The Importance of Leading & Lagging Metrics in Marketing
The Importance of leading & lagging metrics in marketing Goals. Metrics. SQL. AQL. MQL. KPI.
Tony Abena & Matt Abrams | Seven Peaks Ventures
GreenFig’s mission is to enable lifelong learning and to accelerate skill development for high demand jobs with our microdegrees in applied business science With that mission, our instructors are leading in the industry and going outside the box, teaching cutting-edge technology to ensure the students understand digital marketing today and how it is evolving in the future.
Last week, Matt Abrams, a General Partner at Seven Peaks Ventures, taught our Artificial Intelligence (AI) class. Matt has 20 years of experience in data analytics and intelligence so a wealth of information for our class. Our students were engrossed in the topic, the options, and what is coming. Matt wrote this blog and GreenFig wants to share it with you. We think AI can be complicated and Matt takes the time to break down how to think about it in three simple Cs.
We have had a number of conversations recently with founders, CEOs, and product leaders who seem to be running into brick walls when selling their artificial intelligence (AI) / Machine Learning (ML)-based platforms and solutions to medium and larger enterprises. Whether the sales cycles extend too long (nine months and beyond) or decision-makers and key influencers are not aligned, ultimately, these deals do not close. And when they do close, they end up closing as very small, limited pilots/POCs without strong internal ownership to get them to next level.
At the same time, we also see other AI companies achieving the opposite. Much shorter cycles of three to six months, strong buyer and influencer alignment, and deals close with five, six, or seven-figure contracts, often with multi-year terms and the ability to expand their Annual Run Rates (ARR) significantly.
We are seeing a pattern here. In fact, three key go-to-market capabilities play out again and again in the companies building category-leading AI businesses. It all comes down to complexity, consensus, and connection. First, these leaders manage complexity. Next, they build and sustain consensus. Finally, they enable both rational and emotional connections to the solution and the desired outcome. Here’s how we break it down.
We all know to build and deliver AI/ML-based technologies that drive strong, repeatable business results are complicated. In our view, today’s enterprise AI is characterized by three somewhat unique conditions that magnify its complexity:
Many enterprises can deal with “known” tools such as BI/analytics, but dealing with the early adopter risk and uncertainties around AI — the technology itself (what is this black box and how do we actually use it?), the data (do we have enough, and is it usable?), the people (experts, perceptions and politics), and the business requirements (what is success?) — makes it even harder. At the same time, just because AI solutions are, by their very nature, complex, it does not mean that this complexity should be messaged and positioned as a central part of the value proposition.
Too often, we see companies getting too wrapped up in their own “sausage making” and not spending enough time helping companies deconstruct AI and provide a clear, actionable roadmap to get started. Because the vast majority of enterprises have little or no maturity for how to source, assess, contract, and implement AI-based solutions, we see enterprises become skeptical about the “magic” or “black box” they are being sold. The associated sales and evaluation cycles extend and harden as a result.
It is understandable, if leaders do not know what you are talking about, they will take more time to assess and validate, so they do not make a poor choice in what is an increasingly c-suite and board visible decision. What we see in successful sales processes is a program of joint discovery, buyer education that ties an honest organizational readiness assessment to v. 1.0 of the solution, and a clear measurable definition of ROI/success to line it all up. In addition to formal programs, leaders also use creativity and storytelling in playbooks and videos to create narratives and context to help internal leaders better understand how AI solutions work and what it will do for them / their business. These resources can then be used to help ambassadors sell the solution internally.
To get AI or any enterprise technology sold effectively, companies need to identify and align the key direct and indirect players in the decision-making process. Not only is it important to align these teams to the problem and proposed solution, it is often even more impactful to ensure that they are aligned to understand each other’s individual needs and priorities (which we hear time and again is absolutely essential). Corporate Executive Board (CEB, now part of Gartner) has done some interesting research aro how to sell more effectively to enterprises where group buying decisions are becoming more common. Their findings:
Yet given the lack of understanding and alignment around AI, the opposite is typically true for AI companies, which typically end up starting “from scratch” with buyers. As a result, finding and leveraging what CEB calls “mobilizers” versus traditional “influencers” is even more critical to selling AI solutions.
Mobilizers are differentiated in that they provide active (versus passive) help to providers to navigate internal process and progress deals forward. We have seen leading AI companies actively finding and supporting active influencers or mobilizers and arming them with tailored information on not only the business benefits of the solutions but also information that can be helpful to advancing their careers or becoming better leaders.
CEB’s research found that internal champions receiving support on both the “personal value enhancement” and “business value enhancement” fronts were up to five times more likely to be motivated and active mobilizers. Rightly so, mobilizers can become concerned about the risk they are taking to be active, visible promoters, so they need to see the rewards of both business and personal impact as outweighing the associated risk and effort involved. For more info on mobilizers, we recommend the book, “The Challenger Customer.”
Even achieving strong consensus might not be enough. There are traditional Enterprise political hurdles that can be identified and overcome. Startups competing with entrenched incumbents (rarer in AI as an emerging category, but becoming more prevalent as software/SaaS providers add AI capabilities) often don’t fully understand the games that are played within these organizations. Cementing the mobilizers and proving the business benefits of any solution, let alone a new AI solution, is critical — entrepreneurs need to arm themselves to overcome the inertia and politics that play against them.
The starting point in reducing complexity and building consensus is to identify common ground or connection among stakeholders. If you are selling AI-focused solutions you will encounter at least the CIO, the CFO, a business unit leader or leaders, and the procurement team, who all have overlapping, though distinct and sometimes conflicting, interests. Helping those stakeholders connect the dots between their shared interests will set the stage for lower complexity and higher consensus and make it easier and less risky for mobilizers to advocate on your behalf. We see successful AI leaders taking a couple of powerful approaches that help teams see what unites versus divides them.
Selling technology solutions, particularly those that are AI-based, in competitive markets is always challenging. Companies proactively managing the perception and reality around complexity, increase buying group consensus, and build buyer connections are winning more, larger-sized and longer-term deals in the market. There is a real opportunity for AI companies to up-level their go-to-market strategy and execution along these three dimensions, even matching the value of innovation present in their technology. As this occurs and the market matures, we expect to see even more compelling enterprise AI companies succeed.
By Tony Abena and Matt Abrams, Seven Peaks Ventures
The Importance of leading & lagging metrics in marketing Goals. Metrics. SQL. AQL. MQL. KPI.
When GreenFig approached me about participating as an instructor in its new Digital Marketing Science Course, I was not only honored but thrilled to have a chance to participate in a new, innovative program to build savvy digital marketers. Traditional university marketing programs set a great foundation for marketers, but their ability to stay current on the incredibly fast moving world of digital marketing falls flat. I’ve hired many marketers over the years at AT&T Mobility, Microsoft, and others, and I’ve hesitated when hiring early career graduates. The learning curve for being able to hit the ground running for digital marketing roles was just too steep when you have an already stretched marketing team. I’ve typically needed someone who could make an immediate strategic and tactical impact on my digital efforts. The GreenFig Digital Marketing Science microdegree seeks to bridge that gap for the new graduate or the professional wanting to deepen their skill set. Digital marketing is now driving most of the marketing strategies across all industries and it continues to grow. Congratulations students on taking a giant leap forward toward becoming a digital marketing professional. The course I’m teaching is called: Planning for ROI-Baseline KPIs for CMO Dashboards. In this session, we’ll have some fun addressing the following key areas:
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