You know that your sales forecasts are important to the health of your organization.
You also know that your forecasts are important for making critical decisions.
Everything from staffing to to training to investments relies on your sales forecast and the validity of the information.
The problem is that most sales forecasts are riddled with poor assumptions. Worse, forecasts can reflect “emotion” rather than a fact-based evaluation.
Can you trust your forecast? If not, consider implementing predictive analytics in your forecasting process.
Predictive analytics promises to improve your forecasts by using “big data”. Forecasts based on predictive analytics remove emotion from the forecasting process enhancing the credibility of the forecast. It also helps you identify ready to close opportunities hidden in your current pipeline.
In today’s post we’ll review the benefits of predictive analytics and how you can apply it to your current sales forecasting process.
Getting Comfortable with the Numbers
How can you make sure that your forecast numbers are valid? Typically, you have three options:
- Grill the sales manager and the rep. Look for their commitment to the numbers and the validity of the process in coming up with the numbers.
- Dig into the numbers independently. Go back to the client or prospect and gauge the likelihood of closing the deal.
- Apply predictive analytics to the forecast process. Use data to accurately evaluate your pipeline and surface ready-to-close prospects in your pipeline.
You need something more than best guess and intuition to predict customer behavior.
Your forecast numbers will be driving the year-end income targets and marketing budget adjustments. You need something more than best guess and intuition to predict customer behavior.
What’s So Special About Predictive Analytics?
Predictive Analytics is taking the historical performance of the business and applying available data points. These calculations determine what’s going to happen with the pipeline in the future.
What about gut feelings and intuition? While gut feelings and intuition result from actual numbers, those squishy numbers can be affected by the mood of the day or the last closed transaction.
Predictive analytics starts with history and applies known data to produce a valid forecast.
Predictive Sales Analytics: Four Factors
So where do we get the data to calculate a forecast using Predictive Sales Analytics?
- Data from the past Evaluate the historical patterns and decide what they’re telling you. How many interactions are required with the client for the deal to close? What types of products were sold? How long did the sales process take?
- Patterns in the data Look for trends that could help forecast the future. For example, deals that take two weeks or less to reach negotiation phase have a higher probability of closing. Do you have a sale fitting that criteria? If so, apply the predictive analytic.
- What will happen in the future Look for patterns in the data and apply those patterns to deals in the pipeline. For example, three deals took less than two weeks to reach negotiation. Absent of other factors, they have a higher probability of closing.
- Affecting results Assess your current situation and look for ways to include predictive insights. Ask questions such as: How can we use predictive analytics to optimize our pipeline? Can we switch the sale to a rep that has a higher close percentage with specific deals? Can we decrease the time it takes for a deal to reach negotiation?
Predictive Analytics requires a clear sales process to be effective. Before you implement a solution map your sales process.
We recommend focusing on your “Big Deal” process first. Use our Big Deal Strategy tool to inspect your process and uncover potential problems and bottlenecks. Use the information from this review to better target your predictive analytics implementation
Applying Predictive Analytics: Tools and Challenges
The small number of large deals in B2B pipelines limits the available historical data.
Even so, the data will be more valuable because:
- longer deal cycles create more history
- direct interactions with customers result in higher validity
- digging for additional key criteria may uncover other cause-and-effect situations
Best practices calls for automating as much history and data as possible. Look for solutions that will make the job of predictive analytics easy to use, such as Client Relationship Management systems.
If you haven’t rolled out a CRM solution, you may need to start with a manual process. Make sure the process is well documented and put all of the data in one place.
Finally, make sure you have a team onboard that supports predictive analytics. Ensure they are tagged with responsibilities accurately and processes are documented and applied consistently.
Imagine a quarter-end forecast using predictive analytics. No insomnia. No nail-biting. No justifications using gut instinct. Just a powerful tool for predicting future results that everyone can rely on.