A Revenue Marketing Journey: The Conclusion

Sixteen months ago, we started the revenue marketing journey together. We defined revenue marketing as the combined set of strategies, processes, people, technologies, content and result measurements across marketing and sales.

Sixteen months ago, we started the revenue marketing journey together. We defined revenue marketing as the combined set of strategies, processes, people, technologies, content and result measurements across marketing and sales.

What followed was a series of articles that chronicled the major tasks fundamental to transforming your marketing organization to one that influences revenue in a predictable, scalable way. We covered, in the following order, the organization structure, the processes, content, channels, technology and analytics. Links to all 16 posts are provided below.

  1. First Steps in the Revenue Marketing Journey
  2. An Organizational Structure for Modern Marketing Success
  3. Marketing Operations Grows Up: Why Unicorns Rule
  4. Driving Demand Generation: Who Belongs on That Bus?
  5. The Digital and Content Team: Is Splintering a Verb?
  6. 5 Core Marketing Operations Processes to Master
  7. 7 Outrageous Lead Management Errors and How to Fix Them
  8. Is Data-Driven Decision-Making (3D) at the Heart of Your Marketing Organization?
  9. Add Data Operations to Accelerate Your Revenue Marketing Journey
  10. WARNING Don’t Wing Campaign Development: 6 Steps to a Flawless Rollout
  11. Are You Drowning in Content Chaos?
  12. Brilliant Marketing: Why Thomas Edison Was Light-Years Ahead of His Time
  13. How to Formulate Your 2018 Content Marketing Strategy
  14. Your Prospects Are Multichannel. Are You?
  15. How Marketing Operations Chooses Wisely Between Bright, Shiny Objects
  16. Get Revenue Marketing Analytics Right for 2018

Now that we have covered the fundamentals of revenue marketing, it is time to discuss how we operationalize a response to the big challenges facing marketing today using our revenue marketing capabilities. How do we help marketing become even more accountable, fully execute a digital transformation, and embrace the customer experience as the dominant competitive battlefield?

Next month, we will start with accountability and how to shape those quarterly and annual goals of the marketing organization.

Mounting Dysfunction in the B2B Buying Process

Everyone is aware B2B buying is complex. But new research from Gartner suggests that things are even worse these days. It’s buying gridlock. I interviewed Brent Adamson, author of The Challenger Customer and The Challenger Sale, and got the skinny. He also has a set of good ideas for how “prescriptive selling” can help us get out of this mess.

How do you sell when your buyers can’t buy?

Everyone is aware that the B2B buying process is complex. It involves multiple parties over long decision-making cycles. In large enterprise, this can take months, if not years, and involve dozens of individuals in the buying circle.

But new research from CEB, now Gartner, suggests that things are even worse these days. It’s buying gridlock.

I interviewed Brent Adamson, sales principal executive advisor on sales, marketing & communications — also author of The Challenger Customer and The Challenger Sale — and got the skinny. He also has a set of good ideas for how “prescriptive selling” can help us get out of this mess.

Q. Your Research Found a Lot of Dysfunction in the B2B Purchase Process. Please Explain.

At CEB, now Gartner, we’ve been studying sales and marketing for years, which means that we have deep insights into the challenges within these functions. But interestingly, what we’ve been seeing is that it’s become difficult for customers to buy today. It’s certainly hard to sell, but when you look at it from the other side, it is actually harder for customers to make purchase decisions.

Based on CEB’s latest observations, here is a summary highlighting the dysfunction in the B2B purchasing process:

  • Bigger, increasingly diversified buying groups.
    It was only 2.5 years ago that we found the average buying group consisted of 5.4 stakeholders. This number is up 26 percent to 6.8 today, and the average buying group now consists of 3.4 different functions. Diversity also means different tiers within the corporate hierarchy, different geos and different teams. In many cases, these stakeholders have never worked together. Alarmingly, we’re seeing more and more purchases where the customer doesn’t even know who ultimately will be in the decision-making group.
  • Dysfunction runs rampant.
    As customer buying group diversity goes up, so does stakeholder dysfunction. They are having clear disagreements, avoiding key issues, and reporting that they weren’t being heard.
  • Decision-making takes longer than expected.
    A full 84 percent of customers report their purchase process took longer than expected — by nearly double.
  • Even indecision takes forever.
    The average purchase decision now takes 4.9 months, but shockingly, the average “no purchase” decision takes 4.7 months. So whether your average sales cycle is three months or three years, “doing nothing” takes just as long to decide as “doing something.”

