Leading and coaching your sales team effectively involves grounding your strategy in data. Specifically, sales deal data: information from the Opportunity fields in your CRM, packaged into useful metrics from which you can draw insights.
Now, I don’t mean sitting behind your dashboards and then putting on a good show for each SKO (although I’ll never say no to a great kickoff!). What I mean is: leading with data-driven insights allows you to truly command respect since your team knows that your coaching and sales strategy is driven by skilled interpretation of facts and numbers. You can’t just be gun-slinging advice based on “what worked for you back when you were at Xerox”.
Even if your advice is great, your team wants you to understand and interpret the data and KPIs for them to ground your advice in the current reality of your sales team. That’s what we’re going to talk about today: how can sales leaders gather, manage, interpret, and collaborate effectively using data?
It’s easy to get lost in the woods when talking about data, so let’s emphasize: the whole point of using data effectively is so that your team can sell better. Not so that you can improve your spreadsheet-fu for its own sake.
What is Sales Data?
Sales deal data is gathered from the sales process. Usually, from the CRM directly, but also from your pipeline management tool, if you have one. Think: opportunity fields, account level rollup fields, Salesforce reports and dashboards, that kind of thing. But it’s not just the contact, firmographic, and demographic info that exists in the CRM fields: it’s the formulas, ratios, benchmarks, and other key data transformations that allow you to actually interpret that data and act on it.
Why is Sales Data Important?
Without a data-driven approach, you’re flying blind. That sounds generic, but let’s talk about some specifics: let’s say you think the BDR team should focus on making more calls, the AE team should discount on price more than they currently are, and that to increase revenue, you’ll just need to hire more reps.
Okay, great ideas. But how are you backing up and justifying those ideas? Bringing data-driven insights into a single coherent strategy and presenting that both upward to your CEO and board and also within your team of sellers themselves, you’ll gain massive credibility. All stakeholders will know that you’re not just shooting from the hip, you actually have the data proving your ideas.
Maybe in the example above, I come in and present data showing that the BDR team is converting 15% of their connected calls to meetings, but they’re spending too much time researching prospects before calling and not even deploying that research on their cold calls. There we go, that’s a data-driven recommendation to make more calls!
Continuing the example: what if I review sales call transcripts, Next Steps fields, time in stages, and average sales prices sold by rep and realize that, in fact, we shouldn’t be discounting just to get deals done. Instead, what this is telling me is that we should be working on negotiation techniques across the team to get deals across the finish line.
Incorporate the skilled interpretation of data into your decision making as a leader, and your team’s performance will go through the roof. I’ve seen it and done it. This works.
Track These 3 Types of Sales Data
Going from “in the trenches” to “above the clouds” and in between, there are three main categories of sales data you should be tracking:
- Rep Level Sales Process Data
- Department Level Sales Productivity Data
- Business Level Financial Data
Let’s break down each of those.
Rep Level Sales Process Data
Each rep is going to be good at certain things and not as good at others. Paul will leave opps in Negotiation Stage for too long because he’s not as direct of a negotiator, and Natalie will enter opps into Proposals too soon because she doesn’t do as deep of discovery as Rocky does. While every team will have their own focus metrics, here are some examples of what I like to track:
- Prospecting Conversion Rate % by Rep
- Meeting to Opp Acceptance Rate % by Rep
- # Days In Each Stage, by Rep
- Overall Opp Win Rate % by Rep
- Opp Win Rate % by Lead Source and by Rep
- Average Annual Contract Value (ACV) Sold by Rep
- Pipeline Capacity (# of Deals Managed Per Month) by Rep
- Overall Quota Attainment by Rep
In each of these cases, the data brings me a coaching opportunity. Wherever a rep is falling down in one particular area, I know to focus in on that area. Likewise, when a rep is crushing a certain KPI, I’m going to look at what they’re doing in particular to encourage them to share their tips with the rest of the team.
Department Level Sales Productivity Data
In some cases, you’ll want to look not at how each rep can improve but at how the team as a whole can improve. The reason to do this is if you review the data and believe that you’re underperforming in a particular area, but not because of the team you have.
