Today’s consumer and how contact data affects relationships An Experian QAS white paper

1. Data Quality and the Customer Experience
Today’s consumer and how contact data affects relationships
An Experian QAS white paper
January 2013

2. Contents
Page
1 Executive summary 3
2 Introduction 4
Research overview 4
Research methodology 4
3 findings 5
Key
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
2. Data quality and the customer experience

3. 1. Executive summary
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.
With all of these challenges, or behavioral intelligence.
businesses need to ensure that However, businesses need
every interaction, regardless of to ensure accuracy before
the channel, creates a positive depending on data for core
customer experience. Achieving business functions. Without
this 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 resources
customer experience, and damaging the customer
companies must increasingly experience they are working so
rely on data to communicate hard to improve.
with consumers and provide
business intelligence. Despite the overall advances
in analytics and business
Data is a major area of focus intelligence, most businesses
for most businesses in 2013. struggle with data accuracy.
Terms like big data, master data According to the survey, 94
management, data governance percent of businesses believe
and predictive analytics are there is some level of inaccuracy
tossed around as organizations within their system.
try to use analytics and
modeling based on consumer To ensure positive, personal
intelligence to get ahead in the consumer interactions,
marketplace. businesses need to have a
firm understanding of their
Organizations are analyzing customers and accurate data to
the information in their internal help drive business decisions
systems, but a majority of and strategies.
companies also leverage third
party information to gain Thomas Schutz
insight. In fact, according to the SVP, General Manager of
study, 63 percent of businesses North American Operations
append additional demographic Experian QAS
3. Data quality and the customer experience

4. 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.
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 Above
4. Data quality and the customer experience

5. 3. Key findings
3.1 Motivation
Both of these motivations are a
Businesses are driven to strive for accurate data.
Almost all organizations have a data quality direct reflection of businesses
strategy in place; in fact, less than one percent of utilizing analytics and consumer
businesses surveyed lacked such a strategy. The
main reasons cited for maintaining data are to intelligence to inform decision
increase efficiency, enhance customer satisfaction making that will improve the
and enable more effective business decisions.
customer experience.
Over the past few years, motivation for data quality
has shifted. The percentage of organizations
citing efficiency, company reputation, customer in marketing and sales suspect a greater proportion
satisfaction, and compliance has decreased by of their data might be wrong, most likely due to the
varying levels when compared to responses from the fact that these departments experience data quality
past two years. The response that has become more challenges first-hand.
popular is enabling business decisions – up five
percent over the 2011 study. But the level of inaccuracy is improving. The average
percentage of inaccurate data is down eight percent
Another trend lending urgency to data quality over last year. However, 27 percent of respondents
strategies is achieving a single customer view. 37 are unsure how much data is inaccurate, which could
percent of organizations have a contact data quality suggest that accuracy levels have not improved as
strategy in order to support a single customer view. much as respondents seem to think.
This concern was especially important to data
management and IT professionals. The most common types of errors are incomplete or
missing data, outdated information and duplicate
Both of these motivations are a direct reflection data. 92 percent of organizations admit that they
of businesses utilizing analytics and consumer have duplicate data within their system.
intelligence to inform decision making that will
improve 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 of
While most organizations have a data quality internal manual resources, an inadequate data
strategy in place, 94 percent suspect their customer strategy and insufficient budget. Only 14 percent of
and prospect data might be inaccurate in some way. those surveyed cited inadequate senior management
On average, respondents think that as much as 17 support, illustrating that data quality is an important
percent of their data might be inaccurate. Individuals issue for the C-suite.
5. Data quality and the customer experience

6. 3.3 Affects of inaccurate data
Given the level of inaccurate contact data, Methods for Managing Contact Data
businesses are facing several consequences.
First, the bottom line is suffering. 91 percent of Do Not Measure Data Accuracy
organizations think that at least some of their Other
departmental budget was wasted in the past 12 Use Third Party Consultants
months as a result of contact data inaccuracies. Manually Examine Data
On average, 12 percent of departmental budget was Analysis in Excel
wasted. It is worth noting the correlation between
Dedicated Back-Office Software
number of distinct databases within an organization
Dedicated Point-of-Capture Software
and amount of budget thought to be wasted – more
databases directly tie to more wasted dollars. Measure Response Rates
0 5 10 15 20 25 30 35 40
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. Channels Used
The most common problem is sending mailings
to the wrong address. This is followed by sending Physical Location
mailings to the same customer multiple times and Sales Team
Website
staff inefficiencies. 32 percent of respondents said
Mobile
that customer perception is negatively influenced Catalog
by inaccurate contact data. Additionally, 29 percent Call Center
stated that they had lost a customer because of Social Media
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.
6. Data quality and the customer experience

