A Strategy for Successful Leadership of the Website Team

Does your website team try to dazzle you with reams of reports and statistics weekly, so much so that you cannot tell if the website is actually improving in performance? Should you care? If you demand they structure their reports every week as I describe below, the result will be a focused team and an ever-improving website.

Does your website team try to dazzle you with reams of reports and statistics weekly, so much so that you cannot tell if the website is actually improving in performance? Should you care? If you look at SQLs by source, there is a good chance that the website is the best source of SQLs. So, yes, you should care. If you demand they structure their reports every week as I describe below, the result will be a focused team and an ever-improving website.

First, establish and educate on the actual goals of the website. Let’s exclude eCommerce websites because they represent an entirely different set of goals, actions and reports. The goals of your website might include the following:

  • Generate high quality net new leads for the business
  • Provide ongoing education for leads and customers already in the funnel
  • Reinforce brand attributes and brand loyalty
  • Increase brand awareness and advance all thought leadership initiatives

Pretty simple right? So, do you measure your website based on these goals? Yeah, that’s not that simple. The website must do several things to meet these goals.

  1. Attract new visitors and have a plan for how to gain returning visitors
  2. Get more of the visitors to read more than one page (move bouncers to browsers)
  3. Get more of the visitors to engage with the downloadable content (move browsers to downloaders)
  4. Get more of the visitors to fill in a form (move downloaders to converters)

Here are the 5 reporting slides I suggest you ask to see either weekly or monthly that will summarize your website performance as it relates to the goals you set for it.

1. Organic Attract

Measure and report on the trend for weekly visitors and especially new visitors. Give the team a target goal each quarter for new visitors per month. To tune up the website to hit this goal they will have to:

  1. Clean up the website errors that cause poor ranking with the search engines
  2. Identify which keywords should be targeted, and improve the visibility of keywords which are bringing the best browsers, downloaders and converters
  3. Add content which addresses the topics and answers the questions being queried on the search engines, and optimize those blog pages and other web content
  4. Add AMP capability for all pages – i.e. make it more mobile friendly (Google indexes mobile first now)
  5. Move to SSL for all pages and sub-domains
  6. Gain authority with quality backlinks to good performing content from websites with high authority
  7. Reduce page load times to less than 3 seconds by compressing images, reducing scripts, etc.
  8. Identify which channels are driving the best potential new MQLs and SQLs, and focus more resources there

Your weekly one slide “Attract” report should highlight the visitor trend week over week, the hottest entrance pages for attracting new visitors and specify which actions from the list above are planned for this week. The report should also highlight how many of the returning visitors are from existing customers.

2. Bounce to Browse

So you have a great blog that brings in 50% of the traffic, but do they just read that one page and then bounce (exit the website) or are you drawing them deeper into your website? Your team should report weekly on the average duration and page views trend for visitors and what they are doing to improve the numbers.

  1. Add CTAs (calls to action) to all the top performing entrance pages immediately
  2. Consider adding tools like Uberflip or PathFactory
  3. Leverage tools like Crazy Egg to see where visitors are clicking and scrolling and how they are interacting

The one slide report should specify what actions they are taking this week to increase visit duration and the number of pages viewed.

3. Browsers to Downloaders

One of your goals is to provide education to people in the funnel, so you cannot expect to put all content behind gates (forms). A large percentage of your website content will need to be ungated (freemium vs. premium content). Each week your team should report on what percentage of visitors downloaded content and which content was best at driving engagement. What actions are they taking to improve the numbers?

  1. Where is the content placed? Is it in CTAs on all the appropriate pages?
  2. Which content is hot, and can you link to it from more places?

4. Conversions

How good is the website at capturing new leads and getting existing leads and customers to engage with premium content? Each week the one slide report should highlight all the form fills by asset or form type, highlight how many were new leads and share form completion rates by form/asset. The actions pursued each week include:

  1. Fine tune form questions and leverage progressive profiling
  2. Place links to premium content in more hot locations on the website
  3. Add more premium content to the website
  4. Retire older premium content or move to freemium status

The team should have a weekly goal for number of new leads they want to hit.

5. Paid search and paid media summary

Paid campaigns do attraction and conversion all in one, so it is appropriate we report on it separately from the items above. But one slide is all a CMO needs, not reams of Facebook, LinkedIn, Google and other paid media reports. The one slide should include what campaigns you are running by offer and channel, new leads produced, how much you are spending and the ultimate cost per new lead. It would also be good to report on lead quality (cost per MQL and SQL). The actions for the team weekly include:

  1. What new tests or campaigns they are going to try
  2. What campaigns have run their course or need modification
  3. What budget shifts will happen to improve portfolio results

Do you have a similar website performance five slide or less weekly report? If so, please share.

