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Month: June 2023

How Do I Get Management to Listen to Me?

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Hi Joe,

I’m having trouble getting respect from my marketing leadership.

Working in marketing ops means I understand the processes between Marketing and Sales, what’s working well and what isn’t, but I don’t think my boss values my insights.

My role involves lots of procedural responsibilities like building emails and handing leads over, which I think creates the perception that my contributions aren’t important to the big-picture strategy.

How do I get my boss to listen to me? How do I make them see that my work adds value?

Thanks,

Ignored Isabel.

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Isabel, I know this is tough.

Getting your boss to really appreciate the value you provide in MOPs can feel like pushing a boulder up a hill.

After years of progressing my career in MOPs and working with senior leadership figures, I’ve seen a real blind spot from management towards the complexities of marketing operations.

That said, the disconnect goes both ways.

A mistake I often made earlier in my career was to assume that everyone in a company speaks the same language. What comes fluently to us in MOPs can sound downright alien to people in other fields. For instance:

➡️ Data flows
➡️ Systems maintenance
➡️ Martech infrastructure

It’s rarely apparent to leadership at face value how these components help the company to work productively and achieve revenue targets. Add those things together — poor understanding of MOPs, communication that doesn’t touch the bottom line — and you get a lack of respect.

 

“A story of your value in MOPs
that makes your impact on the business clear.”

 

You’re doing great work that’s worthy of recognition. What’s missing is a story of your value in MOPs that makes your impact on the business clear.

Here’s some advice that can help you gain a seat at the table:

 

Unpack the strategy

Automating a ton of processes doesn’t mean your job is simple.

Every email you build or webinar you host comes after weeks of planning to make sure your campaigns run smoothly and reach the right audiences.

This is how you characterize your role to people who think you’re here to take orders; less plumbing, more architecture.

 

Know the room

You’re at a crossroads between technical know-how and commercial priorities.

Your CTO and IT team might relate to the grittier aspects of your work, but for Marketing and Sales, it’s all about how you’re planning and budgeting for successful campaigns and generating leads.

For responsibilities like vendor relations and data governance, you’ll need to surface how doing those things well helps your company be productive and profitable.

 

Unify your data sources

Reporting and analytics aren’t just ‘nice to haves’ — they’re the best instruments for painting the picture of your impact.

Give your tech stack some TLC and join together all the reporting elements that show how you’re performing against KPIs.

 

Share the right numbers

The most compelling move you can make with data is to leave behind the everyday operational challenges — the amount of tickets you’re handling, processes you’re running — and look at revenue.

👉 How many MQLs converted to SQLs?
👉 How many of those turned into closed deals?
👉 What dollar value are they converting?

Those data points prove your contributions to business growth, so own them.

 

“Persistence
goes the distance.”

 

Getting management to listen means changing their perspective of your value. It might not happen overnight, but persistence goes the distance.

Read our post How Do I Show My Boss My Value? for more advice.

You’ve got this,

Joe Pulse.

Help! I Have to Start Attribution

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Hi Jo,

My company wants to start doing attribution, so I’ve been asked to put together a plan.

Here’s the problem: I have no idea how to do this right.

I’m uncertain about the practicalities my plan should account for or what results to expect.

What kind of commitment is attribution really?

How do I create and carry out a plan that works?

Thanks,

Attribution Amy

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Amy, it’s good that you’re thinking critically about this.

Years back, when my marketing team first took on attribution, we were very excited by all the models and ways of understanding how people engage with campaigns.

My expectation

I was under the impression it was a plug-and-play type deal. Three months down the line, it’ll spit out numbers that tell you exactly where to spend, hands-free.

The reality

Three months in, I had nowhere near a confident grasp of how to use different models and data setups, nor was I making any decisions to optimize spend.

My sales and marketing teams were frustrated — the results we thought were coming were nowhere in sight—and so was I.

 

“I learned something important from that experience:
attribution isn’t magic.”

 

I learned something important from that experience: attribution isn’t magic.

To really work for your business, it’s a gradual process that takes:

✅ long-term refinement
✅ consistent methodology, and
✅ clear communication between Marketing and Sales.

That understanding is your plan’s guiding star.

 

8 attribution tips:

👉 Use a dedicated vendor: Unless your entire job is attribution, there aren’t enough hours in the day to build this effectively on your own. There’s no native ability in CRMs to pivot campaign memberships against opportunities, which is how you start to calculate ROI.

