Cheat Sheet: Is Your Database Marketing Ready?

Many data-related projects end up as big disappointments. And, in many cases, it is because they did not have any design philosophy behind them. Because many folks are more familiar with buildings and cars than geeky databases, allow me to use them as examples here.

Many data-related projects end up as big disappointments. And, in many cases, it is because they did not have any design philosophy behind them. Because many folks are more familiar with buildings and cars than geeky databases, allow me to use them as examples here.

Imagine someone started constructing a building without a clear purpose. What is it going to be? An office building or a residence? If residential, for how many people? For a family, or for 200 college kids? Are they going to just eat and sleep in there, or are they going to engage in other activities in it? What is the budget for development and ongoing maintenance?

If someone starts building a house without answering these basic questions, well, it is safe to say that the guy who commissioned such a project is not in the right state of mind. Then again, he may be a filthy rich rock star with some crazy ideas. But let us just say that is an exceptional case. Nonetheless, surprisingly, a great many database projects start out exactly this way.

Just like a house is not just a sum of bricks, mortar and metal, a database is not just a sum of data, and there has to be design philosophy behind it. And yet, many companies think that putting all available data in one place is just good enough. Call it a movie without a director or a building without an architect; you know and I know that such a project cannot end well.

Even when a professional database designer gets involved, too often the project goes out of control—as the business requirement document ends up being a summary of
everyone’s wish lists, without any prioritization or filtering. It is a case of a movie without a director. The goal becomes something like “a database that stores all conceivable marketing, accounting and payment activities, handling both prospecting and customer relationship management through all conceivable channels, including face-to-face sales and lead management for big accounts. And it should include both domestic and international activities, and the update has to be done in real time.”

Really. Someone in that organization must have attended a database marketing conference recently to get all that listed. It might be simpler and cheaper building a 2-ton truck that flies. But before we commission something like this from the get-go, shall we discuss why the truck has to fly, too? For one, if you want real-time updates, do you have a business case for it? (As in, someone in the field must make real-time decisions with real-time data.) Or do you just fancy a large object, moving really fast?

Companies that primarily sell database tools often do not help the matter, either. Some promise that the tool sets will categorize all kinds of input data, based on some auto-generated meta-tables. (Really?) The tool will clean the data automatically. (Is it a self-cleaning oven?) The tool will establish key links (by what?), build models on its own (with what target data?), deploy campaigns (every Monday?), and conduct result analysis (with responses from all channels?).

All these capabilities sound really wonderful, but does that system set long- and short-term marketing goals for you, too? Does it understand the subtle nuances in human behaviors and intentions?

Sorry for being a skeptic here. But in such cases, I think someone watched “Star Trek” too much. I have never seen a company that does not regret spending seven figures on a tool set that was supposed to do everything. Do you wonder why? It is not because such activities cannot be automated, but because:

  1. Machines do not think for us (not quite yet); and
  2. Such a system is often very expensive, as it needs to cover all contingencies (the opposite of “goal-oriented” cheaper options).

So it becomes nearly impossible to justify the cost with incremental improvements in marketing efficiency. Even if the response rates double, all related marketing costs go down by a quarter, and revenue jumps up by 200 percent, there are not many companies that can easily justify that kind of spending.

Worse yet, imagine that you just paid 10 times more for some factory-made suit than you would have paid for a custom-made Italian suit. Since when is an automated, cookie-cutter answer more desirable than custom-tailored ones? Ever since computing and storage costs started to go down significantly, and more so in this age of Big Data that has an “everything, all the time” mentality.

But let me ask you again: Do you really have a marketing database?

Let us just say that I am a car designer. A potential customer who has been doing a lot of research on the technology front presents me with a spec for a vehicle that is as big as a tractor-trailer and as quick as a passenger car. I guess that someone really needs to move lots of stuff, really fast. Now, let us assume that it will cost about $8 million or more to build a car like that, and that estimate is without the rocket booster (ah, my heart breaks). If my business model is to take a percentage out of that budget, I would say, “Yeah sure, we can build a car like that for you. When can we start?”