Next time, learn how the inbound group fits in with the demand generation team in a revenue marketing organization.

Consumer Engagement, But Not Yet Marriage

How many times have we been asked (or asked ourselves) to come up with a valuation of a minute of a prospect’s time and attention, AKA consumer engagement? Almost all advertising is bought and sold using some version of the metric (cost per person, mostly expressed as CPM) and yet no one seems to have nailed an equation that can reliably be used as a baseline.

How many times have we been asked (or asked ourselves) to come up with a valuation of a minute of a prospect’s time and attention, AKA consumer engagement? Almost all advertising is bought and sold using some version of the metric (cost per person, mostly expressed as CPM) and yet no one seems to have nailed an equation that can reliably be used as a baseline.

It’s not that marketers haven’t tried. The most recent expression was reported in Media Daily News at the end of July. Advertisers and agency executives were researched to determine what they “considered” (perhaps better described as their “best guesses”) on the per-minute value of engaged consumer attention and they came up with $1.81. They even produced a bar graph to add verisimilitude.

consumer engagement chart
Credit: Peter J. Rosenwald

This didn’t impress one skeptical reader who commented wryly: “With a sample of 300 people AND no hard guidelines as to how anyone in the survey determined ‘value’ other than for a very narrowly-defined universe, this is just cocktail party fodder.”

Even after a couple of martinis, it would be hard to derive much value from this yardstick of consumer attention. As so-called “opt-in” and “rewarded” advertising models — which let the prospect have some free content before “opting-in” through a paywall or some other device to more content — are becoming increasingly fashionable, it is not surprising that marketers are trying to put some metrics in place to value them.

This illuminates the fact that in today’s multimedia marketplace the “value” of a minute or some other measure of someone’s time, and perhaps even more importantly, attention, depends on a basket of variables that will be unique to each prospect or cluster of prospects. If we can discover which ones are critical to the purchasing process and at what point they influence the customer journey, we may have the beginning of metrics which will intelligently inform our marketing actions. The question is how we get there and the answer remains elusive.

First we need to know what we mean by “engaged consumer”? We all have lots of experience with commercial messages (Wendy’s “Where’s the beef,” for example) which can be described as highly “engaging,” because the creative brilliance attracts the attention of viewers. But that attention has no value whatever for say, vegetarians.

How much the marketer would be willing to pay for an engaged customer, someone who has demonstrated interest in the marketed category and hopefully has the resources to purchase, is more to the point? The Lamborghini dealer should be willing to pay quite a bit more for that engaged minute than the corner taco vendor.

In a September column addressing marketing metrics and suggesting that we stop chasing our tails, I tried to put a figure on the real cost of reaching the target audience for an advertiser like Pampers. Using a $25 CPM cost of a TV spot reaching only women and, after eliminating all women who were neither in the last trimester of pregnancy nor had children under two years old, I came up with a ballpark figure of $208 per thousand. In fact, with a normal average viewing frequency of five times, capturing the engagement of each one of those thousand women for 30 seconds should be worth about $1 ((208*5)/1000), twice that for 60 seconds of attention, not far off of that $1.81 guess.

But will the “engagement” lead to a committed relationship, a marriage if you will, of consumer and brand? Certainly, if the prospect can opt-in or be rewarded with truly relevant and valuable content by clicking to visit the advertiser’s website, and the website can elevate interest to purchase, and the product satisfies and stimulates repeat purchase, the investment in getting that initial 60 seconds of attention will have a quantifiable value.

But putting a figure on that value is as likely to be correct as predicting the length and quality of the marriage.

As a friend of mine says, instead of trying to figure it all out in advance, just start dating.

AI in the Near Future: Dr Merlin Stone on the Parallels of Marketing and Medicine

World-renown researcher, Dr. Mervin Stone, talks with Zuant CEO, Peter Gillett, about the future of AI and the link between marketing and medicine. Learn how data is being used to diagnose and treat today’s toughest health issues as well as marketing’s biggest challenges.  

World-renown researcher, Dr. Merlin Stone, talks with Zuant CEO, Peter Gillett, about the future of AI and the link between marketing and medicine. Learn how data analytics is being used to diagnose and treat today’s toughest health issues as well as marketing’s biggest challenges.

Why Artificial Intelligence? Why Now?

Peter: Why are so many people looking at AI right now?

Dr. Stone:  It all started in the 1960s when the university philosophers got interested in how computers were replicating human decisions. Edinburgh University was the home of that work, but I was at Sussex as a student and there were philosophers interested in AI because they were interested in the power of computers to do better than the human brain. We’ve seen the story of Big Blue playing chess and its serious application for the diagnosis of cancer. So we know that AI can deal with some of the most complex and sophisticated diagnostic problems to help us make decisions. On the flip side, marketing problems are often soft problems such as uncertainty about data quality of. We aren’t certain about the theories or the ideas we use to turn data into a diagnosis of what’s going on, or we’re not certain about what is the best solution when we have got the diagnosis. That’s the world of marketing, but it’s also the world of medicine.

