The RevOps Maturity Model
Revenue Operations is a journey best pursued with a roadmap built on a progressively phased RevOps maturity model.
The model sequences groups of capabilities and competencies. They are ranked in ascending maturity and organized in an evolutionary framework.
The Johnny Grow RevOps Maturity Model is a data-driven and asset-powered roadmap. It shows your current state and the stages to navigate to achieve greater efficiency, automation and revenue growth. Each stage advances the company’s strategy, processes and financial results.
It's born from revenue operations research findings. These findings group performance results into three archetypes of Best-in-Class (top 15%), Medians (middle 50%) and Laggards (lower 35%). The lessons extracted from the Best-in-Class create evidence-based best practices that provide performance benchmarks and prescriptive guidance.
The RevOps Maturity Model Roadmap
The below model can be used as a framework to vision, guide and drive progress toward measurable targets.
It's structured in a way that each investment builds on the prior and contributes to the next. Otherwise, improvements and investments are a series of one-off, disconnected fixes that are generally short lived and fail to create momentum.
Here's how to systematically advance your Revenue Operations and results.
Stage 1: Lagging
The initial stage is characterized by independent operating teams, manual processes and mostly reactive customer engagement.
There is a lack of alignment on shared goals. Instead, teams are organized by department and operate autonomously. Cross-functional communication is random, and collaboration is minimal.
Business systems operate mostly within departments, so data is siloed and cross functional business processes are disjointed.
Different departments patch together their own applications. Business systems have accumulated over time. They were typically acquired as a response to a need or problem and without an overarching tech strategy.
The company recognizes it needs to make customer engagement consistent to improve conversions and ensure customer experience objectives. But without process orchestration and technology automation customer engagement is mostly ad-hoc and reactive.
At this stage few companies have customer intelligence or commercial insights. They struggle with converting raw customer data into actionable insights. Our experience shows that most companies have volumes of customer data. For example, they have buyer digital footprints, recorded sales conversations, exchange data from email and calendars, and seller actions and behaviors in CRM. However, data resides in many places which complicates a data transformation process.
Without data transformation customer insights are not universally shared with others that could use them. Customer engagement is haphazard. Process automation is lost and downstream execution programs such as lead scoring, sales win plan development, and strategic account management are performed manually and inconsistently, if done at all.
Stage 2: Efficient
You need to see your company as customers see it. As a whole. Even though their journey will cross different departments they see the company as a single entity. So, at this stage, companies are eliminating clumsy departmental handoffs, fragmented services and other customer friction.
They are harmonizing cross functional business processes. Even better, they are architecting their processes based on what the customer is trying to accomplish.
This stage also sees a shift from a traditional sales funnel to a bow-tie funnel.
This change facilitates post-customer conversion actions to systemically grow customer share, lifetime value and retention. In fact, unless your sales processes are structured to get more value from existing customers, you're leaving money on the table and missing a growth multiplier.
Customer data is no longer stored in siloes and shadow systems. It is available to be shared outside of the department that created it.
Most companies will organize customer data into a 360-degree customer view in the CRM system. However, at this stage it's generally limited to demographic and firmographic information. The more valuable transactional, environmental, behavioral and social data appear in the next stage.
Customers are not homogenous, and they don't want to be treated as such. So, their top priorities and preferences must be captured and recorded as part of the 360-degree view. Techniques include customer segmentation, personas, voice of the customer, white space mapping, satisfaction measurements (NPS or CSAT), purchase history trends, customer health scores and many more.
With this customer-specific information, the company now delivers relevant, personalized and contextual communications, offers and engagement.
Stage 3: Leading
End-to-end processes are now simplified, streamlined and automated.
Simplified processes remove work. Streamlined processes remove bottlenecks and duplicate effort. Automated processes increase repeatability and accelerate results. Process optimization lowers labor and customer acquisition costs. It directly impacts the company's bottom line.
The CRM system now manages customer data in a rich 360-degree customer view. The data is segmented and organized so it can be quickly searched and easily filtered.
CRM tools are used to automatically deliver customer information to the person that can improve an employee action, a customer experience or a business outcome.
Customer intelligence is created by the transformation of raw customer data into metrics, values and insights.
These insights show things like what customers have purchased (purchase history), what they chose not to purchase (lost sale opportunities), what they have purchased from competitors (third party enriched data) and what they haven't purchased but need (white space).
Customer intelligence is used to improve marketing campaign conversions, lead qualifications, sales proposals and sale opportunity win rates. It's used to deliver differentiated customer experiences.
Data-driven and fact-based customer intelligence now replaces customer knowledge based on anecdotal occurrences, dated historical experience, plenty of assumptions, and personal bias.
Stage 4: Exceeding
A growth mindset permeates the company culture.
The company is now achieving operational excellence and a competitive advantage.
It consistently improves customer experiences, increases sales conversions, grows customer lifetime value, and accelerates revenue growth. The company continues to achieve higher revenue per employee, lower customer acquisition costs, and overall lower cost of sales.
Commercial insights include contextual recommendations, next best actions, and specific guidance that systemically advances the customer journey. That might include recommending the best offer, the right sales play, the most relevant content asset, or the right solution for a customer pain point.
For managers, it might include recommendations to reallocate sales time or resources where they will deliver the biggest impact.
At this stage customer insights shift from hindsight to foresight. Customer behaviors and responses can be accurately predicted.
Shared insights eliminate inter-departmental disconnects. For example, buyer digital footprints acquired by marketing are shared with sellers. This may include which emails, offers and types of content the prospect most engaged. Buyer expectations learned by sellers in the sales cycle are shared with customer service. This may include things like the buyer's internal constraints, timing considerations or blackout periods.
AI is now used in every department.
It harvests sales cycle data, customer data and historical data of comparable customers. It applies this data to determine things like which leads are qualified, which buyers will buy, which sale opportunities will close and which selling actions will advance an opportunity. It shows salespeople how they can better allocate their scarce time to produce better results. Its algorithmic calculations intelligently weigh all available data to deliver highly accurate recommendations that get better over time.
AI recommendations are delivered via email, display notifications and role-based dashboards.
AI-driven Insights provide analysis and visibility to customer health, pipeline progress, and sales forecast accuracy.
They show how to better engage a customer, remedy a variance, implement a course correction or make a more timely and informed decision. In fact, if your information is not continuously suggesting, adjusting or reprioritizing actions, it's not working.
The company now operates an agile and scalable revenue operations model. That's important because as the company grows, so do the growth-related complexities.
But standardized and automated end-to-end processes can accommodate growth without the inevitable firefighting. Even better, they can grow revenues faster than the internal resources needed to achieve that growth.
The More You Know the Faster You Grow
The RevOps maturity model brings structure, sequence and measurement for continuous improvements.
But as you might suspect, the overarching effort to unify cross-functional teams, automate end-to-end processes, integrate departmental systems, and share performance analytics is no easy effort.
Fortunately, there are partners that can help.
Johnny Grow is one of those partners. Rev Ops is our core competency.
Our staff of seasoned RevOps professionals bring research-based insights, evidence-based best practices and prescriptive methods to help clients evolve their revenue operations strategy and results.