Some examples to think about in this case can be:
- Opp Win Rate % When Competitor X is Mentioned
- Prospecting & Opp Conversion Rates by Lead Source
- Average Sales Price per Product/SKU
- Opp Win Rate by Customer Segment Being Sold To
This is where you’re interacting with the product and marketing departments. I like to coach my team as a whole in these areas, but usually, I coach them all in the same way vs personalizing it. For example, if I know that Product X is a difficult sell for now (based on low ASPs or win rates) until we get more case studies from existing customers, I might encourage the team to focus on other SKUs until we get the material we need.
Your interdepartmental relationships are key as a sales leader, so over-communicate these department level metrics to other leaders that you work with!
Business Level Financial Data
Ah, finance: my favorite. (Just kidding, I don’t play favorites with data… or do I?)
This level of data interpretation requires you to remove your I-Was-A-Salesperson-Once hat for a moment and think about how the sales team is performing overall in light of the financial goals the company needs to hit to be successful. Here are some of the financial metrics that I often review with sales teams:
- Cost Per Qualified Opp, by Lead Source
- Quota Attainment % by Rep and as a Team
- Total Customer Acquisition Cost (CAC)
- Sales CAC, excluding marketing and other costs
- Changes in CAC and quota attainment over time
- Burn rate and cash runway
If you’ve identified your Average ACV Closed Per Opp, and your Cost To Generate Each Opp… whoa, baby. You’re in business. That’s the golden formula because it tells you whether the whole motion is sustainable, for how long, and whether and where you can scale it up profitably. See how magical data can be as a sales leader?
Let’s try delivering a micro-presentation to the board, including ALL types of data we just discussed:
“Good morning, everyone, here’s the sales update for the prior quarter. Of our 7 reps, Jackie and Tyson are the clear winners, mostly in terms of their high ASP and superb win rates. Steven is promising but needs work in terms of his late-stage pipeline working abilities, and we are coaching him on that.
The department as a whole is hitting 104% of quota, although we’ve temporarily slowed down our sales of the lower-winning Analytics product until such a time that I can continue to work with the Director of Product Marketing to produce more effective collateral and customer examples. Until then, I hesitate to distract our well-performing reps with this offering, although I’m open to feedback on this portion of the strategy.
Overall, while CAC as a whole has held steady recently, Sales CAC has actually ticked downward to $18k; given our slightly rising ASP of $48k ACV, and the continued improvements we’re making to rep coaching and tech stack by implementing Dooly, I believe that a 2.85X ARR/CAC target is well within reach, by the end of two quarters from now.”
Listen closely… can you hear it? The sound of all the board members being totally impressed at how data-driven your sales leadership strategy is?
It’s a glorious sound, indeed.
Strategies for Managing Sales Data Effectively
By now, we’ve gotten excited about the potential for what you can do as a sales leader when you gather, interpret, and act on data to benefit your team and quota attainment. But the dreaded thought has probably already arisen: do we even have any (clean) data to act on? And if we were to get it, how would we get it? Our reps have enough on their plate as it is.
These are a few best practices you can follow to make headway against this persistent challenge facing sales leaders.
Implement a professional, centralized CRM
It seems obvious, but I’ll start with the reminder to use a professional CRM. And hire someone to implement Salesforce or even Hubspot if you haven’t already. When you do this, avoid “field sprawl”: that’s where the CRM implementation manager builds tons of required fields just because people in your organization ask for them to be created. Make sure the fields you create are necessary, easy to report on, and only make absolute must-have fields required.
Remember, you have to be able to use this data effectively, so capture what you will use and make it easy to work with once it’s in the CRM.
Standardize and automate data entry
The biggest gripe you’ll hear about CRM tends to be from your reps themselves because working in CRM is universally reviled by most professional salespeople. At best, it’s tolerated.
My view on this is simple: don’t fight this, embrace it! Keep CRM simple, try not to burden your expensive account executives with data entry tasks, and spend a few bucks on a pipeline management tool like Dooly to make it actually fun for each rep to manage their pipeline. Seriously, you can give them a powerful but spreadsheet-like tool, or you can give them the equivalent of a cool spaceship console. Build an internal experience your AEs will love, and your team will thank you.
When you do require your team to actually enter data, explain this business need empathetically. Train the team on data best practices: why you need certain things filled out, what we’re all going to do with that data, and discuss how you’re investing in tools and a smart CRM implementation to make the whole thing less laborious. Be clear that it’s not just because you “want them to fill this stuff out” because you feel like having certain fields filled out. Nobody likes that.
