Collecting Accurate Contact Data in a Multichannel Enviornment

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  • Courtney: Welcome everyone. I appreciate you taking the time to listen to us for the next 45 minutes or so. My name is Courtney Fulton and I am the Marketing Programs Manager here at Experian QAS. We just completed a new U.S. based research project around contact data quality perceptions and practices, paying special attention to the multichannel strategy that many organizations find themselves in today. We wanted to share those findings with you, in particular the findings for the financial services and insurance industries. So lets get into the format of the presentation.
  • Courtney: I have my colleague Erin Haselkorn here with me today, who conducted the data quality study. Erin would you mind telling the listeners a little more about what you do here at Experian QAS? Erin: Thank you Courtney. Again my name is Erin Haselkorn and I am a marketing programs and data quality research specialist here at Experian QAS. I manage marketing programs for the finance and insurance industries, but I also have the privilege of working on our contact data quality studies, including this most recent research project. My goal is to provide insight and trends from that study today to help you all benchmark your own practices against those in your industry. Courtney: Thank you Erin. After Erin and I discuss the findings and trends from the data quality study, we will provide some tips to help your business improve contact data quality. We will also be doing a Q&A at the end. I want to mention that if you have questions at any point of the presentation feel free to type them in the chat box in the lower right hand portion of your screen you don’t have to wait until the end of the presentation. Additionally, this webinar is being recorded so we will send out the slides and the recording to the email you entered during registration in about a week or so.
  • Courtney: To start, I wanted to put up on the screen some of the key themes to take away from the presentation. These are the main points we will go over in more detail throughout the presentation. Erin do you want to go through these? Erin: Absolutely. These takeaways are key trends from the research and tips when choosing a data quality strategy that we find helpful in our own businesses data quality, but also from working with our customers. Across industries and countries, we found that organizations do realize the need for data accuracy, but they are struggling to achieve it, partly because of their reliance on manual processes. Because of this, businesses need to move away from manual processes and choose automated solutions that will prevent human error and help businesses ensure the accuracy of their contact data. When choosing one of these solutions there are many options, so each business needs to find a solution to fit its own individual data quality needs. Analysts also need to make sure that they will be able to demonstrate the return on investment to the business. So again, we will go into each of these points in more detail, but these are the main overarching themes we want each of you to walk away with.
  • Liz: I am excited to hear more about each of those points throughout the presentation. But before we jump into the data, Erin can you give us a better idea on the profile of the respondents. Erin: Sure. We surveyed over 1,300 respondents from 7 countries, these included the UK, US, Australia, New Zealand, Singapore, France, and the Netherlands. Financial services, retail, utilities, education and travel, were some of the industries that were included. The companies that were surveyed were large companies with over 250 employees and titles included c-level executives, VPs, Directors, managers and administrative staff that were related to data quality. While we did survey larger companies, smaller businesses can certainly walk away and apply some of this information to their own database. Some of the most common departments that these people work in are marketing, IT, customer service and operations. So it was really a wide range of individuals who all deal with data quality in some capacity.
  • Liz: So now lets start to dive into some of the results we saw from the survey. I think the first overarching theme we saw was that data quality really does matter to a lot of businesses. Erin: Absolutely. We first asked organizations if they had a documented data quality strategy, which means a documented set of processes that are designed to manage all of the organization’s customer and prospect contact data. We saw that 87% of U.S. businesses had a documented data quality strategy, which is a higher percentage than the other countries surveyed. In contrast, if we look at New Zealand, which has a low level of documented data quality strategies, 13% were even unsure if their organization even had a data quality strategy. So countries did vary on the level of data quality investment. From a vertical perspective, those in the manufacturing, financial services, and utilities were most likely to have a documented data quality strategy, where as those in education were the least likely at 70%. So again like with country data, verticals vary on how likely they are to have a data quality strategy. Liz: That is great that so many organizations have data quality strategies, but what are the driving forces behind improving contact records? Erin: Across the global the reasons for improving contact records were pretty consistent. These were to increase efficiency, enhance customer satisfaction and generate revenue. However, other strong benefits were cost savings in the U.S., informing business decisions in APAC and compliance with government regulations for the financial services industry. In fact if we advance a slide…
  • Erin: You can see from this graph the breakdown of these benefits from a holistic global perspective. Again showing that efficiency, customer satisfaction and revenue generation are on top. But brand protection, reducing risk or fraud and informing businesses decisions are also pretty important. However these secondary benefits did change based on vertical. Like we said, financial services cared about compliance with government regulations, this is probably because they have more regulations that are constantly changing, especially with the Dodd-Frank act being implemented, that put an extra burden on them for their data to be more accurate. Again, different verticals are affected by a variety of factors, causing them to improve data quality for different reasons. Liz: So were there any differences between countries that you noticed? Erin: Well more of the variance was actually by vertical instead of country, but we did see that New Zealand and Singapore put more of an emphasis on enhancing customer/ citizen satisfaction and APAC as a hole was more interested in using accurate data in making more informed business decisions. While France was lower than everyone else’s interest in protecting the organizations reputation & brand.