Q. Why Do You Think B2B Buying Has Developed in This Way?

The world that we operate in — selling or buying — it’s a world of “more”: more information, more options, and frankly, just more people involved in a purchase decision.

Many observers wrongly conclude that all this “more” empowers customers. The problem our studies have uncovered is that this “more” is causing decision paralysis.

Q. What Can a Sales Team Do About It?

Sales teams often think they need to be more reactive. As customers demand more, they need to respond more completely and quicker than their competitors. So it’s a race to become the most responsive supplier.

However, if sales teams respond to all their customer’s demands and requests for more, when the customer is already suffering from information overload, this only makes things worse.

Putting Data to Use

The value of data does not depend on size or shape of them. It really depends on how useful data are for decision-making. Some data geeks may not agree with me, but they are generally not the ones who fund the maintenance of spit-spot clean data in a warehouse or in a cloud.

Lead a horse to waterThe value of data does not depend on size or shape of them. It really depends on how useful data are for decision-making. Some data geeks may not agree with me, but they are generally not the ones who fund the maintenance of spit-spot clean data in a warehouse or in a cloud.

From the business perspective, no one would invest an obscene amount of money for someone’s hobby. Sorry for being obvious, but data must be used by everyday decision-makers for them to have any value.

I have shared ways to evaluate various types of data in this series (refer to “Not All Databases Are Created Equal,” where I explained nine evaluation criteria), and even that article, written for businesspeople, can be considered too technical. If I may really simplify it, data is worthless if no one is using it.

Data and information are modern-day currency; piling them up in a safe does not increase their value. Even bigshots like CIOs, CDOs or CTOs should eventually answer to CEOs and CFOs regarding the return on investment. Without exception, such value is measured in dollars, pounds, Euros or Yuans, never in terabytes, megabits per second, instructions per second or any other techy measurements. What incremental revenue or extra savings did all those data and analytics activities create? Or, an even shorter question in a typical boardroom would be “what have the data done for the business lately?”

Like any field that requires some levels of expertise to get things done, there are all kinds of organizations when it comes to data usage. Some are absolutely clueless — even nowadays — and some are equipped with cutting-edge techniques and support systems. But even the ones that brag about terabytes of data flowing through their so-called “state of the art” (another cliché that I hate) systems often admit that data utilization is not on-par with the state of data themselves.

Unfortunately, no amount of investment on data platforms and toolsets can force users to change the way they make decisions. They have to “feel” that using data is easy and beneficial to them. That is why most job descriptions for CDOs include “evangelization” of data and analytics throughout the organization. And often, that is the most difficult part of their job. Another good old cliché would be “You can lead a horse to water, but you can’t make it drink.” Really?

I completely disagree with that statement. First, decisions-makers are not horses, and secondly, we can help them use the data by putting them into bite-size packages. And let’s not even call those packages names that reflect employed processes. When we consume any other product, how often do we care about the process? It’s not just that we don’t want to know what is in the hot dog, but the same is true of even high-tech products, such as smartphones. We just want them to work, don’t we? Sure, some enthusiasts may want to understand everything about their beloved gadgets, but most people could care less about all of the hardships that the designers and manufacturers have gone through.

In fact, I tell fellow analysts to spare all of the details, assumptions and chagrins when they talk to their clients and colleagues about any analysis. Get to the point fast. Tell them major implications and next steps, in the form of multiple choices, if necessary. Have the detailed answers in your back pocket, but share them only when requested. Explain the benefits of model scores without uttering words like “regression” or “decision tree.”

