Create Marketing Reports That Will Thrill Your CEO

Yes, you read that headline correctly. Thrill your CEO. In order to meet that expectation, you will first have to understand what kinds of things get your C-Suite excited. Those are called executive strategic drivers. Some examples of those drivers are Growth Strategy, Competitive Pressures, Regulatory Changes, Risk Management, Shareholder Value, Profitability and Technological Market Disruptions.

                                                                               

Let’s look at what was not in that list of strategic drivers. Unaided Brand Awareness, Total Impressions, Number of Leads, Funnel Metrics… that tactical list of things you are probably reporting on right now. I am not at all saying your current reports are not important. In fact, they are critical to building and maintaining a revenue pipeline. They just don’t address strategic challenges from an executive perspective. And you can change that!

                                                               

Putting Some Perspective on Reporting

If we simply change perspective from Demand Gen Success to Organizational Success, it is very straightforward to realign your reporting. We’ve already established that tying your Demand Gen metrics to revenue is critical to determining success. That never goes away. You are in a business and that business has a key objective (if not the key objective) of generating revenue, which provides livelihoods for employees and products and services for customers. Your executives are charged with not only keeping, but building that revenue stream and the profits it generates. Your owners (or shareholders) want to see a positive return on their investment and your company is but one of a myriad of investment options. How does Demand Gen contribute to success from that perspective? Let’s just look at Demand Gen performance from the perspective of three of the executive drivers:

• Growth Strategy
• Profitability
• Risk Management

Growth Strategy

First, let’s look at the strategy options for growth, which fall into three broad categories: organic growth, partnerships and acquisition. Demand Gen falls into the organic growth category, which means growing the organization by driving increased revenue. This is pretty straightforward, as demand gen is the primary driver of revenue growth. We don’t need to spend a lot of time expounding on the advantages of revenue growth, but we’ll come back to this issue.

Your reporting should reflect both retrospective performance and predictive future performance (for internal consumption only, of course). Your executives want to know how well you performed, but also what to expect in the future. The entire purpose of building a demand gen machine (we’ve often called it a “Marketing Factory”) is to drive efficient, predictable and scalable revenue generation. That includes growth. And growth thrills your CEO.

Profitability

Part of the equation for effective demand generation is how efficiently you can do it. Looking at this graph, we want the best possible combination of demand and the cost required to obtain those results, creating an Effective and Efficient demand gen machine.

As the graph indicates, you can spend more to generate more leads, or you can spend less and generate fewer leads, but neither is an ideal state. You are seeking the best combination of generating demand and reducing the cost of acquiring leads, thereby reducing overall customer acquisition costs.

Creating reports that show your trend over time – and the resultant reduction in cost for acquiring more leads for less cost per lead indicates increased profits. Profit thrills your CEO.

Risk Management

Since we’ve pared down our set of executive drivers, let’s now look at the associated risks. At the highest level, your CEO must balance the risks associated with growth: grow too fast and it is difficult to maintain the organizational infrastructure required to manage the additional business. How will manufacturing produce enough product to satisfy demand? How will the additional burden on staff affect customer satisfaction? Can our supply chain keep up with demand? Can we deliver the additional product through our distribution channels?

On the other hand, if growth is too slow the organization may be too big to maintain its margins and keep up with competition. Will we need to downsize the organization to keep costs in line with revenue? How will the market react to slow growth and potential layoffs? What will we need to do to correct the revenue outlook?

From the profitability standpoint, many of these same questions apply, with some additional flavor. Where can we cut costs with the least impact on product quality and delivery? Where can we consolidate functions or increase efficiencies? Where can we benefit from economies of scale?

To manage risk in each of these categories, your CEO really needs to see the roadmap because changing course before something bad happens is always preferable to changing course afterwards. Fortunately, you are on a course to build a predictable and scalable Demand Gen machine that will help your CEO chart the correct course. By providing your executives with a predictable course, you are helping them avoid risks associated with proceeding blindly on a course into the future. Your predictability reduces risk. Reduced risk thrills your CEO.

Change the conversation.

Your reporting needs to combine retrospective performance with a predictable performance forecast. Looking backwards and projecting forward provides your executives with the information needed to make smart course corrections. Your CEO wants to ramp production, supply chain and distribution that same 15% you have predicted an increase in demand. With that understanding, your reporting becomes a critical information source for business decisions with weighty consequences. Reporting with that perspective makes your Demand Gen team an extremely important part of your organizational success. And you CEO will be thrilled!

