Company Data Opens the Door to Data Business Success

The hype around creating data businesses is at an all-time high in the B2B media sector. With display ad revenue falling off a cliff and lead-gen business models becoming more challenged, there is a clear focus to diversify digital revenue further, and data models are a clear player in that picture.

The hype around creating data businesses is at an all-time high in the B2B media sector. With display ad revenue falling off a cliff and lead-gen business models becoming more challenged, there is a clear focus to diversify digital revenue further, and data models are a clear player in that picture.

But, as I talk to colleagues in the sector, many are trying to find their way on the data front. One obstacle in becoming a data player lies in the fundamental way media brands have their databases set up.

Most B2B media players tout that they have in-depth insights on users through their database. The problem with this approach is that it does not align with the way many companies want to start their data journey. While many marketers will look for one-on-one connections with people, the process of gathering data and gaining market insights often starts at the company/organization level.

There are many reasons why this is the case. On one hand, most of the data searches by a marketer start with trying to understand general trends across a market sector or region. While this data can be obtained at the individual level, it’s much easier to see these trends at the company level.

On the other hand, marketers are getting highly targeted at the companies they want to reach – at Edgell, we had a large technology provider come to us that wanted to reach only 300 companies at one point. While this can be done at the individual level, it’s much easier and more accurate to achieve with a company table structure in place.

Making Company Data Come to Life

Clearly, the lack of a company structure in today’s databases is hurting media brands in their data business launch efforts. But, how do we overcome this obstacle?

There are several ways to start. First, you need to shift your team’s thinking and culture. While capturing info on an individual is important, you have to put a structure in place where you’re equally collecting data at the company level.

Beyond the cultural shift, here are some tips that will help make your company table structure come to life:

Tip 1: Define what you need to capture

By adding a company table, you have a chance to bring valuable data elements to your database. So, you should use this as a chance to bring in data elements that you may not be capturing today. For example, you can bring in things like industries the company covers, overall company revenue, divisions, and other elements that you may not have in your database today.

Tip 2: Align data

The addition of a company table also allows you to align data across individual records. For example, in an audience-driven database, you can have one employee at company X that puts down divisional revenue while another puts down group revenue. Through the company table, you can use publicly available info from Company X’s financial reports or from tools like Hoovers to align all users from that company under a single revenue bracket.

Tip 3: Start with your top companies

Once you’ve built the foundation for the company table, you should develop a strategy for rolling it out to all of your audience members. But where do you start? The 80/20 rule is a good solution here. Take your Top 50 or Top 100 companies and roll the company table out to these organizations first. Likely, you’ll have the most contacts from these companies. That will allow you to create a good sample size for data collection/alignment and also a good way to build a plan for future migration.

Unlocking the Power of the Company Data

Once you’ve started supporting and collecting data, the next trick is putting the reporting tools in place to get value from the company data. Even more important is to have your company reports in place so your customers can leverage them for running market analysis reports, market trend reports, and more.

There are a number of options that can help on the reporting front. To me, one of the most powerful is the integration of a visualization tool like a Tableau or a Good Data. Visualization tools let you pull in different data sets, align them and create reports that can show the combination of data cross companies. This is extremely powerful when dealing with company data because you see trends around companies based on revenue, business/industry, and more.

Start Now

There is no doubt that many media brands are struggling to find their niche in the data business. While not the end-all solution, leveraging a company table in your database architecture is one way that you can better position your organization for data success.

Marketing and IT; Cats and Dogs

Cats and dogs do not get along unless they grew up together since birth. That is because cats and dogs have rather fundamental communication problems with each other. A dog would wag his tail in an upward position when he wants to play. To a cat though, upward-tail is a sure sign of hostility, as in “What’s up, dawg?!” In fact, if you observe an angry or nervous cat, you will see that everything is up; tail, hair, toes, even her spine. So imagine the dog’s confusion in this situation, where he just sent a friendly signal that he wants to play with the cat, and what he gets back are loud hisses and scary evil eyes—but along with an upward tail that “looks” like a peace sign to him. Yeah, I admit that I am a bona-fide dog person, so I looked at this from his perspective, first. But I sympathize with the cat, too. As from her point of view, the dog started to mess with her, disrupting an afternoon slumber in her favorite sunny spot by wagging his stupid tail. Encounters like this cannot end well. Thank goodness that us Homo sapiens lost our tails during our evolutionary journey, as that would have been one more thing that clueless guys would have to decode regarding the mood of our female companions. Imagine a conversation like “How could you not see that I didn’t mean it? My tail was pointing the ground when I said that!” Then a guy would say, “Oh jeez, because I was looking at your lips moving up and down when you were saying something?”

