What are the benefits and drawbacks of using historical data to forecast sales quotas?

1 Benefit: Simplicity and consistency

Using historical data to forecast sales quotas is relatively simple and consistent. You can use existing data sources, such as sales reports, CRM systems, or accounting records, to calculate the average, median, or growth rate of past sales. Then, you can apply a percentage increase or decrease, or a fixed amount, to adjust the quotas for the current or future period. This method does not require complex calculations, assumptions, or models, and it can be easily replicated and standardized across different sales units or regions.

2 Benefit: Realism and fairness

Using historical data to forecast sales quotas can also be realistic and fair. You can set quotas that reflect the actual performance and potential of your salespeople or teams, based on their track record and achievements. You can also account for seasonal variations, market fluctuations, or other external factors that may affect sales, by using data from the same or similar period in the previous year or years. This way, you can avoid setting unrealistic or unattainable quotas that may demoralize or discourage your sales force.

3 Drawback: Complacency and stagnation

Using historical data to forecast sales quotas can also have some drawbacks. One of them is complacency and stagnation. If you rely too much on past sales data, you may miss opportunities to grow your sales or expand your market share. You may also create a culture of status quo or mediocrity, where your salespeople or teams are satisfied with meeting or slightly exceeding their quotas, but not motivated to pursue new or challenging goals. You may also fail to recognize or reward exceptional or innovative sales performance that goes beyond the historical data.

4 Drawback: Inaccuracy and irrelevance

Another drawback of using historical data to forecast sales quotas is inaccuracy and irrelevance. Past sales data may not be accurate or reliable, due to errors, omissions, or inconsistencies in data collection, processing, or reporting. Past sales data may also not be relevant or representative of the current or future sales situation, due to changes in customer preferences, competitor actions, product features, pricing strategies, or market conditions. Using outdated or inaccurate data may lead to setting wrong or inappropriate quotas that may harm your sales performance or customer satisfaction.

5 Alternative: Mixed or adaptive methods

Using historical data to forecast sales quotas is not the only option. You can also use mixed or adaptive methods that combine historical data with other sources of information, such as customer feedback, market research, industry trends, or sales forecasts. These methods can help you balance the advantages of using historical data with the insights and opportunities of using other data. You can also use adaptive methods that adjust the quotas periodically or dynamically, based on the actual sales results or changes in the sales environment. These methods can help you optimize the quotas for the best sales outcomes and customer value.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?