5 Keys For Data-Driven-Sales Team Manager
Sales professionals and the general data-driven public, we decided to highlight the absolutely essential traits and their associated applications to sales. Here are 5 key traits of the data-driven sales manager.
- “Deal Reasonably Well with Uncertainty”
The best sales managers know what they don’t know. Even the most robust data-collection system of the highest quality is not a crystal ball – there will always be uncertainty in the sales world, regardless of how much ‘predictable’ data suggests otherwise. Effective sales managers are comfortable with uncertainty and will do their level best to improve levels of probability and predictability, even as they know that nothing can be etched in stone.
Take the opportunity pipeline, for example. Data-driven sales organizations pump their sales pipelines full of various data filters and risk factors in order to get as close a probability of closing on specific opportunities as possible. Understanding common risk factors – like close date moves, greater-than-average opportunity values, a long duration in the same sales funnel stage, recent momentum – can help sales managers better determine a given opportunity’s likelihood of closing. Variance and probability are critical components of sales, and good sales managers are okay with that.
thanks to : - Gareth Goh, insightsquared
- “Use Data to Develop a Deeper Understanding of their Worlds”
| 5 Keys For Data-Driven-Sales Team Manager |
Data-driven sales managers have a strong sense of what they want to accomplish by having access to all this data and information – namely, to gain a deeper and more meaningful understanding of their sales world. It’s not about boiling the ocean to harness every little bit of data – it’s about using small pockets of segmented data to drive actionable insights.
Does the sales manager want to better manage his or her sales pipeline? Is there no existing method for truly measuring the performance of her sales reps? Does the marketing team need a more effective way of calculating the return on investment of various lead sources? Determining which part of their sales world they want to develop a deeper understanding of will provide a better avenue for data – and the actionable insights derived from that data – to function in.
- “Learn from their Mistakes”
Those who fail to learn from history are doomed to repeat it. That mantra should drive all data-minded sales management decisions, as sales managers pore over reams of data to identify correctable mistakes. Data provides managers with a second chance – it highlights specific areas where managers or reps erred and gives them another opportunity to rectify this.
Sales managers can also use data to learn from the mistakes of others. Studying the conversion rates of each sales rep at different stages of the sales funnel is perhaps the best example of using data to learn from your mistakes. Advanced sales analytics on the sales funnel highlights specific areas of weaknesses among reps. For instance, a rep with a high conversion rate from stage 1 to stage 2 but a precipitously low rate from stage 2 to stage 3 might be struggling with the nuances of that specific stage and need additional coaching. The sales manager who brings this actionable information to a sales coaching session with that rep will find that the rep is more receptive to the constructive criticism, given its support by the data.
Sales Funnel
- “Recognize the Importance of High-Quality Data and Invest to Improve”
A commitment to a data-driven life entails a serious devotion to ensuring the highest-quality data across the board. Effective data-driven sales managers must be strict in their enforcement of data entry and having reliable and accurate data sources. After all, decisions made using inaccurate data are essentially bad decisions.
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To ensure the highest data quality, sales managers must enact a culture of total adherence to data entry and data quality at all times, across their entire team. Every rep must follow a strict process of entering the appropriate information in Salesforce for every opportunity they work with. Without a uniform data entry and data quality process, there will be too many inconsistencies in the information, rendering it ineffective and possibly leading sales managers to make wrong, calamitous decisions.
- “Bring as much Diverse Data to Any Situation as they Possibly Can”
The most data-driven sales managers refuse to take anything at face value. Instead of merely looking at the team’s overall sales cycle, for example, that sales manager is insistent on bringing more diverse data, finding more information and breaking it down into even more specific, meaningful segments.
Did our sales cycle change over the past quarter? The past year? What is our sales cycle among opportunities that are closed/won compared to opportunities that are lost? Which employees are most adept at closing opportunities in a small window and which ones are dragging things out? How do sales cycles in various segments of customers or industries differ? Do specific lead sources provide shorter sales cycles than others?
- Sales Cycle by Won/Lost
Asking these types of hard-hitting questions – and seeking the appropriate data to answer each one – can drive truly meaningful, actionable insights that lead to immediate and positive change. Sales managers who have this type of information are simply better-armed and well-prepared to keep on fighting the evolving sales battles ahead of them.
These are just a handful of key data-driven traits highlighted by Thomas Redman.
{{ The Guest Post Blogger organization was not involved in the creation of this content. - Dalvi Prabhakar B, Founder & Digital Manager (SEO,SEM,SMO) }}
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