 

Cats and dogs do not get along unless they grew up together since birth. That is because cats and dogs have rather fundamental communication problems with each other. A dog would wag his tail in an upward position when he wants to play. To a cat though, upward-tail is a sure sign of hostility, as in “What’s up, dawg?!” In fact, if you observe an angry or nervous cat, you will see that everything is up; tail, hair, toes, even her spine. So imagine the dog’s confusion in this situation, where he just sent a friendly signal that he wants to play with the cat, and what he gets back are loud hisses and scary evil eyes—but along with an upward tail that “looks” like a peace sign to him. Yeah, I admit that I am a bona-fide dog person, so I looked at this from his perspective first. But I sympathize with the cat, too. As from her point of view, the dog started to mess with her, disrupting an afternoon slumber in her favorite sunny spot by wagging his stupid tail. Encounters like this cannot end well. Thank goodness that us Homo sapiens lost our tails during our evolutionary journey, as that would have been one more thing that clueless guys would have to decode regarding the mood of our female companions. Imagine a conversation like “How could you not see that I didn’t mean it? My tail was pointing the ground when I said that!” Then a guy would say, “Oh jeez, because I was looking at your lips moving up and down when you were saying something?”

Of course I am generalizing for a comedic effect here, but I see communication breakdowns like this all the time in business environments, especially between the marketing and IT teams. You think men are from Mars and women are from Venus? I think IT folks are from Vulcan and marketing people are from Betazed (if you didn’t get this, find a Trekkie around you and ask).

Now that we are living in the age of Big Data where marketing messages must be custom-tailored based on data, we really need to find a way to narrow the gap between the marketing and the IT world. I wouldn’t dare to say which side is more like a dog or a cat, as I will surely offend someone. But I think even non-Trekkies would agree that it could be terribly frustrating to talk to a Vulcan who thinks that every sentence must be logically impeccable, or a Betazed who thinks that someone’s emotional state is the way it is just because she read it that way. How do they meet in the middle? They need a translator—generally a “human” captain of a starship—between the two worlds, and that translator had better speak both languages fluently and understand both cultures without any preconceived notions.

Similarly, we need translators between the IT world and the marketing world, too. Call such translators “data scientists” if you want (refer to “How to Be a Good Data Scientist”). Or, at times a data strategist or a consultant like myself plays that role. Call us “bats” caught in between the beasts and the birds in an Aesop’s tale, as we need to be marginal people who don’t really belong to one specific world 100 percent. At times, it is a lonely place as we are understood by none, and often we are blamed for representing “the other side.” It is hard enough to be an expert in data and analytics, and we now have to master the artistry of diplomacy. But that is the reality, and I have seen plenty of evidence as to why people whose main job it is to harness meanings out of data must act as translators, as well.

IT is a very special function in modern organizations, regardless of their business models. Systems must run smoothly without errors, and all employees and outside collaborators must constantly be in connection through all imaginable devices and operating systems. Data must be securely stored and backed up regularly, and permissions to access them must be granted based on complex rules, based on job levels and functions. Then there are constant requests to install and maintain new and strange software and technologies, which should be patched and updated diligently. And God forbid if anything fails to work even for a few seconds on a weekend, all hell will break lose. Simply, the end-users—many of them in positions of dealing with customers and clients directly—do not care about IT when things run smoothly, as they take them all for granted. But when they don’t, you know the consequences. Thankless job? You bet. It is like a utility company never getting praises when the lights are up, but everyone yelling and screaming if the service is disrupted, even for a natural cause.

On the other side of the world, there are marketers, salespeople and account executives who deal with customers, clients and their bosses, who would treat IT like their servants, not partners, when things do not “seem” to work properly or when “their” sales projections are not met. The craziest part is that most customers, clients and bosses state their goals and complaints in the most ambiguous terms, as in “This ad doesn’t look slick enough,” “This copy doesn’t talk to me,” “This app doesn’t stick” or “We need to find the right audience.” What the IT folks often do not grasp is that (1) it really stinks when you get yelled at by customers and clients for any reason, and (2) not all business goals are easily translatable to logical statements. And this is when all data elements and systems are functioning within normal parameters.