As a youngster, I became aware of the idea of AI with HAL in Stanley Kubrick’s 2001 A Space Odyssey, but you pre-date me a tad. Let’s go back to university when you became aware of AI as an undergraduate.

Well, back in the swinging 60s I met lots of interesting people who were philosophers very interested in AI. One of those was a chap called Don Michie who was a British researcher in AI. During World War II he worked for the Government Code and Cypher School at Bletchley Park, contributing to the war effort to solve “Tunny,” a German teleprinter cipher. I think the idea of AI then was actually a misnomer; it was more very clever programming for a long period until we got to the world of neural networks. This was when computers could teach themselves things as opposed to clever programmers looking at all the things that they could see and helping what was basically a dumb machine.

That area is also related to another area of my research, which is how do you make sense of information where there are lots of experts? And that’s not the AI bit; it’s actually another area of interest to marketers, which is where in that wood can you see the wood? That’s not just a question of AI, it’s a question of perception: How do you know what’s really going on? If your sales go down, is it because your customers are leaving or because your products are rubbish? The simplest question, but actually often it would be a combination of both – you need a sophisticated analysis tool to find out. We’re talking about big data now.

AI vs. The Dark Side of Marketing

Peter: What are some current, real world examples to illustrate this?

Dr. Stone: It used to be fairly simple. Much of my work now is looking at information management — One of the journals I publish in is called The Dark Side of Marketing, which is about marketers and business and their attempts to bend data to fit a story about which they had a narrative they had previously given, so that they wouldn’t be blamed for a failure. For instance, we can see that in the papers with some of the governments, and the banks in the credit crisis a few years ago used this smokescreen. And there’s Fred Goodwin, the head of Royal Bank of Scotland who said no, RBS were not exposed to the American mortgage market. This was at the last shareholders meeting before the bank went bust. … Their most recent acquisition had in fact been an American mortgage bank, which was already deeply exposed.

So for sure, AI is also a good way of overcoming the problems of bias — What we have had in the last few years is all the macro economic stuff, which shows us the incredible power of bias to influence the way we interpret a situation and say what works and what doesn’t work. So if you’re a marketer and you’re not truly databased, you’re letting the data speak, or you’re telling people you are, but actually what you’re doing is making the decisions up. That is why I pin a lot of hope on AI. It challenges people’s prejudices about what’s working and what isn’t, and why it works — This is true of science, medicine, and many other areas.

Peter: Let’s move away from fake news, or at least ‘selective information presentation,’ and look at a positive future with AI.

Dr. Stone: We just had this terrible case of the hospital in Portsmouth. An estimated 250 were killed by over prescription of certain drugs. Now the data was at the National Health and Social Care Information Centre in Leeds, which should have been ringing alarm bells. If you’re sitting at the top of the tree, you need some degree of data mining AI to sound the alarm bells automatically. It’s different for the people on the ground. For instance if you work at Dillards, you don’t need to be told what’s going on — if you’re a shop assistant you know because you can see customers walking away, but if you’re sitting on the top of the tree, you often need help particularly if you’re not listening to the people at the bottom of the tree. And if you look at the National Health Service in the UK its outcomes are really poor and the people at the top of the tree are looking hard at the outcomes. You can see the marketing analogy very clearly. Then what happens is ‘bad decisions.’

Improved Marketing ROI Shouldn’t Be Your Metric, This Should

My team often engages in client projects designed to improve marketing outcomes. Many times, clients describe their primary objective as an increased return on marketing dollars or return on investment (ROI). However, this is often the wrong object and their real goal should be improved marketing effectiveness.

My team often engages in client projects designed to improve marketing outcomes. Many times, clients describe their primary objective as an increased return on marketing dollars or return on investment (ROI). However, this is often the wrong object and their real goal should be improved marketing effectiveness.

“That sounds like semantics,” you say? Yes, this is an argument over semantics, and in this case, semantics matter.

When stating the primary objective as improved marketing ROI, the aperture is usually focused on an optimization exercise, which pits financial resources on one side of the equation and levers — such as channel spend, targeting algorithms and A/B testing — on the other side.

A couple of decades ago, marketing analytics recognized that specific activities were easier to link, with outcomes based on data that was readily available. Over time, this became the marketing ROI playbook and was popularized by consultants, academics and practitioners. This led to improved targeting, ad buys and ad content. These improvements are very important, and I would argue that they are still a must-do for most marketing departments today. However, resources are optimally allocated across channels, winning ads identified and targeting algorithms improved, marketing is still not as effective as it can be. Now is when the hard part of building a more effective marketing function actually begins.

