Every modern business function wants to be data-driven. But how do sales teams know what’s worth tracking, how often, and who to share it with?
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Gut feel is seductive. I’m sure everyone setting out to achieve incredible sales and fuel massive growth would like to think that their instincts are finely tuned, their intuition is frighteningly accurate and they have a preternatural understanding of how their prospects think, feel and behave.
The truth is much simpler: data and analytics will always beat gut feel when it comes to decision-making in sales. Even at a tiny, seed-stage startup, with five customers, you’ll always have an easier time closing the sixth if you analyse the data from the first five, rather than going on gut feel. There’s likely to be a commonality about how you won that business - was it a matter of having the right contacts? The entry point? Being aligned on requirements? Targeting the right role? Identifying the right business pain?
Any data is always better than none when you’re scaling sales. Follow the data, and growth won’t be far behind. That’s why setting up the right reporting, analytics and KPIs is amongst the most important things you ever do in this role - so what should you measure? What do you use to measure it? How often do you look at the data, who should see it, and what happens next?
“Even at a tiny, seed- stage startup, with five customers, you’ll always have an easier time closing the sixth if you analyse the data from the first five”
The basics: set up your reporting stack
The core system for setting up your reporting and analytics is obviously your CRM. Salesforce is the weapon of choice for huge numbers of sales teams, but any CRM system that gives you the demand sources for your pipeline will work as the central pillar of a decent reporting stack.
Start with the obvious metrics - splitting your pipeline up by activity across each of your demand sources (i.e. outbound demand-gen, marketing-driven inbound, partnerships, customer referral, and so on).
Split everything down as far as possible and you’ll have the basic ability to see which channels are over- and underperforming, which reps are on target and off target, and particular issues across the board; for example, if a particular region or vertical is underserved by marketing. This granularity will help you surface problems and make decisions day-to-day.
Move beyond the basics and you’ll start to find more nuanced metrics that will really help your decision-making. For example, tracking deal velocity by demand source per country: this level of details arms reps with the ability to execute hyper-tailored plans - even more so if you automate this reporting to be a live feed of data.
However, where CRMs fall short is achieving a level of granularity that can surface all this insight in one place, which you’ll need as you scale. This is why ultimately you should make CRM just one pillar of your reporting stack, along with your marketing automation tool data, and then any Google sheets in which you collect data too.
The goal then should be to find a way to abstract data from all those sources, using tools like Tableau and Domo, that allow you to slice, dice and combine data from multiple sources in one place for a blended view. If you can feed and water those systems and have them run properly, then the data will empower you to be a genuine change agent for the business.
Rather than just feeding back numbers about past events, you can use it to align functions going forward, make smart decisions, and offer valuable insights as to where change is needed.
Of course, that kind of reporting stack doesn’t come cheap. At the other end of the scale, the low-cost way to accomplish something similar is to use a tool like Google sheets and plug in API data directly. Various tools offer rolling contracts of as little as $30 a month to create and define reports in your CRM, with a series of summary tabs, then using filtering to pull the right data in. If you set these up to automatically update on an hourly basis, it’s not quite a live feed, but you can be confident the information you’re using to make decisions is at least relatively recent.
What’s leading and what’s lagging?
Leading indicators and lagging indicators offer different insights to sales teams looking to scale. Lagging indicators let you look back at a certain point in time, helping you to understand how you got where you are; the primary lagging indicator is, of course, closed revenue; conversely, leading indicators get you to that closed revenue. The key leading indicators are metrics like the volume of discovery calls the team is holding. This shows you the level of activity that the outbound sales team is carrying out in terms of understanding new potential customers. This must be tracked through to a meeting, where ideally three things are present:
- An individual (potential champion) with a clear, identified pain point;
- Specific financial impacts; and
- A finite, compelling event (ideally).
It’s up to reps to accept this as a qualified opportunity. The volume of qualified opportunities moving into proof-of-value (POV) is the next key indicator, and then the output of POVs versus closed revenue closes the loop.
More data is always better - that’s an evergreen statement - but how much is enough? The baseline has to be the bare minimum data that you need to progress the opportunity through all its different stages. If you can’t understand the champion, identify the financial metrics and spot a compelling event, you won’t be able to move the opportunity forward.
The next question for data-hungry salespeople is how often they should review the reporting and analytics they gather. In my case, I look at everything daily. There are some numbers where that level of currency isn’t hugely valuable - for example, I can see pipeline creation and receive alerts in real time, but we run monthly reports to enable the serious analysis. Alerts on created opportunities and closed business are great to receive, and meeting volumes are a good metric to be able to see in real time.
