Hypothesis-based marketing repairs

One of the greatest challenges we face as marketers is knowing what’s wrong and why in our sales and marketing funnel. I thought I’d share a useful guide I’ve relied on in order to understand what challenges your organization might face in its marketing.

Let’s start with the three broadest categories of marketing metrics: audience, leads, and sales. We know that audience is driven by PR, media, and advertising. Marketing is about getting qualified members of the audience to raise their hands. Sales is about getting those qualified people to buy. These are the broadest, most gross generalizations, but they’re a place to start.

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Let’s assume then that you have the metrics and analytics from each stage in the funnel, and you’ve broken them out into roughly good and bad categories, based on whether period-over-period growth is positive (green) or negative (red). If you’re going gangbusters and everything seems super with period-over-period math, then you might need to do rate of change calculations in order to detect problems.

Scenarios 1 and 8 are the most obvious. When everything is working great, optimize, but don’t be in a rush to fix what isn’t broken. When everything is broken, fix the fastest, easiest things to get some momentum – any momentum – going. You need wins on the board, and frankly anything will help.

Scenario 2 is a situation familiar to many marketers, and it’s the age old sales vs. marketing argument – the leads are weak vs. you sales people can’t sell. This is a case where the problem may in fact be in the audience itself. An audience that converts to a lead but can’t buy is a targeting issue. Make sure you’re bringing in the right audience via PR and advertising. To ascertain this, you’ll need to look in your sales CRM data to see why opportunities aren’t being created.

Scenario 4 leads to Scenario 7. Scenario 4 is when you’ve tapped out your audience. The audience you have is converting, which means marketing is working, and sales is selling, but it’s only a matter of time before you decline into Scenario 7. The pipe at the top is empty, which means that in a short while, you will run out of leads, and ultimately the funnel breaks down. To remedy it, you’ll need to change up audience capture strategies, using advertising and perhaps hiring a PR firm.

Scenario 5 is a case where your PR and advertising are working, but nothing else is. Start at the bottom of your funnel and figure out why sales isn’t selling. You’ll spend a lot of time with your sales CRM doing that, but it will be worth it. That will get you to Scenario 3, where the bottom of the funnel is converting again and the top is being fed, so it’s just a question of getting your marketing systems optimized and repaired to close the loop.

Scenario 6 is a rarity – rarely do you see marketing flourishing when there’s a decline at the top of the funnel. The exception to this rule is when you have a strong outbound sales force, folks who are going out and networking and building their own pipelines. When you see scenario 6, you know they’re spending too much time sourcing leads and not enough time closing deals. However, fixing the top of the funnel will alleviate this problem in the long-term, giving them leads to call. Start there.

These scenarios are only starting points, but they’re a useful way to begin testing hypotheses about what might be wrong in your sales and marketing funnel – and where you start to fix it!


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Hitting your marketing targets like an archer

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I had the opportunity to do some archery yesterday at one of the local summer camps as I was picking up my daughter. As it was the last day of camp, the instructors invited parents to give it a try.

One interesting thing I noticed was that among the other parents who were trying it out, most focused either solely on the process of operating a bow and arrow, or focused solely on the goal of trying to hit the target downrange.

The people who focused solely on the goal and ignored the process fared most poorly. Some of them didn’t even get their arrows to the target, falling short by half.

Those who focused on the process were able to get the mechanics of operating a bow and arrow down reasonably well, but they still missed the target more often than not.

In the practice of kyudo, the Zen art of archery, it is often said that the archer, the bow, the arrow, and the target must become one, that there must be no distinction. This is a spiritual way of saying that you have to devote a little bit of focus to everything in order to make it all work together, that no one part is more important than another. If you lack focus in any area, things will not work as well as they should.

Making sure that your form is good, making sure that your aim is good, making sure that your body is doing what it is supposed be doing, making sure that you know where the target is – all of these are parts of giving attention to everything that you need to pay attention to in order to ultimately hit the target consistently.

Think about how this applies to your business and marketing. If you focus solely on the process of marketing, doing stuff with no idea what your goals are, chances are you’re going to not hit those goals (if they exist). On the other hand, if you obsess over your metrics and numbers and key performance indicators without actually paying attention to the quality of the work that you’re doing, you also miss the mark.

It is only when you have that blend of attention from the beginning of the process to the goal that will generate the results that you want.

(and in case you were wondering, it had been decades since I last picked up a bow and arrow. Thankfully, years of martial arts experience meant that I acquitted myself honorably, hitting the target every time, if not a bullseye.)


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When bad data can be okay

As marketers, and especially as marketing technologists, correct data, correct metrics, correct information is prized by us (or should be). Incorrect data, faulty data, and misleading data are anathema to our profession and our ability to do our jobs. So it might seem absolutely absurd, even heretical, to make the statement that sometimes, bad data can be okay.

When could wrong data, bad data ever be okay? Here’s a thought exercise for you.

Office clock

Imagine for a moment you wanted to know what time it was. On the wall was a clock that was clearly the wrong time. You know for sure it’s noon but the clock says 3. When you check the clock later that day as you head home, you know it’s 5 o’clock but the clock says 8.

Is the data bad? Yes. Can you still use it? Yes, as long as you know the clock is three hours ahead. The data is bad but predictably and reliably bad. You can develop a mental model (just subtract three) to compensate for the error.

Now imagine the clock shows 3 when you know it’s 12. In an hour, it shows 5. Then it goes backwards and shows 11 within a few minutes. Is the data bad? Yes. Can you still use it? No. In this case the data is bad and unreliably, unpredictable, seemingly random. It’s not something you can develop a model on, and thus it’s totally useless.

Many of the measures we use in marketing come from other derived sources, such as Klout scores and other social influence measures or estimated web traffic. When you’re looking at metrics and tools, the question you should be asking yourself isn’t necessarily whether the data is right (though that’s an important question) but whether it’s reliable.

You can model reliably wrong data that you understand. You cannot model correct data with surety if you don’t know what it’s made of, because things could be changing behind the scenes that you can’t see or compensate for. One day you wake up and what seemed like right data became wrong data overnight.

No better example of this exists than Google’s algorithm. No one knows what’s in it, and thus trying to “win at SEO” is an impossible task because what you think is right today may be wrong tomorrow, but you have no way of knowing it until you lose search rankings. Even worse, because you don’t know what’s in it, you don’t know how to fix what’s wrong except by random experimentation.

Ask how reliable your data is!


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