As a senior finance executive, you’re being tasked with finding ways to accelerate growth and increase profits, and a better understanding of data points like cost efficiency and customer acquisition costs are integral to that goal. This is particularly true in information, media, and event businesses - and at marketing service providers that serve them - because data is the lifeblood of your business. 

However, in our recent survey of over 100 leaders in the industry, we found that less than ½ of all senior executives have high confidence in the trustworthiness of their company’s data. ⅔ of these executives included CEOs and CFOS. If you and your CEO do not have high confidence in your data, then there is a fundamental issue to tackle. 

It begins with a Single Source of Truth for your data (SSOT).

According to Accenture, 76% of CFOs agree that their organization will struggle to meet objectives without one version of the truth across business units.

What is an SSOT, exactly? It is a trusted, unified data architecture and system of management that supports comprehensive, automated financial reporting. It gives finance leaders a place to manage pro forma analysis. Through it, users can clearly see what data has changed, how it was changed, and who made it. Having an SSOT means no longer needing multiple parties to review and validate data sets each time a report needs to be published or shared. Instead, users can feel confident about the integrity of their data, with the information automatically pre-verified.

The Benefits of SSOT for CFOs and Other leaders

• Improve data quality and integrity. Minimize human error.
• Save money. Our internal analysis indicates that if organizations optimized data across functions, they could save $250,000 or more in operating expenses annually, through a reduction of resource time expended and the removal of redundant and underutilized systems.
• Save time.. Data is difficult to manage when it is spread across various systems. Managing decentralized data in silos forces staff to devote significant time to data preparation activities. According to a CrowdFlower survey, data analysts spend 19 percent of their time collecting data and an additional 60 percent of their time cleaning and organizing the dataset
• Improve accuracy of forecasting. Enough said.
• More visibility to make data-driven decisions and add value. When you lack visibility due to data issues, it can limit your ability to see the complete picture of your company’s revenue, sales pipeline, marketing conversion rates, and more. This limited field of vision hurts your ability to add value. A recent report by Accenture found that 81 percent of all CFOs see identifying and targeting areas of new value across the business as one of their main responsibilities.

One example of data-driven decisions from an SSOT is improving your unit economics to improve profitability. Over 8 in 10 leaders at Information, Media and Event companies believe that challenges with improving unit economics based on analytics will have a significant or very significant impact on growth. The inability to get the data house in order is affecting your growth.  You need to synthesize customer data, program data, financial data and other info from disparate sources to do it effectively. Again, it is challenging but very doable, with the right data strategy and platforms. Naturally, we’d be remiss if we didn’t mention Insightify, a data platform specifically designed to meet these and other needs for executives in our industry.

CFOs are Leading the way and Our Roadmap Can Help

In order to minimize the challenges of data management and give CFOs and finance teams access to reliable intelligence, many organizations are moving toward a single source of truth. Increasingly, CFOs are becoming more influential in getting there.

What’s the best path?

Download our E-Book:  Achieving a Single Source of Truth: A New Standard for CFOs and Finance Teams.

We’ll take you through what’s involved in the process, drawing from our 30 years of experience with data strategy and data-driven growth in the industry.

Need help?

H2K Labs has developed a data-first, modular, continuous improvement framework to help customers make revenue a core business process and a center of operational excellence. 

In our first podcast, the CEO of H2K Labs, Heather Holst-Knudsen, talked about data and revenue with Chad Rose, the Managing Partner of Treehouse Technology Group, one of our technology partners.  Chad started as a data engineer at S&P, where he learned how to build predictive models and manage data efficiently. With Phil West and Darton Rose, they founded Treehouse Technology Group in 2014 to serve the middle market's demand for better data and analytics. They identified a gap in the BI landscape and developed InsightOut to deliver enterprise-level analytics at an affordable cost. Heather noticed the same issues within the media, events, and business information industry. No data platform truly understood the complex business environment. 

One-Sided vs. Two-Sided Business Models

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While Chad’s experience has been focused on traditional business models, also known as one-sided businesses, there are some similar issues to the event, media, and business information industry. For example, if a SaaS business is focused on recurring revenue you need to monitor, you need to have your head around the metrics impacting that renewal churn, renewals, and AR. Those metrics are in and of themselves often very difficult to report on as so much of the data around that revenue stream sits in different sources. Another example is events, which really are little independent companies. The sales cycle depends on when the event is happening and works like an individual fiscal year you're managing. Finally, mergers and acquisitions are pretty common within the InsighOut customer base, and it's also common in media and events. 