👉 Establish common terminology: Marketing and sales need shared definitions of what it means to source, touch, and influence leads, the same classification of sources vs mediums, and a mutual understanding of how your CRM accounts for revenue and opportunities. This helps to keep your data clean and for Sales to set accurate goals.

👉 Clear data collection: Use UTMs wherever you can, and be consistent with tagging traffic coming into your website.

👉 Get your tools in sync: Many attribution platforms use Salesforce campaign objects. To keep accurate data flowing, check that these are synced correctly with the relevant marketing automation platform (MAP) programs.

👉 Get your processes in order: Make sure that Sales is using opportunities in Salesforce and regularly reporting pipeline and revenue in there. You’ll need these updates to sync to Marketo or your MAP of choice.

👉 Figure out spend: You might think organic traffic is free — but how much are you paying someone to update and optimize the website? Agree with your boss on how to factor in less obvious expenses. Even estimates are useful for arriving at accurate ROI calculations.

👉 Budget for time: Your platform might take 6-12 months to launch. And if it’s a 2nd or 3rd gen platform that offers website cookie tracking, implement that collection right away if you think you’ll need it in the future.

👉 Small goals = achievable goals: Set goals as part of a gradual roadmap that incorporates more robust models only as you get more comfortable with attribution. Small wins that you actually achieve are better than grand plans gone off the rails.

Attribution is a complex business. You want to go far, not fast.

You’ve got this,
Jo.

Is It Worth It? The Hidden Cost of New MarTech Tools

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TLDR: Adopting a new tool has a range of logistical and financial consequences. MOPs and RevOps leaders should interrogate each potential addition to your tech stack by evaluating the ease of implementation, the experience of your team, the ease of integration with your current or planned tech stack, the potential financial costs beyond the purchase price, and how the tool responds to real business needs and strategic aims.

The motivation for adopting new tools: When new leaders join companies or people move internally to different teams, they take with them the technologies and practices they’re used to. New marketing leaders are often keen to implement tools they’ve had positive experiences with in the past and can be prone to thinking that having more tools makes it easier to surface ROI—more ways to analyze data, more ways to present it, more functions and features to optimize how you work.

The consequence of new tools: In practice, however, this isn’t quite the case. Adopting a new tool has a range of logistical and financial consequences for your business that require thoughtful planning to navigate.

What’s in this article for you? In this Tough Talks Made Easy, we’ll help you explain to your CMO or CRO the problems that can arise from adopting a new tool too soon. We’ll also outline the important things your organization needs to consider before deciding to adopt new technology.

 

New tool consequences

Marketing operations people are frequently asked to take ownership of managing new tools, and so they have first-hand experience of the reality that more tools = more responsibilities.

Adding a new tool to someone’s workload has productivity consequences for what that person can feasibly deliver, especially if they need training to effectively use the tool in question.

If the department leadership is looking at a new core piece of tech—a marketing automation platform, a CRM, a content management system—it’s likely to demand a revamp of your whole MOPs infrastructure.

 

“Before adopting a new tool, you need to understand
if it’s worth it and why.”

 

Without qualified talent on board, you might need to hire someone new to lead on that piece of tech — and the hiring process costs time and money. So before adopting a new tool, you need to understand if it’s worth it and why.

Poorly-conceived additions to your stack will leak revenue, for example:

👉 Wasted subscription fees for unused tools.
👉 Unforeseen disruptions to your team’s workflow and productivity through accommodating new processes.
👉 Suboptimal implementations or maintenance that cause damage downstream.
👉 Integrations that don’t work properly, corrupted data, bloated storage.

 

The questions to ask

 

If you have a robust tech stack

If your tech stack is already robust, your first step should be to evaluate what isn’t working.

Is a new piece of software the best way to address your needs? Encourage your CMO or CRO to explore the solutions existing in your company stack — you might already have the license to a tool that fulfills a similar purpose to a good standard, and you’ll avoid the redundant expense on an overlapping solution.

If leadership’s considering a tool that can change the essential infrastructure of your MOPs/RevOps function (a MAP, a CMS, a CRM), it’s crucial to know what strategic ambitions it supports.

➡️ Are you scaling down to cut costs or simply overhead?
➡️ Are you scaling up because your CMO/CRO has a growth plan and needs the particular capabilities of more advanced tools to achieve it?
➡️ Have they planned for the corresponding investment in the MOPs team (e.g. whether that’s greater headcount, higher training budgets, or a redistribution of role responsibilities) to facilitate a more complex platform?

