Accurate sales forecasting plays a vital role in a company’s success. With confidence in their forecasts, business leaders are equipped to make more informed decisions when it comes to factors that impact revenue, from setting targets and managing cash flow to recruitment and resource planning. This also forms the basis of strong relationships with investors and shareholders, allowing executives to build a reputation as reliable and prosperous. Despite this, almost half of sales leaders say they don’t have confidence in their forecast accuracy today.
Following two years of disruption and with a recession looming, it has never been more important for decision makers to understand the direction of their business and assert its value. Andy McDonald, CEO, Cloudapps, reveals three steps to achieving an accurate sales forecast using revenue intelligence.
1. Complete the data picture
In order to measure the health of their pipelines in a more meaningful way, businesses must build a broader, richer data set. Important decisions on whether to qualify leads in or out of the forecast are reliant upon good quality data, but this depends on the sales team inputting tasks accurately into the CRM. Something they may not feel inclined to do.
To fix the broken data picture, companies therefore need to bolster the information they collect about each deal and identify ways to automate processes. By capturing additional details on how leads are handled, companies will build, a richer picture of deal health over time. Were appropriate discounts applied? Have follow-ups been completed on time? Answers to these questions will allow businesses to gain deeper insights.
When AI techniques are applied to this data, patterns of successful and unsuccessful conversions can then be established. This vital intelligence provides a better indication of deal health moving forward, while also providing insight into best practices. What’s more, AI can identify what is needed to win deals – more resources or greater stakeholder involvement, for example – or even highlight instances where the best course of action would be to qualify out.
2. Incentivise reps to input data into CRM
The other big challenge is getting important information out of reps’ notebooks and into the CRM system in the first place. If sales people don’t see the value of inputting this data, the information available from CRM will always be limited. Businesses need to change their perception; for too many sales people, it’s nothing more than a chore that takes up valuable selling time.
Demonstrating the benefits of inputting information – for sales people directly as well as for the company – will help businesses to overcome this issue. Competitions and leaderboards are often well received, tapping into the competitive nature of sales reps. With a fun incentive to use CRM, the level of insights available for forecasting can be transformed.
3. Agree terminology for each sales stage
Establishing agreed terminology across teams is vital to successful sales forecasting. Any ambiguity on the attributes that a deal should possess before it progresses to the next milestone marker in the sales funnel could result in deal progression being slowed or, in some cases, reversed. A lack of consistency when naming each stage of the sales process and a need for training are often root causes here.
To rectify this, a shared, automated checklist, segmented by sales stage, ensures alignment between reps and managers. Keeping track of indicators like time taken during each stage of the sale also helps to keep a focus on deal velocity.
Utilising revenue intelligence to navigate an unpredictable landscape
With high quality insights gleaned from CRM, forecasting becomes a repeatable and dependable process that business leaders can have confidence in. It is no longer subjective or guesswork, providing businesses with the consistency needed in today’s unpredictable business landscape.
With gamification techniques such as competitions and leaderboards to capture data, combined with the addition of AI to interpret it, businesses can complete the sales data picture and achieve forecast accuracy of up to 95%. And with a 3% increase in forecast accuracy reported to increase profit margin by 2%, the business impact should not be underestimated.
It is time to move on from traditional methods of sales prediction to avoid costly mistakes such as under hiring or over promising to stakeholders. By unlocking and learning from actionable, real-time intelligence from CRM and the tools we use for business purposes every day, companies can continue to drive revenue and prove value.
