Marketing Success Sans ‘Every Breath They Take, Every Move They Make’

Last month, I talked about how to measure success when there are many conflicting goals and available metrics flying around (refer to “Marketing Success Metrics: Response or Dollars?”). This time, let’s start thinking about how to act on data and intelligence that we’ve gathered. And that means we get to touch different kinds of advanced analytics.

Last month, I talked about how to measure success when there are many conflicting goals and available metrics flying around (refer to “Marketing Success Metrics: Response or Dollars?”). This time, let’s start thinking about how to act on data and intelligence that we’ve gathered. And that means we get to touch different kinds of advanced analytics.

But before we get into boring analytics talk, citing words like “predictive analytics” and “segmentation,” let’s talk about what kind of data are required to make predictions better and more accurate. After all, no data, no analytics.

I often get questions like what the “best” kind of data are. And my answer is, to the inquirer’s disappointment, “it depends.” It really depends on what you are trying to predict, or ultimately, do. If you would like to have an accurate forecast of futures sales, such an effort calls for a past sales history (but not necessarily on an individual or transactional level); past and current marcom spending by channel; web and other channel traffic data; and environmental data, such as economic indicators, just to start off.

Conversely, if you’d like to predict an individual’s product affinity, preferred offer types or likelihood to respond to certain promotion types, such predictive modeling requires data about the past behavior of the target. And that word “behavior” may evoke different responses, even among seasoned marketers. Yes, we are all reflections of our past behavior, but what does that mean? Every breath you take, every move you make?

Thanks to the Big Data hype a few years back, many now believe that we should just collect anything and everything about everybody. Surely, cost for data collection, storage and maintenance has decreased quite a bit over the years, but that doesn’t mean that we should just hoard data mindlessly. Because you may be deferring inevitable data hygiene, standardization, categorization and consolidation to future users — or machines — who must sort out unorganized and unrefined data and provide applicable insights.

So, going back to that question of what makes up data about human behavior, let’s define what that means in a categorical fashion. With proliferation of digital data collection and analytics, the definition of behavioral data has expanded considerably.

In short, what people casually refer to as “behavioral data” may include this to measure success:

  • Online Behavior: Web data regarding click, view and other shopping behavior.
  • Purchase: Transactional data, made of who, what, when, how much and through what channel.
  • Response: Response history, in relation to specific promotions, covering open, click-through, opt-out, view, shopping basket, conversion/transaction. Offline response may be as simple as product purchase.
  • Channel: Channel usage data, not necessarily limited to shopping behavior.
  • Payment: Payment and related delinquent history — essential for credit purchases and continuity and subscription businesses.
  • Communication: Call, chat or other communication log data, positive or negative in nature.
  • Movement: Physical proximity or movement data, in store or store area, for example.
  • Survey: Responses to various surveys.
  • Opt-in/Opt-out: Sign-up specific 2-way communications and channel specific opt-out requests.
  • Social Media: Product review, social media posting and product/service-related sentiment data.

I am sure some will think of more categories. But before we create an exhaustive list of data types, let’s pause and think about what we are trying to do here.

First off, all of these data traceable to a person are being collected for one major reason (at least for marketers): To sell more things to them. If the goal is to predict the who, what, when and why of buying behavior, do we really need all of this?

The ‘Who’ of Buying Behavior

In the prediction business, predicting “who” (as in “who will buy this product?”) is the simplest kind of action. We’d need some PII (personally identifiable information) that can link to buying behaviors of the target. After all, the whole modeling technique was invented to rank target individuals and set up contact priority — in that order. Like sending expensive catalogs only to high-score individuals, in terms of “likely to respond,” or sales teams contacting high “likely to convert” targets as priorities in B2B businesses.

The ‘What’ of Buying Behavior

The next difficulty level lies with the prediction of “what” (as in “what is that target individual going to buy next?”). This type of prediction is generally a hit-or-miss, so even mighty Amazon displays multiple product offers at the end of a successful transaction, by saying “Customers who purchased this item are also interested in these products.” Such a gentle push, based on collaborative filtering, requires massive purchase history by many buyers to be effective. But, provided with ample amounts of data, it is not terribly difficult, and the risk of being wrong is relatively low. Pinpointing the very next product for 1:1 messaging can be challenging, but product basket analysis can easily lead to popular combinations of products, at the minimum.

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.

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.