Oct 26, 2023
Sales forecasting is critical to business growth and success. It provides insight into projected future revenue, changing demands and emerging trends, and helps inform both short-term and long-term organisational performance. From entrepreneurs scaling start-up ventures to senior leadership teams working for established industry giants, forecasting models are deeply embedded in strategy and decision making.
Despite this, sales leaders and leadership teams alike continue to face challenges with sales forecasting accuracy:
· 93% of sales managers are unable to forecast revenue within 5%, even with two weeks left in the quarter
· Less than 50% of sales teams have high confidence in their organisation’s forecasting accuracy
· By 2025, it’s predicted that 90% of B2B enterprise sales organisations will continue to rely on intuition instead of advanced data analytics – resulting in forecasting, sales pipelines and quota attainment inaccuracies.
Painting an accurate picture of an organisation’s financial story is key to structured, strategic and sustainable development – from setting benchmarks to allocating resources to planning businesses activities. It doesn’t simply impact sales operations teams, reps and CSOs; inaccurate sales data has ramifications for other teams and stakeholders.
What can be done to ensure accurate sales forecasting? Are there tools that can enhance the sales process?
Why is it difficult to forecast sales?
There are numerous reasons why a sales forecasting process can yield inaccurate results. Here are a handful of the most common:
Tracking customer interactions, storing information and managing relationships are all made possible by customer relationship management (CRM) systems – making it unsurprising that the majority of companies rely on them. However, poor CRM systems data is one of the biggest barriers to accurate future sales projections. Poor data often results from inconsistent record-keeping, data decay, duplication of data and invalid contact details. This, in turn, negatively impacts prospect and customer relationships, makes it challenging for sales reps to perform, and undermines the overarching purpose of using a CRM in the first place.
Outdated and antiquated spreadsheets that aren’t fit for purpose add to the problem. They make it far more likely that a salesperson will miss meaningful or important data, as well as proving confusing, time-consuming and frustrating. Human error is a large factor in sales forecasting using traditional tools, such as Microsoft Excel, which can result in false reporting. Whereas software that specialises in sales forecasting will present real-time data and factor in emerging trends, data stored in traditional spreadsheets can easily become outdated and obsolete.
Cognitive biases, lack of accountability and manual data entry also negatively impact forecasting methods and outcomes. Overestimating and underestimating projections, together with personal predispositions, can regularly lead to ill-informed decision-making and, as a result, ineffective marketing activities. While forecasting does involve an amount of informed judgement-making and relying on data-driven insights, intentional and unintentional manipulation of data – led by, for example, sandbagging, delayed reporting, or overconfidence about making sales – remains a problem.
Organisations who rely on time series forecasting approaches – where future sales are based on historical data related to previous sales – fail to take into account the broader context of sales performance. For example, consumer demands change, markets fluctuate in line with emerging trends, the wider economic climate impacts buying behaviour and cashflow – all have the potential to impact forecasts. This method can be useful for comparison or benchmarking purposes, but should not be the basis of forecasting.
How can sales forecasting systems be improved?
Any improvement to sales forecasting must be grounded in strong sales leadership. Forecasting accountability is paramount and should be reinforced by regular meetings, inspections and adjustments, where necessary. Those involved must be aware of the important role that sales forecasting, via the right metrics and based on the best data, plays.
Having a comprehensive overview of a planned forecasting strategy gives sales teams a strong starting point to work from – that also has the potential to limit inaccurate forecasts. As such, designing a sales forecasting plan should be regarded as a business-critical practice. Salesforce, global experts in sales forecasting and CRM systems, highlight the three primary activities that forecast planning involves:
1. Calculation of number and time period. How will the estimated monetary amount be calculated, and what time period will it cover? Have you accurately assessed historical data in line with the relevant variables? What are the relationships between the variables? How will the numbers calculated impact long-range forecasts?
2. Review and revise. What key milestones are needed to assess the success rate? Will you require real-time oversight of sales activities? How often will it be necessary to revisit sales throughout the quarter and the sales cycle?
3. Break the patterns. Is there another methodology that could be used to boost forecasting accuracy? Have you tried skip-level forecasting? Are there different questions that should be asked? What angles of the data have not been properly examined?
From this point, automated sales forecasting – using reliable CRM software – is a gamechanger. Systems that incorporate artificial intelligence (AI) and machine learning (ML) will improve sales pipeline quality, resulting in better sales operations and conversion rates. They help to drive time-sensitive sales activities by generating highly accurate, real-time data in seconds. Cognitive biases are removed and, as the AI and ML develops over time, data accuracy will only increase. Plus, the right CRM will save teams time, money and effort so that they can focus on matters that require their attention. HubSpot, Zendesk, Salesforce, Pipedrive and Zoho are all examples of popular CRMs.
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