 

If the new tool will play a supporting role in your stack

When evaluating a tool that plays more of a supporting role in your stack, you’ll want to assess how well it integrates with your existing infrastructure.

➡️ What depth of expertise will the tool require?
➡️ How long is the implementation period? Will it require more resources on a temporary or permanent basis?
➡️ Is it best-in-class at providing the functionalities you’re looking for?
➡️ Does it have good momentum in the marketplace?

Getting to the bottom of these points is essential to come up with a realistic assessment of a tool’s total cost of ownership.

 

Questions to ask beyond those above

➡️ Is the total cost of ownership (TCO) worth paying?
➡️ Do the capabilities of the tool respond to the goals your CMO/CRO wants to achieve?
➡️ Can you reasonably estimate that its features can drive revenue and productivity in ways that justify the time, money, and work?

 

“The impact of adopting a new tool
is often poorly understood.”

 

The impact of adopting a new tool is far-reaching and often poorly understood. Remember that strategy defines your outcomes and tools help you achieve them. Read our article Connect the Dots Between Strategy and Technology for more.

Your MOPs and RevOps leaders should interrogate each potential addition to your tech stack by evaluating the:

👉 ease of implementation
👉 experience of your team
👉 ease of integration with your current or planned tech stack
👉 potential financial costs beyond the purchase price, and
👉 tool’s ability to respond to real business needs and strategic aims.

Approach each tech decisions with this degree of intentionality, and you’ll maximize the ROI you gain from your stack.

Get in touch for more guidance on assessing and implementing new technologies.

Lead Scoring: What Marketing & Sales Need to Know

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TLDR: Lead scoring can help Sales focus only on the most valuable or receptive prospects, but the project stands or falls based on the quality of Sales-Marketing collaboration.

What is lead scoring? Lead scoring is the process of evaluating the interest of a prospect and their readiness to engage with the sales process.

The problem lead scoring solves: Lead scoring helps Sales and Marketing concentrate efforts on leads that have demonstrated a higher level of interest or engagement with your brand, increasing the chances of closing deals and generating revenue.

What’s in it for you? In this Tough Talks Made Easy, we’ll cover how to explain the value and reality of lead scoring to Sales – what it is and is not, what it offers and requires. You can incentivize Sales to work together with Marketing with realistic expectations on a project that’s vital for both teams to grow the business.

 

Methods and data points

Companies can score leads in a variety of ways. You can ascribe numeric values, letter rankings, or descriptive terms like “warm” and “cold.” However you choose to score leads, there are several key data points that should factor into the analysis:

👉 Demographics (relevant individual characteristics, e.g. job title)
👉 Firmographics (organization profile, e.g. industry, vertical, size, location, annual revenue)
👉 Behavior (how the lead engages with your brand, e.g. visiting the webpage, interacting on social, requesting a demo)
👉 BANT qualification (the lead’s budget, authority, need, and timeline)
👉 Completeness of the data you have for each lead

There’s no objectively superior method of scoring leads and accrediting weight to different data types. Instead, your Sales team needs to work with Marketing to define the scoring methodology and establish what a “qualified” lead looks like.

An accurate view of lead quality helps Sales to focus on engaging only with the most receptive and valuable prospects. Neither team can make a complete assessment of this without ideas, data, and feedback from the other.

 

Qualify or nurture

Naturally, some leads will show a higher likelihood to buy than others. The task for Marketing and Sales is to determine how to identify and treat leads that fall into one of two groups:

1️ shows an optimal level of interest for Sales to act, or
2️ requires further nurturing by Marketing.

For this process to yield results, Sales needs to agree with Marketing on the benchmark for qualification.

Sales might expect the leads they receive from Marketing to be ready to sign, but there’s only so much your Marketing team can do in advance. As long as Marketing can unearth opportunities with a high likelihood of closure, it’s on Sales to identify where in the process to step in and how to approach each lead.

On the other hand, Sales shouldn’t encourage Marketing to pass leads over who show just enough of a pulse to open an email or click a link. Qualifying leads this way undermines the evaluative power of Marketing’s nurture process. Sales might get a couple of lucky bites, but it won’t translate to sustainable success.