End the FUD: Decision-Making Obstacles in This Election Cycle

Uncertainty blocks the brain from thinking. While talking with a client recently, he acknowledged that he’d been in a bit of a “mind funk” for a while. There was good reason: uncertainty due to lengthy delays of a key decision from someone outside of his company. And it’s a reminder for direct marketers that uncertainty feels more prevalent in this fear-based election year than usual, and it is sure to …

PE1013_stopUncertainty blocks the brain from thinking. While talking with a client recently, he acknowledged that he’d been in a bit of a “mind funk” for a while. There was good reason: uncertainty due to lengthy delays of a key decision from someone outside of his company. And it’s a reminder for direct marketers that uncertainty feels more prevalent in this fear-based election year than usual, and it is sure to block the brain from thinking and acting.

When presented with fear, uncertainty, or doubt (FUD), all logical thinking can shut down. “Mind funk” — or whatever you want to call it — sets in, and instead of moving forward, you shut down. And so do your prospects and customers. Consider how FUD can overtake emotions:

  • Fear frightens and paralyzes.
  • Uncertainly grips and people ruminate over and over.
  • Doubt shakes confidence and buying decisions aren’t made.

It’s human to fall into the FUD trap because this is how our brains are wired. And quite frankly, this nasty and negative election cycle is likely to exacerbate FUD even more, risking a decline in response rates this year.

At any point when approaching your prospective customers, whether you’ve sent them a postcard, letter, catalog or email, you can’t know a person’s state of mind when they receive it. But you can know the persona of the individual, and that alone enables you to consider a person’s core emotional values, and how you can clear away their FUD.

When you stimulate emotion, especially if you can imagine the FUD a person is dealing with at the moment, you get undivided attention. The brain shuts off thinking and is fixated on the problem. Your marketing opportunity?

  • First, when you have identified one’s fears, uncertainty and doubt, promise a solution.
  • Second, redirect thinking by calming the mind with your solution. This dissipates FUD, and decision-making has a chance of returning.

At the rate fear, uncertainty and doubt is being stirred up by this election, another opportunity is to use the news as part of your message. Marketing guru and author David Meerman Scott wrote a book on seizing the news to help advance your marketing message, called NewsJacking. Using news makes you more topical, and these days, you’ll probably hit many emotional core values to help you break through and release decision-making.

Looking for tips about how to attract more customers using FUD? Download my free seven-step guide to help you align your messaging with how the primitive mind thinks. It’s titled “When You Need More Customers, This Is What You Do.” Or get all the details in my new book, “Crack the Customer Mind Code” available at the DirectMarketingIQ bookstore.

Writing Effective InMail and Sales Emails: Don’t Ask for the Appointment

Here’s my best tip on writing effective sales emails or LinkedIn InMail messages: Don’t ask for the appointment. Instead, earn permission for a discussion. Then, execute it (via email) in a way that creates an urge in the prospect to ask you for the appointment. Sound crazy? Sound too difficult? It’s not. I’ll even give you a template.

Here’s my best tip on writing effective sales emails or LinkedIn InMail messages: Don’t ask for the appointment. Instead, earn permission for a discussion. Then, execute it (via email) in a way that creates an urge in the prospect to ask you for the appointment.

Sound crazy? Sound too difficult? It’s not. I’ll even give you a template.

Asking for Appointments Destroys Response Rates
“Any time you begin your sale with an attempt to get an appointment, you are being rejected by approximately 90 percent to 97 percent of perfectly good prospects,” said Sharon Drew Morgen, inventor of the Buying Facilitation method.

That’s because most buyers don’t know exactly what they need. Or they do have a need but aren’t ready to buy yet. Other buyers have not yet assembled the decision-making team.

Setting an appointment with a seller will happen—but not with you.

Because you asked for it (too early).

The Goal of Your Email or InMail Is Permission
The goal of your “first touch” message is to earn the right to have a discussion. Nothing else. It’s exactly like an effective cold call.

It’s a LinkedIn InMail best practice most sales reps don’t know about. It also works with standard email and is surprisingly simple.

Start writing in a way that gets buyers

  1. affirming (“yes, I will be acting on this”) and eventually
  2. inquiring (“can you tell me more about that?”)

The goal of your email or InMail is to earn the right to step up to the plate—not swing for the wall.