But let us stop for a moment and ask why the client would “need” (not “want”) a car like that in the first place. After some user interviews and prioritization, we may collectively conclude that a fleet of full-size vans can satisfy 98 percent of the business needs, saving about $7 million. If that client absolutely and positively has to get to that extra 2 percent to satisfy every possible contingency in his business and spend that money, well, that is his prerogative, is it not? But I have to ask the business questions first before initiating that inevitable long and winding journey without a roadmap.

Knowing exactly what the database is supposed to be doing must be the starting point. Not “let’s just gather everything in one place and hope to God that some user will figure something out eventually.” Also, let’s not forget that constantly adding new goals in any phase of the project will inevitably complicate the matter and increase the cost.

Conversely, repurposing a database designed for some other goal will cause lots of troubles down the line. Yeah, sure. Is it not possible to move 100 people from A to B with a 2-seater sports car, if you are willing to make lots of quick trips and get some speeding tickets along the way? Yes, but that would not be my first recommendation. Instead, here are some real possibilities.

Databases support many different types of activities. So let us name a few:

  • Order fulfillment
  • Inventory management and accounting
  • Contact management for sales
  • Dashboard and report generation
  • Queries and selections
  • Campaign management
  • Response analysis
  • Trend analysis
  • Predictive modeling and scoring
  • Etc., etc.

The list goes on, and some of the databases may be doing fine jobs in many areas already. But can we safely call them “marketing” databases? Or are marketers simply tapping into the central data depository somehow, just making do with lots of blood, sweat and tears?

As an exercise, let me ask a few questions to see if your organization has a functioning marketing database for CRM purposes:

  • What is the average order size per year for customers with tenure of more than one year? —You may have all the transaction data, but maybe not on an individual level in order to know the average.
  • What is the number of active and dormant customers based on the last transaction date? —You will be surprised to find out that many companies do not know exactly how many customers they really have. Beep! 1 million-“ish” is not a good answer.
  • What is the average number of days between activities for each channel for each customer? —With basic transaction data summarized “properly,” this is not a difficult question to answer. But it’s very difficult if there are divisional “channel-centric” databases scattered all over.
  • What is the average number of touches through all channels that you employ before your customer reaches the projected value potential? —This is a hard one. Without all the transaction and contact history by all channels in a “closed-loop” structure, one cannot even begin to formulate an answer for this one. And the “value potential” is a result of statistical modeling, is it not?
  • What are typical gateway products, and how are they correlated to other product purchases? —This may sound like a product question, but without knowing each customer’s purchase history lined up properly with fully standardized product categories, it may take a while to figure this one out.
  • Are basic RFM data—such as dollars, transactions, dates and intervals—routinely being used in predictive models? —The answer is a firm “no,” if the statisticians are spending the majority of their time fixing the data; and “not even close,” if you are still just using RFM data for rudimentary filtering.

Now, if your answer is “Well, with some data summarization and inner/outer joins here and there—though we don’t have all transaction records from last year, and if we can get all the campaign histories from all seven vendors who managed our marketing campaigns, except for emails—maybe?”, then I am sorry to inform you that you do not have a marketing database. Even if you can eventually get to the answer if some programmer takes two weeks to draw a 7-page flow chart.

Often, I get extra comments like “But we have a relational database!” Or, “We stored every transaction for the past 10 years in Hadoop and we can retrieve any one of them in less than a second!” To these comments, I would say “Congratulations, your car has four wheels, right?”

To answer the important marketing questions, the database should be organized in a “buyer-centric” format. Going back to the database philosophy question, the fundamental design of the database changes based on its main purpose, much like the way a sports sedan and an SUV that share the same wheel base and engine end up shaped differently.

Marketing is about people. And, at the center of the marketing database, there have to be people. Every data element in the base should be “describing” those people.

Unfortunately, most relational databases are transaction-, channel- or product-centric, describing events and transactions—but not the people. Unstructured databases that are tuned primarily for massive storage and rapid retrieval may just have pieces of data all over the place, necessitating serious rearrangement to answer some of the most basic business questions.

So, the question still stands. Is your database marketing ready? Because if it is, you would have taken no time to answer my questions listed above and say: “Yeah, I got this. Anything else?”

Now, imagine the difference between marketers who get to the answers with a few clicks vs. the ones who have no clue where to begin, even when sitting on mounds of data. The difference between the two is not the size of the investment, but the design philosophy.

