understanding internal data.

4. Plan – Simply having data isn’t going to make campaigns more effective. Marketers need to have a strategic plan for leveraging consumer intelligence and be able to articulate which data they need to achieve their goals. Businesses should review what they want to accomplish by appending information and decide which attributes will help them achieve this goal. Organizations should use this step to build a complete prospect profile that will enable targeted offers and create models that will actually allow them to execute on that plan.


Adjust Website Displays

Tailor Emails

Target Advertising

Inform Business DecisionsEnhance Loyalty

Uses of Third Party Data

5. Conclusion

Maintaining a consistent, high-level customer experience is a primary goal for many businesses in the year ahead. A positive experience can be challenging to deliver with the volume of channels, disparate data and inaccurate contact information in the marketplace. However, businesses need to provide that unique experience that keeps customers loyal and happy and driving additional revenue.

There are steps businesses can take to improve data capture and aggregation in order to gain

business intelligence. Accurate analytics will allow businesses to make more informed business decisions and operate more efficiently.

Accurate data is the first step in creating a personalized customer experience. Stakeholders should ensure the strategy they have in place for data quality is producing the required results – and that customers agree.


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