Attribution in Sales Forecasting: Increasing Forecast Accuracy
September 30, 2020 •DJ Team
Sales attribution is at the core of any sales manager’s work, allowing them to accurately forecast sales during a given period.
Sales attribution is a critical part of using a forecast model. The modeling allows you to assign a value to some or all of the various touches a lead engages with.
What Is Sales Forecasting?
Sales forecasting is the process of projecting sales your business will close during a future time period – a month, quarter, half-year or year. Sales forecasting can be done for individual sales reps or whole teams.
It helps sales leaders to track performance and, perhaps more importantly, act quickly when results are not progressing according to the model used.
Managers can use sales forecasts to help guide present and future decisions, including:
- Hiring decisions
- Inventory management
- Cash flow management
- Post-sales support planning
- Performance evaluations
Sales forecasting also allows managers to gain a single, unified view of goals and progress and report that progress to leadership.
Understanding Forecasting Methods
There are several common types of sales forecasting available. Here is a closer look at those forecasting methods:
- Length of Sales Cycle Forecasting. Among the simplest forecasting methods, this model uses data on the typical time it takes a lead to convert into a customer. It’s an objective way to forecast and does not rely on sales reps’ assessments of how likely a lead is to close.
- Lead-Driven Forecasting. In this model, the lead source is critical. A value is assigned to each prospect based on the past performance of targets from the same lead source. Data to use include leads per time period for previous intervals, the lead-to-customer conversion rate and the average revenue per lead from that source.
- Opportunity Stage Forecasting. This approach looks at where a lead is in the sales pipeline and uses past performance to project the success rates of leads in each stage.
- Sales Rep Forecasting. Do your sales reps have a good track record at projecting where their prospects are likely to land? If so, then this model can work. It relies on using the reps’ projections to create a forecast. It’s a very risky model, especially given that many sales reps will overestimate their projections.
The Importance of Marketing Attribution
Attribution is about giving credit to the technique, channel, step or action that a lead takes before making a purchase.
Lead attribution is the process of assigning a percentage of each deal amount to the marketing activities that a prospect engaged in before making a sale.
There are multiple models for attributing marketing leads. The most common are:
- First touch, which assigns all revenue to the first interaction a lead had with your company
- Last touch, which credits the revenue to the last marketing interaction a lead took before making a purchase
- Multi-touch, which assigns percentages of the sales revenue to different touchpoints during the sales cycle.
Lead attribution is valuable to sales and marketing teams in that it helps determine future investments in various components of marketing campaigns. Whether you call it deal attribution, marketing attribution or channel attribution, the process is critical.
By using marketing attribution in tandem with sales forecasting, you can build a far more accurate picture of not only your sales projections, but also the channels that lead to sales.
Using this collection of historical data from your sales and marketing work, you can better allocate resources for future campaigns. Allocating a deal amount to various efforts gives you a clear understanding of the relative value of work that moves marketing leads to purchase.
Here’s how it matters. Deal attribution is a backwards-looking process. It takes into consideration what worked for past sales and assigns the value of those deals to the various marketing tactics used. Typically, multi-touch channel attribution models reflect the many aspects of campaigns that lead to sales.
It provides data about customers from the first touch through to conversion. It also allows you to better represent the multi-touch impact of a customer’s journey.
Now consider using a sales forecasting model that factors more than where a lead is in the funnel. By applying the historical trends of how leads behave based on the marketing touches, you have another factor to consider in assessing conversion rates and revenue.
It’s a sales forecasting practice that allows you to consider not just where the lead is, but what the lead is doing.
Challenges to Accurate Sales Forecasting
No matter what types of sales forecasting your business uses, there are internal and external factors that can skew the model. Just as your business is not a static enterprise, neither should your sales forecasting approach remain rigid.
Consider these factors that can affect your forecasting.
- Staff changes. When successful sales reps leave, your revenue is likely to decline until new reps are hired, trained and deployed.
- Territory changes. Moving reps to new accounts means there’s a time lag before new relationships can be established.
- Compensation policies. If your company changes the way reps are compensated, their behavior is likely to change, too.
- Economic uncertainty. Market forces and economic realities require shifts in revenue forecasting and may make historical data less reliable.
- Product variances. Discontinuing and adding new products requires adjustments to forecasts.
Sales forecasting in tandem with marketing attribution provides your business with invaluable insights. The data-driven modeling you use can provide accurate, predictive projections that have ripple effects throughout the organization.
With consistent assessments of past sales, lead behavior and sales funnel progress, you’ll be able to respond to changes faster and provide insights that drive business decisions from budget allocations to product strategy to customer relations.
- Attribution Tracking
- Channel Optimization
- Consumer Insights
- Content Marketing
- Data Science
- Digital Marketing
- Digital Transformation
- Lead Generation
- Market Intelligence
- Marketing Analytics
- Marketing Attribution
- Marketing Management
- Marketing Operations
- Organic Search
- Paid Search
- Programmatic Advertising
- Search Marketing