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.

Best Boston Restaurants

Where to begin. I’m a foodie. I’ve always loved going to nice restaurants and trying their signature dishes. My wife shares this passion, although she’s not as adventurous. Sometmes I have to fly solo for the more interesting foods.

I lived in Brookline for years, so you’ll see lots of local dives below. I’ve also explored most of the top restaurants in Boston. There are some I have no interest in - I’m not really into French food despite some of the restaurant names below. And others that I go back to over and over again - Grill 23, Fugakyu, Giacomos and Mare come to mind.

If you stumble upon this post, please comment. Tell me I’m wrong if that’s your thing. I’m always interested in trying other restaurants too, so please leave suggestions.

Enjoy!

Overall Favorites

Sushi: O Ya (also my favorite restaurant right now)
BBQ: Redbones
Bistro: Mistral
Bakery / Breakfast: Flour (Southie or South End)
Brunch: Tremont 647
Asian: Myers + Chang (I love Joanne)
Fish: Mare
Italian: Giacomos (recommend South End - bigger and they take reservations)
Steak: Grill 23

By ‘Hood…

Brookline: Fugakyu, Zaftigs, Matt Murphy’s, Rod Dee Too, Rami’s, Bottega Fiorentina, Tasca, Audabon (Last two may be Boston addresses, but they are in Brookline neighborhoods)
South End / Roxbury: Myers + Chang, Tremont 647, Giacomos, Mistral, Pho Republic
Back Bay: Grill 23, Casa Romero, Sonsie, Charlies, Clio/Uni, Newbury Pizza, Stephanies
North End / Waterfront: Meritage, Giacomos, Bricco, Mare, Ristorante Fiore, Sail Loft, Sel De La Terre
Cambridge: Not really my world although I love Rialto for dinner and Henriettas for brunch - both at the Charles. The Chowhound folks love Craigie on Main. It’s next up on my list to try. I’ll update once I’ve been

Lightweight Data Governance

Last week I read a great article from First Spike on the upcoming demand for Data Governance work. The author referenced several sources who predicted a sharp rise in demand for Data Governance. One even predicted that it will be a regulatory requirement. I followed up with Mark Cowan from First Spike last week to discuss our definitions of Data Governance.

Mark was very interested in what a Data Governance program looked like at an Internet company. His point was that it’s more typical to see Data Governance in the Health Care and Financial Services industries. That makes sense since those types of organizations are more likely to have higher data quality standards and regulatory requirements. Without going into too many specifics, I let him know my approach on lightweight Data Governance. I think it’s something that I’ll continue to explore, and develop further. I had never articulated it that way before, but it sums up my theory well.

We got to talking about structured vs. unstructured data, and approaches for dealing with each. Lightweight Data Governance is very much unstructured Data Governance. Rather than building formalized organizations to manage data governance and large scale Master Data Management solutions, my approach has been to improve existing infrastructure, systems and processes piece by piece.

This approach can lead to early success in Data Governance programs, backing from colleagues in other departments and an understanding of the value that Data Governance can bring to an organization. It also eliminates some of the arguments from critics regarding high program start up costs, number of dedicated resources, etc. I would highly recommend it as a starting point.

Conversely, most existing theory is based in top-down, large-scale Data Governance. I’ve attended webinars that promote getting buy-in from the CEO down for Data Governance programs. To paraphrase, “Without executive support, Data Governance programs cannot succeed.” I think it’s critical to make some early progress in a new Data Governance program, and get mid-level support. The Directors and VPs who own not only business usage, but also the data and reporting technology need to understand the value of Data Governance. Many do already. If you partner with those leads then executive support will be there when you need it. That’s my theory at least.

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.