The B2B Lead

Marketing Automation



The Care and Feeding of Your Marketing Automation Engine

Not long ago I found a piece of research from Forrester regarding marketing automation best practices and one nugget caught my attention. Consider the following:

“Marketing leaders typically don’t appreciate that automation makes them a database marketing business, and database marketing requires a continuous effort to run campaigns and maintain an accurate, robust marketing database.

The research goes on to say that while establishing a full-time resource to manage sales force automation (SFA) systems and data is standard practice, most organizations simply don’t think of marketing automation in the same way.

While your marketing automation system can be as basic or complex as your needs require, these tools provide a massive amount of options and functionality you can leverage in your campaigns through every stage of the buying cycle. But, according to this research, most organizations simply aren’t providing the support to really leverage them.

Why is that?

I’d love to get your feedback here. Is your organization making the most out of your marketing automation system? Do you have dedicated resources helping to ensure that you do? Or are you like most – wearing multiple hats, doing more with less, and getting it done between the dozens of other things that make up your day?

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Thursday, April 26th, 2012

 

Fuel Marketing Success with 1:10:100

The quandary of bad data has flustered marketing professionals for decades. It’s pretty obvious that outdated customer information wastes time and resources as well as reduces opportunity creation and revenue, but it’s so hard to quantify rarely is action taken. Fear not, there is a way to change this paradigm…

Based upon the visionary work of W. Edwards Deming, the software industry adopted the “1:10:100” rule to measure the financial impact of software defects throughout the development lifecycle. For example, a defect caught in development phase costs a factor of 1x to correct, whereas a defect caught after production release will cost a factor of 100x to correct. Using this system, engineering and IT could justify investments around automation and quality assurance.

The same logic has been employed by industry analysts in the marketing realm relative to the cost of dirty customer data. Research and advisory services firm Sirius Decisions estimates that the cost of each record increases exponentially throughout the customer relationship as follows: $1 to verify record upon entry, $10 to clean and de-dupe after entry, and $100 per record if nothing is done.  When you think about the number of inbound records coming into your marketing database every month, combined with the volume and age of your existing records, you can imagine how these costs (and opportunity costs!) manifest.

Based on our experience at ReachForce, for every 1% of data quality improvement, marketing can generate 5-6% of incremental revenue in concert with sales execution. Another data point from Eloqua’s 2011 Marketing Automation Benchmark Report reveals that companies that employ consistent data hygiene create 7 times the number of inquiries and 4 times the number of leads. Combined with lead scoring, marketers can achieve up to 11x lead generation “lift.” Now that sounds like low hanging (revenue) fruit!

In sum, using the 1:10:100 rule, marketers have another tool to justify critical investments in customer data quality to drive significant improvements in campaign execution and lead conversion rates. Leveraging customer data and industry statistics, ReachForce has developed an ROI model to empower marketers to drive the adoption of data hygiene practices. We’d love for you to “reach” out here to learn more.

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Monday, August 1st, 2011

 

8 Marketing Metrics Keeping the Revenue Engine Humming – B2B Marketing and Sales Tip #310

B2B Marketers listen up.  If you haven’t already heard the finish line has moved.

It’s not at large numbers of impressions anymore (and uh oh if you are). Double digit email open and click thru rates won’t get you there. And, while still cool, it’s not about seeing which social media tool can drive you the most traffic.

The finish line is now at real, measurable revenue.  That’s right, we’ve finally made it to the big league.  Our marketing automation friends and partners (@eloqua and @marketo) are leading the charge, helping educate and pave the road to the new finish line and the ultimate success, more revenue.

Our own teams are counting us to keep the revenue engine humming. How are you doing it?
Here’s a few metrics I like to track to ensure the marketing engine is maintained and performing at expected levels at all times.