Without a proper translator, marketers often self-prescribe solutions that call for data work and analytics. Often, they think that all the problems will go away if they have unlimited access to every piece of data ever collected. So they ask for exactly that. IT will respond that such request will put a terrible burden on the system, which has to support not just marketing but also other operations. Eventually they may meet in the middle and the marketer will have access to more data than ever possible in the past. Then the marketers realize that their business issues do not go away just because they have more data in their hands. In fact, their job seems to have gotten even more complicated. They think that it is because data elements are too difficult to understand and they start blaming the data dictionary or lack thereof. They start using words like Data Governance and Quality Control, which may sound almost offensive to diligent IT personnel. IT will respond that they showed every useful bit of data they are allowed to share without breaking the security protocol, and the data dictionaries are all up to date. Marketers say the data dictionaries are hard to understand, and they are filled with too many similar variables and seemingly conflicting information. IT now says they need yet another tool set to properly implement data governance protocols and deploy them. Heck, I have seen cases where some heads of IT went for complete re-platforming of their system, as if that would answer all the marketing questions. Now, does this sound familiar so far? Does it sound like your own experience, like when you are reading “Dilbert” comic strips? It is because you are not alone in all this.

Allow me to be a little more specific with an example. Marketers often talk about “High-Value Customers.” To people who deal with 1s and 0s, that means less than nothing. What does that even mean? Because “high-value customers” could be:

  • High-dollar spenders—But what if they do not purchase often?
  • Frequent shoppers—But what if they don’t spend much at all?
  • Recent customers—Oh, those coveted “hotline” names … but will they stay that way, even for another few months?
  • Tenured customers—But are they loyal to your business, now?
  • Customers with high loyalty points—Or are they just racking up points and they would do anything to accumulate points?
  • High activity—Such as point redemptions and other non-monetary activities, but what if all those activities do not generate profit?
  • Profitable customers—The nice ones who don’t need much hand-holding. And where do we get the “cost” side of the equation on a personal level?
  • Customers who purchases extra items—Such as cruisers who drink a lot on board or diners who order many special items, as suggested.
  • Etc., etc …

Now it gets more complex, as these definitions must be represented in numbers and figures, and depending on the industry, whether be they for retailers, airlines, hotels, cruise ships, credit cards, investments, utilities, non-profit or business services, variables that would be employed to define seemingly straightforward “high-value customers” would be vastly different. But let’s say that we pick an airline as an example. Let me ask you this; how frequent is frequent enough for anyone to be called a frequent flyer?

Let’s just assume that we are going through an exercise of defining a frequent flyer for an airline company, not for any other travel-related businesses or even travel agencies (that would deal with lots of non-flyers). Granted that we have access to all necessary data, we may consider using:

1. Number of Miles—But for how many years? If we go back too far, shouldn’t we have to examine further if the customer is still active with the airline in question? And what does “active” mean to you?

2. Dollars Spent—Again for how long? In what currency? Converted into U.S. dollars at what point in time?

3. Number of Full-Price Ticket Purchases—OK, do we get to see all the ticket codes that define full price? What about customers who purchased tickets through partners and agencies vs. direct buyers through the airline’s website? Do they share a common coding system?

4. Days Between Travel—What date shall we use? Booking date, payment date or travel date? What time zones should we use for consistency? If UTC/GMT is to be used, how will we know who is booking trips during business hours vs. evening hours in their own time zone?

After a considerable hours of debate, let’s say that we reached the conclusion that all involved parties could live with. Then we find out that the databases from the IT department are all on “event” levels (such as clicks, views, bookings, payments, boarding, redemption, etc.), and we would have to realign and summarize the data—in terms of miles, dollars and trips—on an individual customer level to create a definition of “frequent flyers.”

In other words, we would need to see the data from the customer-centric point of view, just to begin the discussion about frequent flyers, not to mention how to communicate with each customer in the future. Now, it that a job for IT or marketing? Who will put the bell on the cat’s neck? (Hint: Not the dog.) Well, it depends. But this definitely is not a traditional IT function, nor is it a standalone analytical project. It is something in between, requiring a translator.