I just hope that you did not buy a sports car when you needed a truck.

The Future of Online Is Offline

I find it offensive when marketers call anyone an “online person.” Let’s get this straight: At the end of some not-so-memorable transaction with you, if I opt in for your how-bad-can-it-be email promotions, or worse, neglect to uncheck the pre-checked check-box that says “You will hear from us from time to time” (which could turn into a daily commitment for the rest of my cognitive life, or, until I decide finding that invisible unsubscribe link presented in the font size of a few pixels is a better option than hitting the delete key every day), I get to be an online person to you? How nice.

I find it offensive when marketers call anyone an “online person.” Let’s get this straight: At the end of some not-so-memorable transaction with you, if I opt in for your how-bad-can-it-be email promotions, or worse, neglect to uncheck the pre-checked check-box that says “You will hear from us from time to time” (which could turn into a daily commitment for the rest of my cognitive life, or, until I decide finding that invisible unsubscribe link presented in the font size of a few pixels is a better option than hitting the delete key every day), I get to be an online person to you? How nice.

What if I receive an email offer from you, research the heck out of the product on the Internet, and then show up at a store to have instant gratification? Does that make me an offline person now? Sorry to break your channel-oriented marketing mind, but hey, I am just a guy. I am neither an online person nor an offline person; which, by the way, happens to be a dirty word in some pretentious marketing circles (as in “Eew, you’re in the offline space?!”).

Marketers often forget to recognize that all this “Big Data” stuff (or any size data, for that matter) and channel management tools are just tools to get to people. In the age of Big Data, it shouldn’t be so hard to know “a lot” about a person, and tailor messages and offers for that person. Then why is that I get confusing offers all the time? How is that I receive multiple types of credit card offers from the same bank within weeks? Don’t they know all about my banking details? Don’t they have some all-inclusive central data depository for all that kind of stuff?

The sad and short answer to all this is that it really doesn’t matter if the users of such databases still think only in terms of her division, his channel assignment, and only through to the very next campaign. And such mindsets may even alter the structure of the marketing database, where everything is organized by division, product or channel. That is how one becomes an online person, who might as well be invisible when it comes to his offline activities.

What is the right answer, then? Both database and users of such databases should be “buyer-centric” or “individual-centric” at the core. In a well-designed marketing database, every variable should be a descriptor for the individual, regardless of the data sources or channels through which she happens to have navigated to end up in the database. There, what she has been buying, her typical spending level, her pricing threshold, channels that she uses to listen, channels that she employs to make purchases or to express herself, stores she visited, lapsed time since her last activities by each channel, contact/response history, her demographic profile, etc. should all be nicely lined up as “her” personal record. That is how modern marketing databases should be structured. Just putting various legacy datasets in one place isn’t going to cut it, even if some individual ID is assigned to everyone in every table. Through some fancy Big Data tools, you may be able to store and retrieve records for every transaction for the past 20 years, but such records describe transactions, not people. Again, it’s all about people.

Why should marketing databases be “buyer-centric”? (1) Nobody is one-dimensional, locked into one channel or division of some marketer, and (2) Individualized targeting and messaging can only be actualized through buyer-centric data platforms. Want to use advanced statistical models? You would need individualized structure because the main goal of any model for marketing is to rank “people” in terms of your target’s susceptibility to certain offers or products. If an individual’s information is scattered all over the database, requiring lots of joins and manipulations, then that database simply isn’t model-ready.

Further, when I look into the future, I see the world where one-click checkout is the norm, even in the offline world. The technology to identify ourselves and to make payment will be smaller and more ubiquitous. Today, when we go to a drug store, we need to bring out the membership card, coupons and our credit card to finish the transaction. Why couldn’t that be just one step? If I identify myself with an ID card or with some futuristic device that I would wear such as a phone, glasses or a wristwatch, shouldn’t that be enough to finish the deal and let me out of the store? When that kind of future becomes a reality (in the not-too distant future), will marketers still think and behave within that channel-centric box? Will we even attempt to link what just happened at the store to other activities the person engaged in online or offline? Not if some guy is in charge of that “one” new channel, no matter how fancy that department title would be.

