Over the past year, our series of posts on go‑to‑market transformation have made the case for and laid out an approach to operationalizing go‑to‑market activities around the customer lifecycle in a way that better orchestrates engagement, prioritizes customer journey stewardship, and optimizes sustainable client growth. This is an approach we refer to at ANNUITAS as Converged Growth.
More recent posts have laid out the upshots of a reconsidered organizational structure for go‑to‑market organizations and for a transformed go-to-market technology stack through this Converged Growth lens. We have also outlined the role that a Chief Growth Officer should play in this approach.
When implementing this new approach, what is the impact on analytics and KPIs? GTM transformation — specifically, shifting from random acts of marketing and sales to building a perpetual growth engine — has significant implications for the KPIs we use to optimize our go‑to‑market programs and processes. Before we go deeper, we want to level set and focus on the truly critical KPIs for growth leaders. Marketers may focus on a lot more than four KPIs across their day-to-day, but when it comes to building a perpetual growth engine, you really do just need to focus on four key metrics, which we will discuss below.
Our New Growth Goal: Maximizing Lift To Customer Lifetime Value (CLV)
When we transform go‑to‑market through a Converged Growth lens – orchestrating around the end-to-end customer journey and uniting marketing, sales, and customer success teams under a common mission — our framework for ‘successful growth’ shifts. And more unified growth metrics are becoming a mandate: half of Chief Sales Officers recently surveyed by Gartner cited “aligning KPIs across sales and other commercial functions” as a top priority for 2023.
We are no longer as concerned about short-term bookings and renewals, as we are about the impact our combination of GTM interactions have on customer lifetime value (CLV). In other words, we are most concerned about the net “Lift” to CLV that we get from incremental investment in go‑to‑market programs. Our analytical goal subsequently becomes fine-tuning these interactions at every stage of the customer journey to really optimize a long-term, sustainable, and ultimately profitable customer value, to maximize our Lift to CLV.
Almost any company can measure top-line growth with high accuracy; however, when we start to inspect and measure exactly how the orchestrated actions we take — especially in the post-sale phases of the customer journey — affect the overall customer lifetime value, Lift takes on new dimensions.
Classic go‑to‑market reporting has often focused on marketing and sales activity, such as email clicks, page visits, lead conversations, pipeline creation and client portal logins. Yet too often the result is an attempt to optimize literally every activity — even when it reinforces silos. A great example is a content team comparing a blog post with 1,000 views with one that has 100 and judging the former as the most successful blog post when in reality the latter led to 10x more commercial results. Or a customer success team focusing all their time and energy on interactions with system admins for their software product because they’re an available and engaged audience — while neglecting the executives who were the original sponsors of the software (and who are still looking for the results they were promised).
This legacy of ‘random acts’ perpetuates disconnected silos — and marketing, sales and customer success teams that are well-intentioned, but not working together. When we transform our go‑to‑market and transition to a growth engine state, underpinned by an end-to-end Demand Process that tracks through all stages of customer journey (pre- and post-sale), analytical needs change. This new growth engine requires an interconnected system of outcome oriented KPIs to constantly optimize it and the ‘mix’ of interactions (which we refer to below as “Levers.” In this new model, it’s not a single campaign or phone call (i.e., individual Levers) that makes the difference; rather it’s the right combination of Levers — along a critical path — that optimizes our go-to-market results.
This shift is ultimately about treating go‑to‑market programs and processes as an investment portfolio and ensuring our chosen KPIs help us maximize its performance.
New Analytical Cornerstones
The shift to a growth engine and the revised growth goal – maximizing Lift to CLV — must be built on a different foundation than legacy marketing, sales, and customer success reporting.
Thus, our new analytical cornerstones include:
- Multi-interaction tracking (MIT) of contacts — We must be able to see and track every touchpoint with customers — across content, team interactions and engagement channels. We cannot rely on a single, ‘original’ or ‘last’ source interaction to make decisions about go‑to‑market investments. We must see the entire journey of interactions, and how they helped a customer progress through their journey. We also cannot bundle interactions into artificially combined ‘campaigns’ and make assessments based on a combination of activities, unable to decode the actual, specific correlation that led to key commercial outcomes. This leads to the need for a new analytical system of multi-interaction tracking (MIT) — a foundation that is required for effective optimization of your growth engine.
- Converged pre- and post-sale framework — Too often, the metrics used to develop and optimize go‑to‑market programs, pre-sale and post-sale, share little in common. This can lead to optimizing either net new bookings or renewals, neglecting the balance between the two and failing to maximize overall CLV. The framework gap can also lead to a fundamental process gap — failing to ensure the organization is providing stewardship at every stage of the customer journey. Thus, a converged pre- and post-sale framework is a critical cornerstone of a perpetual growth engine. ANNUITAS has developed a model that it uses to organize end-to-end Demand Process — the ANNUITAS Converged Growth OS™ model (see below).
