Sales is a game of numbers: Quotas. Pipeline generation. Number of phone calls placed. Number of demos played. The list goes on. Accurate data collection is crucial for sales, but it shouldn’t stop there.
The ability to make data-based decisions is invaluable for the rest of the organization as well. In fact, it’s how Google runs its entire organization. In this era of big data, the new ABC(D) of sales is: Always Be Collecting Data.
1. Foster a culture of data-based decision-making
A data-based decision-making culture is characterized by collecting data, analyzing information, and conducting experiments.
Encouraging innovation, tolerating mistakes, and emphasizing continual learning all help to create this type of culture. In contrast, an emotion-based decision-making culture is rooted in intuition, gut instincts, and prior experience.
They don’t necessarily have to be mutually exclusive, but in a data-based culture, the insights revealed by the data take priority.
2. Identify a question you want to answer
Generate a hypothesis based on a problem or issue you’re experiencing and that you think data will help solve. Based on this hypothesis, determine what needs to be measured (i.e., what data needs to be collected).
3. Design and conduct experiments
Every experiment needs two requirements, which are (1) a treatment group vs. a control group and (2) a feedback mechanism (i.e., results or outcomes). With initial experimentation, you want to first establish proof-of-concept.
As your experiments become more advanced, you’ll want to test one variable at a time while keeping everything else constant in order to isolate the effect of that variable.
4. Collect the data
Based on steps two and three, decide on what you want to collect in terms of data inputs and data outputs (i.e., metrics). Set up (or buy) accurate data measurement tools, provide clear descriptive labels for your metrics, and ensure data is being captured in a standardized way across the data collection.
5. Analyze the data
The first step in data analysis is generally data cleanup (e.g., correcting data entry errors, merging different datasets together). Look for patterns in your data. Is there a correlation between sales volume and sales experience? Are there mean differences in sales across departments?
6. Interpret the data and take action
Figure out the story your data is telling you and determine which actions can be taken based on that story.
What it looks like when we apply these principles to the assessment and selection of sales candidates
First, make a data-based hiring decision by collecting data on your candidates using a pre-hire psychometric assessment. The hypothesis here is that it will help identify more productive hires. Use candidates’ scores on this assessment as a main basis of your hiring decisions.
Then, track your hires over time. After an initial ramp-up period, assess their sales performance and correlate it with their psychometric assessment scores. This will help test the assessment’s validity. A good psychometric assessment will correlate positively with sales performance.
You can do further testing by creating an experiment that compares the sales performance of individuals who were hired based on the psychometric assessment versus those who were not.
By following the new ABC(D) of sales, you’ll be developing a data-based, analytic culture for your company. The insights created by doing so will allow you to make vastly smarter decisions that save you thousands of dollars in hiring and turnover costs.
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