Customer-Centric Database, Revisited
I have been emphasizing the importance of a customer-centric view throughout this series, and I also shared some details regarding databases designed for marketing functions (refer to: “Cheat Sheet: Is Your Database Marketing Ready?”). But allow me to reiterate this point.

In the age of abundant and ubiquitous data, omnichannel marketing communication—optimized based on customers’ past transaction history, product preferences, and demographic and behavioral personas—should be an effortless routine. The reality is far from it for many organizations, as it is very common that much of the vital information is locked in silos without being properly consolidated or governed by a standard set of business rules. It is not that creating such a marketing-oriented database (or data-mart) is solely the IT department’s responsibility, but having a dedicated information source for efficient personalization should be an organizational priority in modern days.

Most databases nowadays are optimized for data collection, storage and rapid retrieval, and such design in general does not provide a customer-centric view—which is essential for any type of personalized communication via all conceivable channels and devices of the present and future. Using brand-, division-, product-, channel- or device-centric datasets is often the biggest obstacle in the journey to an optimal customer experience, as those describe events and transactions, not individuals. Further, bits and pieces of information must be transformed into answers to questions through advanced analytics, including statistical models.

In short, all analytical efforts must be geared toward meeting business objectives, and databases must be optimized for analytics (refer to “Chicken or the Egg? Data or Analytics?”). Unfortunately, the situation is completely reversed in many organizations, where analytical maneuvering is limited due to inadequate source data, and decision-making processes are dictated by limitations of available analytics. Visible symptoms of such cases are, to list a few, elongated project cycle time, decreasing response rates, ineffective customer communication, saturation of data sources due to overexposure, and—as I was emphasizing in this article—communication breakdown among divisions and team members. I can even go as far as to say that the lack of a properly designed analytical environment is the No. 1 cause of miscommunications between IT and marketing.

Without a doubt, key pieces of data must reside in the centralized data depository—generally governed by IT—for effective marketing. But that is only the beginning and still is just a part of the data collection process. Collected data must be consolidated around the solid definition of a “customer,” and all product-, transaction-, event- and channel-level information should be transformed into descriptors of customers, via data standardization, categorization, transformation and summarization. Then the data may be further enhanced via third-party data acquisition and statistical modeling, using all available data. In other words, raw data must be refined through these steps to be useful in marketing and other customer interactions, online or offline (refer to “‘Big Data’ Is Like Mining Gold for a Watch—Gold Can’t Tell Time“). It does not matter how well the original transaction- or event-level data are stored in the main database without visible errors, or what kind of state-of-the-art communication tool sets a company is equipped with. Trying to use raw data for a near real-time personalization engine is like putting unrefined oil into a high-performance sports car.

This whole data refinement process may sound like a daunting task, but it is not nearly as painful as analytical efforts to derive meanings out of unstructured, unconsolidated and uncategorized data that are scattered all over the organization. A customer-centric marketing database (call it a data-mart if “database” sounds too much like it should solely belong to IT) created with standard business rules and uniform variables sets would, in turn, provide an “analytics-ready” environment, where statistical modeling and other advanced analytics efforts would gain tremendous momentum. In the end, the decision-making process would become much more efficient as analytics would provide answers to questions, not just bits and pieces of fragmented data, to the ultimate beneficiaries of data. And answers to questions do not require an enormous data dictionary, either; fast-acting marketing machines do not have time to look up dictionaries, anyway.

Data Roadmap—Phased Approach
For the effort to build a consolidated marketing data platform that is analytics-ready (hence, marketing-ready), I always recommend a phased approach, as (1) inevitable complexity of a data consolidation project will be contained and managed more efficiently in carefully defined phases, and (2) each phase will require different types of expertise, tool sets and technologies. Nevertheless, the overall project must be managed by an internal champion, along with a group of experts who possess long-term vision and tactical knowledge in both database and analytics technologies. That means this effort must reside above IT and marketing, and it should be seen as a strategic effort for an entire organization. If the company already hired a Chief Data Officer, I would say that this should be one of the top priorities for that position. If not, outsourcing would be a good option, as an impartial decision-maker, who would play a role of a referee, may have to come from the outside.

