Forecasting Future Sales, From the Bottom Up

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Forecasting Future Sales, From the Bottom Up

An AI/ML approach to precision forecasting for the Pharma industry

Much goes into the setting of sales goals and strategizing on how to achieve them. Sales estimates are projected based on a complex analysis of gigabytes of data on the market, HCPs, patients, competitors, and more. While this process can be intensive and time-consuming, sales forecasting is, in no way, one and done. Pre-sales and pre-revenue, it helps businesses better plan their initial sales and marketing activities, and once sales are underway, forecasting becomes essential to the ongoing management and dynamic calibration of marketing campaigns and sales alignment.

Organizations that leverage accurate forecasts are 10 percent more likely to grow revenue year-over-year and are twice as likely to lead their industry. Furthermore, 97 percent of organizations that adopt best-in-class forecasting processes are able to make their sales quotas, while only 55 percent of companies that eschew forecasting achieve the desired amount of sales. That said, 67 percent of organizations have yet to implement any formal approach to sales forecasting, and less than 50 percent of sales representatives place doubt on their forecasting accuracy, likening the process to the reading of tea leaves.

One key to business success is executive managers’ ability to comprehend and use forecasting, and adjust their strategy and execution accordingly. The busier a business gets, the less likely they are to have time to engage in the routine “checks and balances” that sales forecasting becomes. Yet, it is exactly what they need to succeed. Integrating an automated, bottom-up approach to the sales forecasting process is exactly what savvy, future-forward organizations must do to ensure cognizant planning that will keep their sales flowing and gain a competitive edge.

Automation of the forecasting process

Forecasting, of future brand sales includes understanding its entire therapeutic landscape, in terms of HCPs, patients and NBRx-new brand Rxs in terms of units or currency, which is quite a complicated process. There are dozens of existing forecasting models and having an internal team in charge of producing an accurate forecast on a periodical basis, by evaluating and comparing numerous models, is a tedious task. To eliminate the time and complexity involved in the analysis and selection of the best fitting models, automation is key.

Automation simplifies the forecasting process by running all the various (thousands of) available forecasting models, during different time periods. The automation process enables a large scale of possibilities and parameters to be evaluated, so that the best fitting one can be selected and used as a reference. When coupled with an easy-to-use and friendly UI, automation becomes easy and fast to access. This allows the analyst to evaluate different scenarios and optimal parameters, providing a deep understanding of why given models are better than others. In this way, the analyst is empowered to use one or a combination of models that offer the highest precision forecast, which is then presented to brand executives before being moved to production. The result: a high level of transparency and confidence in the forecast.

Bottom-up forecasting produces more realistic, attainable targets

 

Bottom-up forecasting provides a fine-grain evaluation of a given market, resulting in highly accurate forecasts. This method of forecasting allows sales teams to thoroughly analyze distinct forecasts, rather than scrutinize a high-level, nationwide big picture. Analysts can look at the particular slices of their market such as territory level, brand level, portfolio, or category (including competitors), apply different models for different territories, and provide improved, more precise results on a micro level.

Typically, fine-grain forecasting is limited, due to challenges pertaining to data accessibility and the complexity involved in repeatedly running the various models for different territories, brands, etc. However, when automating bottom-up forecasting and combining it with AI analysis algorithms, results can be fine-grain analyzed for precision treatment, or they can be averaged.  The process is made even more accurate, using machine learning algorithms that compare predictions regarding past data to actual results; and improve themselves by comparing how close these predictions were to reality. In doing so, executives are provided indisputable visibility into field results present and future. This injects them with confidence and equips them with strong, accurate, and explainable ‘ammunition’ they can then carry with them to meetings with their management.

Better Understand HCPs’ Prescribing Behavior, with Verix

Automated bottom-up forecasting is not relegated to B2B and B2C sales as conventionally pictured. Verix’s solution for bottom-up forecasting is instrumental in the empowerment of smart campaigning and sales for the pharma industry as well. With Verix, brand leaders can leverage AI-driven insights to examine what-if scenarios at various levels to evaluate strategic directions pertaining to HCPs’ prescribing behavior, and adjust their plan to boost performance and match marketing campaigns and sales activities. This precise, multi-modal forecasting approach turns the traditional model on its head, enabling businesses in the pharma industry to, at long last, generate bottom-up forecasting at multiple levels, to reflect their assets’ true potential. The next step: roll them up to meet top-down targets, and close more sales.

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