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According to Chad, these businesses (media, events, and business information) “with so much in terms of data complexity, trying to meet the targets that they're trying to achieve, and with different business units each acting differently, make it harder to get a full picture of the entire business and just to get an ongoing pulse through an analytics implementation.” The data that's being generated is very much outside the CRM in many cases because of customer behaviors or all the people that are connecting with an event or a media customer. 

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In fact, each of the business units and the brands in those business units really operates like a mini business, with its own P&L, unique fiscal requirements, etc. The data that's being generated holds a lot of value which is being underutilized. It is very overwhelming in terms of figuring out where to start activating the data on the revenue side. 

Challenges in developing an SSOT

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A common misnomer is a lot of businesses feel they can't start an analytics initiative or think they can get anything out of the data. It is often viewed as a linear process where you have to get the data in shape and then be able to start reporting on it or be able to start getting value out of it. You can often do the two in parallel, and they actually reinforce each other if done properly.

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Generally, the types of issues in creating an SSOT are not specific to media, events, and business information but are more around the data cleanliness and the data management practices within the organizations, or lack thereof. A lot of organizations are trying to get a better handle on how to input data and get the team, sales team, to input data consistently within the CRM. 

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However, revenue recognition is a big challenge unique to media, events, and business information. You have these products you're selling, and you have to deliver on what has been sold to recognize the revenue. Businesses need to have a handle on what has been delivered against what's been sold, booked but not yet delivered, those types of scenarios are within any business, but particularly in media, events, and business information; seem to be a pretty big challenge. You have to reconcile what's being done on the operational side against what's been done on the sales side and the financial side. Further, a recurring revenue business model can be pretty challenging, especially if you don't have good systems. Many media and events companies, want people to subscribe to a data product which adds even more complexity to revenue recognition. There hasn’t been a platform developed, until now, that can manage that data complexity. 

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Another challenge in the industry is product customization, for example, industry sectors they serve, like manufacturing, retail, food and beverage, or life sciences, and how their products are structured. Normalizing the data becomes more complex. These industries also have more systems than most where data might be stored; registration, lead scanning, and ad operations, just to name a few. So there are quite a few potential data sets, and those exist across a variety of systems and business units. It isn't critical to start with capturing all of the data when you're developing a single source of truth. But, "I think that within this space is pretty unique in that there's a lot of untapped potential within all those different data sets that if you can establish, it could be quite beneficial," according to Chad.

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Simplifying the data landscape

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The data landscape has changed dramatically, both in toolsets and organizational operations. It's gotten more complex as a business can have hundreds of tools being used for different needs, marketing systems, CRM, sales systems, and finance systems, but you might also have different systems managing your websites or different systems managing your customer engagement, and customer success. And so there are just more and more of those that each company is signing up for and using and leveraging. 

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From a user perspective, it is great as these tools are highly specialized, from a data perspective, it means data is siloed in many different systems and, therefore, highly unusable. Older business intelligence platforms are not designed to be flexible, you need to have a team of engineers to do the implementation and configuration. However, today, there are many more self-service platforms that are much easier to manage and use. Many of them have integrations using APIs or ways to automatically integrate, and many organizations do not leverage the connections.

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Today business intelligence platforms are AI-enabled or becoming increasingly AI-enabled, and they are much faster to get to value and actual successful implementation. Additionally, some elements are even newer in terms of things like reverse ETL or write-backs, which enables businesses to push updates from the single source of truth, from your reporting system back into your source systems. 

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For the end user, you have aggregated all the data, and for example, you've highlighted some churn risks within the dataset, it is pushed back into the CRM, and the sales team gets notified and can action the issue. Until very recently, this was not possible, or you wouldn't be able to do it without having a big engineering team. As the systems have become open with their APIs, as the technology has become more AI-enabled, the result is organizations can achieve better results much faster, and far cheaper. And you can do so with the modern stack that exists today, versus trying to implement the tools designed in prior ecosystems or prior technology stacks.

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In reality, older business intelligence platforms are expensive for mid-market companies. Especially in media and events where the data is complex and lives in multiple layers of platforms. Large organizations can afford to invest in business intelligence solutions, whereas smaller companies cannot. However, that has changed in recent years, in part due to SaaS companies and the availability of products in the market. 

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The decision of which tools to use is where we see many companies go wrong. They are more likely to purchase or hire the well-known name used by enterprises without understanding the implications and the level of support required to implement those tools. As a result, many companies get stuck and end up with failed implementations of powerful tools that are simply not the right fit. 