  1. # of net new companies and contacts from target market sweet spots are added to the marketing mix and where they came from each month. This tells us if which marketing vehicles are working, i.e. advertising, social media, referral programs.
  2. # of contacts being touched with a marketing message each week; net new contacts vs. those in nurture programs and response rates and trends. I can make another list altogether on what you can do with this info. i.e. inactive vs. active programs, segmentation adjustments, etc.
  3. # of inbound requests – both requests for content and contact tracked weekly.  While it may feel like there isn’t enough activity to track it weekly, do it anyway.  As you continue to build out your revenue engine you’ll want to understand what is driving inbound requests.
  4. # of people hitting a landing page, then jumping to corporate site for product/service info.  Newsletter and PPC advertising driving people to best practice content accessible via a landing page, a 2nd click means something peaked their interest.
  5. # of people originating at the company blog and jumping to the corporate site (product pages, solution pages, resources pages).  You’re blogging for SEO, right? Are you driving the right kinds of traffic?
  6. # of new sales meetings set from marketing lead generation programs.  Woot Woot!  We got them to the door, now to convince them to come in and hear more.
  7. # of marketing leads moved to the qualification stage of our sales pipeline.  OOohhh…they like you.  Don’t forget even the best relationships need nurturing.
  8. # of marketing leads moving to a proposal, and of course closing.  Score one for marketing!

Once a new customer is on-board I then go back and identify what activities were involved in moving this lead to being a new customer so I can be sure to do more of it.

Now of course there is a list of metrics similar to this for each initiative you take on.  It’s always important to outline goals and expectations of each program so that you are sure to spend your time and resources on the best producing programs.

Do you measure anything not on this list?  If so, please share.

Oh and a BIG HUGE SHOUT OUT to the marketing automation and CRM partners out there, helping make all of this tracking possible.

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Wednesday, January 19th, 2011

 

Spam Traps – Staying Clear with Routine Data Maintenance

True to the adage “there is nothing new under the sun,” we’ve had a renewed rush of questions from B2B marketers about Spam Traps. I suppose it’s only natural given all of the attention that CRM data quality has been getting over the past few quarters.

Since its become a recurring question, we thought we’d address Spam Traps here.

Spam Traps are Email addresses (or even an entire domain) that appears to be valid, but have been created to use as a lure. Specifically, Spam Trap Email addresses are usually only published (seeded) in areas hidden from view so automated e-mail address harvesters or “skulkers” (used by low end list vendors and spammers) can find the email address, but since no communication is solicited by the originator of the Traps, any Email messages sent to them are immediately, and automatically reacted to as unsolicited.

Reaction is usually meted out by automated anti-spam systems. The automated system can be set to block further e-mail messages with the same content (or from the same source IP address) arriving for other e-mail addresses, because the messages are interpreted as “bulk” or unsolicited e-mail.

New users of marketing automation systems who aren’t attentive to proper list hygiene before, during and after MA implementation often find their source IP addresses (as senders of Email into the Traps) blacklisted, thereby degrading the efficacy of their marketing automation system and marketing program results.

Many Spam Trap addresses show up in search engine results, and anyone can write to these addresses without knowing that all mail will be caught as spam.

Once a Spam Trap becomes tainted (when for example someone discovers what the Spam Trap Email address is being used for) a malicious party could target Traps by sending email to it and taking some degree control over the automated process of determining what is or isn’t being considered bulk, unsolicited e-mail by the anti-spam system. They would then be able to subscribe a Spam Trap address to any legitimate email list and create havoc on a perfectly good subscriber list.

Again, only a solid, routinely cycled CRM data hygiene regimen can mitigate this sort of risk.

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Monday, January 10th, 2011

 

Marketing Automation Software Gains Steam

Filed under Marketing Automation
Drew Sollberger
  • LinkedIn
on October 27th, 2010
 

Marketing automation is quickly being adopted by a larger number of marketers, as evidenced by the success of companies like Marketo, who now boasts over 700 customers. While those using marketing automation still only represent a small percentage of all B2B marketers, it’s obvious that more and more are searching out ways to engage with their prospects more intelligently. Also, many marketers are being held more accountable for their marketing budget and activities. In a recent write-up on the adoption of marketing automation software, Lauren Carlson from Software Advice broke down some of the most pertinent drivers for adoption. Check them out, you might find some good reasons to make a change in your own organization.