For a moment, let’s imagine a typical marketing ROI project from the customer’s perspective. Imagine you are actively shopping for a refrigerator. A retailer uses data to appropriately target you at the right time, across multiple channels, with the right banner ad and a purchase naturally follows, right? Of course not.

  • What about helping you understand the variety of features, prices and brands available?
  • What about helping you understand the value of selecting them over other retailers?
  • What about the brand affinity and trust this process is developing in the consumer’s mind?

Because this purchase journey can play out over weeks or months, these marketing activities are more difficult (but not impossible) to measure and are often left out of the standard ROI project. However, these activities are as impactful as the finely tuned targeting algorithm that brought you to the retailer’s website in the first place.

Back to why semantics over ROI and marketing effectiveness matter. Today, the term “marketing ROI” is calcified within a relatively narrow set of analytical exercises. I have found that using marketing effectiveness as the alternative objective gives license to a broader conversation about how to improve marketing and customer interaction. It also lessens the imperative to link all activities directly to sales. Campaigns designed to inform, develop relationships or assist in eventual purchase decisions are then able to be measured against more appropriate intermediate metrics, such as online activity, repeat visits, downloads, sign-ups, etc.

What makes this work more challenging is that it requires marketers to develop a purposeful and measurable purchase journey. In addition, it requires a clear analytics plan, which drives and captures specific customer behavior, identifies an immediate need and provides a solution so the customer can move further down the purchase journey.

Finally, it requires developing an understanding of how these intermediate interactions and metrics eventually build up to a holistic view of marketing effectiveness. Until marketers can develop an analytical framework which provides a comprehensive perspective of all marketing activity, marketing ROI is merely a game of finding more customers, at the right time and place who will overlook a poorly measured (and, by extension, poorly managed) purchase journey.

Marketing Success Metrics: Response or Dollars?

It’s tempting to ask about whether marketing success metrics should be response rates or money. But you don’t need to ask marketers what they want. Basically, they want everything.

It’s tempting to ask about whether marketing success metrics should be response rates or money. But you don’t need to ask marketers what they want. Basically, they want everything.

They want big spenders who also visit frequently, purchasing flagship products repeatedly. For a long time (some say “lifetime”). Without any complaint. Paying full price, without redeeming too many discount offers. And while at it, minimal product returns, too.

Unfortunately, such customers are as rare as a knight in white armor. Because, just to start off, responsiveness to promotions is often inversely related to purchase value. In other words, for many retailers, big spenders do not shop often, and frequent shoppers are often small item buyers, or worse, bargain-seekers. They may just stop coming if you cut off fat discount deals. Such dichotomy is quite common for many types of retailers.

That is why a seasoned consultants and analysts ask what brand leaders “really” want the most in marketing success metrics. If you have a choice, what is more important to you? Expanding the customer base or increasing the customer value? Of course, both are very important goals — and marketing success metrics. But what is the first priority for “you,” for now?

Asking that question upfront is a good defensive tactic for the consultant, because marketers tend to complain about the response rate when the value target is met, and complain about the revenue size when goals for click and response rates are achieved. Like I said earlier, they want “everything, all the time.”

So, what does a conscientious analyst do in a situation like this? Simple. Set up multiple targets and follow multiple marketing success metrics. Never hedge your bet on just one thing. In fact, marketers must follow this tactic as well, because even CMOs must answer to CEOs eventually. If we “know” that such key marketing success metrics are often inversely correlated, why not cover all bases?

Case in point: I’ve seen many not-so-great campaign results where marketers and analysts just targeted the “best of the best” segment — i.e., the white rhinoceros that I described in the beginning — in modeled or rule-based targeting. If you do that, the value may be realized, but the response rate will go down, leading to disappointing overall revenue volume. So what if the average customer value went up by 20%, when only a small group of people responded to the promotion?

A while back, I was involved in a case where “a” targeting model for a luxury car accessory retailer tanked badly. Actually, I shouldn’t even say that the model didn’t work, because it performed exactly the way the user intended. Basically, the reason why the campaign based on that model didn’t work was the account manager at the time followed the client’s instructions too literally.

The luxury car accessory retailer carried various lines of products — from a luxury car cover costing over $1,000 to small accessories priced under $200. The client ordered the account manager to go after the high-value target, saying things like “who cares about those small-timers?” The resultant model worked exactly that way, achieving great dollar-per-transaction value, but failing at generating meaningful responses. During the back-end analysis, we’ve found that the marketer indeed had very different segments within the customer base, and going only after the big spenders should not have been the strategy at all. The brand needed a few more targets and models to generate meaningful results on all fronts.