7. 3.4 Practices in maintaining data included our survey operate across an average of
four different channels. Overall, organizations in
Most organizations have processes in place manufacturing and retail interact with consumers in
to manage contact data. In fact, 98 percent of more channels than organizations in education and
respondents manage the accuracy of contact the public sector.
data. There are a variety of different tools used
by organizations. 62 percent use some sort of The most common channel for interacting with
automated method, whether that is a dedicated consumers is online through an organization’s
point-of-capture verification tool or a back-office website, with 72 percent of respondents citing this
software product. channel. Other popular channels include call center,
mobile, and face-to-face interaction with a sales
Manual methods are also utilized, with 66 percent team.
stating that they use at least one manual process
to manage data accuracy. Analysis in Excel and Mobile channels continue to be a point of interest
use of response rates from campaigns are the for organizations as consumers utilize them for a
most common manual efforts used by respondents. growing number of transactions. About 50 percent
About 23 percent of organizations only use manual of organizations are capturing customer contact
processes to measure data accuracy. data through mobile applications. About 85 percent
of businesses either have, or are considering or
Software-as-a-service (SaaS) is also a growing implementing mobile data capture.
data quality deployment model. About 60 percent of
organizations are using SaaS tools for data quality About 40 percent of respondents interact with
and 19 percent only use SaaS technology to manage consumers via social media, a relatively new
their contact data. channel for organizations. The importance of the
catalog channel has declined, with only 23 percent
There are regional differences in SaaS usage. SaaS of businesses stating that they interact with
technology is more prevalent in the US than in the individuals via catalogs.
UK and France.
Marketing channels are also important. Email is the
Interestingly, organizations that manage data most important marketing communication channel
accuracy solely through automated methods for 2013. This is followed by social media and
are more likely to be utilizing SaaS technology landline phone.
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
7. Data quality and the customer experience

8. 4. Improving the customer experience
through accurate data
4.1 Preventing human error Then, prioritize projects based on high volume
channels or excessive data quality errors.
To operate effectively in the omnichannel
environment, businesses need to do more than just Second, train staff. Staff education can go a long
exist in each channel; they must create a seamless way toward improving data quality as a lot of
customer 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 to
silo, 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 automated
intelligence is only as accurate as the information verification processes. Software solutions can be
that supplies it, and as mentioned previously, implemented in various channels to help prevent
managing that information is challenging for many inaccurate information, like poor address and
businesses. email contact details. Figure out what data is most
important to the business and evaluate and prioritize
In order to improve data accuracy, businesses need available solutions.
to eliminate human error, the main cause of poor
data quality. There are several steps businesses can Finally, incorporate technology that continues to
take to combat this issue. clean information over time. Even with software
tools working at the point of capture, regular
First, identify data entry points. Businesses need to database maintenance is required. Regular
understand how information enters their system and cleansing allows organizations to review information
through what means. Consider all channels and data and make sure that installed tools are still effective
entry 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. 4.2 Alleviating duplicate data duplicates are identified according to the given
definitions. Once records are identified, then the
Duplicate data has become one of the most common golden record can be determined and the merge
data 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 is
across multiple records. This impedes intelligent important that organizations put processes in place
decision making and can harm the customer to reduce the possibility of duplicates being created
experience. in the future. One way of reducing this trend is to
implement fuzzy matching technology.
Duplicate consumer records are created in a number
of different ways. The majority of respondents blame Fuzzy matching technology uses computer-assisted
human error and multiple points of entry. Other translation to link records that may be less than one
common responses include issues with multiple hundred percent exact. Most CRM systems require
databases and multiple business channels. US an exact match to find an existing record, while
respondents also mentioned that customers provide fuzzy matching allows systems to identify that ‘Sue
slightly different information, often causing new Smith’ could also be ‘Suzanne Smith’. By utilizing
records to be created where an existing record could this software, staff members are more empowered to
be updated. find existing records rather than creating new ones
each time they interact with a customer.
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
9. Data quality and the customer experience

10. 4.3 Using intelligence to create relevant messages provides two business benefits. First, verifying
contact data at the point of entry improves
The omnichannel environment is changing the the accuracy of inbound information so
way companies message to consumers. Today, organizations can get more from marketing
connections happen across various channels: efforts. Second, it ensures that a business can
through telephone conversations, on websites, on get more accurate matches from third party
mobile devices, and across a multitude of blogs data providers, who frequently use contact
and social media sites in addition to in-person information to identify intelligence.
interactions.
3. Enhance searching capabilities – Most
To create meaningful interactions and a positive databases require an exact match to identify an
customer experience, organizations need to be existing record. Enhance capabilities to allow
able to make real-time, dynamic offers. Marketers for matching, even with minor errors, to aid in
need consumer demographic and behavioral pulling and truly understanding internal data.
details to better understand an individual’s need
in order to achieve a personal approach. They need 4. Plan – Simply having data isn’t going to make
to combine buying patterns with purchase history, campaigns more effective. Marketers need to
third party demographic and behavioral intelligence. have a strategic plan for leveraging consumer
While many talk about creating this repository and intelligence and be able to articulate which data
leveraging it in real time, few have actually achieved they need to achieve their goals. Businesses
the goal. should review what they want to accomplish
by appending information and decide which
Appending 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 build
append third party demographic or behavioral a complete prospect profile that will enable
intelligence. Those that are appending these details targeted offers and create models that will
use the information to enhance loyalty efforts, tailor actually allow them to execute on that plan.
emails with specific offers and change website
displays to target prospects.
There are four steps organizations can take in order Uses of Third Party Data
to 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 Loyalty
2. Clean incoming information – Ensuring the
accuracy of data coming into the database
10. Data quality and the customer experience

11. 5. Conclusion
Maintaining a consistent, high-level customer business intelligence. Accurate analytics will
experience is a primary goal for many businesses allow businesses to make more informed business
in 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 a
in the marketplace. However, businesses need to personalized customer experience. Stakeholders
provide that unique experience that keeps customers should ensure the strategy they have in place for
loyal and happy and driving additional revenue. data quality is producing the required results – and
that customers agree.
There are steps businesses can take to improve
data capture and aggregation in order to gain
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11. Data quality and the customer experience