Slow Down Your ‘First Touch’
I recently diagnosed and treated an ineffective InMail message example on recent DMIQ Brunch & Learn webinar, “How to Write Effective Email and LinkedIn Messages that Boost Response.”

In the message, the sales rep is going for the kill. Big mistake. He sent me an InMail message asking me to:

  • Validate the idea of a discussion about his solution
  • Invest time in learning about his service
  • Understand his competitive advantage
  • Refer him to the best decision-maker
  • Consider a “free analysis” (a proposal for his services)
  • Invest time on the phone with him

This is a common (yet ineffective) approach to writing LinkedIn InMail messages.

A Better Approach
The goal of an effective InMail message is NOT to get a meeting or any of the above bullets. If you try to force these you’ll fail. This is what kills your LinkedIn InMail response rate.

Instead, use an InMail message to provoke a “Can you tell me more?” response from a potential buyer. Use the chance to push on a pain—or surface an unknown fact—that the entire decision-making team will applaud you for.

Get on the radar of all decision-makers by asking for permission to facilitate, not discuss need.

Remember, the idea is to present information (content) that helps groups of decision-makers set aside differences, identifies common ground and prioritizes next steps (in the decision-making process).

An Effective InMail Template Example
Here is an effective InMail template for you to try. Let me know how it works for you? Seriously, let me know. Get in touch in comments or email me.

Hi, Sam.

How are you adding new capability to your ______________ [insert area of business your product/services addresses] at any time soon or in future? I work with organizations like ____ [prospect’s business] to make sure ________ [goal].

Would you like to quickly explore, via email, if a larger conversation makes sense? Please let me know what you decide, Sam?

Thanks for considering,
Jeff

Remember, be creative. You don’t need to stick with this template verbatim. Make the tone sound like you. Adjust it. Please get in touch in comments or email me with the results this approach produces for you!

It’s All About Ranking

The decision-making process is really all about ranking. As a marketer, to whom should you be talking first? What product should you offer through what channel? As a businessperson, whom should you hire among all the candidates? As an investor, what stocks or bonds should you purchase? As a vacationer, where should you visit first?

The decision-making process is really all about ranking. As a marketer, to whom should you be talking first? What product should you offer through what channel? As a businessperson, whom should you hire among all the candidates? As an investor, what stocks or bonds should you purchase? As a vacationer, where should you visit first?

Yes, “choice” is the keyword in all of these questions. And if you picked Paris over other places as an answer to the last question, you just made a choice based on some ranking order in your mind. The world is big, and there could have been many factors that contributed to that decision, such as culture, art, cuisine, attractions, weather, hotels, airlines, prices, deals, distance, convenience, language, etc., and I am pretty sure that not all factors carried the same weight for you. For example, if you put more weight on “cuisine,” I can see why London would lose a few points to Paris in that ranking order.

As a citizen, for whom should I vote? That’s the choice based on your ranking among candidates, too. Call me overly analytical (and I am), but I see the difference in political stances as differences in “weights” for many political (and sometimes not-so-political) factors, such as economy, foreign policy, defense, education, tax policy, entitlement programs, environmental issues, social issues, religious views, local policies, etc. Every voter puts different weights on these factors, and the sum of them becomes the score for each candidate in their minds. No one thinks that education is not important, but among all these factors, how much weight should it receive? Well, that is different for everybody; hence, the political differences.

I didn’t bring this up to start a political debate, but rather to point out that the decision-making process is based on ranking, and the ranking scores are made of many factors with different weights. And that is how the statistical models are designed in a nutshell (so, that means the models are “nuts”?). Analysts call those factors “independent variables,” which describe the target.

In my past columns, I talked about the importance of statistical models in the age of Big Data (refer to “Why Model?”), and why marketing databases must be “model-ready” (refer to “Chicken or the Egg? Data or Analytics?”). Now let’s dig a little deeper into the design of the “model-ready” marketing databases. And surprise! That is also all about “ranking.”