Operationalizing data in your organization

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One of the biggest pillars of successful data monetization is democratizing data. It has to be operationalized and part of the daily flow of work. 

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One of the assumptions when InsightOut, and now Insightify, was developed is users are not data scientists, not technically trained, and not experts in determining the right key performance indicators (KPIs), or what to do with the data presented to them. Users want the same user-friendly experience in their business tools as they have in their personal tools. 

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InsightOut was built to simplify the presentation of data and make it less like a dashboard and more like an application. We wanted to strike a balance between flexibility and simplicity. The platform needed to give end users the data they need, when needed, and in a format, they can easily understand and use. 

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Many analytics solutions have failed because they simply provide a few charts and graphs without solving the automation problem or giving users the necessary answers. 

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When we built Insightify, we focused on a handful of visualizations business users and executives commonly use: , , and intelligence. These visualizations are highly customizable, allowing users to slice, dice, and display the data in a way that automates data preparation and report generation while providing all the necessary data points. 

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The level of adoption of InsightOut has been extremely high within the companies that have implemented it, and it has become one of their most popular tools for day-to-day use. The need for exporting data to Excel has significantly decreased compared to other tools that fall short of meeting customer needs. 

The future of data science in business

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Over many discussions with clients, Chad has discovered that the role of data science will change. The change is in part due to advances within AI, or the capabilities to interpret data and decipher what's happening within the datasets, which are becoming very powerful. This will inevitably lead to a situation where a business user should be able to log into a system that has pulled in these different datasets automatically, interpreted the data, and is giving you the outliers or the trends or the specific data points you should be paying attention to today, or for this week, or for this month. 

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There will always be data scientists at the very edge of the development of analytics where they're looking at coming up with brand new ways of determining what's happening or investigating brand new datasets. A lot of companies who share similar business models should and will have the predictive models automated to a certain degree ten years down the road. In the past, if a middle-market company looking to become an enterprise-level company, they would go out and hire engineers to stitch together a bunch of data. Chad doesn’t believe that will happen going forward. He feels the applications will become exceedingly powerful to help enable business users as they intend to make use of this data.

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Becoming a data-driven organization

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A shared problem across all businesses doesn't matter what type, is that becoming data-driven is a critical imperative if you're going to succeed and thrive in the future. However, it requires financial capital, time commitment, leadership commitment, persistence, and in most cases, significant culture and organizational change. Many times businesses are unable (or unwilling) to justify this level of investment. One of the areas H2K Labs does is help customers define success, identify the business value, and ensure it's measured across the journey. According to Chad, businesses need to start small to become a data-driven organization.

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The problem with these types of transformations will only happen and often only start from the executive level. The CFO, CEO, and that level of the executives within the business have to lead change, and they are typically the ones who push transformation, although they are also the ones who might not know or may know the least in terms of technically what's available and what's possible for them to do within their business as it relates to data.

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The view is I want to become data-driven. I wanna aggregate this data, or I want to get to some sort of single source of truth. The reality is there are thousand different systems, the team is telling them the data is a mess and not clean. So the perception becomes this is going to be a giant lift, which has to be a giant lift, but it really doesn't. If you approach the goal with that mindset, then you're most likely going to fail. 

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The solution is simple, start small. Identify the key things which need to take place within the business on a monthly, quarterly basis. If you don't have the technology in place, you're probably doing that manually today. If you're doing that manually, there's a clear ROI to automate that. If you're not automating that, you will be behind very quickly. 

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You start very small, and you automate what you can within the existing reports. Even within those, you must have a certain eye toward what's worth automating. Then you've alleviated work from your team so they can see the value. You have a working solution that's answering some of the questions you are otherwise getting from manual exercises, and you start to generate some buy-in. 

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Once you've accomplished the first goal, the team can see a little bit more about what's possible, and they will start asking other questions as to what could come next. And if you take those iterative steps, one-month, two-month types of projects that should get a result at the end of those and you deliver those, then you build that internal support, you clear the ROI threshold, and you can continue to build on each win.

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Introduction to The Revenue Room™

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The Revenue Room™ empowers mid-market B2B businesses to architect modern revenue organizations using innovative thinking and data-driven strategies. Developed by H2K Labs, this exclusive framework is specifically designed for midsized companies encountering data complexities, such as diverse revenue streams, multi-sided business models, and integrated offerings encompassing services and products. Businesses grappling with data complexity include media, events, buyer-seller marketplaces, business information, and marketing technology and service providers.

