Monday, March 3, 2014

How Your Brand Helps Your "Rank"

Building Your Brand Digitally 

One of the most important aspects of data-driven digital marketing is branding.  Branding can be simply put as a 'promise' or how the company is perceived by its customers.  Google wishes to be the world's information conduit, Facebook its communications conduit and its commerce conduit.  Each of these firms has a clear idea of what and who it is and relates that information to the outside world.

Branding is important in digital marketing because brand equity is the value of our firm to that outside world. Coming up with our brand image is the first step, after which we develop our brand story and generate content on various channels. These activities create awareness and recognition for our brand and finally, brand equity.  Brand equity has both a financial component in terms of our brand's value and a component of competitive advantage in terms of how our brand is positioned.

For most firms, the challenge becomes integrating the brand promise not only with the firm's offline communications but online as well.  In fact, it is my personal belief that branding is more important now that we are so reliant on the internet to achieve our marketing objectives.  Branding as a concept can be elusive to those who seek to quantify their digital marketing efforts but we can see the results on SERPs (Search Engine Results Pages). There are many ways to reinforce our brands online:  Search, social media, mobile communications, email, etc.  Each of these forms of communication must reinforce who we are and how we wish people to perceive our brand.

branding, search
Branding Leads to Search Success

Search Rankings Reflect Brand Strength

However, I have often said that search is strategy.  Understanding who we are and how people search for us is the most critical aspect of our digital marketing management process.  Search engines like Google give acknowledgement to branding efforts.  You may have noticed that when you type in a product name large retailers like Amazon and Walmart come up first, with specialty firms showing up sometimes not until the second page of the search results.

The reason for this result is that Google's algorithms give special attention to strong brands because they are trying to 'cut through the clutter' on the internet and provide searchers something of value.  Small companies need to work even harder than large companies to build their brand.  Starting with a clear story, telling that story through related content through online channels will increase the chances of having a strong brand.  A strong brand will in turn rank highly in search results.

By Debra Zahay-Blatz.
You can find Debra on  and Twitter as well as LinkedIn.

Friday, January 31, 2014

Top Three Reasons why B2B Marketers Struggle with Data Analytics

Why does B2B Marketing Struggle with Data Analytics?

A  report from the CEB (Corporate Executive Board) Marketing Leadership Council on the Digital Evolution in B2B Marketing echoes our own research and indicates that B2B marketers are struggling with implementing data analytics solutions in their firms.  Not Surprisingly, the issue is often one of data quality. This illustration shows that poor data quality and analytics capability leads to a lack of meaningful insight, which in turn can lead to a lack of funding for data quality and analytics efforts.

CEB Marketing Leadership Council Report on the Digital Evolution in the B2B Marketplace, 2012

Lack of Results Leads to Lack of Funding

The result of poor data quality is a vicious circle where marketers cannot get the proper funding for their efforts.  Our research results reinforce the results shown in this graphic but also link data quality to customer performance.  In our recent paper in the Journal of Interactive Marketing volume 27, Issue 1, February 2013co-authors James Peltier, Don Lehman and I present a model with empirical evidence from the banking industry, both B2B and B2C applications.  The article is called Organizational Learning and CRM Success: A Model for Linking Organizational Practices, Customer Data Quality, and Performance.

In the article we measure customer data quality in the organizational context.  We not only show the linkages from customer data quality to ultimate firm performance as measured by growth, we explore this result in an organizational context.  Without the cultural that values analytics and data, data quality cannot be obtained. Culture means not only top management support but parts of the organization working together.

Top Three Reasons Why B2B Marketers Struggle at Analytics

Therefore, the top three reasons I see why B2B organizations cannot achieve analytics success are as follows:

1) Lack of a cooperative culture that supports data quality.
2) Lack of support by upper management for data quality efforts.
3) The resulting poor quality data that leads to poor decisions.

I will follow up this blog post with more information about the link between data quality and customer performance as reported in our research.  I thought the CEB report hit the nail on the head in terms of illustrating the grim consequences of a lack of customer data quality and the conclusions dovetailed nicely with our own research results.   In the meantime, please feel free to contact me with any questions about this material or our article.

By Debra Zahay-Blatz.
You can find Debra on  and Twitter as well as LinkedIn.

Saturday, November 16, 2013

Secrets of Innovation and Data Management from Kayak

Terry Jones, former Ceo of Travelocity, spoke a the recent DMA conference at McCormick Place in Chicago.  Mr. Jones not only founded and acted as CIO of Sabre, Inc, but also functions as Chairman of  Terry has a proven track record of leading companies whose innovations have change the travel industry and has used data-driven digital marketing to spur company growth.

