Last updated: How to master the art of the deal with smarter sales pipeline management and forecasting

How to master the art of the deal with smarter sales pipeline management and forecasting


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B2B sales journeys are dynamic and complex, involving numerous stages, stakeholders, interactions, and variables. But B2B selling also is equal parts data science and relationship building.

‘The art of the deal,” as it is known, is multifaceted. An integral piece is sales pipeline management and forecasting, which is vital to business success. It enables companies to understand current and future opportunities and their influencing factors.

But this task of trying to predict what prospective business will turn into revenue and when is anything but easy.

In fact, Gartner research shows pipeline management and sales forecasting are among the top areas where sales operations functions are least effective.

According to the Gartner, 67% of sales operations leaders agree that creating accurate sales forecasts is harder today than three years ago.

That’s why many B2B sales organizations are looking to artificial intelligence and data analytics for help.

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Old-school sales pipeline management and forecasting

Sales pipeline management is often mismanaged, and sales forecasting is often off-base – representing missed opportunities and a time-consuming manual process that cuts into valuable selling time.

For a sales manager, this is one of the most important tasks, and in the past has been at the mercy of sellers. Traditionally, many organizations have used sales CRM as a system of record, a step-driven data collection tool for capturing critical information and enabling basic reporting. When a sales manager needed to assess the state of business, it came down to manual updates from sellers and scrum-style status checks. While this method works, it inherently has problems.

Sales pipeline—a sales organization’s cumulative prospective business—and sales forecast—the subset of business expected to close in a given period—are traditionally most impacted by user discretion. Visibility into deal progression is based on the discretion of the seller.

As a result, what’s reported is generally skewed in their favor. Sometimes a seller maintains a fictious forecast to cover performance issues or to bide time while they seek new opportunities. Or a seller might “sandbag” and hold back a deal so they can deliver at the last moment and garner praise.

Sales organizations can obsess over number of interactions to try to measure and drive sales performance, but this generally results in more zero-value actions and less successful sellers.

In all cases, the sales manager needs to cut through the noise to determine if the team will hit their goals on time.

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How AI and data analytics change the game

In many ways, the stakes are higher in modern B2B sales. Markets are disrupted, budgets are tight, and deals have more requirements and stakeholders. Sales managers are faced with the job of improving sales velocity while improving forecast accuracy.

The question becomes, “How can you turn around an imperfect and time-consuming forecast process to improve business outcomes?”

Intelligent pipeline and forecast management solutions help organizations improve accuracy, efficiency, and effectiveness by using connected processes, data, and AI to automate the mundane while elevating the important.

With generative analytics and AI insights, sales organizations can understand projected vs. desired outcomes and use modeling to refine sales focus to improve win rates.

Benefits of intelligent pipeline and forecasting management include:

  1. Data-driven decision making. Sales reps and sales managers can leverage real-time signals from opportunity interactions and gain insights to make informed decisions and develop data-driven strategies—instead of relying on intuition or gut feeling.
  2. Better planning. This becomes a repeatable sales process that generates predictable results used for planning, staffing, and compensation.
  3. Focus on the right stuff. Sales reps gain an understanding of opportunity health and can use AI-driven insights to focus on the right opportunities with high propensity to close. For example, based on past sales data, a lead or opportunity score can be generated, and with this, sales reps can prioritize deals based on the winning probabilities.
  4. Uncover opportunities at risk. Sales managers have better insight into forecast strength and seller performance. Individual sales rep performance is visible at each stage, linking deal progression, health, and forecast status to real-time data. These insights can help sales managers identify opportunities at risk or coaching opportunities to improve seller performance.
  5. Sales process insight. Pipeline flow analysis allows a manager to inspect how specific opportunities have changed over time, helping spot issues in the sales process.

Intelligent sales forecasting, in action

Top companies are harnessing the power of AI to improve sales pipeline management and forecasting.

For example, Miele Professional, a division of Miele, the global premium brand in domestic appliances, uses AI and an intelligent sales execution solution from SAP to help their sales reps focus on the right opportunities and help their sales managers better predict future sales. The company’s commercial customers include hotels and hospitals.

Limited visibility into relevant KPIs made forecasting a challenge for sales managers at Sybit, a professional services company based in Germany. Recognizing the complexity of today’s sales environment, the company modernized its sales operation by implementing intelligent sales forecasting solution. Productivity shot up by 50%.

“We saw the value immediately: greater transparency, better conversion rates, more revenue,” said Thilo Kerner, chief revenue officer at Sybit.

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