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The Imperative of Data-Driven Revenue Growth

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In an environment marked by inflation, achieving revenue growth has become a top priority for companies. However, to ensure lasting impact, businesses must focus on retaining and growing their existing customer base, emphasizing high-quality revenue, and investing in capability building across all revenue-critical functions. This requires adopting an approach where capabilities build upon each other, gaining strength over time.

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The Compelling Financial Returns of Data-Driven Selling

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The transition to data-driven selling is not just a trend but a necessity. According to the Gartner Future of Sales 2025 Report, 60% of B2B sales organizations will shift from intuition-based to data-driven selling by 2025. This shift promises substantial financial returns, as evidenced by data from Boston Consulting Group. Companies with data-driven revenue organizations can expect a 10–20% increase in sales productivity, a 15–20% increase in customer satisfaction, a 30% reduction in GTM expenses, 19% faster growth, 15% more profits. Data from Forrester Consulting shows that public companies enjoy 71% improvement in stock performance.

The Urgency of Revenue Modernization in a Changing World

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Several factors underscore the imperative for modernization in the revenue organization. As the business landscape continues to evolve at a rapid pace, new and previously unknown competitors continuously emerge, and radical changes reshape your customer’s preferences and the way they consume information and purchase. Economic challenges and evolving customer demands further intensify the pressure felt by businesses while investors and boards increasingly demand results. 

Data assumes a crucial role, transcending its status as a mere tool and becoming a pivotal driver of operational efficiency, margin improvement, value creation, revenue generation, and competitive advantage.

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Recognizing the Signs for Modernizing Your Revenue Organization

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There are clear metrics you can track to see if your revenue organization requires modernization. Some easy ones to monitor include consistently missed forecasts (especially those presented to the board), decreasing quota attainment levels, increasing churn rates, maverick discounting, decreased net revenue retention, low NPS and CSAT scores, and higher-than-expected attrition rates on the revenue team.

In addition to the readily noticeable signals, some are more elusive or recognized but deliberately unacknowledged by the business due to the overwhelming magnitude of the anticipated change.

Inefficiency in Data Management: The extensive time, personnel, and effort required to aggregate, cleanse, organize, and analyze data can be daunting and inefficient. This process, though necessary for deriving insights, is often overwhelming and takes valuable time away from strategic activities that could drive the business forward. Reports are inconsistent, unreliable, and incredibly frustrating to employees. 

Missed Revenue Opportunities: Often, there's a persistent concern about potential revenue slipping through the cracks due to inadequate data insights. For example, the finance team might struggle to identify the reasons for customer attrition or to effectively track how well a media business is offsetting print decline with digital growth. Understanding lifetime value is also another challenge. 

Challenges in Key Data Metrics Extraction: Investors and boards demand more frequent and more complicated data insights such as gross and net revenue retention, ARPC (average revenue per customer), and audience engagement metrics over a rolling 12-month period. Finance teams and/or CROs cannot produce these reports and data quickly and effectively.

“I know no one apparently gets fired for choosing IBM, but I do know a few CEOs who did get fired for providing imprecise forecasts to the board four times in a row.” - Me at a recent breakfast with a private equity investor. The Revenue Room™ is here to help.  

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Mastering Revenue Kaizen: The Path to Excellence and Continuous Improvement

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To be successful in making revenue a center of excellence, businesses must embrace Revenue Kaizen.

Revenue Kaizen is an incremental method for enhancing revenue outcomes and is essential for continuous improvement in revenue strategies. It involves making small but significant improvements, decreasing waste, gaining buy-in, iterating processes, and encouraging embedded learning and skills development. The importance of revenue kaizen lies in its focus on continuous process improvement, risk-taking, change management, and constant evaluation and iteration.

The Net Net

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The journey to revenue excellence is complex and challenging but crucial for business success. Identifying and understanding the red flags, embracing the principles of revenue kaizen, and investing in data transformation and advanced business intelligence help B2B midsized businesses navigate the complexities of the modern economy and move towards becoming data-driven, efficient and profitable organizations. Request a Complimentary Strategy Call

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In today's marketplace, data is more valuable than ever before. Businesses that use data analytics and AI to inform and support their decisions often outperform their competitors who rely on gut instincts alone. A recent HBR Pulse survey on transforming data into business value through analytics and AI found that 75% of respondents say having a data-driven culture is critical to their organization's overall success. But, where is your go-to-market source of truth? How can you ensure that the data you are using is accurate and up-to-date? In this post, we'll explore the survey results and discuss some effective strategies for transforming data into business value.