The following are the major steps:

  1. Formulate Questions: “All of the above” is not a good way to start a complex project. In order to come up with the most effective way to build a centralized data depository, we first need to understand what questions must be answered by it. Too many database projects call for cars that must fly, as well.
  2. Data Inventory: Every organization has more data than it expected, and not all goldmines are in plain sight. All the gatekeepers of existing databases should be interviewed, and any data that could be valuable for customer descriptions or behavioral predictions should be considered, starting with product, transaction, promotion and response data, stemming from all divisions and marketing channels.
  3. Data Hygiene and Standardization: All available data fields should be examined and cleaned up, where some data may be discarded or modified. Free form fields would deserve special attention, as categorization and tagging are one of the key steps to opening up new intelligence.
  4. Customer Definition: Any existing Customer ID systems (such as loyalty program ID, account number, etc.) will be examined. It may be further enhanced with available PII (personally identifiable information), as there could be inconsistencies among different systems, and customers often move their residency or use multiple email addresses, creating duplicate identities. A consistent and reliable Customer ID system becomes the backbone of a customer-centric database.
  5. Data Consolidation: Data from different silos and divisions will be merged together based on the master Customer ID. A customer-centric database begins to take shape here. The database update process should be thoroughly tested, as “incremental” updates are often found to be more difficult than the initial build. The job is simply not done until after a few successful iterations of updates.
  6. Data Transformation: Once a solid Customer ID system is in place, all transaction- and event-level data will be transformed to “descriptors” of individual customers, via summarization by categories and creation of analytical variables. For example, all product information will be aligned for each customer, and transaction data will be converted into personal-level monetary summaries and activities, in both static and time-series formats. Promotion and response history data will go through similar processes, yielding individual-level ROI metrics by channel and time period. This is the single-most critical step in all of this, requiring deep knowledge in business, data and analytics, as the stage is being set for all future analytics and reporting efforts. Due to variety and uniqueness of business goals in different industries, a one-size-fits-all approach will not work, either.
  7. Analytical Projects: Test projects will be selected and the entire process will be done on the new platform. Ad-hoc reporting and complex modeling projects will be conducted, and the results will be graded on timing, accuracy, consistency and user-friendliness. An iterative approach is required, as it is impossible to foresee all possible user requests and related complexities upfront. A database should be treated as a living, breathing organism, not something rigid and inflexible. Marketers will “break-in” the database as they use it more routinely.
  8. Applying the Knowledge: The outcomes of analytical projects will be applied to the entire customer base, and live campaigns will be run based on them. Often, major breakdowns happen at the large-scale deployment stages; especially when dealing with millions of customers and complex mathematical formulae at the same time. A model-ready database will definitely minimize the risk (hence, the term “in-database scoring”), but the process will still require some fine-tuning. To proliferate gained knowledge throughout the organization, some model scores—which pack deep intelligence in small sizes—may be transferred back to the main databases managed by IT. Imagine model scores driving operational decisions—live, on the ground.
  9. Result Analysis: Good marketing intelligence engines must be equipped with feedback mechanisms, effectively closing the “loop” where each iteration of marketing efforts improves its effectiveness with accumulated knowledge on a customer level. It is very unfortunate that many marketers just move through the tracks set up by their predecessors, mainly because existing database environments are not even equipped to link necessary data elements on a customer level. Too many back-end analyses are just event-, offer- or channel-driven, not customer-centric. Can you easily tell which customer is over-, under- or adequately promoted, based on a personal-level promotion-and-response ratio? With a customer-centric view established, you can.

These are just high-level summaries of key steps, and each step should be managed as independent projects within a large-scale initiative with common goals. Some steps may run concurrently to reduce the overall timeline, and tactical knowledge in all required technologies and tool sets is the key for the successful implementation of centralized marketing intelligence.

Who Will Do the Work?
Then, who will be in charge of all this and who will actually do the work? As I mentioned earlier, a job of this magnitude requires a champion, and a CDO may be a good fit. But each of these steps will require different skill sets, so some outsourcing may be inevitable (more on how to pick an outsourcing partner in future articles).