Notes:

Align your reports with executive strategic drivers.

Don’t just look backwards, look forward.

Be Predictable.

                                    

Thank You!

I want to thank the thousands of demand gen professionals who have spent the past 40 weeks on this journey with me. I am often surprised at what Google tells me about the number of readers of this humble blog post. We’ve traveled together down a long road to learn about building, organizing and managing our Demand Gen teams and I hope you’ve learned something that will make you a better marketer.

What’s next?

I’m going to take some time off from posting through the holiday season and the remainder of the year to regroup and refuel. I will be taking the fundamental ideas from this post and organize and compile them into an e-book that I will publish early next year. I hope you will be as excited to read it as I am to publish it. I’ll also be looking for new and exciting topics to cover in next year’s blog posts. In the meantime, I wish you much success and ever-increasing demand!

Deodorant Goes Viral! Test Intelligently.

In my last post, we took a look at a framework for optimization that creates results that will inform future programs and tactics. Using Scientific method, we created control and test groups to prove out our hypotheses. We also looked at ways to increase our testing flexibility using test matrices allowing us to test multiple factors simultaneously to get the best combination of variables. Now let’s look at a real-world example that might challenge our assumptions.

Deodorant goes Viral!                                                  
Let’s say our good friends at Acme, a Consume Packaged Goods company, is releasing a new mens' deodorant brand. The marketing brain trust has spent plenty of time, resources and money testing and validating the brand positioning and customer value proposition. Acme has settled in on a particular brand voice that is best presented in a humorous light.

Testing on several control groups have settled on a particular series of video clips that have shown the potential to “go viral” and spread the new brand’s message over the Internet for very little money. The demand gen team packages and presents the idea to brand leadership and everyone agrees the videos are engaging and hilarious and will definitely spread like wildfire over the Internet, so the project gets the green light. Time to light ‘em up! The Demand Gen team posts the videos on You Tube and eagerly waits for the results.

Within hours the video does, indeed, go viral as expected. Views are in the millions, and rapidly increasing. Within days the videos are at the very top of the view lists and are making the rounds via Social Media channels. All is good, right?

And now, a word from our sponsor…

Expectations are high, but word from the distribution team is the product is staying on the shelves in droves. Expected backfill orders are non-existent. The videos are obviously a smash hit, spreading the new brand across the Internet at record speed. Why no product sales as a result? Quick, somebody check their Twitter account to see what’s going on!

Houston, we have a problem. While everyone seems to absolutely love the humor in the video, the reaction is not exactly what product planners have in mind.

#worldsdumbestdeodorant

#dumberthandirtdeodorant

#lmaodumbdeodorant

#smellslikestupiddeodorant

#canyousmellthatgoodandbethatstupid

 Yes, the videos are hilarious, but nobody wants to be associated with a product whose promotional videos depict its users as complete idiots. And it’s too late for those tens of millions of viewers to “un-see” those videos.

What should Acme have done?

This is a clear example of where spending a little time and money on a limited pilot could have saved a lot of embarrassment later. Executing tests in a controlled environment with limited, but representative audiences can provide insight into those unintended consequences. These pilots should concentrate on understanding not only what will happen, but why it happens.

Change the conversation.

Your pilot should be conducted with the end objective in mind. These pilots are good at bridging assumptions you may have made in causal relationships. In this case, ACME assumed that millions of Internet views would cause millions of product sales. Instead, those views led to brand ridicule and corporate embarrassment.

Pilots can also be used to test specific audience segment reactions against the whole. It is a great way to determine how message/offer variants track in one segment vs. another. Do customized messages really improve responses? Better to pilot and know than spend additional time and money only to find out after the fact you generated no additional lift for your efforts.

Notes:

Pilot before committing to an untested plan.

Pilot with the end objective in mind.

Pilot to correct causal assumptions.

                                    

We’re now piloting testing and optimizing every bit of success out of every program we execute. It’s time to tell your C-Suite about the fantastic work you are doing. Unfortunately, Marketing historically has done a less-than-stellar job of reporting on its success. In our next installment, we’re going to begin a series on reporting to overcome that legacy: Create Marketing Reports That Will Thrill Your C-Suite!

