Why shiny objects kill growth

Let me introduce you to Random Rick. He's energetic, eager, and full of ideas. He's just started at Pipeline Ninjas, a HubSpot CRM implementation agency, and he's determined to drive growth. Rick reads everything he can find about growth marketing, watches YouTube videos, and keeps a running list of tactics he wants to try. He's ready to make an impact.

But here's the problem: Random Rick's approach is killing growth, not creating it. Every shiny object on the internet looks like an opportunity. Every new tool promises to be the answer. Every blog post suggests a different priority. So Rick tries everything, jumping from tactic to tactic, convinced that if he just works hard enough and tests enough things, growth will follow.

I've been Random Rick. Early in my career, I was doing exactly this. Implementing a new tool one week, pivoting to a different channel the next, never quite understanding whether anything was actually working. It was exhausting, and the results were frustratingly inconsistent. Most growth marketers start this way, and many never escape it.

This chapter reveals why scattered tactics kill growth, even when you're working harder than everyone else. We'll follow Pipeline Ninjas as they start at €10,000 in monthly revenue and watch what happens when Random Rick takes charge. His story isn't unique. It's the pattern I've seen hundreds of times in my consulting work, and it's probably costing you more than you realise.

Rick starts his new role at Pipeline Ninjas with genuine enthusiasm. He knows the company needs to grow, and he's determined to figure out the right approach. Like most marketers in his position, he decides to begin by "getting up to speed" with the data.

He opens Google Analytics 4 and immediately faces a wall of metrics. Sessions, engagement rate, conversion events, user acquisition reports. He clicks through the interface, looking at traffic sources, bounce rates, demographic data. But how do you interpret all of this? Which metrics actually matter? He's drowning in data without a framework to make sense of it.

Next, he pulls up the marketing dashboard in HubSpot. Lead conversion rates, email open rates, form submissions. Then the sales data. Pipeline value, win rates, average deal size. Each system has its own dashboard, its own way of presenting information. After an hour, Rick is more confused than when he started. He closes his laptop and decides to move on. He doesn't have time for this.

Instead, Rick turns to external resources. He starts reading blog posts about growth marketing, trying to absorb as much information as possible. One article says to focus on retention. Another says acquisition is everything. A third argues that you can't grow without fixing your product-market fit first. Every piece of advice makes sense in isolation, but how do they fit together?

He moves to YouTube, hoping for more practical guidance. He watches a video about scaling Facebook ads, another about optimising conversion rates, a third about building a content engine. By the end of week two, Rick has consumed dozens of articles and videos. He's taken notes, bookmarked resources, and created a list of potential tactics to try.

But he doesn't feel more confident. He feels overwhelmed. The sheer volume of information has paralysed him rather than empowered him. He's got 50 ideas and no way to decide which one to pursue first. So he does what most marketers do in this situation: he picks something that sounds good and starts experimenting, hoping that momentum will create clarity.

This is where Random Rick's approach begins to break down. Without a framework to filter information and prioritise actions, preparation becomes procrastination dressed up as research. Rick has spent two weeks learning, but he hasn't learned the most important thing: where to start.

Rick decides to start with cold email outreach because he's read that it can generate leads quickly. He signs up for Lemlist, a tool he's seen mentioned in several blog posts, and starts building his first campaign. His hypothesis is simple: if he can improve his email performance through A/B testing, he'll generate more leads.

He tests different subject lines and email copy, watching his metrics carefully. The results are encouraging. His open rates double from 20% to 40%, and his click rates improve from 6% to 12%. Rick feels vindicated. This is working. He's making progress.

[IMAGE: Lemlist dashboard showing A/B test results with improved open and click rates]

For his second experiment, Rick decides to tackle the booking process. He's read that the scheduling tool you use can impact conversion rates, so he switches from Calendly to Lemcal. The setup takes a few days, and he's not entirely sure it's made a difference, but it feels like forward progress.

His third experiment targets the landing page. Looking at Pipeline Ninjas' website, Rick thinks it looks a bit outdated. He decides to redesign it using Unbounce, hoping a more modern look will improve conversions. He changes the layout, updates the colours, adds new visuals. It looks better, but did it actually help? He's not sure, but he's already thinking about the next experiment.

The fourth experiment happens almost by accident. Rick realises that leads aren't being nurtured after they submit a form, so he sets up a basic email sequence in HubSpot. He's not expecting much, but surprisingly, activation rates increase by 20%. This is his best result yet, though he doesn't fully understand why it worked.

For experiment five, Rick shifts his attention to the proposal process. He's heard good things about PandaDoc, so he switches the sales team over to this platform. The implementation takes longer than expected, and the sales team grumbles about learning a new tool, but Rick is convinced it will help close more deals.

By experiment six, Rick circles back to cold outreach. His initial A/B testing showed promise, so he decides to scale those efforts. He runs more tests in Lemlist, refining subject lines and call-to-action placement, hoping to replicate his earlier success.

On the surface, Rick looks busy and productive. He's running six experiments across different areas of the business. He's testing tools, trying new approaches, and generating data. But there's no thread connecting these experiments. No strategic logic determining why he chose these six things instead of six others. He's operating on intuition, blog post recommendations, and whatever feels urgent in the moment.

This is the Random Rick pattern: constant motion without strategic direction. Each experiment makes sense in isolation, but together they form a scattered, reactive approach that wastes energy on low-impact activities.

