Sales forecasts are generally important in the preparation of the upcoming fiscal year and also in the management of current year sales targets. Moreover, they are based on qualitative and quantitative data and are only problematic when the sales team and managers confuse optimistic targets with forecasts. Here, you will find out how to improve sales forecasting.
Leverage historical data
Most large companies have a history of data that they rely on to make realistic sales forecasts. In addition, analytics, or other forms of tracking hard data related to conversion rates and targets, are essential to formulate accurate forecasts. However, this data does not guarantee the accuracy of forecasts, which can be distorted by new product launches, increased competition, or exploration of new markets. The historical data then represents a hard basis from which it is possible to build sales forecasts, provided that it includes contingencies that may have a negative and positive effect on future year results. Alternatively, these elements can be incorporated into a final version of the sales forecasting solution report. Each link in the supply chain introduces inventory, decoupling from downstream demand. For more information, click on www.verteego.com
Implement the action plan for a sales pipeline
If the level of lead qualification has an undeniable influence on the conversion potential, then the quantity of leads generated has an impact on the number of closed transactions. It is therefore essential to define the action plan, which will enable the number and type of leads needed to achieve the objectives. If the sales team closes, for example, 25% of transactions with qualified leads, then they could also aim to double the number of leads that are generated in the next quarter. In this way, they would theoretically be able to close an additional thirty to fifty percent of deals. The action plan should also give the same importance to both lead generation and sales forecasting software, regardless of the end goal. In addition, analysing the conversion rates at each stage of the sales journey helps optimising the development of action plans. This predictive analysis also relies in part on the expertise of sales representatives. They are able to identify the factors that drive the progression of leads through a sales journey, the stages of the journey and the percentage of leads that is converted at each stage. They are also able to identify the factors of lead qualification, whether it is an online demo or a registration form, as well as the number of leads that are needed to achieve defined transaction objectives.
Prepare for multiple scenarios
Imagining the worst-case scenario is usually never a good idea. However, whatever the goals are, it is essential to consider contingencies that could jeopardise them. For example, the retail predictive solution must take into account extreme scenarios such as the simultaneous loss of three sales representatives recruited by the competitor, a product recall, or the unsolicited re-evaluation of sales processes. It is not intended to list all such scenarios, but to predict the margin of error for each of these problems. Analysis of the previous year’s results may reveal the factors behind the successful performance. However, there is no guarantee that the factors, let alone the performance, will be recurring. A campaign that produced excellent results last year may not be as successful this year. This means that the random nature of financial results in a business sector must be an integral part of sales forecasts.