This sponsored post is produced in association with The TAS Group.
There is no deficit in the amount of data available to sales managers to help them do their job. There is, unfortunately, sometimes a deficit of insight into that data. The reasons for this inconvenient truth are multiple:
- Sales managers are really busy and data-based sales management can take a lot of time.
- Even if the time and the will are there, it’s very hard to interpret sales data consistently.
- Most analytics are predictive (here’s what might happen) or leap to being prescriptive (here’s what you should do), without first being descriptive (here’s what this means in the context of this deal).
All of this adds up to a situation where many sales managers – who are already preoccupied with pipelines, sales forecasts, and quarterly reviews – simply eschew data analytics as a cumbersome waste of time, and thereby end up missing-out on the potential goldmine hidden in all that data.
And there’s the problem with most sales analytics in a nutshell. Now, what’s the solution?
Fantasy? Nope. Not if you know the right questions to ask.
The right questions (and how to ask them)
As a matter of rote, every sales manager should constantly assess the answers to these key questions:
- What are my must-win deals?
- Are there opportunities in my pipeline that are inactive or stalled?
- What is my actual win rate (as measured by dollar value, not just count of opportunities)?
- What happened to the deals John forecasted last month?
- Are we losing deals late in the sales cycle?
- What is the difference between our performance for qualified opportunities (deals that get to Stage 2 or 3 in the funnel) and all opportunities?
The questions are as old as time. Getting to the right answers, however, is a constantly evolving challenge based on client demands, morphing competitive landscapes, and a rising seas of big data that can sometimes overwhelm even the best-intentioned of sales managers.
So what’s the differentiator in getting to the right answers?
Learn more: Join our upcoming live webinar “The Secrets to Maximizing Sales Performance.”
Domain expertise
Nothing trumps experience. If you don’t know how to enrich data signals (e.g. those data points that have specific value in telling you what matters in getting a deal done), if you don’t know what metric to pay attention to in your pipeline, if you don’t know what questions to ask of your key performance indicators and how to interpret them into detailed action plans, then you are setting your sales team up for failure.
Until every sales manager in your organization is capable of doing these things, you’ll need to rely on automated solutions to do the heavy lifting for you. In the ideal world, this technology will free the sales manager from a world of reports, charts, and best-guess interpretations, and allow them to focus on managing their sales team and pipeline to optimal performance.
The Holy Grail is the ability to apply the solution today and understand what’s going on in your business better tomorrow. Ideally, your unicorn tech will be native to a top CRM, such as Salesforce, and will deliver specific, contextual advice to get the most out of every sales opportunity while leaving no room for misinterpretation – superior sales-force management, no analytics needed.
Describing the ideal solution
The typical approach most tools take to analyze data and provide answers to these questions is to predict sales performance in three or four buckets:
- Core Data — The data that exists in your CRM
- Situational Analytics — Typically represented by reports or visualizations, these tell you what happened in the past
- Predictive Analytics — Based on patterns from the past, what is likely to happen in the future? (Lots of challenges here to differentiate between causation and correlation)
- Prescriptive Analytics — Based on those predictions, what should we do about it? (Flawed, of questionable value, and risky)
The missing component goes back to being descriptive — determining what parts of the data actually matter and what data enrichment is needed before the data can be a reliable source for prediction and, ultimately, prescription. The TAS Group’s solution, Sales Performance Manager, does a good job of adding in the descriptive part of the equation and the end result is technology that seamlessly leverages the power of hard-core analytics to enable sound, metrics-driven coaching that sales managers in any sized organization can apply with confidence.
The true potential of big data in sales management is to identify small opportunities to improve the outcomes of each lead in the pipeline. Done well, this means letting sales managers ask key questions and get detailed answers that provide the level of granularity required to consistently manage risk out the pipeline, and squeeze out small but meaningful performance improvements across individual sales people, accounts, and opportunities.
Making it simple for sales managers to make sense of data and create plain-language action plans to optimize sales performance is what good automation tools can and should do, because sales management shouldn’t (and doesn’t have to be) so hard.
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