Data-driven Sales Performance Management (SPM) can bring tangible benefits like more profitable sales, better quota setting, insight into your sales funnel, improved retention, and operational efficiency that are compelling to any organization. But what does it take to deliver a data-driven SPM solution?
The first step is to plan your data strategy: what data do you have and what value can it provide to drive sales performance? Artificial Intelligence (AI) and Machine Learning (ML) can automate a lot of the number crunching, but it is up to the business analysts to understand your data environment and envision what might be possible. At a high level, there are three key considerations in defining a data strategy to support data-driven selling.
1. Decide what to measure
Most organizations have massive amounts of information. You likely have a lot of data that is relevant to understanding your customers, channels, and sales cycles. It’s worthwhile to do an audit of the data that is available and then consider what else you might want to measure. The data that you collect will depend on what you want to accomplish. Here is where you should consider the power of AI and ML to provide predictive value by providing the best path to get to your sales numbers.
For example, let’s say you are a parts manufacturer with sales reps who are delivering product stock along a given customer route. You want to be able to forecast orders, so that you can pre-order appropriately. Using predictive analytics, with inputs like customer selling history, route, and seasonality, you can predict the quantities of various products that you are likely to need. It’s not hard to see that these predictions have value beyond the sales rep knowing what to order and being able to estimate commissions; for example, they also could provide value in terms of budgeting, forecasting, and accruals, as well as managing inventory and production.
Think about what you want to accomplish, and use some imagination to envision what data might be able to be captured that could have an impact on sales performance. Based on that analysis, define your operational, financial, and predictive metrics.
2. Ensure data exists upstream
With traditional SPM, you needed to worry about only the data required for calculating commissions and other variable compensation, but for data-driven SPM, you will also want to consider the additional overlay data you need for predictive analysis and reporting. You will need to map out your data integration strategy.
As a downstream system, a well-designed SPM solution requires quality data to deliver valuable analysis and outputs. This means it’s important to think about your upstream information sources and how you structure that data.
So, what data do you need? The typical metrics you want to be able to measure include:
Financial metrics such as sales spend and profitability (from the Finance system), including deal margins (bid vs. list from CRM sources at the transactional level)
Operational metrics and indicators of sales efficiency, such as conversion rates, close rates, and pipe aging (from the CRM system)
Field metrics relating to sales transactions and the related context
3. Think about new high-value SPM outputs
When an SPM solution includes transaction-level crediting, it also has the potential to generate some very useful summary statistics (for example, how many people were paid on the deal, what was the margin, what total incentives can be allocated back to the deal, and how does that compare to the booking amount?). Think about the information that is collected at the transaction level: what value you can extract by correlating that data with other metrics?
In the era of big data, next generation SPM systems can equip you to make decisions that will drive revenue and motivate sales teams like never before. Get the tips you need to implement an effective strategy and system with our eGuide: The Data-Driven Approach to Sales Performance Management: New techniques that will drive revenue in 2019.