 

Building lead profiles

Marketing’s nurture programs build insightful lead profiles through rich data collection, which allows Sales to approach the highest quality leads in the most engaging ways, showing awareness of their interests and the situational context. Without that basis, Sales risks burning effort on premature leads and failing to hit targets.

The point to make is that lead scoring best allows Sales to identify and win business from the highest value leads when two things are in place:

✅ clearly defined and realistic models for scoring and qualification, and
✅ time for Marketing to nurture developed engagement data from their campaigns.

 

Fuel your growth machine

To get started with lead scoring, Sales needs a good grasp of their past successes. Your reps should dig into historical data about past deals and lead journeys until they can answer these key questions:

👉 What makes a person qualified enough?
👉 What behaviors and traits did closed-won leads show?

 

The quality of collaboration

From there, lead scoring stands or falls based on the quality of your collaboration. Sales and Marketing should participate in healthy, ongoing discussions until you agree on a scoring methodology and handover process that both teams can comfortably deliver.

With that agreement in place, you stand the highest chance of seeing the benefits of lead scoring—the ability for Sales to prioritize quality leads, better insight for Marketing into the most valuable lead characteristics, and increased alignment and revenue that both halves of your growth machine can enjoy.

Want more guidance on lead scoring? Revenue Pulse is here to help.

AI Fatigue is Here

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TLDR: Over the last few weeks, sentiment towards AI has shifted from optimism to fatigue. On the Gartner Hype Cycle, AI is now entering the “Trough of Disillusionment,” a phase where hype-driven expectations have been left unmet. But while it’s easy to dismiss AI in the short term, history has shown that those who continue to experiment with new technology as it approaches the “Slope of Enlightenment” and eventual “Plateau of Productivity” will greatly benefit in the long term.

Welcome to the Trough of Disillusionment.

Wow, that was quick!

In the course of a week, I’ve started to see the bright lights shift from optimism to fatigue. LinkedIn, Twitter, National News Media, colleagues, friends, and family are all starting to roll their eyes at any discussion of AI. This is predictable, natural, and ok.

 

“It’s perfectly normal
to be skeptical.”

 

The AI hype has been a bit omnipresent. Hyperbole or not, the idea that AI is the next big step for humanity is being tossed around. It’s perfectly normal to be skeptical. It’s also predictable that the hype can not deliver the promise in the short term.

 

AI has achieved a lot in the last 6 months.

GPT-4, Bard, Midjourney, and Adobe Firefly have taken exponential leaps forward – with outputs almost indistinguishable from magic. People are concerned about the route this “choose your own adventure” AI will take from the incredibly positive (think cancer cures) to the extremely negative (think Terminator AI soldiers). It’s easy to dismiss this in the short term because the crystal ball is cloudy today.

We’ve been pretty bad at predicting the future when it comes to AI. We predicted we’d see factory AI robots first and AI creative last. It’s actually been inverted.

We’ve entered a new phase of the technology Hype Cycle called the Trough of Disillusionment.

 

Hype Cycle

 

This was developed by Gartner in 1995 and has been consistently used to monitor the phases of technological introduction to adoption. It’s pretty bang on when we look at the current phase of AI.

 

Peak of Inflated Expectations

We’ve had our Trigger event; In late November last year, ChatGPT was released to the world and it was the fastest technology to reach a million users in history. From December to June, we’ve gone up the curve toward the “Peak of Inflated Expectations.” What have we been told? The world is going to be changed forever. White-collar jobs are going to be replaced. A million new AI software tools are being launched weekly.

 

Trough of Disillusionment

Now we’ve reached or have passed the “Peak of Inflated Expectations.” Interest is starting to wane because the expectations of the hype aren’t being met. I think we’re now just entering the downward slope to the “Trough of Disillusionment.”

For example, I saw a post by MOPs meme master Jason Raisleger and the gist was, “OK, OK, I know I’m using ChatGPT wrong.” And today I woke up and read a newspaper opinion piece titled, “Will AI really change everything? Not likely.” It concludes with, “So the next time you hear a platitude spoken in the worship of AI, feel free to roll your eyes.” Even when technology moves fast, and AI definitely has, we humans can be predictably impatient.

 

“Those who continue to experiment
will benefit in the long term.”

 

Some people are getting to the trough quicker than others. But history has shown that those who stick around and continue to experiment and iterate with the technology will benefit in the mid and/or long term.

