An overview of the sales management technology landscape

Predictable sales execution is the most important aspect of successful sales from leadership to the sales rep. Today, the ability to deliver on these core responsibilities is not optimized. The day-to-day tasks of reviewing the pipeline, recommending next best actions, and creating forecast visibility suffer from imperfect data. Data that is only partially recalled by the reps and therefore entered incorrectly. Data that falls through the cracks and is left out of the database altogether. Data that is not optimally captured and therefore not visible. Data that is visible, but not easily scanned by busy sales leaders. Data, in other words, that is suboptimal—and therefore leads to unsatisfactory sales results.

What is the sales management category?

The sales management category includes technology that provides the efficient collection, analysis, and visualization of data that is purpose-built for sales leaders to manage more effective, productive, and predictable sales organizations. It’s about making the sales leaders’ and sales reps’ jobs easier and, ultimately, about maximizing revenue.

The category’s value lies in two critical areas:

1. Day-to-day management of sales execution (e.g., reviewing current pipeline and recommending actions)
2. Organization-wide predictive insight into revenue (e.g., providing forecasted revenue to the executive team).

Sales leaders are tasked with managing sales execution and delivering insights into how revenue attainment is achieved. However, the process of bringing the right data together has been largely manual and inefficient. As a result, sales management decisions result primarily from gut feelings and imperfect recollections.

In theory, customer relationship management (CRM) enables the features that the sales management category vendors provide. But for most organizations, the CRM alone is not sufficient. Two issues stand out with regard to the current incarnation of CRM:

1. It is not easy for the reps to keep their CRM data up-to-date, including the critical fields required for effective sales management such as updating activities, contacts, accounts, and opportunities.
2. Reporting modules are either outdated or require intricate custom reporting. The key to effective sales reporting is to focus on the information a leader needs to focus the rep on the right deals (those more likely to close) and to recommend the best actions for progressing their deals.

The promise of the sales management category is enabling data-driven decision-making via more efficient processes that pave the way for better decisions and free up reps and managers to focus on their core job—engaging with customers. The category is about harnessing data from within the organization and allowing managers to intelligently gather, visualize, and extract maximum value from information in a way that boosts reps’ effectiveness.

The figure below represents the major segments of the sales management stack. Sales engagement is a separate category that feeds engagement data to the sales management stack while output from sales management analysis informs engagement activities. The sales management segments (recommended actions and analysis) sit on top of the CRM.

The sales engagement stack consists of the following elements:

1. Planning is the process of determining the go-to-market approach for an organization, account, or territory. It involves key activities such as quota and territory planning, account segmentation, and sales capacity.
2. Pipeline provides visibility into potential revenue for periods beyond the near-term forecasting range. For example, a company on quarterly quotas views the current quarter as the forecast and the future quarters as pipeline. Pipeline has traditionally been difficult to manage due to a lack of trusted data and visibility, but it is critical to ensuring repeatability.
3. Engagement consists of the various interactions (digital, in-person, phone, etc.) that sales reps and others in the organization have with prospects and customers. Engagement tracking provides visibility into engagement history across multiple channels (email, phone, f2f) and across multiple stakeholders. Sales leaders use engagement visibility to determine a rep’s current status in an account. For example, recent interactions between the champion and the sales rep are a likely indication of engagement and progress, whereas a lack of recent engagement signals that a deal might be at risk. This helps sales leaders determine where to provide coaching to help progress deals.
4. Forecasting predicts sales revenue for a near-term period. If a company is on a quarterly revenue model, the forecast reflects the revenue that is predicted to close in that particular quarter.
5. Sales activity automation proactively automates mundane and manual tasks without human intervention. Capabilities include automated activity logging, notes capture, and contact identification and logging (identifying contacts in a meeting invite and entering them into the CRM).
6. Recommended actions leverage historical data to provide guidance on next steps. An example is the post-meeting follow-up email. The system reminds the rep to send a follow-up email, and recommend who to send the next meeting invitation to, based on who was on the last meeting invite.
   

Improving sales execution is a strategic imperative

The rise of the sales management category boils down to the two major challenges of sales management: sales execution and predictability. Execution has always been a key part of a sales leader’s job description, but data from the TOPO Sales Benchmark points to execution as being a top priority and a top challenge for most sales leaders.

Roughly half of all sales leaders lists execution as a challenge.

Sales Execution and Enablement Challenges

A lack of quota achievement has pushed execution to the forefront. Data from the TOPO Sales Benchmark, which surveys world-class sales organizations, shows that at least 40% of sales reps do not meet quota. The main takeaway is clear: There is significant upside to improving execution.

Quota Achievement Based on ACV

For sales technology applications, a key metric of successful adoption is daily active users (DAUs)—the number of users who access the solution daily. The sales management technology category would have been doomed to fail if users were only accessing forecasting data at the end of the quarter. Instead, sales management applications have become the single source of information shared by sales leaders and their reps to engage more effectively. As a result, forecasting and pipeline applications have become a part of regular workflow. This consistent user activity is one key drivers in the success of the category.

Predictable revenue is a requirement and in a data-driven business world the organization expects visibility

Forecasting is a core competency for sales leaders. But the process has been cumbersome and resource-intensive (data being assembled from disparate locations, multiple meetings to “understand” deals, etc.). Traditionally, the ineffectiveness and inefficiency of forecasting have been unavoidable, but organizations are now becoming more data-centric in their forecasting approach. For example, CFOs are leveraging data and models to develop the overall financial plan for the organization, looking quarters and years out. Sales can’t credibly forecast that far out without supporting data and sales leaders know this. In a recent TOPO survey, 49% of sales leaders are typically 20% or more off target at the beginning of a quarter (let alone a year out). The inability to forecast into the future has severe effects on overall financial planning, including hiring decisions.

