KQIs (Key Quality Indicators) To Measure Data Quality

At the recent MIT Information Quality Industry Symposium, the hot topic was measuring the impact of data quality programs. In a bad economy, it makes perfect sense. If your company is cutting programs, you need to justify your data quality initiatives, or they too will be cut. My favorite presentation on the topic was from Delphine Clement, whose topic was the, “Cost of Non Quality Data.” I thought that was an interesting way to look at it, and she presented a very mature view of Data Management. Delphine credited sessions from previous MIT Information Quality Symposiums with some of the underlying theory. I’m sure there are others to credit as well, and if you know the history please comment.

Delphine reports on the Key Quality Indicators (KQIs) that matter the most to her business partners. She has taught the business community that KQIs are needed to build confidence in the KPIs. I like that the KQI approach mirrors the KPIs (in naming and level of importance), and that they are presented as a complementary report. Think of this as the metadata for the KPIs. That’s the way I rationalized it.

KQIs would make sense to any Data Quality lead, but it might not to a VP of Marketing or VP of Sales. It’s not their job to care how we do ours. So how do you bridge the gap with the executive KPI users? You must understand their needs, and show them that the KQIs are driving the data quality projects in your organization. They will only care if the KQIs help to resolve their issues. Also, KQIs may be used to show them progress in your data quality programs. When you complete a project and are able to turn a yellow (cautionary) indicator to green (good), they will understand how the project affected their work.

Delphine’s approach begins by asking business leads and other data users a simple question, “How should we measure data quality.” She gathers feedback via surveys from her business customers and measures progress through response trending over time. Sounds like internal Marketing, right? Delphine also presented a methodology for measuring direct vs. indirect cost savings from Data Quality initiatives. She has clearly spent a lot of time working on this approach and is doing a great job. I really enjoyed this presentation.

She also recommended involving the end users early on to define:

• What are the Key Quality Indicators (KQIs) that are important to the business?
• Should the KQIs be global or local?
• What is the cost of poor quality data?
• Are the KQI’s different by country?

I love these questions. Simple, direct, and open. Rather than telling our peers how we should be measured, ask them and include them in the KQI process.

Demographic vs. Firmagraphic Appends

Note: This is a continuation of the B2B vs. B2C series. I will also be presenting some related material at the MIT Information Quality Symposium next week (MITIQS 7/15-7/17 2009). If you are attending, please introduce yourself.

Data Acquisition is an important part of any data driven Sales and Marketing program. I associate the append process with Marketing since that’s where these programs are generally funded, but the clear beneficiary of the work is the Sales organization. And of course, it takes a partnership between Technology, Sales and Marketing to make data acquisition programs successful. A data file on a shared drive or standalone table in a database has little or no value. The data has to be integrated with customer and prospect lists and loaded into the CRM and SFA systems and / or presented through your BI/DW tools to show ROI. Sorry… I got on a roll with the acronyms. They’re defined at the bottom. I’m in a Data Governance role now. Acronyms and definitions are my life (in a scary, scary way).

This raises the topics of Data Cleansing, Address Standardization, Merging & Matching, Deduplication, Archiving, etc. I’ll save those topics for future posts, but would encourage questions in the comments section. It’s fair to say that without a Data Quality focus you won’t achieve the optimal results for either Demographic or Firmagraphic Data Acquisition program.

Demographic Appends

Demographic data is generally used by Marketers and Analysts to determine the best way to target a consumer base. Starting with a universe of customers or prospects, a company can buy information on:

• Credit History
• Purchasing Patterns
• Housing Location and Situation, e.g. owner, renter
• Salary
• Lifestyle / Family

You can also buy data that segments customers based on the provider’s best information. Some is based on geographic of financial information. Others are based in buying patterns. Sometimes they even have cool segment titles like “Boomer Barons,” “True Blues,” and “Jumbo Families.” (All of those are from the Acxiom Personicx family of pre-defined segments). The goal of a demographic append is really bulk segmentation. How do I get enough information about my consumers to segment them for marketing and analysis. In most cases, the purchaser doesn’t care about any single individual or household, other than to get them into the correct program. The marketers are looking at sets of consumers, and then targeting from there.

In very high end consumer sales, e.g., luxury items, high net worth banking etc, you may find cases where sales people will use the appended data at the individual level. But generally it’s used for grouping customers into buckets, finding buying patterns and trends across like customers, and performing analyses such as next best product, lifetime value, and similar. Consumer sales is so transactional, that there isn’t time to research before any one conversation.

Firmagraphic Appends

B2B data, which I prefer to call “Firmagraphic” but have also seen called Firmographic or Firma-graphic, can provide value for not only segmentation, but also for pre-call research. B2B Sales is moving toward a more consultative approach where the sales rep becomes more of a partner with the purchaser. The best sales reps do this across all businesses. Rather than try to sell one product or service (transactional), the sales rep tries to understand the company’s need and deliver a suite of prodcuts & services and sometimes even the workers to use them (in an outsourcing arrangement). I’m sure if you’re reading this you’ve seen the varying styles of transactional and consultative sales reps.