But the case that should not be is the IT team or the analytics team solely dictating the whole process. Creating a central depository of marketing intelligence is something that sits between IT and marketing, and the decisions must be made with business goals in mind, not just limitations and challenges that IT faces. If the CDO or the champion of this type of initiative starts representing IT issues before overall business goals, then the project is doomed from the beginning. Again, it is not about touching the core database of the company, but realigning existing data assets to create new intelligence. Raw data (no matter how clean they are at the collection stage) are like unrefined raw materials to the users. What the decision-makers need are simple answers to their questions, not hundreds of data pieces.

From the user’s point of view, data should be:

  • Easy to understand and use (intuitive to non-mathematicians)
  • Bite-size (i.e., small answers, not mounds of raw data)
  • Useful and effective (consistently accurate)
  • Broad (answers should be available most of time, not just “sometimes”)
  • Readily available (data should be easily accessible via users’ favorite devices/channels)

And getting to this point is the job of a translator who sits in between marketing and IT. Call them data scientists or data strategists, if you like. But they do not belong to just marketing or IT, even though they have to understand both sides really well. Do not be rigid, insisting that all pilots must belong to the Air Force; some pilots do belong to the Navy.

Lastly, let me add this at the risk of sounding like I am siding with technologists. Marketers, please don’t be bad patients. Don’t be that bad patient who shows up at a doctor’s office with a specific prescription, as in “Don’t ask me why, but just give me these pills, now.” I’ve even met an executive who wanted a neural-net model for his business without telling me why. I just said to myself, “Hmm, he must have been to one of those analytics conferences recently.” Then after listening to his “business” issues, I prescribed an entirely different solution package.

So, instead of blurting out requests for pieces of data variables or queries using cool-sounding, semi-technical terms, state the business issues and challenges that you are facing as clearly as possible. IT and analytics specialists will prescribe the right solution for you if they understand the ultimate goals better. Too often, requesters determine the solutions they want without any understanding of underlying technical issues. Don’t forget that the end-users of any technology are only exposed to symptoms, not the causes.

And if Mr. Spock doesn’t seem to understand your issues and keeps saying that your statements are illogical, then call in a translator, even if you have to hire him for just one day. I know this all too well, because after all, this one phrase summarizes my entire career: “A bridge person between the marketing world and the IT world.” Although it ain’t easy to live a life as a marginal person.

What Does a Data Marketer Look Like?

The currency of nearly all marketing today is data. Ten years ago, we might have said much the same of digital marketing, and all the email, display, social, search, and mobile that’s came forward from it.

The currency of nearly all marketing today is data.

Ten years ago, we might have said much the same of digital marketing, and all the email, display, social, search, and mobile that’s came forward from it.

Twenty years ago, we could have said the same of database marketing and customer relationship management.

And wind back—measurability and accountability, the hallmarks of direct marketing—always have relied on data. We may have called it lists back in the day—but data are what lists have become. The inherent value of data is to know the shared attributes among the data elements and to use that knowledge.

Without a doubt, the “marketing of data” has evolved and transformed as much as marketing itself. Every day in our world, it’s not enough to have contact details on people, or any number of the hundreds of demographic, psychographic, contextual, social and behavioral overlays that may be available, we also need analytics power.

Recent research from The Winterberry Group underscores this point: data is now an $11 billion business in America, and that includes analytics services revenue. I recall an unofficial guestimate of a $2 billion data market back in the early 1990s, when that meant a North American directory of 30,000 plus response and compiled lists available for rental and exchanges.

Next month, the Data Innovators Group will host its annual Data Innovator of the Year Award dinner in New York. This year’s honoree is Auren Hoffman, CEO of LiveRamp (now owned by Acxiom), who says his mission “to connect data to every marketing application.” And so it shall be… Soon.

But who is going to all make it work? Let’s welcome the data marketer and the data scientists and strategists they employ.

Still, too many brands keep customer data in siloes. And while responsibly using offline data with online data is fast coming down the pike, marketing organizations need people in place who can help clients navigate the brave new world of data management platforms, data quality strategies, programmatic media exchanges, big data and small data, and all the algorithms that drive this important “stuff” often in real time. A list sale exists largely no more. Instead data is a pathway to opportunity, a challenge overcome, by way of a data-to-insights-to-strategy recommendation, and a discipline for testing and data quality that leads brands (and their agencies and data marketer partners) to succeed.

It’s more difficult than ever to be a successful data marketer, but our field is producing the partners that businesses, brands and chief marketing officers need. Now if we could just go find a few.