We Need Not "Big Data' But "Big Wisdom"

One of Jones'  themes was data-driven digital marketing and its role in innovation.  Terry said, and I agree, that as marketers we don't need 'big data' as much as 'big wisdom'.  Only about one percent of data is acted upon in corporations.  We need people with vision to know how to harness the data the companies already have. Jones said we need to get rid of the "Bozone" Layer that stifles innovation by using smaller, more effective teams and other techniques to encourage new ideas.
bozone layer, innovation
Terry Jones from Kayak on How NOT to Stifle Innovation
Jones spoke of the changes in marketing and the challenges these changes post to marketers.  He said it took 40,000 years for men to invent the fishing net, another 23,000 to invent the fish hook and less than four years for the majority of American households to have a tablet computer.   When customers trust reviews from people they have never met on the internet instead of the brand, the customer is extremely powerful, "wired and dangerous."  And this customer expects your site to be the best, 24 by 7.

We Must Inspire Employees to Innovate

To reach the current customer, who is Internet empowered, tech savvy, time starved and information, rich requires managing and inspiriting innovation in the corporation.  This approach is consistent with research my co-authors and I have conducted on how to organize effectively to manage data in the corporation.  This research indicates that top management support is critical in creating quality, actionable data.  We have also conducted research about how data is used throughout the corporation in the innovation process.

Using his own firm as an example of innovation, Jones said that Kayak relies heavily on data analysis in designing its web site.  He said 20% of everything you see at the Kayak web site is a a test and that he encourages testing and does not punish failure.   He encouraged companies to "Kill Projects, Not People" and hire people who don’t 'fit' the mold.  He also said most innovation comes from the bottom, from those who are talking to customers.

Those Who Survive, Adapt

Jones quoted Darwin saying those who survive are not the biggest and stronger but those who most respond and adapt to change.  Today, we can use the internet and data analysis to build virtual firms that react quickly.  Jones said Kayak, a comparison engine for travel sites, went public for 1.8 billion dollars using open source code, search engine technology and only 220 people.  Certainly this model poses a challenge to us all to innovate and create by managing our people and data well and using 'wisdom' and not just 'data.'

By Debra Zahay-Blatz.
You can find Debra on  and Twitter as well as LinkedIn.

Monday, October 14, 2013

Top Takeaways on Big Data Talk from Don Hinman

Big Data versus Small Data

Don Hinman from Epsilon Data Services was the keynote speaker yesterday at the +Marketing Edge Marketing EDGE Research Summit. Don spoke about big data versus, small data (how we used to do data analysis).  Don said data has four elements, which are volume, variety, velocity and veracity. The volume and velocity of data is increasing exponentially and at a rapid rate.  Hence, we have a need for analyzing large data sets.

big data, small data, data scientist

Therefore,  big data allows company to take data from and about their customers and put it together in such as way as to get new insights and revenue streams.  Big data sets are data that is too large for traditional analysis and also includes not only numeric but unstructured data that might be text information from customer interactions, videos, etc. 

Data Explosion Means New Analysis

Marketing data has exploded with  new data types, such as web logs and addressable TV. For example, soon the number of cell phone users will exceed the population. Don gave some examples of a big data application using tools Epsilon has to understand the effects of social media on other marketing channels, using big data techniques.  Epsilon can help companies take customer records, match to Facebook, connect the information on the email records and CRM to target ads more effectively on Facebook.  By appending Social URLS the company can match likes and followers  to identify the pattern of friendship and target and segment more effectively.  

Don joked that he did not realize until recently he was a Data Scientists, in spite of being in the field of statistical analysis, playing with data,  for 30 years.  Big Data will require Data Scientists, those who can work with math, statistical  computer science and marketing and have healthy curiosity and skepticism,

What's Next?

He also said that the future holds

  • More focus by government agencies on regulating data firms
  • The Internet of things,  which connects the physical world to the internet
  • New versions of Internet protocol and use of more sophisticated ways to locate resources on the web

Thanks Don for taking the time to speak to us.  It was an informative talk and we appreciated your time.

By Debra Zahay-Blatz.
You can find Debra on  and Twitter as well as LinkedIn.

Monday, August 19, 2013

Middle Management Secrets for Big Data

Middle Management Lost in the Big Data Shuffle

If data quality gets lost in the shuffle in Big Data implementation, so also does the role of Middle Management.  Despite the promise of data management and big data, many of the firms investing in customer information technology have  witnessed limited financial success from their data-driven efforts designed to get close to customers.

big data, middle management

As a consequence, many adopters became disillusioned and learned for themselves that customer-centricity is difficult to accomplish, requires a high level of coordination between IT and marketing, and involves a cultural shift with regard to how customer data are integrated and shared within and between functional areas (Zahay and Peltier, 2007). 