- Granular, closed-loop reporting — Interactions must be judged not by the volume of activity but by sustainable commercial outcomes. Every interaction must be able to be analyzed according to its content and channel “elasticity.” This means we need to understand the probability of each interaction against key commercial outcomes for key customer segments. This requires a closed-loop system that can match point interactions — Levers — to specific commercial outcomes at the most granular level.
- Conversation Track segmentation — A major go‑to‑market organizing principle for Converged Growth organizations and for their growth engines is Conversation Track Architecture. It’s the most scalable way to organize and orchestrate interactions across marketing, sales and customer success along the customer journey. It’s also a key design principle for programs and organizations. Our reporting must allow us to segment analysis by Conversation Track to optimize our playbooks around these critical paths.
- Both contact-level and account-level visibility — Go‑to‑market for B2B products and services requires the ability to optimize interactions at both the contact and account level. If the question is whether your organization wants to embrace ‘ABM’ or ‘MQLs,’ the answer is ‘yes’ … to both. And your reporting should be able to slice and dice interactions from both points of view.
- Customer lifetime value (CLV) and customer profitability (CP) — The most critical frame of analysis must be customer lifetime value (CLV), per period and overtime. A Converged Growth go‑to‑market approach looks at both pre-sale and post-sale activity; individual pipeline deals, sales or renewals are insufficient to really optimize customer value. Return on go-to-market investments also must also be profitable. Thus, a key frame is not only top-line value but also profitability against go-to-market investments.
KPI Layers
What are the KPIs we need to optimize our Growth Engine? Our KPIs encompass three layers across four categories.
Levers drive Conversion and Velocity, which subsequently drives Lift.
The graphic below lays out this system of outcome oriented GTM KPIs:
The system of KPIs is focused on how we can optimize the portfolio of Levers to drive greater Conversion and reduce Velocity, in order to optimize and sustain Lift to CLV (and to CP).
Here is a breakdown of each category:
- Levers — These are our interactions with customers and the resultant efficacy of these interactions. Major categories include content touches, team interactions (e.g., a sales or customer success call) and engagement channels. The basis for Lever-based KPIs is the amalgamation of MIT data and being able to slice it at the customer/account level OR being able to slice it at the interaction level. Our analysis of levers looks at activity volume, how these interactions drive funnel outcomes, the elasticity (or productivity) of these interactions, and both the cost and ROI of these interactions. Ultimately, we want to be able to see the portfolio of interactions for a given customer contact, Conversation Track, or account, and be able to optimize our mix to drive the greatest CLV and CP.
- Conversion — This looks at movement through demand states (i.e., funnel stages) — both pre- and post-sale — and allows us to identify friction in movement between these demand states. This is a critical piece of ‘telemetry’ we must track in go‑to‑market programs, so we can zero in on bottlenecks and improve throughput.
We also look at Conversion through the lens of ICP. For ideal customers, we want to see a combination of engagement, demographic/firmographic fit and lower funnel/intent behaviors to ensure prospects ‘look like’ our ideal and highest CLV customers. So, while one view of Conversion is about constantly improving throughput, the other view is ensuring there is not so much throughput that what is getting through is lower quality because the prospect doesn’t fit these criteria.
- Velocity — This looks at throughput time — i.e., the speed (or lack of it) at which a prospect moves between demand states — both pre-and post-sale — and the overall time attributed to the interaction-to-spend cycle for customers. Our goal is to be able to track the velocity between interactions and outcomes and to optimize this over time.
It’s important to also know that for some customers in their buying journey, velocity cannot be improved — i.e., we cannot get them to ‘spend’ faster than their current buying process will allow. We can reduce frictions, but there is an organic cycle time. Thus, another important perspective on Velocity is understanding how long it really takes from initial interaction through demand states to achieve commercial outcomes, making it a key input for forecasting and demand planning.
- Lift — This is the ultimate goal — driving greater CLV and greater CP. So, we want to be able to measure this at the customer, Conversation Track, and account levels, and we want to be able to tie it back to the mix of Levers that optimized net Lift to CLV — and did so with maximum profitability (i.e., Lift to CP).
Next Steps
Shifting from random acts of marketing to a perpetual growth engine model — operationalizing go‑to‑market around the customer journey — also changes the analytics we need to be able to optimize this engine. Thus, we need a new system of KPIs that can maximize our net Lift to CLV and to CP.
If you’re looking for more resources on go‑to‑market transformation and KPIs, see the content pieces linked below:
- A broader set of KPIs for the full customer journey
- Making the shift from tactical to strategic demand experience management
- ANNUITAS CEO Adam Needles breaks down the KPIs that matter in his Ignite B2B keynote
- The benefits of taking a Converged Growth approach to go-to-market
Want even more great Converged Growth and Chief Growth Officer content? This piece is part of our broader eBook, “The Chief Growth Officer’s Handbook.” Sign up now to be among the first to receive the complete book — coming soon.