When you go after any type “look-alikes,” do not just go after the ideal targets in your head. Always look at the customer profile reports to see if you have dual, or multiple universes in your base. A dead giveaway? Look at the disparity among the customer values. If your flagship product is much more expensive than an “average” transaction or customer value in your own database, well, that means most of your customers are NOT going for the most expensive option.

If you just target the biggest spenders, you will be ignoring the majority of small buyers whose profile may be vastly different from the whales. Worse yet, if you target the “average” of those two dichotomous targets, then you will be shooting at phantom targets. Unfortunately, in the world of data and analytics, there is no such thing as an “average customer,” and going after phantom targets is not much different from shooting blanks.

On the reporting front — when chasing after often elusive targets — one must be careful not to get locked into a few popular measurements in the organization. Again, I recommend looking at the results in every possible way to construct the story of “what really happened.”

For instance:

  • Response Rate/Conversion Rate: Total conversions over total contacted. Much like open and click-through rate, but I’d keep the original denominator — not just those who opened and clicked — to provide a reality check for everyone. Often, the “real” response rate (or conversion rate) would be far below 1% when divided by the total mail volume (or contact volume). Nonetheless, very basic and important metrics. Always try to go there, and do not stop at opens and clicks.
  • Average Transaction Value: If someone converted, what is the value of the transaction? If you collect these figures over time on an individual level, you will also obtain Average Value per Customer, which in turn is the backbone of the Lifetime Value calculation. You will also be able to see the effect of subsequent purchases down the line, in this competitive world where most responders are one-time buyers (refer to “Wrestling the One-Time Buyer Syndrome”).
  • Revenue Per 1,000 Contacts: Revenue divided by total contacts multiplied by 1,000. This is my favorite, as this figure captures both responsiveness and the transaction value at the same time. From here, one can calculate net margin of campaign on an individual level, if the acquisition or promotion cost is available at that level (though in real life, I would settle for campaig- level ROI any time).

These are just three basic figures covering responsiveness and value, and marketers may gain important intelligence if they look at these figures by, but not limited to, the following elements:

  • Channel/Media
  • Campaign
  • Source of the contact list
  • Segment/Selection Rule/Model Score Group (i.e., How is the target selected)
  • Offer and Creative (hopefully someone categorized an endless series of these)
  • Wave (if there are multiple waves or drops within a campaign)
  • Other campaign details such as seasonality, day of the week, daypart, etc.

In the ultimate quest to find “what really works,” it is prudent to look at these metrics on multiple levels. For instance, you may find that these key metrics behave differently in different channels, and combinations of offers and other factors may trigger responsiveness and value in previously unforeseen manners.

No one would know all of the answers before tests, but after a few iterations, marketers will learn what the key segments within the target are, and how they should deal with them discriminately going forward. That is what we commonly refer to as a scientific approach, and the first step is to recognize that:

  • There may be multiple pockets of distinct buyers,
  • Not one type of metrics will tell us the whole story, and
  • We are not supposed to batch and blast to a one-dimensional target with a uniform message.

I am not at all saying that all of the popular metrics for digital marketing are irrelevant; but remember that open and clicks are just directional indicators toward conversion. And the value of the customers must be examined in multiple ways, even after the conversion. Because there are so many ways to define success — and failure — and each should be a lesson for future improvements on targeting and messaging.

It may be out of fashion to say this old term in this century, but that is what “closed-loop” marketing is all about, regardless of the popular promotion channels of the day.

The names of metrics may have changed over time, but the measurement of success has always been about engagement level and the money that it brings.

How to Make Marketing a Revenue Center and Not a Cost

Many marketers struggle to have their departments viewed as the valuable, revenue-generating entities that they are. They are viewed as sales support teams, at best. That shouldn’t be the case, and if it is, it may be your own fault. To get your marketing viewed as a revenue center and not a cost requires the right metrics, solid sales and marketing integration, and excellent analytics.

In a word: metrics.

Many marketers struggle to have their departments viewed as the valuable, revenue-generating entities that they are. They are viewed as sales support teams, at best.

That shouldn’t be the case, and if it is, it may be your own fault. If you can’t create a line that ties your activities to actual revenue, you can’t prove that your marketing activity is generating revenue.

You only make the problem worse if you insist on talking to C-level folks about fans, followers, likes, and subscribers. They just don’t pay the rent. And acquiring them has costs.

But drawing the line between marketing and revenue isn’t always easy, particularly for B2B marketers without transactional websites. Which means that my claim for metrics being the answer is a bit simplistic. You really need:

  • The right metrics
  • Integration between sales and marketing systems
  • A measurement process

The Right Metrics

As I mentioned above, not just any metrics will do. Process metrics like fans, followers, clicks, etc. are important to us as marketers but not important to those with profit-and-loss responsibility and not important to most businesses as a whole. (If you’re in the publishing business, that’s another story.)