I have been saying this all along, but let me say it again. The future of online is offline. The distinction of such things would be as meaningless as debating if interactive TV of the future should be called a TV or a computer. Is an iPhone a phone or mobile computer? My answer? Who cares? We should be concentrating our efforts on talking to the person who is looking at the device, whether it is through a computer screen, mobile screen or TV screen. That is the first step toward the buyer-centric mindset; that it is and always has been about people, not channel or devices that would come and go. And it is certainly not about some marketing department that may handle just one channel or one product at a time.

The Big Data movement should about the people. The only difference this new wave brings is the amount of data that we need to deal with and the speed in which we need to operate. Soon, marketers should be able to do things in less than a second that used to take three months. Displaying an individually customized real-time offer built with past and present data through fancy statistical model via hologram won’t be just a scene in a science fiction movie (remember the department store scene in “Minority Report”?). And if marketing databases are not built in a buyer-centric structure, someone along the line will waste a lot of time just to understand what the target individual is all about. That could have been OK in the last century, but not in the age of abundant and ubiquitous data.

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.

New Developments in B-to-B List Acquisition

To reach cold prospects among business audiences, sales and marketing teams often begin by developing a list of prospective targets. Marketers can find just about every target company, title and job function they need from traditional list suppliers. Plus, the Internet has made possible the introduction of some excellent new opportunities for identifying prospects at various stages of the buying cycle. Let’s look at what’s new in B-to-B lists these days

To reach cold prospects among business audiences, sales and marketing teams often begin by developing a list of prospective targets. Marketers can find just about every target company, title and job function they need from traditional list suppliers. Plus, the Internet has made possible the introduction of some excellent new opportunities for identifying prospects at various stages of the buying cycle. Let’s look at what’s new in B-to-B lists these days.

Traditionally, the first step in list development has been working with a list broker who has experience in your target audience category. There are more than 40,000 business lists available for rent in the U.S., plus numerous databases and online data enhancement services to choose from.

Business lists can be divided into four general types:

  1. Compiled files assembled from directories, the Internet or other public and private sources, by such suppliers as D&B, InfoGroup, Data.com, NetProspex and ZoomInfo. In recent years, many compilers have been making their data available for rent via an online interface, vastly enhancing the speed and flexibility of ordering.
  2. Response files created as a by-product of other businesses, like catalog/e-commerce sales, seminars, trade organization memberships, or magazine and newsletter subscriptions. Response files tend to be more current and accurate than compiled files.
  3. Cooperative databases from multiple list owners, offered in either open format, where you pay for what you use (examples being MeritDirect’s MeritBase, InfoGroup’s b2bdatawarehouse and Mardev DM2’s Decisionmaker database), or closed format, where only members who put customer names in can take prospect names out (examples include Epsilon Abacus Cooperative and the b2bBase, a joint venture of MeritDirect and Experian).
  4. Internal databases populated from billing systems, lead management systems, and website registration systems. Many companies today use their marketing automation or CRM systems as their marketing databases, and populate them from a variety of internal and external sources.

A New Direction in B-to-B Lists
The B-to-B list industry has changed considerably in the last decade, with the proliferation of social networks. But the big new development today is the trend away from static name/address lists, to dynamic sourcing of prospect names complete with valuable indicators of buying readiness culled from their actual behavior online. Companies such as InsideView and Leadspace are developing solutions in this area.

Leadspace, created by a team of former Israeli intelligence officers, is a leader in targeted, real-time prospecting data for business marketers. Their process begins with constructing an ideal buyer persona by analyzing the client’s best customers, which can be executed by uploading a few hundred records of name, company name and email address. Then, Leadspace scours the Internet, social networks and scores of contact databases for look-alikes and immediately delivers prospect names, fresh contact information and additional data about their professional activities.

How LevelEleven Took its Prospecting to the Next Level
LevelEleven provides a cloud-based platform where sales managers can create fresh and compelling sales contests within Salesforce.com. For example, the Detroit Pistons recently used LevelEleven to organize a sales contest for skyboxes at their arena, and drove sales of over half a million dollars. In other words, 50 percent of the skybox annual sales target was closed in a mere six weeks.

