Are You Spamming Your Customers?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Have you had a similar experience? Please comment.

B2B vs. B2C Matching for Sales and Marketing

I recently read the KnowledgeBanks article Why is b2b marketing different from b2c marketing? The article works to disprove the common misperception that “B2B marketing is just marketing to consumers who happen to have a corporation to pay for what they buy.” I completely agree, and would like to extend the points made to include the differences in matching, sales, and marketing. I’ll also point out ways to know if you’ve reached your goals.

Some background before I begin. I spent two years working for Harte-Hanks implementing primarily B2C matching for Financial Services Marketing systems. Did you ever wonder how the banks knew that your accounts were linked even though you opened them as different names and addresses? Think about Bank of America’s history. It’s a collection of dozens of banks, and you could have opened accounts any time in the past. To merge that data which could include 10-100 million rows of accoutns, massive B2C matching systems are required, and equally complex logic. My work at Monster has centered on B2B data and how it’s used by Sales and Marketing teams. I’ve worked primarily with matching engines based on Trillium software, but I’ve gotten to know most of the other technologies used at an Enterprise level over the years. I’m a free-agent when it comes to technology, and I’d recommend all technologists embrace an open mind when it comes to vendor selection.

B2C Matching

B2C matching is absolutely nothing like B2B matching. The difference? Householding. In B2C the goal is to household different contacts at the same address. You household John’s accounts, then you household Mary’s accounts, then you merge them both together if they live at the same address. Sounds simple right? Well, sometimes John goes by Jack, and sometimes Mary uses her maiden name, and their phone company and credit card records are all listed as “M & J.” These are the troublesome records to figure out, and most of the records have some flavor of variation similar to this.

B2C Sales and Marketing relies on volume, and the companies focused on matching process huge data sets. You need to find many interested parties because the available money from each is very small. A person may buy $100 worth of software, a $2000 computer, or even a $20,000 car. But they will never spend $1MM with you unless you sell houses to the moderately rich or toys to the ultra rich.

B2B Matching

B2B matching is also about householding, but we don’t call it that. We call it Parent-Child Hierarchies. The goal is to determine all of the locations of a business that you are selling to, and try to figure out how they fit together. Of course this is an enterprise perspective, and SMB would be more focused on single locations. So the enterprise question is, which locations are headquarters of other branches? Does that headquarters control purchasing for the child branches? Or are the branches empowered to buy on their own? What does the sales history tell us about them? Do the reps know anything that can help, and how do we capture that data in an automated matching process. All that, and I haven’t mentioned that companies buy, sell, and merge all the time. Think about GE, Berkshire Hathaway and Tyco. Is each of those 1 business or 20?

For B2B Matching each sales and marketing person would like to know how much each location has purchased and which has the purchasing power. Some of those locations will purchase services that could result in multi-million dollar deals. When I worked for Accenture the philosophy shifted to “Big Bets” and the partners (who functioned as sales people) only targeted accounts willing to commit to $25MM per year. That year they sold several Billion dollar deals. Think about that for a minute. The sales reps will need appropriate level high quality contacts and contact information at each location. You can’t sell a $25MM deal to a line manager or team lead. There are many ways to get B2B contacts - list purchase, telemarketing, partnerships, etc. - but that’s a different post.

Measuring Results

If you think you’ve reached your goal and found success with your matching, you’re wrong! Matching is more of an art than a science, and as soon as you get to an acceptable level of completeness and accuracy you need to start looking for the next round of improvements. Matching (like data warehousing in general) must change with the business. As new products are developed, new technologies released, and new business processes implemented, the matching must be updated to dovetail with those changes.

To Address in Future Posts…

Preferences and Opt-out Management
B2B vs. B2C Analytics
Number of Services per Contact (B2C) vs.
Number of locations per Company (B2B)
Demographic and Firmagraphic Appends

Kaushik’s 10/90 Rule applied to B2B Data

Avinash Kaushik is the leading expert on Web Analytics basics. In his book Web Analytics: An Hour a Day he describes the baseline understanding you need to build and maintain an analytics driven website. I would highly recommend the read. In one section, Avinash describes his 10/90 rule, which he has also posted on his blog, Occam’s Razor. The shorthand version is Kaushik recommends spending 10% on tools and 90% on intelligently trained people to get the most return from your analytics investments.

How else can the 10/90 rule be applied? What about B2B Data? Are you getting the most value from the lifeblood of your outbound customer acquisition strategy? Are you paying too much for leads and focusing on quantity rather than quality. I know I’ve fallen into that trap.

Data Acquisition of B2B Leads

When planning your budget for Data Acquisition, you need to consider not only your list spend, but also the analysts empowered to develop your Leads Strategy. It’s easy to buy a 10MM row list of B2B Leads. All you need is money and a list broker to sell it to you. The more mature model is to understand your customer base and only buy prospect information for companies who have a high likelihood to purchase your product.

My title was previously “Director of Marketing Analytics.” I’m reminded of this daily when vendors call and leave messages for Mark Goloboy, Director of Marketing Analytics. My contact information was gathered from website and magazine subscriptions, webinars attended, and likely my LinkedIn profile. I’m on Jigsaw, Pipl, and ZoomInfo. Those companies gather and sell your data. That’s what they do. If you’re reading this, you are likely on several large aggregated lists that are built from dozens of smaller niche lists. I would recommend googling yourself and opting out of any services that look like aggregators, unless you enjoy hearing vendors’ sales pitches.

But wait. I buy list data. And you don’t want to hear from me just like I didn’t want to hear from you. And therein lies the problem. A vast majority of the leads you can purchase don’t want what you’re selling. No matter how good your sales people are, most of the leads you can provide them will never be sold. So how can you improve results? You need to understand your customer base through analytics, develop patterns of purchasing customers, and only buy those leads who have a high likelihood to purchase from you. That’s easy to say in two sentences, but it requires business analysts with deep understanding of your company and industry, statisticians who know your customer data and can transform it into insightful scoring analyses, and sales and marketing strategists who know how to work with your front line sales people and deliver complimentary messages to your customers.

The 10/90 Rule Applied

So to avoid overpaying for unfiltered B2B lists, you need to follow Kaushik’s rule. If you’re planning to pay $10 for data then you need to assume $90 worth of analytic commitment. That needs to be applied at four different stages.

1) You need to commit to analytics before the list purchase to determine which prospects are likely to become customers. Then you can avoid buying full data sets and instead buy targeted niche lists. Also at this stage you should develop program revenue projections and determine if the list is worth the purchase price. If not. Don’t buy it! Spend the money elsewhere.

2) Once you have the list in house, you need to score it based on previous buying patterns, filter out any existing customers, and prepare the list for distribution to your sales teams.

3) During the program you need to analyze results quickly and course correct if the campaign is not producing results.

4) Following the program you should analyze what worked and what didn’t. Did your models and projections hold up? What was the actual ROI? Was it better for some segments? All of these questions need to drive future programs.

And don’t forget…

Throughout the process, your analysts need to coordinate with Marketing and Sales leaders, CRM and SFA systems owners, and financial strategists to align with other programs and schedules. Following the process communicate with all constituents. Let them know where you’ve had success, and MORE IMPORTANTLY let them know where you can do better. You build credibility if you point out areas of improvement and show the ability to mature your programs.

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

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

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

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

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