Thank you to the Hudson Valley Direct Marketing Association for enabling my participation at its recent “Meet the Masters” event. Ryan Lake (Lake Group Media), Mark Rickard (Rickard Squared) and Rob Sanchez (Merit Direct) are three CEOs of data marketing organizations who have a few suggestions on where we can all go to look.

B-to-B Prospecting Data Just Keeps Getting Better

The most reliable and scalable approach to finding new B-to-B customers is outbound communications, whether by mail, phone or email, to potential prospects, using rented or purchased lists. B-to-B marketers typically select targets from prospecting lists based on such traditional variables as industry, company size and job role, or title. But new research indicates that B-to-B prospecting data is much more detailed these days, and includes a plethora of variables to choose from

The most reliable and scalable approach to finding new B-to-B customers is outbound communications, whether by mail, phone or email, to potential prospects, using rented or purchased lists. B-to-B marketers typically select targets from prospecting lists based on such traditional variables as industry, company size, and job role or title. But new research (opens as a pdf) indicates that B-to-B prospecting data is much more detailed these days, and includes a plethora of variables to choose from—for refining your targeting, or for building predictive models—to pick your targets even more effectively.

My colleague Bernice Grossman and I recently conducted a new study (opens as a pdf) indicating that B-to-B marketers now have the opportunity to target prospects more efficiently than ever before. In fact, you might say that business marketers now have access to prospecting data as rich and varied as that available in consumer markets.

To get an understanding of the depth of data available to B-to-B marketers for prospecting, we invited a set of reputable vendors to open their vaults and share details about the nature and quantity of the fields they offer. Seven vendors participated, giving us a nice range of data sources, including both compiled lists and response lists.

We provided each vendor with a set of 30 variables that B-to-B marketers often use, including not only company size and industry, but also elements like the year the company was established, fiscal year end, Fortune Magazine ranking, SOHO (small office/home office) business indicator, growing/shrinking indicator, and other useful variables that can give marketers insight into the relative likelihood of a prospect’s conversion to a customer. We learned that some vendors provide all these data elements on most of the accounts on their files, while others offer only a few.

We also asked the participating vendors to tell us what other fields they make available, and this is where things got interesting. In response to our request for sample records on five well-known firms, the reported results included as many as 100 lines per firm. Furthermore, two of the vendors, Harte-Hanks and HG Data, supply details about installed technology, and their fields thus run into the thousands. The quantity was so vast that we published it in a supplementary spreadsheet, so that our research report itself would be kept to a readable size.

Some of the more intriguing fields now available to marketers include:

  • Spending levels on legal services, insurance, advertising, accounting services, utilities and office equipment (Infogroup)
  • Self-identifying keywords used on the company website (ALC)
  • Technology usage “intensity” score, by product (HG Data)
  • Out-of-business indicator, plus credit rating and parent/subsidiary linkages (Salesforce.com)
  • Company SWOT analysis (OneSource)
  • Whether the company conducts e-commerce (ALC)
  • List of company competitors (OneSource)
  • Biographies of company contacts (OneSource)
  • Employees who travel internationally (Harte-Hanks)
  • Employees who use mobile technology (Harte-Hanks)
  • Links to LinkedIn profiles of company managers (Stirista)
  • Executive race, religion, country of origin and second language (Stirista)

Imagine what marketers could do with a treasure trove of data elements like these to help identify high-potential prospects.

Matter of fact, we asked the vendors to tell us the fields that their clients find most valuable for predictive purposes. Several fresh and interesting ideas surfaced:

  • A venture capital trigger, from OneSource, indicating that a firm has received fresh funding and thus has budget to spend.
  • Tech purchase likelihood scores from Harte-Hanks, built from internal models and appended to enhance the profile of each account.
  • A “prospectability” score custom-modeled by OneSource to match target accounts with specific sales efforts.
  • PRISM-like business clusters offered by Salesforce.com (appended from D&B), which provide a simple profile for gaining customer insights and finding look-alikes.
  • “Call status code,” Infogroup’s assessment of the authenticity of the company record, based on Infogroup’s ongoing phone-based data verification program.

We conclude from this study that B-to-B prospecting data is richer and more varied than most marketers would have thought. We recommend that marketers test several vendors, to see which best suit their needs, and conduct a comparative test before you buy.