LevelEleven’s target prospect is a sales manager or sales operations manager in any company that uses Salesforce.com as its CRM system. Today, LevelEleven’s sales team gets leads from four sources:

  1. The Salesforce.com AppExchange, where other Salesforce users search for partners.
  2. Conferences and trade shows, like Dreamforce.
  3. Registrations from content downloads at the LevelEleven website.
  4. Rented lists of prospects.

LevelEleven has tried a variety of list sources over the years, with mixed results. In the first half of 2012, the prospecting sources produced zero in closed sales. In June 2012, they began experimenting with Leadspace. In the second half of 2012, a full 30 percent of LevelEleven closed deals came from this source.

According to Bob Marsh, CEO, the power of Leadspace for LevelEleven is its close targeting based on the LevelEleven customer profile. “Leadspace helps us infer pretty accurately whether a prospect is using the Salesforce platform,” he says. “And they deliver to us a short list of highly likely contacts in the account, like the Salesforce administrator or the sales operations manager. Everyone on our sales team has a Leadspace license, and it is performing for us.”

It’s a good thing that the B-to-B list business is continuing to evolve in new directions. What new developments are you seeing?

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

Left Hand? I’d Like to Introduce Right Hand

What happened to good, old fashioned, “please” and “thank you”? As a customer, it’s nice to be thanked for my business, or appreciated for my subscription to a service. It makes me feel part of the brand and valued for my investment. But as a cold prospect, it’s even more important since making a good impression should always be part of the process. So why is it missing from so many marketing communications programs?

What happened to good, old fashioned, “please” and “thank you”?

As a customer, it’s nice to be thanked for my business, or appreciated for my subscription to a service. It makes me feel part of the brand and valued for my investment. But as a cold prospect, it’s even more important since making a good impression should always be part of the process. So why is it missing from so many marketing communications programs?

After attending a B-to-B webinar recently, I fully expected to receive a follow-up email thanking me for my attendance, and a continued nurturing of me along their sales cycle: A request for a meeting, an invitation to participate in a live demo, or even a link to a case study or two that were geared to my industry. Instead, I got an email that sounded as if they were talking to a cold prospect.

Perhaps the marketing manager failed to merge/purge the webinar registration/attendee list against their cold prospecting list (tsk, tsk, tsk). But I suspect this business didn’t even think to conduct a merge/purge. Why?

Because, like most mid-to-large B-to-B organizations, one marketing manager is responsible for acquisition and someone else is responsible for sales support—and it seems that neither of them talk to each other … EVER.

If this company maintains a database, I should be flagged as “responded” AND “attended an event” so the sales team can take over the management of this “lead.” I’ve met with many, many organizations that don’t have a lead database (or, even worse, they have multiple databases because no one is happy with the company solution, or the solution is too hard to manage/maintain). Worse still, they may have a customer database, but it’s not well maintained, or is too difficult to access/use. So when it comes time to upsell or cross-sell a product, they don’t even know who their customers are, or how to talk to them in a meaningful way.

Thus we circle back to my dilemma. How can you thank me for attending an event and start to sell me on your product/solution, if you don’t know that I attended in the first place?

As marketers, we’re all busy with our heads down, trying to get work out the door. I get it. But at some point, you have to stop all the day-to-day madness and realize that you’re just putting off the inevitable. Insist on investing in a proper marketing database and a database manager to help your company communicate with more intelligence and insight. In turn, that will lead to your ability to target any particular audience and craft smarter, more relevant marketing messages, which will, in turn, lead to better results. I guarantee it.

Oh, and you’re welcome.

Updating Your Marketing Database

It’s amazing how quickly things go obsolete these days. For those of us in the business of customer data, times and technologies have changed along with the times. Some has to do with the advent of new technologies; some of it has to do with changing expectations. Let’s take a look at how the landscape has changed and what it means for marketers.

It’s amazing how quickly things go obsolete these days. For those of us in the business of customer data, times and technologies have changed along with the times. Some has to do with the advent of new technologies; some of it has to do with changing expectations. Let’s take a look at how the landscape has changed and what it means for marketers.

For marketing departments, maintaining updating customer data has always been a major headache. One way to update data is by relying on sales team members to make the updates themselves as they go about their jobs. For lack of a better term, let’s call this method internal crowd-sourcing, and there are two reasons why it has its limitations.