Three Ways to Use your Demand Funnel Part 3. Optimize Your Lead Processes.

We just explored way to properly use your Demand Funnel to diagnose and fix problems with your Lead processes. Using this diagnose-and-fix methodology, you should be able to get leads moving and plug and any leaks in your process. This should create a positive lead flow with negligible leakage or stuck leads and an “always-on” diagnostic to keep you from developing more problems. So you’re good, right? Not so fast! You now need to use the Demand Funnel infrastructure you’ve created to optimize your process. Here’s how.

Understanding Optimization
Optimizations makes some assumptions, so let’s make sure we have properly aligned on those.

1.       Optimization assumes your process works. If your process is broken, optimization will not help. It may make matters worse.

2.       Optimization should be built in to your Lead Management process. It is not an optional thing you do every once in a while for specific processes or parts of your process. To be really successful, optimization needs to become a part of your Demand Gen culture – something that is an expected part of the way everyone does their jobs.

3.       Optimization requires a framework and methodology. You cannot optimize against a moving target.

                                                           

We have already covered many ways to perfect your process, so we’ll spend little time here addressing that subject. At this point we need to assume your processes are defined and properly implemented.

Building Optimization into your process

If we think of Demand Generation as a series of actions taken to achieve success against a given strategy, we would normally execute these actions with a plan (as opposed to random acts of marketing). Organizations manage those projects in a variety of ways with a variety of tools – most often referred to as project management. The three pillars of project management are: 1) Scope, 2) Resources and 3) Time. To build optimization into your project plan, you are likely affecting all three of those pillars, with scope being the key. Let’s look at a simplified example.

For every Demand Generation project, no matter which tactics or media are used, major project categories remain relatively constant. Starting with the idea that we would like to do X by Y date, you may see a project plan similar to this:

1.       Initiation and Planning: What do we want to do? Who is our target audience? What defines success in Demand Funnel terms?  How will we measure it?

2.       Discovery: What are the requirements, resources and constraints to doing it?

3.       Ideation: Based on our discovery, how should we do it?

4.       Design: Based on ideation results, what do the deliverables look like?

5.       Development: Based on approved design concepts, build the deliverables.

6.       Testing: Based on the design requirements, make sure the deliverables meet requirements.

7.       Deployment: Execute the delivery of tactics to the target audience.

8.       Reporting: How did we do against our plan?

As a Demand Gen professional, you probably recognize project plans that look similar to this. Within each of these major categories will be many sub-categories, each dependent on the scope, time and resource availability. Of course, your organization will have its unique twist to this structure.

What’s missing? Optimization. Where should optimization fit in this plan? Let’s take a look at a revised project plan including optimization.

1.       Initiation and Planning: What do we want to do? Who is our target audience? What defines success in Demand Funnel terms?  How will we measure it? How will we optimize it?

2.       Discovery: What are the requirements, resources and constraints to doing it?

3.       Ideation: Based on our discovery, how should we do it? What should be optimized?

4.       Design: Based on ideation results, what do the deliverables look like? What components of the deliverables should be optimized and compared to other similar programs.

5.       Development: Based on approved design concepts, build the deliverables. Build the optimization components.

6.       Testing: Based on the design requirements, make sure the deliverables meet requirements. Make sure the optimization components and tests work per specifications.

7.       Deployment: Execute the delivery of tactics to the target audience. Implement the testing components.

8.       Optimization: Evaluate test components on the prescribed cycle. Report and record results within the framework. Deploy winning components.

9.       Reporting: How did we do against our plan?

Notice we only added one major step to the plan, but optimization has become completely integrated into the planning, design and execution of the overall plan.

Optimization Framework

Optimization on a particular program is great. Great for that particular program. But how do we scale optimization so we can leverage optimization learnings across Demand Gen programs for the entire organization? For that, we need a framework.

A framework is simply a method by which we can organize and execute our testing and optimization such that results can be compared to other, similar tests. Think back to high school science classes where you learned the Scientific Method. Your testing framework should emulate this methodology to maximize your results and create the ability to measure them across multiple programs of similar – and, in some cases, dissimilar – types of programs. To get to an operational framework, let’s start with objectives and work backwards.