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Unified Source of Truth in the Cloud

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The survey found that organizations already highly data-driven before the pandemic doubled down and became even more data-driven while struggling organizations fell further behind. Leaders in the survey invested in and accelerated data, analytics, and AI initiatives at higher levels than their counterparts. The reason for this is obvious: with COVID-19 completely transforming how businesses operate in weeks, executives were more inclined to adopt data-driven cultures and accelerate industry-specific solutions, including AI.

In addition, it was discovered that having a unified data cloud is important for organizations that desire significant value from their approach. A Unified Cloud approach enables businesses to share data more efficiently and effectively, increasing collaboration and reducing redundancy, resulting in the ability to identify opportunities for innovation, market entry, competition, scale efficiency and agility, reduce risk, and improve operating margins.

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The Challenge and Critical Need for Data Democratization

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“While it appears that many companies have their data management acts together, in fact, many don’t,” says Doug Levin, executive in residence at Harvard Business School and lecturer at the Harvard Business Analytics Program.

However, the survey found that organizations still struggling to keep up often face challenges in analyzing data across multiple sources and data quality issues.  In the past, data silos have been creating barriers to analyzing data across the whole corporation and restricting access to real-time data. Sourcing data from various places remains challenging, including complexities in integrating/consolidating data from several systems/sources. Additionally, data quality issues often arise from poor data governance practices.

Multicloud adoption is emerging as a popular solution, but it presents challenges in data governance/management and service integration and management. However, democratizing access to data and analytics tools and AI capabilities is key to remaining competitive.

Bye Bye Data Chaos, Hello SOT in the Cloud

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Organizations with data complexity due to high levels of daily transactions, mergers and acquisitions, diverse customer segments, and revenue streams have even greater challenges.

CRM and data warehouses are used for data management, but they are not well-suited for most B2B organizations. For instance, a CRM cannot handle detailed digital data such as product analytics, payment/booking/subscription information, call intelligence data, among other areas. Moreover, data warehouses limit you to high-level dashboards that provide minimal insights, or you have to hire a data science and engineering team that can translate the data into valuable insights. Organizations in highly transactional environments with multiple revenue sources and customer segments such as business information, media, events, and marketing platforms require more innovative approaches to data management and reporting.

Now organizations can use end-to-end data management and advanced analytic solutions such as Insightify. Insightify sits on top of core systems such as CRM, Marketing Automation, Finance, Operations, CDP, and even existing business intelligence tools and seamlessly aggregates data from decentralized sources, unifying it into a Single Source of Truth (SSOT) at the platform level. Insightify preserves data in its original format at its source so that businesses can avoid the high costs and risk levels associated with waterfall-level data transformation projects.

Using this unique approach, users are empowered with high-level dashboards and visualization tools coupled with in-depth drill-down capabilities to diagnose, interpret, and prescribe actions and decisions.

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Measuring  Financial Returns

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Finally, the survey highlights the importance of measuring and reporting data and analytics investments, business value, or outcomes. Investing in data and analytics is only useful if it can deliver concrete business outcomes, such as new product/service introduction, operational efficiency, customer satisfaction, revenue, and market share. Leaders' organizations reported improved performance in each of these key areas. Maximizing business value from data requires leadership support and enterprise-wide strategies for data and analytics.

In conclusion, organizations that prioritize their data analytics initiatives can expect significant returns. With data-driven cultures becoming must-have requirements for modern-day business, businesses must ensure they have a significant head start over their competitors. Leaders who invest in data, analytics, and AI initiatives position themselves to address the effects of disruptions while driving innovation effectively. The right approach, using the correct data from the right platform, can create significant business power. Organizations that utilize effective strategies for transforming data into business value can improve their operational efficiency, customer satisfaction, revenue, and market share while also 21differentiating themselves from their competitors.

In our previous blog, we delved into The Revenue Room™ and Revenue Kaizen, highlighting the immense financial returns of centering revenue as a focal point of excellence in B2B businesses. Now, let's explore why revenue is your most critical business process and the guiding principles of The Revenue Room™ Operating Framework.