After six experiments, Rick sits down to review his progress. He's worked incredibly hard, and the revenue graph shows growth. Pipeline Ninjas has gone from €10,000 to €14,000 in monthly revenue. That's 40% growth. It should feel like a win.

But Rick doesn't feel like he's winning. He feels exhausted and uncertain. Yes, revenue is up, but why? Which experiments actually drove that growth? What should he do next? The data raises more questions than it answers.

When he breaks down the results using the Solid Growth System framework, a troubling pattern emerges. The 40% revenue growth wasn't evenly distributed across the four growth engines. Engaged sessions barely moved, growing only 1%. The marketing funnel showed decent improvement at 26%. The sales funnel contributed 10%. And contract value? Zero growth.

This uneven distribution reveals the fundamental problem with Rick's approach. He worked on whatever felt important in the moment, without considering how different parts of the system compound together. Some experiments moved the needle, others barely registered. But without a framework to prioritise, Rick had no way to know which experiments to run before starting them.

The most damning evidence comes from the traffic analysis. Remember those cold outreach experiments that felt so successful? Rick's open rates doubled, his click rates improved dramatically. But here's what the donut chart reveals: cold outreach represented only 0.2% of Pipeline Ninjas' total traffic.

Rick spent significant time and energy optimising a channel that was nearly irrelevant to the business's overall growth. Even doubling performance on cold outreach couldn't materially impact revenue because he was working on such a tiny slice of the total opportunity. It's like optimising the boot space of your car when the engine isn't working properly.

This is the data trap that catches Random Rick. He's collecting metrics, running experiments, and seeing some positive results. But without understanding the relative importance of different channels and metrics, he's optimising randomly. He's working hard on things that don't matter enough, whilst the real opportunities remain untouched.

The 40% growth Rick achieved came despite his approach, not because of it. Some experiments happened to target areas that mattered. Others were essentially irrelevant. But Rick has no systematic way to tell the difference, which means he can't reliably repeat his success or scale his efforts.

The obvious cost of Rick's approach is time. Six experiments over several months, with significant effort invested in each one. But time isn't the only thing Rick wasted. Let's look at what this scattered approach actually cost Pipeline Ninjas.

First, there's the opportunity cost. Whilst Rick was doubling down on cold outreach (0.2% of traffic), the organic channel (which represented significantly more traffic) went completely unoptimised. Whilst he was redesigning the landing page with Unbounce, the actual conversion bottlenecks in the marketing funnel remained unaddressed. Every hour spent on a low-impact experiment was an hour not spent on something that would have generated compound growth.

I've seen this pattern dozens of times in my consulting work. A marketer spends three weeks implementing a new tool or testing a new channel, generates a 15% improvement, and feels productive. But if that 15% improvement happens on a metric that only contributes 2% to overall revenue, the total business impact is negligible. Meanwhile, a 10% improvement on a metric that drives 40% of revenue would have created five times more value.

[IMAGE: Comparison chart showing 15% improvement on 2% of revenue vs 10% improvement on 40% of revenue]

Second, there's the team morale cost. Rick's colleagues watched him jump from experiment to experiment without a clear strategy. The sales team had to learn PandaDoc mid-quarter, disrupting their workflow. The marketing team saw the landing page redesigned without understanding why. When results came in inconsistent and hard to interpret, it created organisational confusion about whether the growth efforts were actually working.

This erosion of confidence is subtle but dangerous. When a team doesn't trust that their growth marketer knows what they're doing, they become hesitant to implement recommendations. They start second-guessing decisions. They hedge their bets by continuing old approaches whilst half-heartedly trying new ones. This organisational friction slows everything down.

Third, there's the budget cost. Rick tried six different tools and experiments. Subscriptions for Lemlist, Lemcal, Unbounce, and PandaDoc. Ad spend on channels that didn't matter. Time spent in meetings coordinating experiments that shouldn't have been priorities. When you're working without a system, you can't accurately estimate return on investment because you don't know which investments will actually drive growth.

But perhaps the biggest cost is the mental one. Rick is exhausted. He's working evenings and weekends, reading constantly, trying to stay on top of the latest tactics. He feels like he's running on a hamster wheel, working incredibly hard but never quite getting ahead. This isn't sustainable. Burnout isn't far away.

I know this feeling because I've been there. The constant nagging doubt about whether you're working on the right things. The anxiety when someone asks "what's our growth strategy?" and you realise you don't have a coherent answer. The fatigue that comes from trying to hold too much complexity in your head without a framework to organise it.

Random Rick's story isn't about a lack of effort or intelligence. He worked hard, tried multiple approaches, and even generated 40% revenue growth. His failure was a failure of system, not capability.

The lesson is clear: without a framework to prioritise experiments, you're optimising randomly. You might get lucky and work on something that matters. You might waste months on things that don't. Either way, you won't know the difference until after you've spent the time and money.

Rick needed three things he didn't have: visibility into which metrics actually drive revenue, a method to prioritise which metrics to work on first, and a way to measure whether his experiments were moving the needle on what matters. Without these, even his successes were accidents he couldn't reliably repeat.

But what if you're not Random Rick? What if you're an expert in your field, with a structured approach and proven tactics? Surely that's better? In the next chapter, we'll meet Specialist Steve, who has all the discipline Rick lacks. And we'll see why even that isn't enough.