The first reason is technology. Typically, customer data is stored in a data hub or data warehouse, which is usually a home-grown and oftentimes proprietary database built using one of many popular database architectures. Customer databases tend to be proprietary because each organization sells different products and services, to different types of firms, and consequently collects different data points. Additionally, customer databases are usually grown organically over many years, and as a result tend to contain disparate information, often collected from different sources during different timeframes, of varying degrees of accuracy.

It’s one thing having data stored in a data warehouse somewhere. It’s quite another altogether to give salespeople access to a portal where the edits can be made—that’s been the real challenge. The database essentially needs to be integrated with or housed in some kind of tool, such as an enterprise resource planning (ERP) software or customer relationship management (CRM) software that gives sales teams some capability to update customer records on the fly with front-end read/write/edit capabilities.

Cloud-based CRM technology (such as SalesForce.com) has grown by leaps and bounds in recent years to fill this gap. Unlike purpose-built customer databases, however, out-of-the-box cloud-based CRM tools are developed for a mass market, and without customizations contain only a limited set of standard data fields plus a finite set of “custom fields.” Without heavy customizations, in other words, data stored in a cloud-based CRM solution only contains a subset of a company’s customer data file, and is typically only used by salespeople and customer service reps. Moreover, data in the CRM is usually not connected to that of other business units like marketing or finance divisions who require a more complete data set to do their job.

The second challenge to internal crowd-sourcing has more to do with the very nature of salespeople themselves. Anyone who has worked in marketing knows firsthand that it’s a monumental challenge to get salespeople to update contact records on a regular basis—or do anything else, for that matter, that doesn’t involve generating revenue or commissions.

Not surprisingly, this gives marketers fits. Good luck sending our effective (and hopefully highly personalized) CRM campaigns if customer records are either out of date or flat out wrong. Anyone who has used Salesforce.com has seen that “Stay in Touch” function, which gives salespeople an easy and relatively painless method for scrubbing contact data by sending out an email to contacts in the database inviting them to “update” their contact details. The main problem with this tool is that it necessitates a correct email address in the first place.

Assuming your salespeople are diligently updating data in the CRM, another issue with this approach is it essentially limits your data updates to whatever the sales team happens to know or glean from each customer. It assumes, in other words, that your people are asking the right questions in the first place. If your salesperson does not ask a customer how many employees they have globally or at a particular location, it won’t get entered into the CRM. Nor, for that matter, will data on recent mergers and acquisitions or financial statements—unless your sales team is extremely inquisitive and is speaking with the right people in your customers’ organizations.

The other way to update customer data is to rely on a third-party data provider to do it for you—to cleanse, correct, append and replace the data on a regular basis. This process usually involves taking the entire database, uploading it to an FTP site somewhere. The database is then grabbed by the third party, who then works their magic on the file—comparing it against a central database that is presumably updated quite regularly—and then returning the file so it can be resubmitted and merged back into the database on the data hub or residing in the CRM.

Because this process involves technology, has a lot of moving parts and involves several steps, it’s generally set up as an automated process and allowed to run on a schedule. Moreover, because the process involves overwriting an entire database (even though it is automated) it requires having IT staff around to supervise the process in a best-case scenario, or jump in if something goes wrong and it blows up completely. Not surprisingly, because we’re dealing with large files, multiple stakeholders and room for technology meltdowns, most marketers tend to shy away from running a batch update more than once per month. Some even run them quarterly. Needless to say, given the current pace of change many feel that’s not frequent enough.

It’s interesting to note that not very long ago, sending database updates quarterly via FTP file dump was seen as state-of-the-art. Not any longer, you see, FTP is soooo 2005. What’s replaced FTP is what we call a “transactional” database update system. Unlike an FTP set-up, which requires physically transferring a file from one server and onto another, transactional data updates rely on an Application Programming Interface, or API, to get the data from one system to another.

For those of you unfamiliar with the term, an API is a pre-established set of rules that different software programs can use to communicate with each other. An apt analogy might be the way a User Interface (UI) facilitates interaction between humans and computers. Using an API, data can be updated in real time, either on a record-by-record basis or in bulk. If a Company A wants to update a record in their CRM with fresh data from Company B, for instance, all they need to do is transmit a unique identifier for the record in question over to Company B, who will then return the updated information to Company A using the API.

