- Revenue Technologies are purpose-built applications to drive company growth.
- The volume of these business applications can seem overwhelming. That's why a revenue technology stack (RevTech stack) is used to define the applications that most increase customer acquisitions, customer share and customer retention, and most contribute to the company growth strategy.
- A strategically designed Rev Tech stack replaces ad hoc and piecemeal systems with a holistic software portfolio that maximizes revenue growth, minimizes technology complexity and future-proofs technology investments.
A Revenue Technology stack, commonly referred to as a RevTech stack, is a group of synergistic business software applications and cloud services designed to accelerate company revenue.
It's often designed pursuant to an IT application strategy and managed as a technology portfolio. It includes apps to grow the sales funnel, convert leads to revenue, drive innovation, apply analytics and benefit from advanced technologies such as artificial intelligence.
The goal of a Rev Tech stack is to organize the most effective technologies that drive top line revenue growth. That means the apps that increase customer acquisitions, customer share and customer retention.
Without a RevTech stack, most companies acquire departmental and piecemeal systems to solve an urgent but isolated problem. That may help with the problem of the day, but quite often contributes to software sprawl, data siloes, lack of integration and temporary results. It also fails to contribute to a more strategic technology portfolio that aligns with the company's priorities.
Executives seeking business growth know that technology can help. What they may not know is which technologies and how they work together. The RevTech stack answers these questions.
The RevTech stack is a holistic approach to technology-enabled revenue generation. It consists of the following five tech domains.
Fill the Funnel Applications
Growing revenue starts by growing the sales funnel.
Growth technology research found that some of the most effective apps include the following:
- Marketing Automation Platforms (MAP) were the top ranked business application in this category
- Website optimization, including SEO, SEM and online conversion optimization
- Customer Data Platforms (CDP) for account and contact data enrichment and data management
- Digital engagement, for both leads and customers, including apps such as conversational marketing, real-time interaction orchestration, nurture campaigns and social marketing
- Omni-channel engagement and integrated, multi-channel campaigns, with cross-channel identity resolution and accurate campaign attribution
- Campaign portfolio management, for inbound and outbound campaigns; Account-based marketing (ABM) campaigns were ranked the most effective
- Dynamic customer segmentation for improved target audiences and increased relevancy, personalization and context for better offers, engagement and campaign conversions
- Marketing Resource Management (MRM) and Digital Asset Management (DAM) for streamlined and efficient business processes, resource management and asset management
That's a lot of apps, but fortunately most companies can grow their sales funnel with only a few. And because it's not realistic to acquire every helpful technology at once, a Rev Tech roadmap is needed to show how they fit together and how progressive applications build upon each other and can be acquired over a multiple year period. The roadmap is dynamic, linked to revenue outcomes and incurs adjustments during the journey.
Fill the funnel technologies are responsible for the lead to opportunity processes. They are sometimes illustrated in the lead management loop.
Convert the Funnel Applications
Convert the funnel growth technologies turn leads into won opportunities and revenue.
These business systems include:
- CRM Platforms
- Sales enablement apps (for sales training, content and coaching)
- Using the MAP to track digital footprints for explicit (behavior) scoring and customer sentiment detection
- Sales force automation for sales process orchestration or tasks such as predictive lead and opportunity scoring
- Recommendation engines, for things such as push-based content and next-best-action recommendations
- Alerts for real-time coaching
- CDP for buyer insights and customer intelligence
- Sales intelligence tools (i.e., LinkedIn Sales Navigator and ZoomInfo)
- Customer self-service apps
Each app adds value, but as mentioned with the prior category, not all apps are needed.
To get the most bang for your buck, you can apply customer intelligence and sales cycle data to identify the optimal combination of technologies and processes that will drive the biggest revenue growth.
The interactive dashboard below models sales uplift strategies, evidence-based best practices and technology tools to show which combination delivers the biggest increase to top line revenue.
It's this type of data-driven revenue engineering that shifts planning from guesswork to systemic execution and precisely forecasted results.
There is a direct and inextricable link between innovation and business growth. The opposite is also true. A lack of innovation accelerates product commoditization and lowers margins and revenue generation.
But how do you improve or create products and services that are enthusiastically embraced by customers? First, you recognize innovation is born from customer problems. Second, you ask customers what problems are worth solving. And technology is needed to do this at scale.
Your innovation ideation is only as good as your customer intelligence, which should reside in your CRM system. Your CRM software can enrich your customer data model and use tools to continuously gather customer input, problems and feedback.
Sample tools include Voice of the Customer (VoC), social listening, crowdsourcing, speech analytics, conversational marketing and conversational intelligence. These and other tools can automatically capture, tabulate and route customer data that feeds the ideation engine.
Below is an illustrative example of how we use VoC.
