In every organization, the sales forecast is critical to decisions on everything from budgets to spending. Forecasting is a necessary task to ensure an organization develops and plans successfully, but it is still resented. The amount of effort put into the process and the time it takes is not trivial for most organizations. The task can be incredibly time-consuming, especially when there isn’t a defined process in place.

Organizations with multiple locations and business units have to rollup the forecast into a single forecast. Each individual team member adjusts their own spreadsheet to come up with their number for the quarter, but how they arrive at the final number is up to their own discretion, expertise, and sometimes bias.

Sales operations then collects and combines all these spreadsheets, make the necessary adjustments, aggregate them across the organization, and supply the forecast number. At this point, it’s speculative how long the actual process takes, but what isn’t speculative is the data used to inform the forecast result is already stale and out of date.

This process is then repeated next week or next month, and each time the tediousness of this task doesn’t decrease. If anything, the sales force begins to resent the task altogether. An outcome of an accurate forecast is to plan, monitor and develop sales efforts and expenses. If the forecast is continuously missed, this can have a direct impact on other areas of the organization such as unforeseen cash flow, problems in production, staffing and financing needs.

As previously stated, there is a common understanding that the forecasting process itself is a problem, but there are also other significant challenges sales team face to achieve reliable forecast accuracy. Some of these challenges include:

• Multiple manual tasks (such as spreadsheet aggregation) in the end-to-end sales process, which takes away time from closing a deal or engaging with customers
• Multiple BI tools
• Limited or no access to the right information and insights to inform or more accurately predict the sales forecast

We’ll explore the role of predictive analytics, and how it can help improve overall forecast accuracy. By consolidating the data from Salesforce, sales teams can more effectively assess the present, and use predictive analytic models to more accurately predict the future of sales and financial forecasting. By feeding data into predictive analytic models, sales teams are enabled with analytics-based insights and recommendations.

Predictive Analytics for Sales Forecasting

First, let’s begin with a standard definition of predictive analytics:

“A branch of advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning and artificial intelligence to analyze current data to make predictions about the future.”

Patterns can then be found in historical and transactional data and can be used to identify risks and opportunities in the future. One role of predictive analytics in relation to forecasting is models can capture relationships among different factors and assess risk with a particular set of specified conditions, and then assign a score or weight to the risk assessment. From this information, the sales team can then take this assessment and target opportunities with higher scores. This allows sales teams to more effectively manage their time, and improve overall sales effectiveness by accurately targeting the right (more likely to close) opportunities.

It’s important to note that in order for predictive analytics to be valuable, the data that is collected also needs to be accurate, in the same way, that the data that informs the forecast needs to be accurate.

The question then is, where should data come from to build the models, and what is considered “good” data?

Data Sources for an Accurate Forecast

There are many approaches to sales forecasting, but not all approaches will result in an accurate forecast. When searching for a sales forecasting solution that will improve forecast accuracy, a solution that uses historical analysis and predictive analytic techniques is generally superior to one that doesn’t. The data that is fed into the model must be accurate. If the data isn’t accurate, then the result isn’t going to be accurate either.  When first approaching this, organizations can take steps to make sure that all data is of the highest quality. When it comes to data quality, here are some suggestions for places to start:

Use all historical data: The more historical data your organization has, the better. Historical trends can suggest future trends. This means the longer the amount of historical data an organization has, the more likely it is that the model will be able to pick up on factors such as seasonality, cyclical events, and long-term trends to enhance the forecast.

Correct missing data: It’s inevitable that there will be some missing data, especially if you’re a large organization. Missing data can cause big problems for organizations because it doesn’t represent all the historical data of an organization, so it can’t spell out the big picture. If an organization has missing data, the forecast model needs to understand this, and not interpret ‘missing’ as ‘zero’.

Include causal factors: Sales history should include causal factors as independent variables in the model. Not all causal factors will have a significant effect, but if they do it could improve forecast accuracy. For example, elements of the customer profile, e.g. the customer’s industry, may not seem relevant, but the models measure the effect of these factors and use them appropriately.

Investing in data quality is a valuable contribution to forecast accuracy. It can be seen as a low priority activity, but not tackling this issue can have unintended consequences.

When it comes to an actual sales forecasting software, an option that has an integrated data store in the cloud and merges with different data sources is preferable, not only for predictive analytics purposes but also overall security. The software should also be able to merge with different data sources such as:

• CRM data, including all historical sales activity
• Financial data, including all product margins
• External data which could include industry-specific data sources and cross-industry data sources

We’ve covered the data, now let’s discuss how predictive analytics actually improves forecast accuracy.

How Predictive Analytics Techniques Improve Forecast Accuracy

It’s not just the case that there is a hype around predictive analytics right now–there are real benefits to choosing an application that uses these techniques. After all, when all your competitors start using applications that employ the same techniques, your organization is going to be left behind and simply playing catch-up.

A software that employs predictive analytics will continuously improve the models with accurate, historical, and ongoing feedback from the data sources. Comparisons between what happened in the past and current circumstances make it easier for sales teams to understand performance, and predict future circumstances.

When organizations use predictive analytics two important outcomes occur:

Complete Coverage: All data sources are objectively considered, weighted based on the empirical impact, and combined to provide an accurate factor analysis and predictive function.

Detect Changes: Machine learning techniques are used to discover repeating patterns in the data, and can also identify anomalous patterns as well. Data anomalies can relate to factors such as changes in business conditions. Users are alerted to statistically significant changes and use their own insight and data to understand where the business conditions might have changed, and in turn, find out what it means.

The result of these two features is a model that covers all underlying factors, including reporting changes to take corrective action. The role of predictive analytics is to give organizations the opportunity to become more proactive and anticipate outcomes and behaviors based on data and not just assumptions or biases.

For sales teams tangible benefits can include:

• Enable sellers and sales teams to make data-driven decisions, which makes them more effective and efficient
• Spend more time on valuable customer-facing activities and reduce time spent on manual tasks

Applied predictive analytics allows businesses to successfully use and interpret big data, and also use it as a guide for planning.

Take Advantage of Predictive Analytics

Current approaches to sales forecasting that continue to use spreadsheets or multiple business intelligence tools are no longer effective, and this means it’s time to take advantage of advances in predictive analytics so you can stay ahead of your competitors. Predictive analytics when applied to sales forecasting models gives organizations the opportunity to plan effectively, and ensure higher forecast accuracy.

If you want to learn more about predictive analytics and sales forecasting, join us for a demo. We can discuss Vortini’s forecasting methodology and get a better understanding of the nuances of your organization’s forecasting process.