  • Liz: The next trend from the research was the surprising low level of accuracy given the desire for accurate contact data. I think this image really represents what businesses don’t want to see, which is a lot of returned mail that not only decreases efficiency, but represents a lost opportunity to communicate with customers, prospects or constituents. Erin: That’s right Liz. Since businesses understand the benefits of accurate data, none of us want to see mail piling up because of inaccurate addresses. However, based on the results of our survey, many organizations are feeling the negative consequences of inaccurate data. Liz: Utilities mail room story
  • Erin: If we look a little more in depth at the low level of accuracy we see that 89% of U.S. organizations suspect their customer/ prospect data to be inaccurate in some way. However, on average, U.S. organizations thought as much as 24% of their data was inaccurate. That is a very large percentage of data to be inaccurate. However, this is the first year that we are seeing organizations realize how low their level of accuracy is. We have seen this from data tests for a long time, but this is the first year that our surveys reflect this high level of inaccurate data. On the vertical side, respondents in financial services have experienced more issues than those in retail and utilities. However, the level of trust in contact data has actually gone up in the past year. We asked about the level of trust in our 2009 global research study and at that time 97% of global organizations did not completely trust their contact data in terms of it being completely clean, accurate and up to date, however, this level went down to 92% of organizations not completely trusting their contact data in terms of it being accurate and up to date. This means that organizations are starting to gain more trust in their data, partly because they are starting to realize its importance and are starting do something about cleaning it. Liz: Are there any consequences to all of this bad data? Erin: Absolutely, in the survey we see that 79% of U.S. organizations experienced at least one negative consequence in the last 3 years from data accuracy issues. However, this number went up to 91% in France, so the US was on the lower side of negative responses seen globally from inaccurate data, but remember, they are the most likely to have a documented data quality strategy. Some of the most common consequences in the U.S. were negative impacts on customer perception, staff inefficiencies, mailings sent to the wrong address, mailings sent to the same customer multiple times and lost customers. All of these negative impacts hurt a companies bottom line and efficiency, which are some of the things organizations are trying to avoid and why they are investing in contact data management projects. On the vertical side, we still saw the same consequences, but respondents in financial services have experienced more issues than those in retail and utilities. Additionally, respondents were asked what percentage of the annual budget was wasted in the past 12 months as a result of contact data inaccuracies. Globally, 90% of organizations think at least some of their departmental budget was wasted. On average, 15% of budgets were wasted, but this jumped to 25% for marketing respondents. Liz: Wow that is a lot of negative results. Especially that budget number. If we think about all of the cost cutting measures that are in place for businesses to reduce just a small percentage of annual budget, we see that just by improving basic contact data, businesses can get back a pretty large chunk of their annual budget. But what seems to be causing all of these inaccuracies that are having such a dramatic effect on businesses.
  • Erin: Well if we move to the next slide, we will see that the main cause of data inaccuracy is human error. This is significantly above the others. Liz: So what exactly do you mean by human error? Erin: These are errors that we naturally tend to make, like leaving fields blank, incorrectly typing numbers, misspelling, all of that is human error. While human error was far and away the biggest reason for inaccuracy across all verticals and countries insufficient budget, lack of internal manual resources and inadequate data strategy were some of the secondary reasons for data inaccuracy that trended globally. We think that the lack of internal manual resources and insufficient budget are due to the current economic situation, with budget and employee cuts. But secondary reasons did vary by vertical market. In financial services, more respondents felt that a lack of relevant technology contributes to their lack of trust where as manufacturing focused more than other industries on the inadequacies of current relevant technology.
