Creating a Data-Driven Sales Forecast
The sales forecast is simply a projected measure of how a market will respond to a company’s marketing efforts based on the sales that happened weeks or months before. For publicly traded companies, sales forecasts confer market credibility.
Forecasts essentially add value across the organization. The production uses sales forecasts to plan their cycles; sales ops plan territory and quota, the supply chain can plan their material purchases and production capacity, and sales strategy with channel and partner strategies.
These are use cases. Unfortunately, many companies stay disconnected from sales forecasting, adversely affecting business outcomes. What if sales forecast information is not shared?
The marketing team may create a demand plan that does not align with sales quotas, leaving companies with too much or too little inventory, inaccurate sales targets, hurting the company’s bottom line.
Keys to a Successful Sales Forecasting
There needs to be automation, strong organizational coordination, analytics-based processes to improve the accuracy of your sales forecasts. Some of the keys to success are:
- Form a collaborative team and source inputs from various stakeholders, salespeople, business units, and regions. Additionally, the frontline sales team must provide a pulse on the market unconsidered before.
- There must be a data-driven process using predictive analytics to assist the team.
- The analytics must produce real-time insights that allow sales leaders to make informed decisions and update the forecast based on demand or market changes.
- The data comes to a single point giving greater visibility into agents and regions, helping sales teams align different business functions across the organizations.
- Finally, a plan to improve the sales forecasting process creates more refined and accurate forecasts that will enhance over time.
You can create an accurate sales forecast for your business with these steps.
1. Establish a sales process
Create a structured and documented sales process to convert a lead. You must also set standard opportunity, lead, prospect, and close definitions. Everyone must come to a consensus on counting leads entering and exiting a sales funnel.
2. Assess historical trends
Examine sales from the previous year and break down the numbers by price, product, sales period, agents, and other variables. Add them into the projected sales per sales period to form the basis of your forecast.
3. Include business plans
Add strategic business plans and build them into your forecast. What is your growth mode? What are your hiring projections? New target markets? Itemize everything to understand the forecast at a granular level.
4. Set quotas
Work with sales teams to set individual and team sales quotas which will serve as financial baseline goals to compare alongside your sales forecasting.
5. Invest in a CRM tool
CRMs give your sales representatives a database to track and provide accurate predictions, even if your business is new. Accurate data will provide accurate forecasting.
6. Choose a forecasting method
The sales forecasting method depends on the age of business, team size and pipelines, data quality, and tracking habits. The forecasting model will depend on the industry, the business model, data tracking, and sales team.
7. Incorporate changes
In the future, you must incorporate changes and modify them according to the number of changes, especially in pricing, number of customers, promotions, channels and locations, and product updates.
8. Monitor competitors
You must consider the campaigns and products of competitors. Check both new players entering the market and also major players in the space.
9. Anticipate market trends
Keeping tabs on competitors will help you anticipate trends. Will any competitors go public? Will there be any acquisitions? Will any government policy or legislation affect how consumers receive your product?
10. Keep your teams informed and accountable
Regardless of what sales forecasting method you choose, keep your sales teams informed and communicate changes and decisions. Besides, CRMs will keep representatives informed about every interaction with leads. Inform your team of any feedback. The sales team will be accountable for their performance against quotas and forecasts.
Finally, predictive analytics and machine learning are already transforming many business areas, including sales forecasting. To conclude, companies can put relevant stakeholders on the same platform and do the following:
- Improve decision-making.
- Increase accountability.
- Standardize sales forecasting and pipeline management.
- Provide accurate and trusted forecasts.
- Align sales quotas and revenue expectations.
- Access data-driven trend analysis and sales benchmarking.
- Reduce territory coverage planning time, and
- Benchmark trends to assess in the future.
Draup for Sales is a sales intelligence platform that aids users with AI-powered sales insights. It deep dives into various indices from a company’s outsourcing probability to predict and quantify the high probable opportunities for sales and marketing teams, thereby saving time & costs spent chasing low potential leads.