Other data sources include CRM transactions such as customer support cases. You can also include bots and virtual agents on the website or social channels that capture data from visitors who are not customers and thereby greatly expand the innovation data set.
Customers are not homogenous so the data should be categorized by customer segment and persona.
Once the data is captured it a simple process to tabulate, weight, segment and prioritize the top ranked problems that are then ready for innovation solutioning.
Most low growth companies are data rich and information poor.
A recurring pattern among low growth companies is that they don't know what actions and systems deliver the biggest revenue uplift, so they pursue tactics based on speculative ideas and experimentation. They most often revert to what they know instead of what is most effective. This results in incremental and often temporary results and preserves the status quo.
The highest growth companies plot revenue growth strategies using data and predictive analytics.
Revenue analytics show how data rolls up throughout the enterprise to produce financial results. It allows pro forma modeling to compare alternatives and trade-offs and plot the shortest path to the desired destination.
Revenue analytics are created in a 4-step sequence.
- Data transformation is needed to convert data from an unused byproduct to actionable information. Data patterns and trajectories shift data from historical to predictive. When done at scale, this transforms data from a raw material to the company's most valuable asset.
- Tools such as dashboards, reports and predictive analytics are needed to get the right information to the right person at the right time. This allows them to improve a customer interaction, make a more informed decision or immediately intervene to resolve a performance deviation.
- Information insights are needed to elevate information from being merely interesting to inducing action. Information is powerless until it creates action.
- A data driven culture is one of the important growth technology best practices. That's because it is needed to deliver the last mile of the journey. Management must promote a culture that shifts decision making from intuitive, gut-based and subjective decisions to data-driven, fact-based and objective decisions. This is often done with what many analytics experts call at DDOM (data driven operating model).
Converting data to information that shows precisely how to maximize company growth in the least time and cost is a complex undertaking, which is why those who succeed will outperform those who don't.
Artificial Intelligence (AI) creates insights based on algorithms that sift through large volumes of customer data. It identifies information that correlates with revenue growth outcomes and makes specific recommendations to improve or achieve those outcomes.
AI is the technology to transition a customer, product and sales data depository into a predictor of customer behaviors, creator of customer insights and facilitator of increased customer acquisitions and upsell.
When AI integrates with daily work processes, staff become more efficient and effective. For example, sales managers can use AI to surface the leads being neglected, the sale opportunities that need attention and the forecasted deals that are at risk. Sales staff benefit from guided selling recommendations.
Marketers use AI to identify the highest performing mix of campaigns or determine the best combination of content, offer and channel to maximize conversions for each target audience. They may use an AI-driven product recommendation engine to advise high a propensity product for upsell, cross-sell or bundle by customer segment or for an individual customer.
AI is especially effective at recommending next-best-offers and next-best-actions based on account and contact attributes (i.e., account firmographics, contact demographics, customer segmentation) and online behaviors (i.e., digital footprints, buyer propensity).
Contact center managers may use AI to personalize customer engagement, deliver faster resolutions, lower cost to serve, improve customer experiences and scale customer service operations. And it doesn't just benefit customers; it delivers an improved agent experience that increases staff productivity and decreases agent turnover.
The above examples illustrate that AI deployments should be designed pursuant to the biggest revenue uplift opportunities or the biggest problems that need to be solved. Below are some additional use case scenarios.
AI is no longer in the early days. We crossed that chasm and any company not using AI today is substituting labor for technology. The question for business leaders standing the sidelines is not if AI will impact their business, but how and when.
Market leading CRM systems such as Microsoft Dynamics 365 and Salesforce have removed technical barriers with simpler tools, such as Azure Machine Learning and Salesforce Einstein, to put AI applications in the hands of business analysts and power users.
Data is the fuel, AI is the engine, and insights are the destination.
Two RevTech Success Factors
The previously referenced revenue technology research uncovered some unexpected results. Most Rev Tech applications were rated as both effective and ineffective by large numbers of respondents. Even those revenue technologies with the highest scores had large numbers of low scores and vice versa.
The data suggests that most revenue systems could be effective or ineffective depending on how they are designed and implemented. This suggests starting with growth technology best practices can deliver a big impact.
The Best-in-Class archetype data surfaced two influential success factors. First, when revenue technologies are tightly aligned with revenue growth outcomes, they are much more likely to be successful.
Second, a centralized rather than federated technology management model is extremely helpful in achieving standardized data structures, cross-departmental business process automation, enterprise-wide information reporting and much lower system support costs.
Finally, RevTech is delivering exceptional ROI and that is driving some skyrocketing growth. But it's still a nascent and fragmented software technology market. There's no single vendor with a majority market share.
To manage this risk, RevTech buyers should consider a mix of platform and point solutions and above all, define your RevTech stack pursuant to your business growth plan. Don't be tempted to start with technology as that puts the cart in front of the horse.