Readers who would like to see our past studies on the quality and quantity of prospecting data available in business markets can access them here. Bernice and I are always open to ideas for future studies. We welcome your feedback and suggestions.

A version of this article appeared in Biznology, the digital marketing blog.

Building Your B-to-B Marketing Database

The single most important tool in B-to-B is, arguably, the marketing database. Without a robust collection of contact information, firmographic and transactional data about customers and prospects, you are at sea when it comes to customer segmentation, analytics and marketing communications of all sorts, whether for acquiring new customers or to expand the value of existing customers. In fact, you might call the database the “recorded history of the customer relationship.” So what goes into a marketing database? Plent 

The single most important tool in B-to-B is, arguably, the marketing database. Without a robust collection of contact information, firmographic and transactional data about customers and prospects, you are at sea when it comes to customer segmentation, analytics and marketing communications of all sorts, whether for acquiring new customers or to expand the value of existing customers. In fact, you might call the database the “recorded history of the customer relationship.” So what goes into a marketing database? Plenty.

First, let’s look at the special characteristics of B-to-B databases, which differ from consumer in several important ways:

  1. In consumer purchasing, the decision-maker and the buyer are usually the same person—a one-man (or, more likely, woman) show. In business buying, there’s an entire cast of characters. In the mix are employees charged with product specification, users of the product and purchasing agents, not to mention the decision-makers who hold final approval over the sale.
  2. B-to-B databases carry data at three levels: the enterprise or parent company; the site, or location, of offices, plants and warehouses; and the multitude of individual contacts within the company.
  3. B-to-B data tends to degrade at the rate of 4 percent to 6 percent per month, so keeping up with changing titles, email addresses, company moves, company name changes-this requires dedicated attention, spadework and resources.
  4. Companies that sell through channel partners will have a mix of customers, from distributors, agents and other business partners, through end-buyers.

Here are the elements you are likely to want to capture and maintain in a B-to-B marketing database.

  • Account name, address
    • Phone, fax, website
  • Contact(s) information
    • Title, function, buying role, email, direct phone
  • Parent company/enterprise link
  • SIC or NAICS
  • Year the company was started
  • Public vs. private
  • Revenue/sales
  • Employee size
  • Credit score
  • Fiscal year
  • Purchase history
  • Purchase preferences
  • Budgets, purchase plans
  • Survey questions (e.g., from market research)
  • Qualification questions (from lead qualification processes)
  • Promotion history (record of outbound and inbound communications)
  • Customer service history
  • Source (where the data came from, and when)
  • Unique identifier (to match and de-duplicate records)

To assemble the data, the place to begin in inside your company. With some sleuthing, you’ll find useful information about customers all over the place. Start with contact records, whether they sit in a CRM system, in Outlook files or even in Rolodexes. But don’t stop there. You also want to pull in transactional history from your operating systems-billing, shipping, credit—and your customer service systems.

Here’s a checklist of internal data sources that you should explore. Gather up every crumb.

  • Sales and marketing contacts
  • Billing systems
  • Credit files
  • Fulfillment systems
  • Customer services systems
  • Web data, from cookies, registrations and social media
  • Inquiry files and referrals

Once these elements are pulled in, matched and de-duplicated, it’s time to consider external data sources. Database marketing companies will sell you data elements that may be missing, most important among these being industry (in the form of SIC or NAICs codes), company size (revenue or number of employees, or both) and title or job function of contacts. Such elements can be appended to your database for pennies apiece.

In some situations, it makes sense to license and import prospect lists, as well. If you are targeting relatively narrow industry verticals, or certain job titles, and especially if you experience long sales cycles, it may be wise to buy prospecting names for multiple use and import them into your database, rather than renting them serially for each prospecting campaign.

After filling in the gaps with data append, the next step is the process of “data discovery.” Essentially this means gathering essential data by hand—or, more accurately, by outbound phone or email contact. This costs a considerable sum, so only perform discovery on the most important accounts, and only collect the data elements that are essential to your marketing success, like title, direct phone number and level of purchasing authority. Some data discovery can be done via LinkedIn and scouring corporate websites, which are likely to provide contact names, titles and email addresses you can use to populate your company records.

Be thorough, be brave, and have fun. And let me know your experiences.

A version of this article appeared in Biznology, the digital marketing blog.