To arm those consultative sales reps with the appropriate information, companies often purchase firmagraphic data and load it into their CRM systems. This data may be grouped into:

• Parent Child Relationships
• Locations of Related Companies
• Contact Information for Executives
• Industry Codes
• Number of Employees
• Revenue

This allows the rep to quickly look at the companie’s situation, and taylor their initial pitch accordingly. “I see you’re in a fast moving industry, with over 100 employees, and that you’re company has decentralized offices across the country. Have you heard of Product A that might meet your needs?” That conversation can only happen to a perspective customer if a firmagraphic append has happened previously. Which reminds me that another form of firmagraphic purchase is the ubiquitous, “Get 200 leads in your target industry,” but that’s not what I’m talking about here.

Segmentation of B2B customers is also sometimes based on Firmagraphic appends,  but as mentioned in the original B2B vs. B2C post, householding in B2B is focused on creating parent child relationships among your customers and prospects. You should also use all of your existing customer information including location, purchasing and servicing history, and past communication response to segment your customers. Once you have those relationships built, you can begin to analyze your coverage of headquarters and branches based on firmagraphic data.

Analytics then focuses on similar models for next best product, lifetime value, likelihood to purchase, likelihood to respond to certain campaigns and others. The tools to d

Acronym Glossary

CRM: Customer Relationship Management. Generally refers to the systems used by Sales and Marketing teams to store and organize customer contact information, purchasing, and servicing history. CRM systems also pump out lots of data used for operational reporting and as inputs to customer analysis and segmentation.

SFA: Sales Force Automation. Tools used by Sales Reps to manage their actitivities. This would include follow ups/reminders, appointments, leads, renewals, etc.  SFA systemsalso includes the operational reporting of those activities in some cases.

BI: Business Intelligence. The analytics and reporting tools built on top of the Data Warehouse. Business Intelligence can also be used to describe the practice of analyzing data to determine important insights and drive strategy. These tools look across CRM, SFA, manufacturing and financial systems.

B2B: Business to Business. Marketing and Sales activities along with associated products and services targeted at business customers.

B2C: Business to Consumer. Marketing and Sales activities along with associated products and services targeted at individuals or households of consumers.

ROI: Return on Investment. A simple calcualtion that takes the difference between the cost of a program and the returned value from it, and divides it by the cost of the program. So if I spend $100 and make $150, my ROI would be the $50 of benefit over the $100 of cost: .5 ROI. People often forget to subtract the cost from the numerator and inflate their ROIs.

Upcoming Blog Posts

June has been a light month for blog posts. I’ve settled on upcoming topics, and even drafted my next couple of posts out. Why the slow month? I took a detour into Lean BI research that proved uninteresting to write about. I’ll scrap that. Gone also is my brief foray into Cloud Computing for BI. I’m just not an expert there. Good to learn about, but nothing I want to associate my name with yet. I also had to knock out a conference presentation for this Summer.

I’ve settled on the following upcoming topics. Bear with me while I get them out there. Enjoy the previous posts in the Data Governance folder. Also, please comment if there are any areas you’d like me to explore.

1) Lightweight Data Governance: A Starting Point

A continuation of the previous post. I’ve finished my MIT Information Quality Symposium presentation for this Summer, and think it would be helpful to write some background on the theory I’m developing. In a recent conversation with a Data Governance colleague, they referred to the work as “different from what everyone else is doing.” I hope that’s a good thing. Either way, it confirmed that the presentation will be provocative for that group. So I’ve got that going for me.

2) Data Quality and Data Governance Blogs I’d Recommend

I’ve been keeping a list of those bloggers I think are really good. I look for people who put their opinions out there, and keep the topic light. I’m also a fan of those who are tool agnostic. Too many in our field are married to their vendors. It’s a bad position to take.

3) Demographic and Firmagraphic Appends

A continuation of my B2C vs. B2B series. In this post I’ll explore where the value is, how to incorporate and whether you should even bother. Have you maximized your own data first? Are there other ways to get access to this data for free? Some podcaster’s I follow think so. More to come in the blog post.

Are You Spamming Your Customers?

Marketing Compliance is a critical area that most companies don’t do well. If you haven’t read the summary of the CAN SPAM act, you should. As a consumer, you should know. As an email marketer, you need to know. From the always hilarious FTC Consumer Protection page:

“The CAN-SPAM Act of 2003 (Controlling the Assault of Non-Solicited Pornography and Marketing Act) establishes requirements for those who send commercial email, spells out penalties for spammers and companies whose products are advertised in spam if they violate the law, and gives consumers the right to ask emailers to stop spamming them.”

Can I start by pointing out that the government thinks of Marketers and Pornographers in the same category? I love that. Think of the ramifications! These guys should go into comedy. What’s next? Auditors and Strippers grouped together for Wall Street regulations by the SEC?