At least three related factors contribute to inadequate forecasting:

1. Sales data relies on individuals to enter data consistently and in a standardized way. This has proven close to impossible to accomplish: No one doubts that the possibilities for machine learning and AI are endless in sales, but some doubt whether data can ever be truly usable because sales has to first enter it. This introduces a conflict: Is it more important to hit the number or to collect the data?
2. Lack of a single, trusted source of data. The CRM should take care of this, but data is not entered consistently. True-to-life, potent visibility comes from multiple data sources that have to be aggregated and sifted through.
3. Forecasting is a resource-intensive process that remains inaccurate. Countless hours are spent by executives to reps trying to make sense of the forecast.

Compared to the other categories in the sales technology stack, tech innovation has been lacking in sales management. The massive growth in sales tech has predominantly focused on prospecting tools (contact data, sales intelligence), engagement (sales engagement platforms), and what has been known as quote-to-cash (CPQ and e-sign). The other areas of the sales process became more efficient (for example, CPQ and e-sign cut down manual work at the end of the sales process), yet day-to-day sales management has continued to be inefficient.

1. Sales activity automation provides the breakthrough required to make data capture easier and more standardized. Sales activity automation takes much of the burden of data updating from sales reps. For example, most sales activity today can be automatically logged via sales engagement platforms. Applications such as Clari, Olono, and People.ai identify additional stakeholders by capturing attendees from meeting invites and automatically entering them into the system.
2. New user interfaces have made data capture much easier. Rapid technology innovation enables reps to engage with critical systems via preferred interfaces (not the CRM) and avoid disrupting reps’ current workflow. Examples include simpler mobile experiences (Clari has received high satisfaction marks for its mobile experience), enabling CRM updates via Slack (with tools such as Troops.ai), updating deals from LinkedIn (LinkedIn’s new Deals feature), and updates directly from email applications (Microsoft Dynamics allows for updates without leaving Outlook). We anticipate that capturing and harnessing data will become even easier as voice assistant technology continues to improve and, eventually, proactively engage with sales reps for data updates via voice.
3. A market realization that engagement data is as important as predictive data. The initial value proposition in the sales forecasting market was to look at deals won in the past and try to ascertain firmographic and demographic attributes to predict the current viability of the pipeline. This is a factor that needs examination, of course. But there was a missing piece. Engagement activity is still a better predictor of deal strength than all other factors. For example, a deal that the system predicts to close likely will not if we have not spoken to the prospect in a month. Now engagement data is becoming available across multiple touch points (e.g., website visits) and stakeholders to provide reps and managers with an even deeper understanding of their accounts and opportunities.
4. Practical uses cases of machine learning and AI are promising. The sales market has healthy skepticism about AI. Sales leaders know that AI is the future, but they trust themselves and their reps more than the machines…for now. Many of the AI vendors came into the market with big promises of reducing human-centered activities and, frankly, it didn’t work. To overcome this, vendors had to refocus messaging on how machine learning facilitates better choices rather than on replacing human decision making. Practical use cases are finally starting to emerge. For example, AI can determine how to successfully engage with customers with specific recommendations for which channels to use and how to customize the conversation.
5. The CRM-UX for sales leaders does not provide the easy-to-understand, complete interface to assess the health of the business. Traction in the market today is driven by the simple idea of providing sales leaders with a better, easier interface to one source of data.
   

Management and insight are critical

Sales management technology enables the efficient collection, analysis, and visualization of data, making it easier for sales leaders to manage more effective, productive, and predictable sales organizations.

The category’s value centers on two critical areas:

1. Day-to-day management of sales execution (e.g., reviewing current pipeline and recommending actions)
2. Insights into revenue attainment (e.g., providing insights around forecasted revenue to the executive team).

Let’s break down this definition into the elements that comprise it:

• Data collection is the process of capturing all information pertinent to prospects, customers, and deals on the part of the reps and management. Accurate collection of up-to-date data is key especially as organizations look to leverage AI/machine-learning, where the technology needs to assess all the variables and act on them in real time.
• Data analysis means reviewing the available data and drawing conclusions that result in a more effective sales strategy. The more easily accessible and the more comprehensive the data, the more successful predictability and execution will be.
• Data visualization is the production of images that present data in ways that spreadsheets and raw numbers do not. The easier, the quicker to grasp, and the more automated the visualization, the more effective the sales execution.
• Sales execution (or sales effectiveness) is about delivering on or exceeding sales quota by acting on sales insights. If data collection, analysis, visualization, and predictability suffer, execution inevitably falters.
• Rep productivity reflects sales reps’ efficiency—that is, their ability to deliver the highest quantity of quality engagement with the right customers. To achieve maximum levels of productivity, organizations optimize sales reps’ days to eliminate as much non-selling tasks as possible and to use data to ensure they are spending this selling time with the right people.
• Predictability in sales is about accurate forecasting. Being predictable means understanding past performance and present conditions to determine the likelihood of reaching the defined revenue quotas. The ever-elusive art of forecasting has tended to involve a labor-intensive information-gathering process that includes correlating spreadsheets, interrogating sales reps, etc. The sales management category offers the promise of solid, data-driven decisions because it provides a more comprehensive view of what’s happening, so sales manager and sales reps can work together to proactively drive positive outcomes.

Sales management technologies evolve sales organizations by enabling them to change the way they operate, focusing their efforts on high value activities. How can the elements that make up the sales management category evolve if the category is put to work? The table below shows the progression from status quo as we know it today to a new data-driven reality.

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