  • Liz: So how are people managing their contact data quality currently and is that contributing to the errors? Erin: Globally, organizations are managing data accuracy. However, they are using a lot of manual processes, which do not prevent human error, the main cause of contact data quality errors. There were several common manual processes that were used. The first manual process is measuring the response rates from marketing campaigns, which can be through sorting through return mail or email bounce backs. Next is analysis in excel spreadsheets, perhaps through pivot tables or sorting, and finally manually examining the data in the database row by row. Liz: I can’t imagine how much data some of these large organizations must be manually going through. Erin: It must be very time consuming for staff members to manually go through all of that data. But despite the fact that organizations are using manual methods to clean data, there are companies that are using automated processes like point-of-capture software and dedicated back-office software. In fact, more organizations in the US use at least one of these automated method for managing the accuracy of contact data than any other country we surveyed in Europe or APAC. Liz: What about in the vertical space? How do they compare to one another? Erin: They are still using manual processes across industries. However, more organizations in utilities are using dedicated point of capture software, which verifies data prior to entry, than any other industry and education is the least likely to use these tools. However, financial services are most likely to use back-office tools that clean existing data. But for those organizations that are improving their data quality they are seeing a big pay off. 81% of U.S. organizations say they have upgraded their data management system in the past two years. For those that made improvements, they saw an average return on $1.29M in extra profits. You can see from the graph on the screen that the U.S. was one of the countries that saw a lower return on investment from these improvements. The UK saw over $9M while New Zealand saw just under $5M. However, you do see lower levels of return in The Netherlands. Across verticals there were also larger returns. On average, the financial sector saw $2.316M in extra profits and Utilities on average saw $13M. This could be partly because these two industries are most likely to be using automated methods for data entry, rather than manual processes. Liz: Wow, those returns are surprising, but when you consider how much contact data is used across an organization, it really does touch a lot of areas.
  • Liz: But when we talk about strategies, software as a service always comes up as a method of introducing new tools and it is certainly a hot topic within the industry right now. Did we ask any questions around Software as a Service in the study? Erin: Yes we did and I agree that it I a hot topic. Many organizations are looking at how they can reduce their IT infrastructure. That’s part of the reason why companies like and Oracle On Demand are becoming so popular. We asked the respondents in our survey if they had any concerns around putting their contact data in the cloud and 90% said yes. Most listed security as a top concern, but a secondary concern was loss of control. In the vertical sector, organization in financial services and education have more concerns about putting data in the cloud compared to all of the other sectors we surveyed. We think people will use this tool to manage their contact data, but it will take some time before they are willing to put all of their data in the cloud.
  • Liz: Another hot topic now is the environment and going green. Organizations are always talking about more ways to become environmentally friendly, did we see that as a priority from the research? Erin: Well while organizations do think improving contact data can help them becoming green, it is certainly not one of the main reasons they improve contact data. Globally, 92% of organizations admit that their contact data management and marketing practices could be improved to support more environmentally-friendly marketing. When we asked them how they would do that, 54% said updating contacts who move would help environmental practices. Other practices mentioned were increasing email communications over direct mail, more frequent de-duping of databases and better targeting through segmentation and profiling. However, when organizations were asked about why they maintain quality contact records, only 13% said they do it to help to environment. So efficiency gains, customer satisfaction and revenue far outweigh the green initiative.
  • Liz: So now that we have gone over the research and what companies around the globe are doing about data quality, how can organizations go about picking the right practices to clean their data. Erin: Well first businesses need to automate processes and not rely so much on manual processes that can not prevent human error. Because each business has different contact data quality problems and needs, it is important that analysts tailor solutions to fit their own needs. To accomplish this, they need to spend time with their data before selecting a solution. First, look at data usage. See what data is used more frequently. Does the business rely more heaving on email or direct mail to communicate with customers? Those types of questions will help analysts see which data sets are used most, so they can decide on tools that will prevent errors in future data and repair existing data the most frequently used pieces. Next, look at data entry points. Understanding the flow of data allows businesses to select tools for those given points of entry. Websites, call centers, POS locations, and in-house data entry should all be reviewed and see what data is captured at each point and the common errors that occur at each entry. Finally, review common data errors. Ask yourself what are the most error-prone pieces of data. You can do this by running information through a batch-type engine and it can flag what is incorrect. Again businesses said that 15% of their database might be inaccurate, that means that there are probably trends and data that is consistently inaccurate throughout the database. Having a better understanding of the data will help businesses select the right tools. And even though most of the data quality errors related to human error, some did relate to budget. By reviewing this information ahead of time, it will allow businesses to prioritize projects, allowing them to improve data in stages to help with budgetary restraints.
  • Liz: that is great advice. While getting to know your data is an important step before you choose a solution, with today’s economy stakeholders also have to show return on investment with any project, but especially in IT. What can businesses do to help demonstrate that return on investment? Erin: That is a great point Liz. To prove a return on investment, businesses really need think through the data and processes before they start a project, kind of like when selecting a data quality tool. First, they need to set objectives for the overall business and benchmark the current situation. You can use some of the same data you collect before using a tool but that performance will allow you to compare yourself against competitors and industry standards. Then, set target metrics so where you want to be when the project is finished. Think about the main reasons you are maintaining contact data. Is it to improve efficiency, reduce expenses, or improve customer service like the rest of the globe? Maybe you have different reasons that we didn’t talk about today. Your metrics should relate to those main overarching themes and then you need to communicate that vision to stakeholders. Then after the project is rolled out and some time has past, evaluate those same metrics again and take a new benchmark. See if there was a list in performance or what changed. Then you can communicate the performance to the businesses and see if the project was really successful. If you chose to roll-out the project in stages, it will give you enough information to decide if the next roll-out is going to be worth your while.
