The accuracy of revenue forecasts is heavily reliant on information from the field. But, salespeople often times aren't estimating the correct time frame for a large sale, which inevitably throws sales calculations off balance.
Depending on how many optimists and pessimists you have on your team, there are typically some adjustments that need to be made to arrive at a number you are willing to commit to your executive team. As a sales leader, it's imperative to understand the key metrics in determining the length of a sale.
Sales Cycle Length measures the average time between making first contact with a sales lead and that same sales lead completing a purchase order that can be recognised as a sale. Sales Cycle Length measures the speed of execution within a sales force, particularly in comparison to an industry peer group or world-class companies.
It is sometimes difficult to establish a common average Sales Cycle Length because there is a wide variety in what constitutes the terms "lead", "first contact", and "closed sale". Once your company has a clear definition of these terms, the calculation becomes more meaningful. Getting everyone speaking the same language within an organisation will go a long way toward improving your forecasting ability.
Having a common sales language helps salespeople organize their funnel and determine where they are in the sales cycle. Some sample definitions for "lead", "first contact", and "closed sale" are as follows:
- A "lead" occurs when a prospect requests product information and/or is engaged about a product or solution offered by your company
- "First contact" occurs when an inside sales representative confirms this information request over the telephone or in person
- A "closed sale" occurs when a purchase order or payment is received
Drivers affecting sales cycle length
There are three key drivers affecting Sales Cycle Length:
- Deal size - There is a direct correlation between Average Deal Size and Sales Cycle Length (i.e. the larger the deal size the longer it typically takes to close). This is primarily attributable to the required number of people needed to approve larger expenditures.
- Market segmentation - The shortest sales cycles are associated with increasing product usage by current customers. Market segmentation also refers to increasing market share in one's existing market and is also defined by sales campaigns where a deep price penetration strategy is deployed, designed to steal share in an adjacent market. The longest sales cycles are found where a new product is trying to have success in a new industry.
- Poor sales process - Organisations that do not map their sales process to the market's buying process have longer sales cycles. Unfortunately, some organisations feel they can force their sales model on a particular customer segment.
This rarely works given the presence of a competitor willing to accommodate the buyer's every request. An inward-looking sales process results in several inefficiencies and self-imposed bottlenecks, thus delaying the average sales campaign. Also, lack of strategic process on opportunities leads to lack of efficiency and effectiveness in the actions of the sales rep.
Time-series analysis: A best practice for shortening sales cycle length
Time-Series analysis is a forecasting technique used to evaluate sales cycle length data to determine patterns and trends. The findings can help companies determine the best sales opportunities to pursue. Time-Series analysis is a useful method for using past quantitative data to predict future performance.
Good ... better ... best. Three methods for determining a Time-Series Analysis: Naïve forecast, Moving averaging forecast, and Weighted moving averages forecast. Each method uses demand from previous periods as the key variable.
- Good: Naïve forecast
Naïve sales cycle length assumes the next period's sales cycle length will match that of the previous period. Though very easy to calculate, using this approach is crude and can introduce much error into the calculation, especially for organizations that have even a moderate amount of volatility in their business.
- Better: Moving averages forecast
Moving averages select a representative number of sales campaigns and calculate the average of those cycles. The result becomes the sales cycle length assumption for the next period.
The moving average sales cycle length helps correct the simplistic assumptions of the Naïve Forecast since it is likely that the previous period's sales cycle is not perfectly repeatable in the next period.
Moving averages help smooth over variations attributable to seasonal patterns. The moving average of sales performance based on the preceding three months reduced the chance that any one month's exceptional performance (good or bad) will unduly influence the next month's sales cycle assumptions.
However, more recent sales cycle length data is usually considered more reliable than older data since it may be indicative of current market conditions. Moving average sales cycle lengths do not account for this since the impact of recent data is reduced by the inclusion of older data in the average.
- Best: Weighted moving averages forecast
The weighted moving average can help overcome this bias. The weighted moving average assigns weights to data in different periods with, generally speaking, more recent periods receiving a higher weighting because they are considered more influential. The sum total of all the weights equals one; therefore, each weight is a fraction of one.
Executives must ask themselves a key question when considering sales cycle lengths: Is the cycle length increasing, decreasing, or flat? Time-Series Analysis, specifically moving averages, can be helpful in answering basic trend questions as it may suggest emerging opportunities or conversely, warning signs. Time-Series Analysis is a good first step toward developing a better understanding of sales cycle length.