Data Quality Improves When Middle Management Feels Involved

The interpersonal and organizational factors of big data implementation, which  my co-authors and I have been studying since 2007, has only recently come to the forefront as critical to the success of Big Data projects.  In fact, our research shows that companies where middle management believes it is involved in customer data management and feels supported, data quality improves and so does firm performance. So it is critical to involve middle management in customer information processes.  

Middle managers, as our Dilbert, cartoon illustrates, often take a back seat or are assigned limited importance in organization. However, these managers play a key role in strategy execution, particularly in cross-functional efforts such as CRM and Big Data.  Prior work from the Gartner Group suggests that among the building blocks of successful CRM implementation, which is a customer data-dependent application are Organizational Collaboration, and Organizational Processes.    

How to Involve Middle Managers in Big Data

What does it mean to involve Middle Management and improve organizational collaboration?  These results are based on 128 responses from managers in the financial services industry.  In our survey we asked middle managers three key questions to which they responded on a scale of 1-5 with 5 being strongly agree and 1 being strongly disagree.  The questions were as follows:

  1. We feel comfortable calling our upper management when the need arises
  2. Our marketing management is responsive ot our customer information ides
  3. Marketing managers can easily schedule meetings with upper management

Marketing management support was strongly correlated with customer data quality, which in previous research we have demonstrated to be related to ultimate firm performance.  In a regression analysis, marketing manager support was also significant in predicting customer data quality.  These results are consistent with other research we have conducted (Zahay and Peltier 2008) and show the importance of involving middle management in the process.

Bottom-Up Strategy is the Key

It appears that in order to ensure data quality, the firm’s middle management and upper management must have an open and communicative relationship.  Consistent with findings from practice, the most successful organizational relationships in these companies had a clear role for middle management in translating the language of quality customer information management to upper management.  These results are also consistent with several schools of thought in strategy, which support the idea that strategy comes from the bottom up, or at least from middle management back to top management (Bower 1986).  

By Debra Zahay-Blatz.
You can find Debra on  and Twitter as well as LinkedIn.

Saturday, July 6, 2013

Being Smart about Customer Data Quality

Data Quality Impacts Firm Performance

With all the talk about Big Data, data quality sometimes gets lost in the shuffle.  The fact is, if your database is not of high quality, there will be a number of serious consequences to your business. I recently gave a talk at eMetrics in Chicago on Organizing for Customer Data Management.  As part of my presentation, I spoke about how to organize your company for data quality and the fact that customer data quality leads to better firm performance.

Are B.D. Zahay and D.Z. Blatz the Same Person?

If you are skeptical, let's look at an example below.  I recently received two credit card offers from the same company, two different names.  Let me explain that I was Debra Zahay and then upon my marriage four years ago, I became Debra Zahay Blatz.  I changed my name legally with the DMV and social security administration. 

customer data quality

How Come I Get a Better Offer if You Don't Know Me?

Like any marketing professional with roots in direct marketing, I enjoy analyzing my mail offers.  Perhaps the most interesting mail recently was this set of offers.  The offers were to the same address, but two different offers to two different names, one person.  The first offer was to my real name, Debra Z. Blatz for a credit card I hold, with an offer for 0%  promotional APR for balance transfers until 6/1/2014.  Since I don't really carry balances on my card, this was not a very interesting offer or relevant to me.

The second offer was to someone called Blatz D. Zahay.  Apparently in some computer systems,upon marriage, the woman retains her maiden name, but takes the man's last name as her first name and makes her old first her middle name.  Blatz D. Zahay got a really good offer for a credit card with 0% APR on balance transfers AND purchases for 21 months, not just until 6/1/2014.  The card also offers travel rewards.

As I look at these offers I can see that the company though that B.D. Zahay was a new customer and made a better offer to her than D.Z. Blatz, a current customer.  If you know anything about data cleansing and data quality, most merge/purge processes would have picked this up because the address was the same.  I am guessing that what really happened is that my name got mixed up on some list or other and that this list was purchased by the company for prospecting purposes.  Most companies still tend to treat new customers better than their current customers, even though it is customer retention that leads to profitability.

Poor Customer Data Quality Can Impact Customer Relationships

The negative aspects of what happened to me could be that I realize that the company doesn't know who I am and that I would get a better offer if I were a complete stranger.  This realization could lead to my seeking credit card relationships elsewhere.  This data management strategy also leads to the wrong offers going to the wrong current customers. A little research would have shown that I don't really carry card balances on my personal credit card accounts. Being smart about customer data quality means not making offers that have no relevance or might anger the customer.  