  • Liz: So to summarize today’s presentation we wanted to reiterate that businesses are investing in data quality and are seeing a significant uplift to the businesses by improving it. Erin: Yes but organizations are relying too heavily on manual processes to clean up data. They need to move to more automated methods, but they should choose a solution that is right for them. Evaluate the data and get to know it before making a decision and before that you take benchmarks before and after so you can make sure you prove the return on investment required by most businesses in today’s economy. Liz: Well Erin thank you for all of that great information. We are going to bring you back in just a minute for a Q&A but first….
  • Liz: I wanted to provide a brief overview of the Experian QAS products and services. These are more automated methods for cleaning contact data that will help eliminate the possibility for human error. Lumped into two categories: Pro software is front end AV that can sit on a PC, be a SaaS, or web based. We also have real time Email and Phone verification Our back end cleansing solutions include our batch process, bulk processing, and phone and email Our Enhancement solutions include a de-duplication tool and NCOAlink service. In addition Experian EMS has a host of other enhancement products and services.


  • 1. Data Quality Survey Review: Collecting Accurate Contact Data in a Multichannel Environment Wednesday, August 17, 2011 Teleconference: Dial-in: X Passcode: X
  • 2. Welcome! Introductions and Overview of Today’s Session
    • Experian QAS findings from June 2011 research
      • Review research findings
      • Discuss interesting trends within and across markets
      • Provide tips for improving multichannel contact data quality
    • Today’s speakers:
      • Erin Haselkorn
        • Marketing Programs and Data Quality Research Specialist, Experian QAS
      • Product Manager
        • Product manager
      • Courtney Fulton
        • Marketing Programs Manager, Experian QAS
  • 3. Key Takeaways
    • Channels of communication are diversifying
    • Current accuracy levels are low
    • Manual cleansing processes are common
    • Consider all channels for cleansing
  • 4. Research Methodology
    • December 2010
    • 1,320 respondents from 7 countries
    • Produced by Dynamic Markets
    • Multiple industries
    • 250+ employees
    • C-level executives, vice presidents, directors, managers and administration staff
  • 5. Data Quality Matters
    • 87% of U.S. businesses have a documented data quality strategy
      • Data quality strategies are most common in the U.S.
    • Companies improve contact records to:
      • Increase efficiency
      • Enhance customer satisfaction
      • Generate revenue
    • APAC is more focused on informing business decisions
    • The financial industry wants to improve contact records to comply with regulations
  • 6. Data Quality Matters
  • 7. Low Level of Accuracy
  • 8. Low Level of Accuracy
    • 89% suspect customer/ prospect data inaccurate in some way
    • 24% of total records inaccurate
    • U.S. most confident; APAC region least confident
    • Education was least confident; utilities and financial services are most confident
    • 79% experienced at least one negative consequence in the last 3 years from data accuracy issues
      • 87% wasted departmental budget
      • 30% negatively impacted customer perception
      • 26% lost a potential customer
  • 9. Reasons for Inaccuracy
  • 10. Current Strategies
    • 87% manage data accuracy in some way
    • 73% of U.S. businesses use at least 1 manual process, compared to 64% globally
    • 81% of organizations upgraded data management systems in the last 2 years
    Return from Data Quality Improvements
  • 11. Current Strategies
  • 12. Current Strategies
  • 13. Find the Right Contact Data Quality Tool
      • Review data usage
      • Examine entry points
      • Evaluate data errors
  • 14. Demonstrate ROI
      • Set objectives and benchmark current performance
      • Establish target metrics
      • Communicate vision to stakeholders
      • Evaluate based on metrics
      • Present results to stakeholders
  • 15. Summary
    • Businesses are investing in data quality
    • Significant uplift is seen from improving data quality
    • Organizations rely too heavily on manual processes
    • Evaluate data prior to choosing a solution
    • Demonstrate ROI and make sure the solution is working
  • 16. QAS Products & services Real-time verification
    • Address
    • QAS Pro
    • QAS Pro On Demand
    • QAS Pro Web
    • QAS Pro API
    • Phone and Email
    • QAS Phone
    • QAS Email
    Clean & enhance
    • Clean
    • QAS Batch
    • QAS Bulk Processing
    • Phone & Email Batch
    • Enhance
    • QAS de-duplication
    • NCOA Link®
  • 17. Questions ? Submit your questions now! Questions after the event? Email: Call: 888-727-3985 Visit:
  • 18. © 2011 Experian Information Solutions, Inc. All rights reserved.
  • 19. Please visit for more information.