Tailwinds for Marketing Automation Software

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Wednesday, October 27th, 2010

 

It’s a Contact Data Management “Revolution” – Part III

This is the third post in a series discussing the RPM (Remediate ― Provision ― Maintain) Contact Data Management strategy. Last week we covered the first track, “Remediate,” or the proactive process of fixing problems within contact data that, among other things would at best hinder, at worst prevent effective market segmentation and different types of post remediation analysis.

The next track up for discussion is Provisioning. This hinges on post remediation analysis of firmographic and contact data to create what we call a “Smart Contact Data Provisioning Plan.” A simple way to describe this is to think of provision planning as being analogous to your weekly trip to the grocery store. One of the best pieces of advice my Mom gave me as I left the nest:  “Never go grocery shopping when you’re hungry … make a list first.”  As time passed the benefits became clear. Checking my pantry and refrigerator to know what I did and didn’t have, and making a “fill the gap” list saved me money and time. It also ensured that at meal preparation time throughout the week I had exactly what I needed.

Post remediation analysis is the same thing. It’s an automated way of looking at your in-house data to check what you do and don’t need before you start planning your funnel-filling programs and calling list vendors to buy contact data for fuel. The different types of analysis needed to form a Smart Contact Provisioning Plan are:

Title Density Analysis – First step toward human-level segmentation. It is based on grouping contact records that have similarities in title. An effective way to structure this is to break the data by 1) IT versus non-IT records 2) departmentalization of non IT records and 3) high-medium-low ranking based on seniority of title. The results provide three useful views:

-   To see if you have enough records in the right areas of existing target organizations, i.e. high-medium-low ranking titles, and by silo in IT and non-IT departments ― and provision accordingly

-     To cross-tabulate contact groups with wins-analysis to see if certain contact data  “sweet-spot” sets should be provisioned in similar target companies (existing & net-new) in order to replicate success  

-   To see and quarantine records for contacts working in areas and/or have titles which aren’t relevant to your sales and marketing mission. This helps ensure outbound efforts are not wasted in trying to engage with the wrong people          

 Title Clustering Analysis – Very useful in maximizing the yield of targeted data pulls when provisioning or sorting lists based on business function. This is the inclusion and sorting of custom fields to define areas of responsibility. For example, if you wanted to aim a message or program at a “Sr. Sales Leadership” audience, Title Clustering maps all associated Title Groups to the desired persona. This broadens search matches for obvious keyword hits such as “VP Sales,” as well as less direct hits where more ambiguous titles (in relation to the function) such as “VP Business Development” are less likely to surface using typical search techniques.               

 Contact Gap by Persona Analysis – Every sales and marketing organization needs to have an acute understanding and categorization of the various Personas within an organization’s Decision Making Unit (DMU). The science of Persona development captures and communicates relevant insights about such things as the target buyers’ roles, responsibilities, priorities and problems. Each contact record you are working through the deal making process needs to be tagged with this important information for human-level segmentation purposes. At ReachForce we use an automated Role-Title correlation application to assist customers who want –but don’t already have– Persona tags in their existing records. Once these tags are appended, your database can then be automatically examined for “gaps” to show which DMU Persona types are and aren’t present on an account-by-account basis. This information is then used to create a provisioning plan to fill the gaps.        

 Contact Darning Analysis Guides the Smart Contact Provisioning Plan in terms of pinpointing which contact records are needed (by Persona) as replacements for older records that have proven to be inaccurate or outdated during the remediation process (counter-attritional).

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Tuesday, April 13th, 2010

 

It’s a Contact Data Management “Revolution – Part II”

Our last post previewed the RPM (Remediate ― Provision ― Maintain) Contact Data Management strategy as a way to maximize your investment in Sales & Marketing Automation systems. It follows a very logical, yet sometimes difficult to execute progression … 1) clean the data to add and/or replace missing or incorrect information 2) analyze it to pinpoint gap fill and net new persona acquisition provisioning requirements and 3) maintain and continually compile objects that generate the most business benefit to the end users.