The metrics you need to seek out are business metrics. These are metrics related to profit, revenue, sales, lead volume, lead quality, and so on. The problem, of course, is that not only are these metrics harder to measure, they are harder to tie to specific marketing actions.

Despite the increased degree of difficulty, this is the first step in turning marketing from a cost to a revenue generator. In fact, you may have to make inroads here before you can secure the resources you’ll need to take the next step.

Sales and Marketing Systems Integration

That next step is tying your various sales and marketing systems together in such a way that you can track not just how many times a piece of content, for example, has been consumed, but what content a particular prospect has consumed. This requires coordination between your CRM system and the CMS that underpins your website.

It’s of even more value if you can track which pieces of content are most frequently consumed by prospects who convert to customers.

As an added benefit, coordination like this can also increase your marketing’s effectiveness by allowing you to tailor the content you present to individual visitors. For example, once you know what content a site visitor has already consumed, you can replace a static “You Might Also Like” links in your sidebar with links to content that might be the next logical step for someone who has already consumed introductory materials.

Progressive profiling, as it’s called, will also help you hone your content offerings and create content ladders that lead from introductory materials through education and establishing trust to, we hope, conversion.

Measurement Process

Finally, we need to measure what’s working and what is not. We need to know what content resonates with our audience and which audience segments we’re connecting with. Much of the data you’ll need for this will be available in your CRM, though you may need to tie in other analytics data gathering tools

The only downside to this is that implementation of these ideas tends to be quite customized. There’s no off-the-shelf solution that is likely to fit your needs – your audience, your CRM and CMS, your goals. Making yourself an educated consumer is critical, even if you aren’t going to implement with internal resources. Different vendors will present different solutions and doing an apples-to-apples comparison requires at least a basic understanding of the various moving parts.

Turn the Funnel Upside Down for Better ROI Planning

Many conventional marketers depict the progression from prospect to buyer as a funnel starting with impressions at the top and working down through the sales cycle to responses, leads, qualified leads and finally buyers. This approach tells a top-to-bottom chronological story of the promotion process.

Many conventional marketers depict the progression from prospect to buyer as a funnel starting with impressions at the top and working down through the sales cycle to responses, leads, qualified leads and finally buyers. This approach tells a top-to-bottom chronological story of the promotion process.

Better ROI chart
Credit: Chuck McLeester

But turning the funnel upside down provides a much more useful approach to planning ROI. It has its roots in the fundamental principles of direct response: customer lifetime value (LTV) and allowable acquisition cost (AAC).

Better ROI chart 2
Credit: Chuck McLeester

You can start out at the top of the upside-down funnel using your customer lifetime value, or if you’re interested in getting a specific return on a short-term promotion, you can use the value of a one-time transaction. Either way, you’re starting with the value of a customer — be it short-term or long-term.

Once you determine a revenue point to work with, set a target ROI and calculate your AAC (allowable acquisition cost). For this illustration, let’s assume that the transaction is worth $200 and our target ROI at 2:1. This results in an AAC of $100; that is, the amount we can spend to get the transaction.

AAC chart by Chuck McLeesterAs you move to the lower portions of the upside-down funnel, you apply assumptions about the conversion rates at each stage. You may have some historical data on which to base these assumptions, but if you don’t apply industry standards or make educated guesstimates. Ultimately, you’ll learn what the actual rates are in a well-constructed test scenario. For example, if you assume that 30 percent of all qualified leads will convert to buyers, then the allowable cost per qualified lead is $30.

Qualified lead formula for better ROI
Credit: Chuck McLeester

Similarly, you can calculate the allowable cost per lead, cost per response and cost per impression all the way to the base of the upside-down funnel. So if you estimate that two-thirds of your leads will be qualified, your allowable cost per lead is $20, and so on.

Allowable cost/lead formula for better ROI
Credit: Chuck McLeester

 As you reach the bottom of the upside-down funnel, you can determine the required response rates from each medium under consideration. You can either make an assumption about the percentage of clicks, calls or responses that will turn into leads, or you can go straight to calculating the number of leads you need from each medium based on the media cost as shown here.

Final graphic for better ROI
Credit: Chuck McLeester
  1. Divide the cost of the media by the allowable lead cost to determine the number of leads required from each medium
  2. Divide the number of leads required by the circulation or number of impressions associated with that medium

For example,

Final formula for better ROI
Credit: Chuck McLeester

 (These calculations can also be done on a CPM basis).

Then, do a gut-check. Is that response rate realistic? Don’t know? Test it. A carefully controlled small test will quantify your assumptions at each point of the upside-down funnel.