Being smart about data quality also requires patience, good processes in the organization and good support from upper management.  The focus can't just be the current bottom line but what will happen with the customer relationship. Maybe the company has figured out that it doesn't matter if it makes a few people re-think their relationship with the company.  That is not a chance I would like to take but I do understand that the firm may have analyzed its bottom line and realized that some customer opinions and reactions don't have that much impact on performance.  

Learn How to Organize for Customer Data Management.

You can look at my Slideshare for the presentation on how Organizing for Customer Data Management, leading to data quality in the organization and better firm performance. Int the talk I ask if everyone knows that customer data quality is important, why isn't everybody doing it?  The answer to this question is because data quality is difficult to achieve and maintain and requires top-level involvement.   I will be exploring various aspects of this presentation over the next few weeks.  My talk covered over ten years of research with over 400 firms so there is a lot to explore and discuss. I look forward to your comments. 

By Debra Zahay-Blatz.
You can find Debra on  and Twitter as well as LinkedIn.

Friday, June 7, 2013

How I Became a Data-Driven Digital Marketing Specialist

Who I Am

This is my first blog post for my new Data-Driven Digital Marketing blog and I wanted to introduce myself.  My name is Debra Zahay-Blatz (publishing as Debra Zahay) and I am a Professor of Digital Marketing, the co-author of the leading textbook in Internet Marketing, Internet Marketing, Integrating Online and Offline Strategies and the Editor of The Journal of Research in Interactive Marketing. I also have had my own Data-Driven Digital Marketing consulting firm since 1993, Zahay, Inc.

What I Do

With my co-authors, I have published widely in the area of customer information management. I currently writing a book that I hope will help companies understand how to organize and manage customer information.  You can read more about me on my company website or come here me speak on this subject at eMetrics next week on "Organizing for Customer Data Management."

Although I have a strong statistical and analytics background, my primary research interests are the managerial side.  I research how customer information can be managed for competitive advantage and recently I have been exploring the role of data quality in this process.  The above graphic illustrates my personal journey in customer data management. I can saw I came by this professional  pursuit honestly and, like many of you, through direct and database marketing. I teach Internet Marketing, including search and social media, as well as database marketing, analytics and data mining.

How I Began

The journey really began with receiving my Master's in Management from the Kellogg school at Northwestern University. (I should say that I went to the greatest marketing school in the world and studied Quant Methods and Finance, figuring if I knew something about statistics, numbers and the computer I would always get a job; who knew how marketing would evolve?).

This background eventually led me to a division of Dun & Bradstreet where we sold data and software to the investment community, banks, brokerage firms, investment advisers.  There I learned about software development and implementation, data cleansing and management.  We used direct marketing techniques and customer databases because we marketed B2B and it was the most cost-effective approach and produced the best results.  I was promoted from support, to sales to marketing management and eventually back to the field for sales management.  Along the way we always used customer information to inform our sales and marketing plans. (I always say if I had a nickel for every database I have created I would have about $1.50 right now.  Data drove many of my decisions).

What I learned about customer databases I employed in my next company as Sr. Manager of Vertical Marketing programs in the Central Division for MCI Telecommunications (now Verizon).  I got the job based on the success we had in the Teleconferencing division with targeted vertical campaigns.  For the division, we developed and implemented a targeted, integrated marketing communications campaign for our insurance vertical using multiple channels and integrating sales force communication.  Critical to our success was the development of a customer and prospect database, which involved integrating five different databases across the company.  It was a classic case of data silos and no consistent way to map customer data through a single identifier.

Our efforts paid off and we made millions on a $15,000 out of pocket investment and our staff time.  I presented the results of the campaign at my first global marketing conference, ESOMAR in Europe, and started thinking from that point on that I wanted to know more about customer information management and the link to profitability.

The Big Question

So for several years after that experience I would go to practitioner conferences and ask people, "What is the link between managing company information well and performance and do you have any information on that link?"  Long story short, I never found anyone who had answered this question to my satisfaction.  I was teaching at DePaul University by then and the department chair encouraged me to get a Ph.D. and the answers to my questions.

The Ongoing Answers

So I enrolled in the Ph.D. Program in Marketing at the University of Illinois at Urbana-Champaign and received my degree in 2000.  My thesis was on the role of strategy (positioning, customization and personalization), customer information management and ultimate firm performance. My econsultancy guest blog "Why Doesn't Everyone do Big Data?" summarizes some of this research.   I got the answers to my questions and have been sharing them with the academic and professional world.  I look forward to sharing my ideas on my new blog, listening to and interacting with my readers.  I have found specializing in Data-Driven Digital Marketing to be a wonderful career, rewarding intellectually and professionally and I look forward to giving back to our great community through this blog.

By Debra Zahay-Blatz.
You can find Debra on  and Twitter as well as LinkedIn.