Revenue: Your Most Critical and Vital Business Process

For many businesses, sales are often seen as an immediate outcome rather than a process that needs careful management. This perspective is particularly prevalent in companies formed through acquisitions, owned by private equity, or that are very decentralized. These companies often suffer from fragmented and siloed sales organizations and processes. This fragmentation leads to challenges in controlling, measuring, scaling, and optimizing revenue goals, resulting in lost revenue, wallet share, and market share. Therefore, transforming these outdated, siloed sales practices into a modern, data-driven center of revenue excellence is crucial.

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The Revenue Room™ Operating Principles

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1. Leadership-Driven Transformation

Transformation starts with leadership. Leaders must define and communicate a clear vision and mission for driving transformation and articulate how it will benefit individuals, teams, and the business. This communication needs to be consistent and ongoing.

“A key foundation for change success lies in data-driven leadership. The report finds that leaders who explain the benefits of data use and lead by example can increase the likelihood of change success by 23 percent and have been shown to increase employees’ willingness to work in a data-driven manner.” - Cap Gemini, Data-Driven Change Management is Crucial for Successful Transformation, January 2023 

2. Data-Driven Decisions

The journey must be fueled by data. Utilizing a single source of revenue truth coupled with advanced business intelligence enables the activation of analytics, predictive insights, risk and opportunity identification, and embedding data-driven decision-making into daily workflows.

3. Defining Success: Transparent Metrics & KPIs

Smart KPIs are critical to data transformation for a few compelling reasons: focused direction, improved decision-making, enhanced accountability, performance tracking, improved employee engagement and alignment, continuous improvement and risk management.

Establish clear, transparent, measurable objectives and performance metrics to evaluate the success of revenue initiatives. This helps in tracking progress and making necessary adjustments.

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4. Effective Revenue Governance

It’s essential to exercise control over all customer-facing, revenue-generating activities. This includes establishing clear metrics, definitions, functional accountability and responsibility, and processes.

Revenue governance in a business context refers to the policies, procedures, and standards to manage and oversee all aspects of revenue generation, recognition, and reporting within an organization. It is a comprehensive approach that ensures accuracy, consistency, and compliance in revenue-related processes. 

Revenue governance includes: revenue recognition, pricing and discounting, sales and contract management, internal controls and audits, pipeline management, GTM motions, data management and reporting standards, risk management, cross-functional collaboration, technology and systems, compliance and regulatory requirements, technology and systems, training and development and performance monitoring. 

5. Collaboration Across Functions

Align every revenue-critical employee across various departments such as sales, marketing, finance, customer success, product, and operations. This ensures a unified focus on goals and collaborative efforts in opportunity identification, acquisition, retention, and expansion.

In B2B information environments, collaboration cuts across sales marketing, audience marketing, sales, customer success, content, operations and finance.
Yes, even content and operations need to connect to revenue directly.

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6. Embracing Evolutionary Transformation

Transformation is not instantaneous; it's an evolutionary process that unfolds over time. It requires continuous learning, experimentation, risk-taking, and a collaborative team spirit. And again, this must start from the top and permeate every facet of the revenue organization.

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Think Revenue Kaizen. 

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Interested in learning about Revenue Kaizen?

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7. Forecasting as a Core Competency

Covid proved that Mike Tyson was right when he said, “Everyone has a plan until they get punched in the mouth.” 

Customer behaviors and demands are in a constant state of flux, rendering prior assumptions inadequate for future predictions. To adapt to these changes, forecasting must become a fundamental skill, placing significant emphasis on analyzing data from diverse and even unconventional sources. This analysis not only allows us to discern the intentions of our customers but also to anticipate shifts in their demographics and how we can deliver value to them.

Predictive and prescriptive forecasting should be a core competency across all areas, including sales, revenue lifecycle, and operational performance. Forecasting should be in lockstep with pipeline motions.

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8. Customer-Centricity

“Customer-centric businesses are 60% more profitable than their counterparts.” - Deloitte The value of experience:  How the C-suite values customer  experience in the digital age

Customer-centricity is a common theme among market leaders and includes keeping the customer at the center of all revenue initiatives. This involves understanding their needs, pain points, and buying behaviors. 

Impact on revenue includes: 

• Personalized marketing and sales
• Enhanced customer experience and service
• Data-driven product development
• Predictive analytics for upselling and cross-selling
• Optimizing pricing strategies
• Real-time feedback and agile response
• Better risk management

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9. Transparency and Accountability

Maintain transparency in revenue performance throughout the organization. This should include metrics that reflect actual performance and the impact of transformational changes. Reporting, analytics, and data visualization should be made available through governed business intelligence dashboards to all.