Content Marketing: Better Metrics Mean Better Measurement

It isn’t always easy to measure the impact of your content marketing or attribute sales, leads or lead quality accurately. One way to improve the quality of your measurement is to improve the quality of your metrics.

It isn’t always easy to measure the impact of your content marketing or attribute sales, leads or lead quality accurately. One way to improve the quality of your measurement is to improve the quality of your metrics.

Analytics data is easy to come by. In most cases it’s even available free or is included in services you’re already using, like an email service provider. That doesn’t mean all that data is useful.

In fact, having all of that data is a problem of its own — the firehose problem. As in, if a firehose is your only source of water, you’re either going to have to be creating in how you use it, or quenching your thirst is going to be a painful proposition.

Understanding Different Types of Metrics

Assuming you’ve solved that problem, and can isolate the data you want, it’s important to focus on business metrics rather than process metrics. We define these as follows:

  • Business metrics directly impact your businesses profitability (Sales, for example)
  • Process metrics are proxies that tell us about our marketing but not necessarily our business (Twitter followers are a good example)

As you’d imagine, business metrics are far more important, but many marketers focus more on process metrics because, well, they’re much easier to come by. Just log in to Twitter, Google Analytics or just about any other digital marketing platform and you’ve got your data.

What’s missing from that data for many B2B businesses is what we might call the last mile: the connection between content consumed and a sale consummated. That means measurement and attribution take much more effort. Marketing automation tools can help with this — and we certainly recommend that you implement the sort of tracking that these tools make possible — but even if you aren’t quite ready to make that commitment, there are adjustments you can make to the tracking you are doing.

Differentiating Between Initial Contact and Greater Engagement

Perhaps the best adjustment you can make is to move away from first-level metrics and toward second-level. So, rather than focusing on the number of Twitter followers you have, focus on the number of clicks you get to lead magnets you tweet about, the number of shares your posts get, and the number of likes your content garners.

That’s not to say that tracking your followers isn’t worthwhile. It is, particularly if you track the data over time. But it’s so much further removed from usable business metrics than the process metrics that measure the next step. That is, the folks who have not only followed you, but who have engaged with you via Twitter.

This is true of other social media channels and other content types. Rather than measuring just blog post page views (which I would argue are more valuable than Twitter followers), measure how frequently a blog post is shared, leads to a newsletter signup, a lead magnet click, or even clicks to other pages on your site.

While nothing is a replacement for the direct connection between content consumption and sales, tracking the engagement-based process metrics mentioned above is a much more accurate way to assess your content marketing’s health and effectiveness than relying on those metrics that measure just an initial interaction.

How Numbers Lead Us Astray So Easily

Frogs, fish, dogs, spiders, hyenas, chimps and others in the animal kingdom all have an innate ability for counting. But we humans are easily fooled by numbers, especially when they’re presented in context. Learning to exploit the power of context can pay off big for marketers, but at the same time, marketers need to be careful not to be fooled themselves.

numbers
Creative Commons license. | Credit: Pixabay by fotoblend

Frogs, fish, dogs, spiders, hyenas, chimps and others in the animal kingdom all have an innate ability for counting. But we humans are easily fooled by numbers, especially when they’re presented in context. Learning to exploit the power of context can pay off big for marketers, but at the same time, marketers need to be careful not to be fooled themselves.

Consider the example of the Economist subscription offer discussed by Dan Ariely in his book “Predictably Irrational.” Ariely duplicated this subscription offer with a group of 100 MBA students:

Chuck McLeester Chart 1
Credit: Chuck McLeester

Which would you have chosen?

Repeating the exercise without the “decoy” offer of the print only subscription yielded the following results:

Chuck McLeester Chart 2
Credit: Chuck McLeester

Which would you have chosen this time? Clearly, context will fool us into perceiving the value of offers differently.

Fish are not so easily fooled.

“Small fish benefit from living in schools, and the more numerous the group, the statistically better a fish’s odds of escaping predation. As a result, many shoaling fish are excellent appraisers of relative head counts. Three-spined sticklebacks are … able to tell six fellow fish from seven, or 18 from 21 — a comparative power that many birds, mammals and even humans might find hard to beat.” Beastly Arithmetic, NYTimes Feb 6 2018

Psychology-based marketing expert Jeanette McMurtry says,

“When marketers discover the inconsistencies and irrationalities about how consumers make choices, they can create messaging that engages consumers’ minds, both conscious and unconscious. When that happens, there’s a lot more to ‘count’ when it comes to sales, revenue, ROI and lifetime value.”

One of the reasons we’re so easily tricked by numbers is our reliance on verbal intuition. In his book, “Thinking Fast and Slow,” Daniel Kahneman provides several illustrations of how our intuition gets in the way of arithmetic when we’re presented with numerical problems in a verbal context. What is your initial response to Kahneman’s word problem?

