When we launched a new product in September 2020, our go-to-market team was determined to do so in a data-driven, highly iterative manner.
Conversation intelligence vendors were putting out a ton of “Labs” content at the time, so I spent a year running the most popular one — Gong Labs — on our discovery and sales calls.
What we learned came as a big surprise.
By every conversational intelligence measure, I shouldn’t have been selling anything:
- I was talking too much (I speak for over 60% of the talk time)
- I was asking too many close-ended questions (over 25 per meeting)
- I was interrupting prospects a lot (over 10times per meeting)
- I was taking forever to follow up after meetings (4.2 days)
But the weird thing is —
the worse my Labs stats got, the better my sales were.
We saw a 10% increase in pipeline velocity in Q3 of 2021, followed by our best ever Q4.
What explains this gap? Let’s dive into what happened in my case, and why I think sales teams should trust themselves more than generic sales intelligence tools.
Where sales intelligence tools go wrong
Sales labs are popular because they supposedly tell salespeople how to reach a set of targets — X leads, X clients, X revenue.
That appeals to our long held ideas of what success looks like, and preys on our desire to bring simple structure to what is inherently a very messy process with no guarantees of success.
But in truth, generating 100 leads a week could be a hindrance for your company and a boon for another one your size, if success to you means spending time on more qualified leads, while success to another means educating a larger array of leads.
So forget the labs. Instead, focus on these four principles.
1. Start measuring your own stats
First things first. Stop reading cookie cutter “labs reports” and start measuring the only stats that can give you an accurate insight into your journey: your own data.
Stop reading cookie cutter “labs reports” and start measuring the only stats that can give you an accurate insight into your journey: your own data.
The data used by sales intelligence tools to generate recommendations is not based on your company and how it reaches success. The tools probably draw an average across businesses, although the source of their data is unclear.
So executing on those recommendations blindly is almost certainly going to cause you problems.
Your statistics need to focus on optimizing the customer journey, not the sales rep’s journey. So hone in on metrics that reveal what potential customers are getting out of the process:
- How educated are they on your product coming into the first meeting?
- What kinds of problems do they come to you with?
- How many touch points do they need to have with your company in order to complete a purchase?
The answers to these questions are often right there in your communications, but harnessing them is burdensome unless you’re automating the process. Conversation intelligence software is widely available, so there’s no excuse to not get started with even one of the cheaper applications.
In our case, we pivoted towards focusing on customer-driven behaviors — such as talk time, questions asked, monologues, sentiment — and their relationship to pipeline progression (deal stages moved per period of time).
2. Use the RevOps machine to work the stats into your operations
To optimize any process, you need to work those statistics into your operations and iterate continually until you get the best results.
For sales, that requires an agile “machine” that takes information from the pipeline, marketing leads, finalized deals, customer satisfaction, product ease of use, and more. It’s not actually sales at all, but more of an umbrella operation called RevOps.
A RevOps system relies on continuous, accurate data collection. That means automated data collection and automated data entry to CRMs. Trust me, you can automate so much more of your data-oriented tasks than you think, and my clients are always shocked at how much extra work they’re doing.
That frees up reps to do the smart work — understanding client issues, tailoring their interactions with possible clients to be the best experience for them, and improving on the product itself based on data about what clients want from you.
Observe how your metrics change depending on your sales and marketing decisions. They will be the key insights into which roads lead to success, and which don’t.
3. Identify your personal success metrics
The next step is lining up the data that tracks your business’ progress and performance to your individual success criteria.
- What does success mean for your business in terms of end results — is it getting a high-value customer up the chain, or is it prioritizing customers that will sign up and stay loyal for years?
- What does success look like on the customer journey — being able to buy your product with minimal face-to-face time with a sales rep, or being educated on your product before having to purchase it?
By having a clear idea of your business’ personalized success metrics, rather than just performance metrics, you’ll personalize your approach to marketing and sales.
That’s the difference between blindly trusting an app to tell you how to behave, without knowing what the results will be, and knowing how your business and customers work so intricately that you can confidently build your own models.
Your RevOps machine should be continuously analyzing the data and looking for patterns in your achievements. Dig deep on those successful interactions and results to find underlying causal factors that got you there. Run experiments on your company’s own performance and success data, not general data that might not accurately represent you.
4. Embrace results that work for you, and you alone
After taking a hard look inwards, the team at Truly realized that our approach had to be to seeking truth (what the customer wants) versus blindly seeking success (adopting formulas we think will push up our metrics, without much regard for customer experience).
It’s the difference between spamming people to sign up to your newsletter six times because statistics say that after six times they are more likely to sign up — versus figuring out why they are not signing up in the first place.
What we actually learned from our Labs results
So instead of being anxious about our poor Labs results, we used our own metrics to gain the context and understand why our behavior had actually worked. Then we transformed those behaviors into standards we should be following to get the best outcomes. These were some of the results:
- Yes, I talk too much in meetings … but I do so because our marketing has brought in a warm lead who is better acquainted with Truly, so now they crave specific information, and I’m providing it.
- Yes, I ask closed ended questions … but that’s because I’m collaboratively trying to qualify if we’re a good fit. Prospects actually love this.
- Yes I’m interrupting a lot … but when I do, I resurface our mutual goal of achieving our goal in the 30 minutes we put aside, so I don’t need to ask the customer for even more of their time.
We’d understood that simplicity and ease were top of mind for potential clients. So, we made sure marketing was providing companies with more granular information on how Truly worked, rather than putting out general/ambiguous messaging to rack up MQLs.
Before, discovery calls were about letting the customer talk at length so we could figure out whether or not we were a fit in the first place. Now they were more about understanding the mechanics of how we might collaborate. We simplified our product to reduce the number of decision-makers within a company that needed to interact with us to get the ball rolling, leading to fewer meetings and quicker adoption.
By understanding customer needs, we were also able to narrow down the type of customer we could work best with. A business’ needs either matched our solutions, or they didn’t, which is why they appreciated us being concise where we needed to be.
Even though those are typically thought of as “selfish” behaviors, they’re actually very buyer-centric, as long as we’ve done the work of understanding what a successful buyer journey looks like beforehand.
All of the above totally changed our relationship with our leads, and set up a model that was completely different from any prescribed formula we might have been fed. And we’re just one example of many companies out there that are doing something totally different in their go-to-market motion.
Sales is changing, your customer is changing, and you should probably change too. Sales is no longer about casting a wide net and bringing in as many potential buyers as possible, at the expense of the user.
Customers demand respect for their time and care for their needs. So drill down on your customer, what they want from you, your own metrics, and what they mean to you.