 

Slope of Enlightenment

The “Slope of Enlightenment” happens when the ways the technology can benefit the enterprise start to crystallize. Think internet and e-commerce in the late 90s and social media and targeted social ads in the late 00s. It takes a while for new technology to demonstrate its commercial value. Social ads were pretty effective at targeting up until we asked apps to stop tracking us on our phones.

 

Plateau of Productivity

The final stage in the Gartner Hype Cycle is the “Plateau of Productivity.” This is when the benefits, applicability, and relevance of the technology are very clear and investments are paying off. You can argue about if and when this is going to take place, but it is ultimately a predicted path for the future of AI.

You could even say that Adobe’s Firefly AI product, released in beta in Photoshop, is already approaching the plateau. There is no doubt that for creatives, the Slope of Enlightenment has been embarked upon. And while not everyone is a creative, I encourage you to ask an art director about AI – ask them if they think this is a fad.

 

The Route We’re Taking at RP

Our crystal ball, like at most times, is cloudy and unclear. What is predictable, though, is our behavior and impatience. The Hype Cycle helps us understand that this is what we do.

While some may pack up their AI enthusiasm for now, that’s not the route that we’re choosing to take at RP. We’re going to continue to learn, experiment, and iterate with AI. It’s probable that AI will impact our work and our client’s work for the foreseeable future. We’re going to push through the Trough of Disillusionment for the promise of the Slope of Enlightenment.

We hope to see you along the way, but we can always catch up at the Plateau of Productivity.

Staying Up To Speed

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TLDR: AI tools allow us to work faster than ever before. But with this speed comes several organizational challenges, including quality control concerns, integration issues, and increased pressure on decision-makers. Companies must identify these problems and prepare for them to fully benefit from the productivity and efficiency increases that AI can provide.

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When I think about the impact of AI on businesses, the most significant factor is speed; The countless AI tools at our disposal allow us to work faster and more efficiently than ever before.

But in the wake of such speed, it’s crucial to acknowledge the organizational challenges that may emerge – and the need to identify and prepare for them.

Let’s take a closer look at specific problems companies will face as AI accelerates operations.

 

“AI tools allow us to work faster than ever before.”

 

Approval Lags & Quality Control Challenges

Once teams streamline and optimize their processes through the use of AI systems, projects might move faster than management can review and approve them. If managers don’t have the capacity to audit and control these fast-moving projects, the result will be either:

(1) significant delays as managers catch up or
(2) decreased quality as unchecked work slips through.

It will be crucial for managers to remain highly detail-oriented throughout this operational transformation; overlooking finer points or skipping essential steps in a process could lead to costly problems down the line.

 

Integration Issues

When it comes to implementing AI systems to speed up tasks, many teams may face early integration issues with existing tools and workflows. Organizations who fail to configure their processes properly and troubleshoot technical setbacks effectively will face significant disruptions and risk falling behind.

 

Quality vs. Speed

This also complicates the delicate balance of quality vs. speed. While AI systems certainly have the ability to speed up our work, there are many situations where rushing tasks could lead to compromised quality. It’s essential to carefully design processes in a way that maximizes AI assistance while maintaining the standards you’ve set for your business.

 

Increased Pressure on Decision-Makers

This quality vs. speed problem not only applies to day-to-day work but higher-level decision-making as well. As projects move more quickly, leadership teams and C-Suite executives will be pressured to make high-impact, informed decisions on accelerated timelines. To effectively adapt and thrive in this fast-paced environment, companies may have to restructure traditional decision-making hierarchies in favor of new strategies and agile methodologies.

And pressure on decision-makers will also come in the form of heightened expectations from company stakeholders. Consistently maintaining high-quality output at increasing speeds will be a real challenge that can lead to disappointment and friction between leadership and ownership groups.

 

Managing Rapid Change

It’s clear that the implementation of AI has the potential to rapidly change the way we work and make decisions — and this will likely cause disruption throughout many levels of your organization. If this rapid change is managed poorly, leaders will be met with resistance as employees become overwhelmed, confused, and even less productive than before.

 

“We must pay attention
to the fast-moving
developments of AI.”

 
 

There are many potential challenges ahead when it comes to utilizing AI systems to speed up our work.

But if we prepare ourselves and manage the integration of these tools skillfully, the resulting increase in productivity and efficiency will be game-changing.

Now more than ever, we must pay attention to the fast-moving developments of AI.

That’s all for this week.

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