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10. Focusing on People

Recognize the need for upskilling or reskilling in certain roles and skills, while others may require new talent acquisition. Training and upskilling, especially in the area of data fluency, needs to be uniform within the context of functional roles as well as from a business-wide perspective. 

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The Net Net

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Understanding and implementing The Revenue Room™ Operating Principles is crucial for any business that aims to excel in revenue generation. By focusing on these principles, companies can transform their revenue processes, making them more efficient, data-driven, and customer-centric. This approach not only revitalizes outdated practices but also positions the company for sustainable growth and profitability in an increasingly competitive and complex business landscape.

The Revenue Room™ is here to help. Sign up here to get alerted when we publish our next blog.

About The Author

Heather Holst-Knudsen has deep roots in the B2B world, growing up in the renowned Thomas Publishing Company. With years of experience and a passion for the industry, she has significantly contributed to digital innovation and monetizing audience engagement. Holst-Knudsen's expertise led her to found H2K Labs, specializing in generating financial returns from data and driving revenue and profitability.

Have a question or want to share your perspective? Email me and let’s set up a time to speak.  

Follow me on:

‍‍LinkedIn  &  Twitter

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In our preceding blogs, we've shed light on The Revenue Room™ and Revenue Kaizen, presenting pivotal operating principles to elevate revenue into a center of excellence in B2B enterprises. Our focus now shifts to the intricate components that make revenue an essential business process, exploring how to implement and manage these elements effectively.

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The Essence of Revenue as a Business Process

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Revenue Operating Plan Steps

Following the steps of the Revenue Operating Plan is crucial, especially in the B2B sector, as it lays the groundwork for a systematic and efficient approach to revenue generation. By aligning products with customer needs and demands, and engaging effectively with target segments, businesses ensure that their offerings are both relevant and compelling. The execution phase, which focuses on managing the sales pipeline, leverages data-driven strategies to optimize sales processes and reduce revenue risks. The delivery stage is key to fulfilling customer expectations and building long-term value, fostering customer loyalty and retention. Finally, the expansion phase concentrates on growing business within the existing customer base, through upselling and cross-selling, which is essential for sustainable growth. The iterative nature of the 'Rinse, Lather, Repeat' step, using data to continuously refine and scale operations, ensures that the business stays agile and responsive to market changes. Implementing these steps effectively results in a robust, customer-centric revenue model that drives business success and resilience.

• Build: Align products with customer needs and demands, ensuring future offerings are equally aligned.
• Engage: Generate demand within target segments and ensure alignment across marketing, business development, sales, and product teams. This stage is crucial for pipeline development and acceleration.
• Execute: Optimize pipeline management for predictable closures. Utilize data for identifying risks and seizing opportunities while enabling sales leaders to provide real-time coaching and deal guidance.
• Deliver: Commit to delivering value that exceeds customer expectations, using data to pinpoint product opportunities and unmet needs.
• Expand: Focus on retaining and enlarging your client base through upselling and cross-selling within existing accounts.
• Rinse, Lather, Repeat: Continually refine and scale processes using data-driven insights.

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Aligning Revenue-Critical Roles in Phases

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Implementing a phased approach to align revenue-critical roles is vital. Starting with the most obvious alignments, the process evolves through four distinct phases, each adding more departments into the alignment, from Sales and Customer Success to including Marketing, Operations, and Product teams. Data is pivotal in this alignment, fostering collaborative, data-driven decision-making.

"Revenue critical roles'' refer to key organizational functions that directly influence and drive revenue generation. These roles are pivotal in ensuring the financial success of a company. In the B2B information sector, the roles are more expansive than in other B2B businesses. They span across various departments, each contributing uniquely to the revenue stream. Here's a breakdown of some of these roles and their primary functions:

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SALES

Function:  Responsible for directly selling products or services to customers. They play a crucial role in meeting sales targets, developing sales strategies, managing the sales team, and building relationships with key clients.

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SALES MARKETING

Function: Focus on creating and implementing marketing strategies that enhance brand recognition and generate leads for the sales team. Their role involves market research, campaign creation, digital marketing, and tracking the effectiveness of marketing efforts in terms of ROI and lead generation.

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BUSINESS DEVELOPMENT

 Function: Tasked with identifying new business opportunities, partnerships, and markets. They are crucial in expanding the company’s client base and finding new revenue streams. Their role often overlaps with sales and marketing and while common in most B2B businesses, not as common in B2B information sectors.

