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You are at:Home»Technology»Three steps to boosting sales and enterprise value using revenue intelligence
Revenue intelligence

Three steps to boosting sales and enterprise value using revenue intelligence

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By sme-admin on July 19, 2022 Features, Technology

There is a direct correlation between accurate sales forecasting and a company’s success.  Business leaders who have confidence in their forecasts can make more informed decisions when it comes to factors that impact revenue, such as setting targets, managing cash flow and recruitment. Vitally, this forms the basis of a strong reputation with investors and shareholders, too. Despite this, only 45% of sales leaders say they have confidence in their forecast accuracy today. 

After two years of disruption and now the prospect of a recession to overcome, it has never been more important for decision makers to understand the direction of their business. Andy McDonald, CEO, Cloudapps, reveals three steps to achieving an accurate sales forecast.

  • Fix the broken data picture

Businesses need to build a broader, richer data set to measure the health of their sales pipelines in a more meaningful way. The decision to qualify leads in or out of the forecast is dependent on that data but all too often it is based on tactical tasks inputted by the sales team into the CRM. It might even be incomplete.

To address this, companies need to bolster the data they track about a deal and look to automate processes. Over time, a richer picture of deal health can be built by capturing deeper insight into how each lead is handled. For example, were appropriate discounts applied? Have follow ups been completed on time?

By applying AI techniques to this data, patterns of successful and unsuccessful conversions can then be identified, which will provide a better indication of deal health moving forward, while also providing insight into best practices. In addition, AI can pinpoint what is needed to win deals – whether that’s more resources or greater stakeholder involvement – or even whether the best course of action would be to qualify out.

  • Encourage reps to use CRM

Now onto the next challenge when it comes to achieving a high quality data picture: how to move the information out of reps’ notebooks and into a CRM system. If salespeople aren’t able to see the value of inputting this data, then it’s nothing more than a chore, something that takes up valuable selling time.

Businesses need to shift this perception by demonstrating the benefit of inputting information – for them directly as well as for the company. Competitions and leaderboards are often well received within a business and a sales role is competitive by nature. By providing a fun incentive to use CRM, the level of insights available for forecasting can be vastly improved.

  • Provide guidance on each sales stage 

Key to successful sales forecasting is the consistent application of terminology. If reps and managers are unclear on the attributes that a deal should possess before it progresses to the next milestone marker in the sales funnel, deal progression can be slowed or even reversed. Using confusing names for each stage of the sales process and a lack of training are often root causes here.

A shared, automated checklist, segmented by sales stage, ensures reps and managers are aligned. Indicators like stage duration also help to keep a focus on deal velocity.

Dependable processes in an unpredictable landscape

With high quality insights gleaned from CRM, forecasting is no longer subjective and discussion-based but a repeatable and dependable process that business leaders can have confidence in. In today’s unpredictable business landscape, no business can afford a low success rate.

By applying gamification techniques such as competitions and leaderboards to capture data, combined with the addition of AI to interpret it, businesses can achieve sales forecast accuracy of up to 95%. And with a 3% increase in forecast accuracy reported to increase profit margin by 2%, the business impact could be transformative.

Traditional methods of sales prediction are neither precise nor prescient. Without accurate forecasting, businesses risk over or under-hiring and overpromising to stakeholders – costly mistakes that could ultimately lead to lost revenue, even falling share prices.

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