Let’s step back into the marketing world, where folks are not easily offended by the subject matter. If I give a spreadsheet that contains thousands of leads for your business, you wouldn’t be able to tell easily which ones are the “Glengarry Glen Ross” leads that came from Downtown, along with those infamous steak knives. What choice would you have then? Call everyone on the list? I guess you can start picking names out of a hat. If you think a little more about it, you may filter the list by the first name, as they may reflect the decade in which they were born. Or start calling folks who live in towns that sound affluent. Heck, you can start calling them in alphabetical order, but the point is that you would “sort” the list somehow.

Now, if the list came with some other valuable information, such as income, age, gender, education level, socio-economic status, housing type, number of children, etc., you may be able to pick and choose by which variables you would use to sort the list. You may start calling the high income folks first. Not all product sales are positively related to income, but it is an easy way to start the process. Then, you would throw in other variables to break the ties in rich areas. I don’t know what you’re selling, but maybe, you would want folks who live in a single-family house with kids. And sometimes, your “gut” feeling may lead you to the right place. But only sometimes. And only when the size of the list is not in millions.

If the list was not for prospecting calls, but for a CRM application where you also need to analyze past transaction and interaction history, the list of the factors (or variables) that you need to consider would be literally nauseating. Imagine the list contains all kinds of dollars, dates, products, channels and other related numbers and figures in a seemingly endless series of columns. You’d have to scroll to the right for quite some time just to see what’s included in the chart.

In situations like that, how nice would it be if some analyst threw in just two model scores for responsiveness to your product and the potential value of each customer, for example? The analysts may have considered hundreds (or thousands) of variables to derive such scores for you, and all you need to know is that the higher the score, the more likely the lead will be responsive or have higher potential values. For your convenience, the analyst may have converted all those numbers with many decimal places into easy to understand 1-10 or 1-20 scales. That would be nice, wouldn’t it be? Now you can just start calling the folks in the model group No. 1.

But let me throw in a curveball here. Let’s go back to the list with all those transaction data attached, but without the model scores. You may say, “Hey, that’s OK, because I’ve been doing alright without any help from a statistician so far, and I’ll just use the past dollar amount as their primary value and sort the list by it.” And that is a fine plan, in many cases. Then, when you look deeper into the list, you find out there are multiple entries for the same name all over the place. How can you sort the list of leads if the list is not even on an individual level? Welcome to the world of relational databases, where every transaction deserves an entry in a table.

Relational databases are optimized to store every transaction and retrieve them efficiently. In a relational database, tables are connected by match keys, and many times, tables are connected in what we call “1-to-many” relationships. Imagine a shopping basket. There is a buyer, and we need to record the buyer’s ID number, name, address, account number, status, etc. Each buyer may have multiple transactions, and for each transaction, we now have to record the date, dollar amount, payment method, etc. Further, if the buyer put multiple items in a shopping basket, that transaction, in turn, is in yet another 1-to-many relationship to the item table. You see, in order to record everything that just happened, this relational structure is very useful. If you are the person who has to create the shipping package, yes, you need to know all the item details, transaction value and the buyer’s information, including the shipping and billing address. Database designers love this completeness so much, they even call this structure the “normal” state.

But the trouble with the relational structure is that each line is describing transactions or items, not the buyers. Sure, one can “filter” people out by interrogating every line in the transaction table, say “Select buyers who had any transaction over $100 in past 12 months.” That is what I call rudimentary filtering, but once we start asking complex questions such as, “What is the buyer’s average transaction amount for past 12 months in the outdoor sports category, and what is the overall future value of the customers through online channels?” then you will need what we call “Buyer-centric” portraits, not transaction or item-centric records. Better yet, if I ask you to rank every customer in the order of such future value, well, good luck doing that when all the tables are describing transactions, not people. That would be exactly like the case where you have multiple lines for one individual when you need to sort the leads from high value to low.

So, how do we remedy this? We need to summarize the database on an individual level, if you would like to sort the leads on an individual level. If the goal is to rank households, email addresses, companies, business sites or products, then the summarization should be done on those levels, too. Now, database designers call it the “de-normalization” process, and the tables tend to get “wide” along that process, but that is the necessary step in order to rank the entities properly.