  • A ball and a bat cost $1.10
  • The bat costs one dollar more than the ball.
  • How much does the ball cost?

You would not be alone if your initial response was 10 cents. But you would be wrong. Because if the ball cost 10 cents and the bat costs one dollar more than the ball then the total cost would be $1.20.

Consider how you might take advantage of people’s intuitive responses when constructing offers, but don’t let your own intuition get in the way of making decisions. Sometimes marketers are fooled by test results because they look for cause and effect in results that could easily have happened randomly. If you’re testing creative variations with samples of 25,000 impressions and your usual clickthrough rates are in the range of 1 percent (which yields a results pool of about 250 clicks), you should know with that sample size and that average response rate, your results can vary by 10 percent. So, statistically there’s a 90 percent chance that you could have gotten 225 clicks or 275 clicks. Yet, if you got both those extremes in an A/B test, it would be easy to conclude that one cell beat the other by a lot.

We are similarly confused by percentages. Psychologists Rochel Gelman of Rutgers University and Jennifer Jacobs Danan of the University of California, Los Angeles, have studied how often reasonably well-educated people miscalculate percentages. We hear that the price of something rose by 50 percent and then fell by 50 percent, and we reflexively, mistakenly conclude, “Oh good, we’re back to where we started.” Beastly Arithmetic, NYTimes Feb 6 2018

Feel free to comment with your answer to this percentage problem, or with any thoughts or experiences you have on using consumers’ proclivity for intuition over rationality to better your marketing efforts.

Get Revenue Marketing Analytics Right for 2018

Here’s a trap many marketers fall into in the early part of the journey: The marketing VP received additional marketing budget, but the price is that she has to report marketing numbers to the CEO each month. How do you start? Here are your best bets for initiating revenue marketing reporting this year

Last month on our revenue marketing journey, we discussed how to develop use cases as a way of teasing out specific technology requirements for marketing. This month, we turn our attention to revenue marketing analytics and, more importantly, how to choose the right metrics for where you are in your revenue marketing journey.

Here’s a trap many marketers fall into in the early part of the journey: The marketing VP received additional marketing budget, but the price is that she has to report marketing numbers to the CEO each month. So the organization is turned upside down attempting to create marketing results reports for the first time.

How do they start? Marketing ROI analysis, or marketing influenced revenue, or, harder still, predictive reports? The outcome is predictable.

6 Steps to Accurate Revenue Marketing Analytics

If you are in the lead generation stage of your Revenue Marketing journey, moving into demand generation, and recently acquired marketing automation technology, here are your best bets for initiating revenue marketing reporting this year:

1. Avoid Ego Metrics for 6 Months

Marketing ROI and marketing influenced revenue. These require a lot of pieces to be in place and working and are simply not a good place to start. We recognize that they are important, but don’t try to start here. Avoid creating the ego metrics the first six months.

2. Define the Decisions

Start by defining what decisions the demand generation and content teams are making weekly and monthly and asking what reporting related information they need to make better investment decisions. Create those reports for them first. Good examples are:

  • Weekly database engagement by campaign, content, channel, region, product interest, and contact type. Are they a prospect or a customer? Engagement means they downloaded or clicked on an offer, registered for something or visited one of your digital properties. It also includes engagement on the social channels (likes, replies, forwards, clicks). It does NOT include email opens.
  • Form completion rates (or the converse, form abandonment rates).
  • Net new leads by region, product interest, lead source and content/asset that attracted them.
  • MQLs and SQLs by lead source, region and product interest.
  • Cost per MQL from inbound sources.
  • Funnel conversion rates, by contact type, region and product interest.
  • Funnel age in stage (qualitative measure of the funnel), by region and product interest.

3. Fix the Errors

Reports like these will reveal all sorts of issues with your data and with the processes that update your data. You will spend months fixing these process issues and amending the data. You will probably also find that your data has serious omissions precluding you from reporting the way you want and a data enrichment project may be initiated.

4. Take Your Time, Before Sharing

Do not share the initial reports throughout the organization because it is likely that they are wrong. There will be errors from simply not having enough good data to be a representative sample to incorrect data to faulty report configuration.

If you share the early reports widely and the errors are uncovered by the recipients, it may take a while to recover your credibility. Take your time, validate your early reporting and gradually start to share them more broadly.

5. Are the Initial Reports Helping?

Sit in with the demand gen teams and content teams and see how they are using these initial reports. Are they useful for making decisions on a weekly or monthly basis? I.e. is the reporting cadence aligned with the required decision-making cadence? Are they getting the detail they need? Is there drill down required?

Modify your reports to fully satisfy this audience before you move to the next audience.