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CUSTOMER SUCCESS & OPERATIONS

Function: Ensure customer satisfaction and retention, which is critical for recurring revenue and upselling opportunities. They work to understand customer needs, address issues, and provide solutions that enhance the customer experience. In B2B information, customer operations includes:

• Digital advertising operations
• Demand generation operations
• Event operations
• Subscription management
• Membership management
• And more

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‍REVENUE OPERATIONS

Function: Oversee the integration and alignment of sales, marketing, and customer success operations. They are crucial for streamlining processes, optimizing the sales funnel, and ensuring that the different departments work cohesively towards revenue goals.

PRODUCT

Function: Oversee the development and lifecycle of a product. Product teams ensure that products meet market demands and customer needs, directly influencing the sales potential of the product. In the world of business information - eg.  media, events, price reporting, marketplaces - the product eco-system includes but is not limited to:

1. Audience: Build and sustain a viewership, readership or attendance base, which in turn can drive advertising revenue, sponsorship revenue, tradeshow revenue, subscriptions, and brand partnerships.
2. Content: Position the company as a trusted source of information. Create and curate relevant, high-quality content that meets the specific needs and interests of their business audience. In price 
3. User Experience: Various roles within B2B information businesses impact user experience including event and digital operations.

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FINANCE

Function: Play a key role in revenue forecasting, budgeting, and financial analysis. They provide insights into financial performance, cost control, and investment opportunities, aiding in strategic decision-making.

Each role contributes to the revenue pipeline in different but interconnected ways. Their functions are crucial in driving sales, optimizing marketing efforts, maintaining customer relationships, and ultimately, ensuring the financial health and growth of the organization.

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Aligning KPIs and Outcomes

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Creating revenue-impacting KPIs for the aligned organization is a crucial step. These KPIs span across different departments - Sales, Marketing, Customer Success, Operations, Product, and Finance - each with its specific set of metrics that align with overall revenue goals. We will do a deep-dive into the specific KPIs in an upcoming blog. Make sure to sign up for blog alerts so you can be notified when we post.

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Standardization in The Revenue Room™

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Standardization plays a crucial role in data-driven revenue organizations for several key reasons:

1. Enhances Data Quality and Integrity: Standardization ensures that data across the organization is consistent and uniform. When data is standardized, it reduces errors and discrepancies, improving the overall quality and reliability of the data. This is especially important in data-driven businesses where decisions are based on data analysis. Poor data quality can lead to incorrect conclusions and poor decision-making.

2. Facilitates Data Integration: In today’s business environment, data often comes from various sources, including internal systems, social media, IoT devices/event tech, and third-party sources. Standardization allows for easier integration of these diverse data sets. It ensures that data from different sources can be combined and used together effectively, which is essential for comprehensive analysis and insights.

3. Improves Efficiency: Standardized data streamlines processes and saves time and resources. It reduces the need for manual data cleaning and transformation, allowing data scientists and analysts to focus on higher-value tasks such as analysis and interpretation. This can lead to faster insights and more agile decision-making.

4. Enables Scalability: As businesses grow, so does the volume and complexity of their data. Standardization creates a framework that can easily scale, accommodating increasing amounts of data without sacrificing quality or performance. This scalability is vital for data-driven businesses that need to adapt to changing market conditions and business needs quickly.

5. Supports Compliance and Data Governance: With the increasing importance of data privacy and security regulations (like GDPR, CCPA, etc.), standardization aids in compliance. It ensures that data handling across the organization meets legal and regulatory requirements. Moreover, standardized data supports robust data governance, allowing organizations to manage their data more effectively and securely.

6. Facilitates Better Analytics and Machine Learning: Standardized data is critical for accurate analytics and machine learning models. If you need predictive analytics, they won’t happen without standardization. There is no way to create the model accurately. Inconsistent or poor-quality data can lead to biased or invalid model results. Standardization ensures that the data fed into these models is accurate and consistent, leading to more reliable and actionable insights.

7. Enhances Data Sharing and Collaboration: In large organizations and across different departments, data sharing and collaboration are essential. Standardization ensures that everyone is on the same page, allowing different teams to understand and use the data similarly. This uniform understanding is crucial for collaborative projects and cross-functional teams.

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The Net Net

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Transforming revenue into a critical business process is not just about adopting new strategies or technologies; it’s about a holistic transformation of your business’s approach to revenue generation. It requires continuous improvement, standardization, and alignment across all departments. By following the outlined components and phases, businesses can effectively make revenue a center of excellence, paving the way for sustained growth and success in the competitive B2B landscape.

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