Set up automatic data enrichment and cleaning processes
When it comes to the simple firmographics and demographics: as much data entry as possible should be purely automated. With a wealth of contact data enrichment tools available, nobody on your team should be keying in Job Titles, Company URLs, or LinkedIn interactions manually. Set up auto-enrichment schedules that make sense, and sit back as your CRM becomes a self-cleaning, pleasing tool.
With respect to pipeline level data, such as the ol’ “What’s going on with this deal? dooly.my.salesforce.com/best-deal-ever-totally-gonna-close” question, try to tackle these in 1:1s or pipeline review meetings. Things like Close Dates, Stages, Next Steps, Blockers, etc, should be well taken care of if your reps are using a pipeline management tool, but if you’re using CRM out of the box, quick 5-min blocks during sales meetings, and simple reports, can do the job in a pinch.
Maintain a data-driven culture of collaboration
Being a data-driven sales leader who communicates effectively with their team and with other stakeholders and executives is one thing. But to go above and beyond, try to influence others in the organization to get them truly on board with your philosophy as a revenue strategist. All department heads should be looking at the same source of truth data, analyzing it to come to similar insights, debating those, and agreeing on a course of action.
To do this, you need to take charge by demonstrating that you won’t stand for unclean CRM data, field sprawl, or the passé idea that full cycle reps should be responsible for full cycle data entry. You know that garbage in means garbage out, and bad data means low-quality strategy. Lead by example. Sometimes, that means publishing your sales forecast (and its inputs) more widely than you otherwise would. It can also mean illustrating how that forecast is sometimes dependent on those other than your own department, such as when you’re collaborating with marketing on demand gen and cost per opp metrics.
At Dooly, we’re fans of smashing silos. Not in an agricultural sense (although that does sound a bit fun after a long quarter of crushing quota), but we believe that cross-functional collaboration is more than just having cordial relationships with the CEO and VP of Marketing. It means agreeing on what data you’ll all use and why, how you’ll provision the right tools for gathering and interpreting data, and that you won’t ask high-paid reps to do data entry jobs.
Empower and Appreciate Your Sellers
The collaboration doesn’t stop with leadership: every leader knows that their relationships with front-line managers and sellers are the most important of all.
By communicating your approach to data-driven sales management to your team, you’re reminding them of the business reasons why certain data is necessary. You’re remaining open to feedback about which required data, and fields might not actually have a business purpose and potentially making those not required. You’re ditching the “my way or the highway” mentality!
Take the opportunity, too, to help equip your team with insights that you’ve derived from your focus on data. Write monthly sales team updates with trends and other interpretations of the data that help the team sell better – why are we winning, where are we losing? To which competitors? Include relevant screenshots from Dooly and SFDC where applicable, but don’t bore them with entire dashboards. Which reps are doing what well, and how can we learn from them?
Not only that but which reps contributed valuable anecdotal insights that ended up influencing strategy overall, both on the sales team and on the product team as they evaluate feature performance?
Marketing, too, can incorporate anecdotal sales data to analyze competitors and understand how to better market against them, or to adjust positioning and website messaging.
Lastly, one of my favorite subtle tips for adopting and entrenching a culture of sales data is to recognize and reward data-driven success stories. A little Slack shout-out for AEs who close deals with plenty of data on them, allowing the CSM or implementation manager to onboard smoothly never hurts! Perhaps you might even spiff the rep, team, or department with the “cleanest opps” with a nice dinner at the end of the quarter.
How to Automate Sales Data Entry with Dooly
Dooly was made for sellers, by sellers! We Dooligans are vigorous proponents of effective, effortless selling. And that means letting automation do the job it was meant to: the stuff our customers’ reps don’t need to be doing. Dooly automates sales data entry to help you manage pipeline, directly integrates with Salesforce to ensure up-to-date info, and leverages AI to keep your team focused on the customer conversation and enjoying the craft of sales. We believe sales leaders should be strategizing, coaching, leading, and getting promoted! Nagging reps takes you away from the best parts of the role, and we’re here to free you from it.
Take a spin today if you want to stay ahead of the game.
Join the thousands of top-performing AEs who use Dooly every day to stay more organized, instantly update their pipeline, and spend more time selling instead of mindless admin work. Try Dooly free, no credit card required. Or, Request a demo to speak with a Dooly product expert right now.
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