Now, the starting point in all the summarizations is proper identification numbers for those levels. It won’t be possible to summarize any table on a household level without a reliable household ID. One may think that such things are given, but I would have to disagree. I’ve seen so many so-called “state of the art” (another cliché that makes me nauseous) databases that do not have consistent IDs of any kind. If your database managers say they are using “plain name” or “email address” fields for matching or summarization, be afraid. Be very afraid. As a starter, you know how many email addresses one person may have. To add to that, consider how many people move around each year.

Things get worse in regard to ranking by model scores when it comes to “unstructured” databases. We see more and more of those, as the data sources are getting into uncharted territories, and the size of the databases is growing exponentially. There, all these bits and pieces of data are sitting on mysterious “clouds” as entries on their own. Here again, it is one thing to select or filter based on collected data, but ranking based on some statistical modeling is simply not possible in such a structure (or lack thereof). Just ask the database managers how many 24-month active customers they really have, considering a great many people move in that time period and change their addresses, creating multiple entries. If you get an answer like “2 million-ish,” well, that’s another scary moment. (Refer to “Cheat Sheet: Is Your Database Marketing Ready?”)

In order to develop models using variables that are descriptors of customers, not transactions, we must convert those relational or unstructured data into the structure that match the level by which you would like to rank the records. Even temporarily. As the size of databases are getting bigger and bigger and the storage is getting cheaper and cheaper, I’d say that the temporary time period could be, well, indefinite. And because the word “data-mart” is overused and confusing to many, let me just call that place the “Analytical Sandbox.” Sandboxes are fun, and yes, all kinds of fun stuff for marketers and analysts happen there.

The Analytical Sandbox is where samples are created for model development, actual models are built, models are scored for every record—no matter how many there are—without hiccups; targets are easily sorted and selected by model scores; reports are created in meaningful and consistent ways (consistency is even more important than sheer accuracy in what we do), and analytical language such as SAS, SPSS or R are spoken without being frowned up by other computing folks. Here, analysts will spend their time pondering upon target definitions and methodologies, not about database structures and incomplete data fields. Have you heard about a fancy term called “in-database scoring”? This is where that happens, too.

And what comes out of the Analytical Sandbox and back into the world of relational database or unstructured databases—IT folks often ask this question—is going to be very simple. Instead of having to move mountains of data back and forth, all the variables will be in forms of model scores, providing answers to marketing questions, without any missing values (by definition, every record can be scored by models). While the scores are packing tons of information in them, the sizes could be as small as a couple bytes or even less. Even if you carry over a few hundred affinity scores for 100 million people (or any other types of entities), I wouldn’t call the resultant file large, as it would be as small as a few video files, really.

In my future columns, I will explain how to create model-ready (and human-ready) variables using all kinds of numeric, character or free-form data. In Exhibit A, you will see what we call traditional analytical activities colored in dark blue on the right-hand side. In order to make those processes really hum, we must follow all the steps that are on the left-hand side of that big cylinder in the middle. Preventing garbage-in-garbage-out situations from happening, this is where all the data get collected in uniform fashion, properly converted, edited and standardized by uniform rules, categorized based on preset meta-tables, consolidated with consistent IDs, summarized to desired levels, and meaningful variables are created for more advanced analytics.

Even more than statistical methodologies, consistent and creative variables in form of “descriptors” of the target audience make or break the marketing plan. Many people think that purchasing expensive analytical software will provide all the answers. But lest we forget, fancy software only answers the right-hand side of Exhibit A, not all of it. Creating a consistent template for all useful information in a uniform fashion is the key to maximizing the power of analytics. If you look into any modeling bakeoff in the industry, you will see that the differences in methodologies are measured in fractions. Conversely, inconsistent and incomplete data create disasters in real world. And in many cases, companies can’t even attempt advanced analytics while sitting on mountains of data, due to structural inadequacies.

I firmly believe the Big Data movement should be about

  1. getting rid of the noise, and
  2. providing simple answers to decision-makers.

Bragging about the size and the speed element alone will not bring us to the next level, which is to “humanize” the data. At the end of the day (another cliché that I hate), it is all about supporting the decision-making processes, and the decision-making process is all about ranking different options. So, in the interest of keeping it simple, let’s start by creating an analytical haven where all those rankings become easy, in case you think that the sandbox is too juvenile.