The first of the three RPM tracks is Remediate, which simply means fixing a problem. A good place to start is fixing your data so you are able to configure targeted deal making activities with precision. The best business case for data remediation is for market segmentation purposes. With or without automated B2B sales and marketing applications, I have rarely been in a situation where “one message does it all” in terms of relevancy. Whether configuring lead generation campaigns or selling directly, the thoughts and values you are communicating in your messages have to directly relate to your target. That means the type of business they are in, the characteristics of their organization, and the job they do. Opportunities increase when unique target groups with varying needs, wants, and what we call “segment vernaculars” are recognized.

This concept (taken from the Wind & Cardozo Model) is known as Two-Stage Market Segmentation.  It is based on “broad two-step classifications of macro-segmentation and micro-segmentation.” That sounds complicated, but it’s really not. In fact, it’s one of the most commonly used methods in B2B marketing and is often extended into more complex models that include multi-step, and three and four-dimensional models which many Marketing Automation systems support. To make segmented campaigns happen, the data in your system(s) needs to have the following (at the very least) segmentation data points to sort, slice and dice by:

  • Segmented Messaging by industry – If you market to more than one industry segment, your outbound communications need to take into account unique industry-by-industry needs, wants and vernaculars, which is a good approach for application-based selling. You need SIC and/or NAICS codes in your company and account records for this type of segmentation. However, the former is based on very basic and standard industry classifications and shouldn’t be relied on exclusively. For instance, many industries that have a lot of technologies, or have new, innovative products are classified as ‘other.’
  • Segmented Messaging by company size – Messaging that pinpoints needs and wants relative to the size of a company can be a big deal when it comes to being relevant to whoever is on the receiving end. You need to append employee headcount and reported revenue (when available) for this type of segmentation. It’s best to have both since many companies don’t report earnings. This is one of the most practical and easily identifiable criteria. It can be good indicator of the potential business for a company. However, it needs to be combined with other factors to provide a reliable picture.
  • Segmented Messaging by geographic region –A company’s location is just as important as its size because it’s very important to relate to its culture, language, general business attitudes, and communication requirements. For example a company would adopt a different selling strategy with a prospect if it’s based in EMEA instead of the U.S. Geographic segmentation is another important element, especially for multi-national and global B2B businesses and brands. Many marketers have regional and national programs which alter their messaging strategies to meet the individual needs, wants and (obviously) languages of geographic areas.  You need to append complete postal address information for this type of segmentation.
  • Segmented Messaging by role or function – Too many marketers fail to take good segmentation practices to the human level and rely solely on a person’s title to decide whom to target and with what message. It’s best to go through a process of determining what personas make up the ideal decision-making unit (DMU) for the product or service you’re selling, and craft a messaging approach that highlights your value in a very personal way. This borrows from the concept of Micro-segmentation and focuses on factors that matter most to people in the course of daily business. At ReachForce we use proprietary software and data processing tools to conduct title density and clustering analysis in combination with role title correlation tagging to append role-function information for our customers who want to add this important segmentation capability.
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Thursday, April 1st, 2010

 

It’s a Contact Data Management “Revolution”

Wikipedia defines “RPM” as Revolutions Per Minute (abbreviated rpm, RPM, r/min, or r•min−1), a unit of frequency of rotation: the number of full rotations completed in one minute around a fixed axis. It is used as a measure of rotational speed of a mechanical component.

I’d like to borrow from this a bit for the sake of discussing strategy for Contact Data Management for Sales and Marketing Automation applications. Since the bottom line objective should be accelerating the B2B sales cycle, all things that optimize how fast and efficiently prospect contact information & sales intelligence data rotates into and around your deal making processes are important. Hence, our product development group is starting to see and define the acronym “RPM” in a new, data-centric way ― Remediate ― Provision ― Maintain.

It’s relates to sales funnel velocity ― how fast deals make it through from qualification to close ― but it is specifically about drilling down to those little noticed things that go on within the data and systems themselves to facilitate fast, efficient and predictable deal making.

Over the next few posts we’ll take a detailed “check the boxes” look at the three components of an “RPM” Contact Data Management strategy.