But seriously… Every time I receive a commercial email, I look for the common traces of a well produced email marketing campaign. Often I find critical elements missing. CAN SPAM points out that all campaigns must 1) Not contain false or misleading header information; 2) Contain an opt-out mechanism that leads to an opt-out request processed within 10 days, and 3) Include a valid postal address. Pretty basic, right? Most don’t. Email addresses are spoofed all the time. Subject lines contain misleading information from real companies. Opt-outs are non-functional or not even included. And there are many companies that fail to include an appropriate footer including a postal address.

In one recent example, I opted out of a campaign at the Corporate Executive Board. They are an established organization and should be very good at Marketing Compliance. I don’t usually include real examples, but you’ll see that the CEB earned this review. I wasn’t interested in purchasing their services, so I sent a note to the sales rep who had been contacting me to let him know that I’d like to opt-out of any future communication. I included the subject line “Unsubscribe,” which will be important later. At that point a series of steps should happen.

1) The rep should flag me in whatever CRM system (Siebel, Salesforce.com, etc.) he uses to track his communication with customers. If he’s doing it manually, he should delete my email address from his Outlook, but I assume the Corporate Executive Board is more sophisticated than that. Maybe not.

2) He should alert the Marketing department that I no longer have interest in receiving their campaigns. This could happen systematically by integrating a Marketing Campaign Management system with the CRM system, or it could be an email to a marketing administrator. The latter is more likely.

3) My email address should be flagged as opted out, and all future marketing campaigns should suppress that email address.

I know that something broke down in that normal process flow. How? I received another email in a campaign during the next month. Now at this point there’s been no violation of CAN SPAM. I didn’t use the systematic opt-out in the email. I worked through a sales rep, so all that has occurred is a poor customer interaction. This time, I clicked on the opt out (to ensure the process would work) and sent a friendly note to the rep letting him know that something broke down. I forwarded my previous confirmed request and included a new subject line - “Unsubscribe - 2nd Request.”

Two months later I received another email. This time I clicked on the opt-out link, and reviewed the company’s privacy policy. They suggested on their own site that any questions should be directed to their Legal Department, and even included an email address for convenience. After seeing that I again forwarded the previous thread, CCed the Legal team, and let them know that I had a background in Marketing Compliance, and that they were not living up to their CAN SPAM responsibilities to opt me out. Subject line - “Unsubscribe - 3rd Request.”

I received an apologetic email from the Sales rep letting me know that they had converted their email marketing system to a new vendor and that they were still working out the kinks. That prompted me to do a little research, and I quickly discovered that they were using Eloqua (look for the default server name of en25 in the email address). Eloqua is a SaaS marketing campaign tool that has received high marks from the community. I know a little about Eloqua, and was elated. The tool is unique by email address, so there is no chance for ambiguity. All they needed to do was flag me using the built in functionality, and no more emails to Mr. Mark Goloboy.

The next month I received another email. At this point I was dissappointed more than mad. I now knew how easy it was for the Corporate Executive Board to get it right. As a marketing compliance expert, I felt that they were letting the whole community down. I sent another email, but this time let them know that they had now twice violated CAN SPAM. I again included the Legal Department and detailed the history including who had sent recent emails. You guessed it. Subject line, “Unsubscribe - 4th Request.”

The next day was the clincher. A new sales rep reached out with an unsolicited email (read: SPAM) to an opted-out prospect who had already put the Corporate Executive Board’s Legal Team on notice and documented process failures. At this point I asked for the email to be forwarded to the CEO, CMO and Head of Sales so they could see the team’s failure to accomplish basic communication tasks. The subject line, “Unsubscribe - 5th Request.”

That was the last I heard from the Corporate Executive Board, but I’ll be sure to update this blog post if they send any more.

Have you had a similar experience? Please comment.

Letting B2B Data Die or: How I Learned to Stop Worrying and Ignore the Problem

I had a conversation with a colleague from another company recently. They mentioned a data quality project to clean up old B2B CRM Data.  I had to stop and ask, “Why?” This wasn’t just old data, but really old pre-acquisition data from another source system.

We discussed further and I found that the data in question was sales data from 2006. When it was migrated the database keys were butchered, and many of the relationships were lost. It was going to take  four FTE resources over 6 months to fix the problem. In a bad economy, I just couldn’t justify why any company would spend that effort. My suggestion… Dump the data from the reporting tables. That’s right. Delete it. Archive it for audit purposes, but get it out of the way and focus on today’s problems.

My colleague was shocked. Here I am, a Data Governance expert and Data Quality evangelist telling them to ignore the problem. My reasoning? You need to constantly prioritize your projects. If something is more valuable in terms of revenue, or presents a greater risk, or is a bigger pain to more people, fix that first! Don’t dwell on perfection. You’ll never get there. Just try to make the most improvements you can, as quickly as you can. Grab the low hanging fruit rather than re-planting the tree.

If this offends your data quality sensibilities, please comment. I’m curious to know whether my opinion resonates.