How to Forecast Sales With a CRM

Now let’s get into the nitty-gritty of sales forecasting using a CRM.

As a reminder, sales forecasts can mean the difference between sleepless nights.

• Sleepless night A): Worrying about your business
• Sleepless Night B): Partying in Vegas with your sales on autopilot.

Here are the two easiest ways to forecast sales as a small business owner.

1. Sales Funnel Forecasting (my favorite)

Sales Funnel forecasting involves analyzing each stage of the sales funnel to predict future sales.

The sales funnel typically includes stages such as lead generation, lead qualification, proposal, negotiation, and closing.

Businesses can predict sales volume and revenue by examining conversion rates and time spent at each stage.

Example:

Here’s an example of what each stage of the funnel looks like:

• Initial outlook: 10% chance of conversion
• Qualified prospects: 25% chance of advancement
• Proposal phase: expected 50% conversion rate
• Negotiation phase: 75% chance of winning
• Closing deals: 90% chance of closing

Business owners can more accurately project sales results by quantifying the potential success rate at each of these crucial milestones.

Pros:

• Detailed insight: Provides a detailed overview of the sales process, allowing targeted improvements at every stage.
• Adaptable: Can be tailored to a company’s specific sales process and stages.
• Predictive power: Helps predict sales volume by analyzing historical conversion rates.

Cons:

• Data intensive: Requires detailed and accurate data on conversion rates and time spent at each stage.
• Complexity: It can be complicated to set up and maintain, especially for companies with long or complicated sales cycles.
• Based on assumption: This relies on the assumption that past performance is indicative of future results, which may not always be the case.

2. Forecasting based on lead score

Forecasting by Lead assigns a score to each opportunity based on its likelihood of closing.

Factors such as customer engagement, buying signals and historical data are used to calculate these scores.

Sales teams can then use these scores to prioritize efforts and predict sales revenue.

Example:

Imagine assigning each lead source a numerical value based on its likelihood of conversion:

• References: With a score of 8.1, referrals often produce high-quality leads thanks to the pre-established trust.
• Email campaigns: With a score of 7.2, they can effectively target and nurture leads with personalized content.
• Landing pages: Marked 6.5, these are the bread and butter tools for capturing interested parties and collecting data.
• Social media: With a score of 5.4, social platforms allow you to reach a wide audience, albeit with different levels of intent.
• Cold calling: With a score of 3.6, it remains an outreach tool despite lower conversion rates in today’s digital age.

Pros:

• Prioritization: Helps sales teams prioritize their efforts on the most promising leads and opportunities.
• Dynamic: Scores can be updated in real time based on new data, making the forecast more responsive to changes.
• Efficiency: Increases sales efficiency by focusing resources on leads with the highest likelihood of closing.

Cons:

• Complex scoring models: Developing and maintaining accurate scoring models can be complex and require advanced analytics capabilities.
• Data quality: The accuracy of predictions depends on the data quality used for scoring.
• Adaptability: Scoring models may need to be updated regularly to remain accurate as market conditions and customer behavior change.

Both Forecasting by Sales Funnel and Forecasting by Score provide powerful insights for sales forecasting.

The choice between the two is yours!

Sometimes (I hate to say this) selling is more of a feeling than a fact…