Here’s a quick preview:

1. Remediate – If you are serious about leveraging all of the benefits that sales and marketing automation systems offer, think of these systems as your “engine.” Then, think of the data you are feeding into your engine as the “fuel.” Clean, high octane fuel means data that has complete, valid account/company level firmographic segmentation data for list sorting and targeted messaging purposes (If you target by industry, size of company, geographic location etc). Also, business card information contained in your database degrades at a rate of 3-6% per month. The RPM Contact Data Management strategy puts the power of automation to use in stabilizing and transforming your existing data assets into actionable, campaign ready lead generation assets.

2. Provision – Not accidentally, the best thing about a great contact data Remediation process [above] is it helps you understand things about the data you’ll need to plan the purchase for additional contact data wisely. I compare this to going grocery shopping, because before you start buying you should look at your provisions carefully to identify what data you do and don’t need. A good contact data provisioning plan helps you understand which contacts you need to buy to fill in gaps left by “Remediation Fallout” ― that is, replacing or updating outdated contact information you had in your database. You’ll also be smarter about what contacts you need to buy to expand your audience from a net-new perspective. Some RPM techniques used here are 1) title density and gap analysis to pinpoint missing personas within your existing target organizations and/or 2) identifying new organizations and persona-groups that you don’t already have on your radar with sales funnel wins analysis and “looks like” list profiling.

3. Maintain – Sales and marketing automation systems need a built in “fuel filter” and synchronizer in place to make sure that the investments you’ve made in them are not diluted by the effects of dirty data. Data is perishable and once introduced into your lead generation engine it starts aging and dying. Good data maintenance is a process of automatically monitoring and stress testing records by comparing them to trusted, up to date sources. This sort of data rationalization is the use of meta data to determine the best assortment of objects that generate the most business benefit to the end users and not only keeps data in a high state of campaign readiness, it also creates an opportunity to enrich standard business card records with valuable details that can transform a run of the mill lead or contact record into a much more powerful sales intelligence compile.

If you have any thoughts as we go through this, please chime in!

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Friday, March 26th, 2010

 

How to Get More From Your In-house Database

To get more from your in-house data for lead generation, you need to keep it clean ― but that’s like hitting a moving target. With companies merging or going out of business and a workforce that is constantly changing, marketing databases get dirtier at an alarming rate.

In fact, 2.1% of data goes bad every month, according to MarketingSherpa. This means  over 25% of contact data goes bad per year.

The only problem is knowing which 25%.

The good news is that marketers can dramatically improve their campaign results by using the right strategies to consistently update, clean and refresh their lead-generation database. According to SiriusDecisions, “the company that markets with a healthy data-cleansing routine can realize nearly 70% more revenue than an ‘average’ organization, based purely on data quality.”

Lately, we’ve been talking about the best ways to start with data cleanup, what to consider when marketing to your in-house database, important signs that your database might need help, and more.

Armed with this information, you should be in a better position to optimize your in-house database, reduce wasted efforts, improve conversions and get more revenue from your marketing dollar.

Do you have some great tips for starting the clean up process?

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Tuesday, March 9th, 2010

 

Move your Sales and Marketing Database Out of the Slow Lane – B2B Marketing and Sales Tip #281

Aberdeen recently released a great report on leveraging customer data to better serve your marketing efforts. Here are some of the take-away points we thought were worth sharing:

If your database is in the slow lane:

  • Start by using data for activities that will have a positive impact on revenue. Demonstrate the value of your data to justify investment in its ongoing health.
  • Develop timelines and processes for cleaning your data. The importance of good data hygiene can’t be understated. If you clean it once and walk away it will get dirty again. Only constant attention will yield golden results.
  • Invest in tools to help analyze customer data. Data analysis and marketing automation technologies will help you improve the effectiveness of your marketing campaigns.

If your database is already on the right path but needs a boost to hit the fast lane:

  • Implement a formal data hygiene strategy. Create repeatable processes for de-duping, cleaning, and appending data as well as documenting best practices for users of your data.
  • Engage multiple departments for data analysis. Encourage collaboration between your sales, marketing, customer service, IT, and finance organizations.
  • Democratize all customer data. Centralize your database to measure and optimize the performance of your multichannel campaigns.
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Thursday, March 4th, 2010

 
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