Objective: Create 1,000 MQLs in Q1

Let’s break this down. (Although your number is different, this is probably a very common objective for Demand gen teams, so it will be easy to relate.) We have two real options here. The first is to create MQLs from your unknown TAM – those contacts outside your database, but fit your target market. The second is to convert current Inquiries (those contacts already in your database who have previously interacted with your brand) to MQLs. This sounds like the basis for an optimization test, so what would we want to understand from this program test?

1.       Total Volume of conversions: did more Inquiries or Unknowns convert?

2.       Speed of conversions: Which converted faster – Inquiries or Unknowns?

3.       Cost of conversions: Which converted at a lower cost - Inquiries or Unknowns?

As a result of this optimization framework, we would begin to understand how best to apply our marketing resources to the challenge of creating MQLs. However, we need to dig deeper into optimization because we need to understand why these conversions happen. Without that understanding, we could wind up applying the wrong tactics for the wrong reasons. This is where our use of Scientific Method gains us an advantage in optimization.

We start with a hypothesis and devise a method to test that hypothesis against a baseline. Let’s say our proposed program is designed to execute a very typical multi-touch mix of tactics, including an email and a direct mail message. We have looked at the previous arguments and decided to address current Inquiries in our database as our target audience, with the objective of creating our 1,000 MQLs. It have been determined that conversions (via form submit) from this program will generate sufficient scoring points to “auto-MQL” any Contact already in Inquiry status. Let’s also assume our benchmark conversion rate is 5%, so we require a total audience of 20,000 Inquiries for this program. Our optimization objective is to convert at least 6%, beating our benchmark rate by 20%. How, then should we design our optimization tests?

Let’s start with our hypothesis. We believe offer messages work better than product messages because our product is well-known and comparable to other products in the industry. Price drives conversions.

The conversion points for this program are:

Data and list hygiene, along with good emailing practices, are the keys to deliverability. We don’t test for that at a program level, so let’s assume static deliverability for this program.

Open rates are a direct result of the combination of sender and subject lines (for email) or packaging for direct mail. We’re going to test two specific messages:

1.       Better Widgets, Better Price

2.       20% off Widgets until Friday.

Once the email or direct mail is opened, we need to get the recipient to read far enough into our message to respond to our call to action (CTA). We’ve already established that our products are well known, so we don’t need a lot of description, other than why the reader should respond now. Here, we are going to test two messages and two CTAs in a multivariate matrix to understand what combination provides the best results. This test covers both the Read and Respond steps in our conversion path.

Message

1.       Our great widgets do more for you.

2.       Our great widgets are on sale.

CTA

A.      Better Widgets, Better Price.

B.      20% Off this week only

The matrix is very simple, covers every possible combination, and looks like this:

                1A           2A

                1B           2B

Conversion happens once the recipient has responded, and is the final measure of how committed the recipient is to your message and offer. Whether the response is to purchase or to engage more deeply with your brand, this commitment is the critical step. Do not assume that once your recipient has clicked the CTA, he or she will go ahead and execute the conversion. Abandonment here indicates you have not solidified your “closing message.” Let’s try something to test that commitment here – I call it the bonus close.

For three of the four message/offer combinations, a sale price is expected. Only one expects just a “better” price. Let’s align each of the four combinations with a bonus close test.

·         1A expects only a better price.  Bonus Offer = 20% off through Friday

·         1B expects 20% off this week only. Bonus Offer = 22% off if you order by Wednesday

·         2A expects an unidentified sale price. Bonus Offer = 20% off through Friday

·         2B expects 20% off this week only. No Bonus Offer = 20% off through Friday

These tests will help us understand the best mix of offers and bonus offers as they meet or exceed expectation when the recipient hits the conversion form.

                                                      

Change the conversation.

Optimization is a conversation that needs to be a part of every program execution. As you have seen here, it is a lot more than a few random tests thrown into the mix to get a few extra conversions; it is an entirely new way of thinking about what drives those conversions. The conversation should not be if we test, it should be how we test. Smarter tests lead to smarter programs.

Notes:

Test against solid hypotheses to provide scalable optimization.

Optimization is a culture, not an action.

Test every conversion step in your program process.

                                    

Are you all in on optimization? I hope you are. One last thing to consider: what if one of your tests goes wildly wrong? (This is not a bad thing – it is why we test!) Is there a way to mitigate risk and not put your Demand Funnel in jeopardy? Excellent question, and we’ll look at ways to do that next week in: Deodorant Goes Viral! Test intelligently.