But the true determining factor of how often you should report on any given metric is how quickly you want to be able to react to changes in it. For example, if your pipeline suddenly increases 10%, do you want to know straight away? Was it one big deal falling happily into your pipeline, or has an inbound/outbound strategy suddenly paid dividends? Similarly, if pipeline, or SDR calling volume, slows down in week 2, do you want to be able to take corrective action mid-month? The frequency with which you report and share data should be driven by how actionable that data is - and what you plan to do with it.
"It’s a huge mistake to discard a datapoint now as being irrelevant, only to find that a year from now, you need to start collecting it”
Less is not more
In an enterprise environment, if you were to join a sales team and start receiving alerts at the level I’ve described - opportunities created, deals won, and so on - the volume would be overwhelming. But if you’re building a sales function from the ground up, and then scaling it, then getting the full picture from day one is never a bad idea. As you scale, you’ll build regional teams to be responsible for particular datasets, giving them a narrow focus and filtering out the rest. You can’t provide that laser-focused snapshot without capturing as much as possible from day one.
It’s crucial to find a way to incorporate data from existing users of the product into your reporting streams. It informs your conversations with prospects, helping you to understand usage and adoption, arming you for the discussions that start when a renewal is looming, and surfacing any red flags you need to see. It also helps SDRs to understand the challenges and pain points that their prospects face - never a bad thing.
Managing the reporting/analytics transition to multiple teams, verticals and regions seems complex but it should be straightforward if your source data lives in one CRM. Define a master report that encompasses absolutely everything as a basis, then use your abstraction tools to filter and manipulate it as per the requirements of each team. The important thing is that you start out with clean reliable data, and above all, that you avoid building something that’s not agile. Make sure your reports can support, for example, changes to individual fields, without causing repercussions that break your reporting and force you to rework them from the base level. If you want to scale sales, then design for scale - set up a reporting stack that can handle, and enable, rapid iteration and growth.
Remember that you don’t know what you don’t know. It’s a huge mistake to discard a datapoint now as being irrelevant, only to find that a year from now, you need to start collecting it. In a high-growth environment if you’re suddenly asked to double the size of the business, you might need that data to make decisions - keep those reports running in the background and collect as much data as you can, even if you haven’t defined what it’s for yet. It might prove to be your secret weapon a year from now.
What your C-suite needs to see
Deciding the level of detail that your leadership need to see is a reporting challenge for every sales team. Sometimes the C-suite want the same level of access as you have to sales reporting and KPIs, so you make need to provide access to Tableau, Domo or whichever system you use, with an executive dashboard that surfaces the same slice of data you see. Somewhere in the middle are the leaders who just want direct and reliable answers to ad hoc questions, meaning you need to pull data on the fly as and when they require it. Then at the other end of the scale are the traditionalists who prefer to view everything through the prisms of PowerPoint and Excel. If you get your basis dataset right, then the process of abstracting the right data and translating it into decks and sheets can be relatively painless.
A common demand is that the key indicators and metrics are reduced down to one slide. This is a worthwhile exercise in focus, but it’s important to remember that there’s always a big story and a bunch of lagging indicators behind the reporting headlines. Rolling up the numbers into headline figures can easily be misleading - blended averages can obscure weakness and overperformance that may need to be addressed. The right level to share depends on how interested senior stakeholders are in the stories behind those numbers.
“Rolling up the numbers into headline figures can easily be misleading - blended averages can obscure weakness and overperformance”
In my experience dealing with tech companies, C-suites often include a CTO co-founder from an engineering background, who leads on the product side; and a CEO with a more sales-oriented skillset. CEOs excel at peer-to-peer executive selling, and by volume they’re often the person in the company who’s held the most sales meetings, having been involved in scaling the company from day one. This kind of personality will often want details and granular access into the sales data - even if they’re no longer involved in sales processes. Make sure you’re able to provide it, because the knowledge they have about selling the specific product never goes away. You might be surprised at the insight they can add even years later.
Use it or lose it
The final key lesson about reporting and analytics to keep front of mind as you scale sales is very simple: make sure you actually use the data you gather. Don’t let your number-crunch meetings drift into being forums where data collectors sit around reading out the numbers on screen. Functional owners must confront the data and make actual business improvement decisions. If all you’re doing is describing past events, then you probably all have more valuable things to do with your time.
Every company wants to be data-driven - particularly in tech - but to achieve the pace of scaling that most startups want, getting reporting and analytics right, and turning those insights into upside for your business, is a winning formula you can’t ignore.