Perhaps the best part of the transactional update architecture is that it can be set up to connect with the data pretty much anywhere it resides—in a cloud-based CRM solution or on a purpose built data warehouse sitting in your data center. For those using a cloud-based solution, a huge advantage of this architecture is that once a data provider builds hooks into popular CRM solutions, there are usually no additional costs for integration and transactional updates can be initiated in bulk by the CRM administrator, or on a transaction-by-transaction basis by salespeople themselves. It’s quite literally plug and play.

For those with an on-site data hub, integrating with the transactional data provider is usually pretty straightforward as well, because most APIs not only rely on standard Web technology, but also come equipped with easy-to-follow API keys and instructions. Setting the integration, in other words, can usually be implemented by a small team in a short timeframe and for a surprisingly small budget. And once it’s set up, it will pretty much run on its own. Problem solved.

The Database Marketer Superhero: Expanded Role, Big Impact

Riddle me this, Batman: What sort of marketing strategies today require deeper, strategic database insight? Not so puzzling, is it? Pretty much everything a marketing team does today is driven by data — e.g., digital outreach, content, media, attribution, return on investment analysis, lead nurturing, PR and social community participation. In fact, the list would be shorter if we tallied up those marketing functions that don’t benefit from data-driven decisions.

Riddle me this, Batman: What sort of marketing strategies today require deeper, strategic database insight?

Not so puzzling, is it? Pretty much everything a marketing team does today is driven by data — e.g., digital outreach, content, media, attribution, return on investment analysis, lead nurturing, PR and social community participation. In fact, the list would be shorter if we tallied up those marketing functions that don’t benefit from data-driven decisions.

Database marketers were traditionally the geeks of the marketing department. They kept to themselves, ran queries to answer questions posed by other strategists, and worked hard to keep data clean and updated. Today’s database marketers are part of an emerging and essential marketing operations team that’s driving a lot of brands’ strategies. One marketer said to me recently, “Whomever knows the customers best gets to make the call.” Who knows your customers better than the people working with the data every day? All of a sudden, database marketers are superheroes — or at least have the opportunity to wear capes if they choose to accept the challenge.

There are two factors driving this trend, one being consumer habit. Given the ability and choice to interact with brands in many ways and across many channels, consumers are taking full advantage. It’s a me-centered consumption world where customer preference and whim create habits. At the same time, marketing automation technology is advancing and data integration is possible. Marketers can track and, more importantly, react to customer behavior in order to meet needs across channels.

Consider these five initiatives that have become imperatives for many chief marketing officers today:

1. Obtain a 360-degree view of the customer. One B-to-C marketer told me that there are more than 25 ways customers can interact with her brand, from a kiosk to a store counter to email to mobile commerce to branded website to call center to social communities. Most consumers participate in three or more of those channels. Communications can only be optimized if those habits and experiences are captured — and actionable — in your database.

2. Respond to customer behavior in the channel where the interaction occurred. This also has to be aligned with self-selected preferences.

3. Select the optimal channel for your next offer. A hotel owner uses past booking behavior to send last-minute alerts via SMS to those who have opted in and accessed the brand’s mobile commerce site. All others get the information via email. Response has boosted overall 8 percent.

4. Outline personas representing key customer segments. Do this in order to profile audience types and improve communication messaging and cadence.

5. Test and optimize your mix of channels for lead nurturing campaigns. For a live seminar event, one B-to-B marketer emailed reminders and offers based on interaction with previous email campaigns. Those who didn’t respond got simple reminders on date, location and keynote speakers. Those who did respond got more robust offers. Revenue from the offers increased 50 percent over the previous year and spam complaints dropped 25 percent. This is surely because those who demonstrated a willingness to engage prior to the event were nurtured with offers that made sense to their actions, and the others were left alone.

I’m sure there are infinite variations of these opportunities. Perhaps you’re testing some of them now. It will also be great to see how database marketers react to this new level of attention and interest from the C-suite. Will you embrace it and join the strategists, or will you run back to the corner and take orders?

How are you and your team embracing the need for a data-driven marketing approach? Please tell us by posting a comment below.