How to Create Accurate and Realistic Sales Forecasts. With David Griffin [Episode 557]

David Griffin, CEO of Vortini, a sales forecasting system, joins me on this episode.

KEY TAKEAWAYS

David says the single biggest challenge facing sales reps today is that the level of competition increases quarterly, making it difficult to achieve predictable income. The buyer is also more informed, both on you and on your competition.

Vortini came to be when David was doing sales data analytics. David wanted to specialize, as it is a crowded market. He looked for core business processes that were not well-supported with software solutions, and found forecasting.

David sees heavy investments in CRM, and pipelines of opportunities. However, pipelines don’t tell the whole story for a solid forecast. Managers create spreadsheets, send them to opportunity owners, and get their forecasts a week later.

Forecasts matter because revenue expectations must be met. SaaS has issues around staffing, and manufacturing has problems around inventory, if forecasts are inaccurate.

Vortini takes data from the CRM, compares it to history, and considers collaboration for pipeline deliverability. Then it creates a scenario around the pipeline and resources.

Andy cites Philip Tetlock, saying that we should train people to become better forecasters. It is a skill that can be learned. Vortini focuses on history and information available, to step through the assumptions that create a forecast.

Reps are nervous about committing. Under-forecasting is as great an issue as over-forecasting. It can mean canceled orders if the goods or services are not available on time. Corporate forecasts are built from many smaller forecasts.

Forecasting tip: first, ensure opportunities are realistic and achievable. The last day of the quarter is not a credible close date. Are targets set too high by management? Setting targets 15% higher this year than last is a hope, not a target.

It is essential to manage biases. Don’t put the forecast in a spreadsheet. Keep it in the CRM, so forecasts and the quarterly results can be compared within the CRM. People can see their bias by looking at the evidence.

Make sure you are staying connected to the overall plan. Are your quarterly forecasts supporting the annual forecast? David compares day 70 in history with day 70 of the quarter and day 70 of the forecast. Watch for going off track.

It might be better to work on fewer opportunities, and do a better job on them. Carefully convert as many as possible. Don’t burn your way through them. They represent the base of future wealth to the company.

The forecast that works uses machine learning to look at history and make defensible assertions about times to close. Forecasting does not say a quick close is impossible, but that it does not match past observed behaviors. Talk about it.