To be an effective sales leader, you must have strong communication skills and be driven by data and process. Strong communication, especially a coach-like mindset, is extremely important in terms of supporting reps to achieve quotas. Positive and effective communication between a leader and their reps allows for a smooth flow of information, which creates an environment that motivates the team to work towards achieving goals. More than ever, being analytical and process driven is key to creating and scaling a high performing team. It’s important that leaders understand the story in the data, make impactful decisions based upon it and motivate their team with data. In terms of nice to haves, having an eye for great sales talent is something that can take time to develop as a sales leader. I’ve been in sales my entire career and being able to identify great talent is something I'm always working on. If early on in their tenure a sales leader can hone in on a candidate's desire to learn and succeed, they are set up for success.

The most important skills for account executives are also the most important skills for many roles. I believe a successful AE needs to be great at the following: 1. Active listening: first rule of sales is to listen to your buyer and truly comprehend what they are saying and what they need 2. Problem solving: handling the many challenges, distractions and roadblocks that come up in the sales process 3. Storytelling: relaying information in an engaging, authentic manner that drives the point across 4. Mentalizing: understanding non-verbal queues, empathizing, emotional intelligence, etc. Process and product can be taught by the company that you sell for - but the above skills, no matter the company or the role, will be invaluable in your career.

To effectively define the metrics for which you should hold sales accountable, I look at a few things: 1. Understand the "Sales Math" of the business across some core universally applicable SaaS Sales metrics 2. Compare the performance of the top 1/3 AE's against the bottom 1/3 AE's and look for which metrics contribute the most to high performance. 3. Go deep in those categories and correlate the activities top performers do differently to achieve these results. Quantify these activities to define supporting metrics which will lead to success. To break this down, let's understand the foundational "Sales Math." This is the equation to hit quota. The equation is fairly simple, but everyone's vernacular is different. It is actually extremely important to have very well defined steps in the equation to get consistency across your entire team. For example, we use opportunity stages with clear exit criteria for the buyer & seller to provide consistent insight into our Sales Math. So I would actually use a Stage 1 Opp Created - instead of Discovery Call, and Stage 3 Opp instead of Demo. For the purposes of this article, I'll use general sales terms that each business should be able to use as a starting point and customize from there. Here are the metrics that go into the Sales Math equation: * Activities to create a Discovery Call * # Discovery Calls per quarter * # Demos per quarter * Discovery Call to Demo conversion ratio * # Closed Won Deals per quarter * Demo to Closed Won conversion ratio * Average Deal Size * Average Deal Cycle These metrics will allow you to create the math to hit quota. If the current team's metrics do not consistently lead to the results you're looking for, then the Sales Math may be aspirational. If your team is executing against plan, then this may be your actual current metrics. Regardless, this is what you should feel confident telling AE's is the realistic, attainable and surpassable way to hit quota. For example, it could look like: $250k Quarterly Quota Average Deal Size of $84k 3 Deals to hit quota Close ratio of 33% 9 Demos needed per quarter 60% conversion ratio of Disco to Demo 15 Discovery Calls needed per quarter 50 Activities to create a Disco 750 Activities needed per quarter* *one note on activity. It's a metric I'll always track to understand a baseline level of effort, but I will often leave this out of the Sales Math when dealing with higher complexity sales and more senior AE's. Up to you if this should be in your Sales Math equation. Now take your Sales Math, and map your high performers against your low performers to look for which metrics have a high correlation with success. This exercise can be extremely surprising, so be open to what the data shows you, and hold your strong opinions loosely. Let's extrapolate this exercise across two different scenarios: Scenario 1 - Enterprise Here's how the exercise played out when running it against a more enterprise business (numbers are directional): 1. Activity, Discovery Calls and Demos were almost identical across high & low performers. This told me that pushing "more activity" was only going to have so much impact on performance. 2. The Closed Won conversion of top performers was 46% vs. 25% for the low performers. This was a huge gap, and had major implications on the Sales Math. 3. The Average Deal Size of top performers was $160k vs. $70k for low performers. This is also a huge gap compounded the success or struggles of each group when combined with the stat above. So the key metrics to optimize were Average Deal Size and Demo to Close Ratio. We wanted to maintain our activity levels, but really lean into increasing ADS and strategies to help with Deal Execution. Based on this knowledge of what would have the biggest impact in high performance vs. low performance, we added in some metrics & activities that would contribute to these results: * Updated our account prioritization to ensure a focus on the top deals & tracked activity against Priority 1 accounts * We blocked off time each week to prospect into our top accounts & scheduled strategy sessions to help get more meetings with these accounts * We tracked # of Discos with P1 accounts * # of Demo's with $100k+ Opportunities For Deal Execution * We tracked multi-threading in each account * Have we made an executive connection? * We created a cross-functional meeting to lean into competitive differentiation strategy * We set a threshold for accounts that needed a key deal review & updated our process to improve efficiency and make room for more accounts reviewed each week. Scenario 2 - Transactional Here's how the exercise played out when running it against a more transactional business (numbers are directional): 1. There were two camps of high performers. Those with extremely high activity, and those with higher disco to demo efficiency. Our most consistent top performer was a combination of both. Low performers fell into a similar pattern of either low activity or low conversion of discos to demos. 2. Deal size and win rate didn't have dramatic differences outside of 1 AE who closed the largest deal in segment history. This wasn't repeatable so we eliminated that result instead of putting too much time in hunting whales. 3. Average Deal Cycle for top performers was 39 days vs. 52 days for lower performers. Top AE's were closing deals faster, which allowed for more time to close more deals. From this data we defined additional metrics and activities to drive better results: * Upped the baseline activity volume expectations - there is a diminishing point of returns, but higher volume was almost always a component of success. We raised the bar, but also coached our highest volume AE's to lean more into their efficiency metrics instead of pushing to just do more. * Managers went deep on quality of discovery calls coming into the funnel * Title & Seniority level of Prospects - lower conversion was correlated with lower titles. * Was the company in our Ideal Customer Profile? Quality of company greatly impacted conversion * Why now? Did we offer someone a gift card or just bug them until their defense was worn down? Or was this call predicated on funding, a new hire, an inflection point in the business, intent or some other business catalyst? * Managers inspected quality of prospecting messages * Managers inspected quality of discovery calls * We rallied around creative promos to help the team close deals faster * We replicated decks top AE's were using to build value and establish trust faster In both Scenario 1 and 2 - we started with the baseline Sales Math, and through comparison of top performers vs. low performers we were able to lean into the 2 key metrics that had an outsized impact on performance. We then defined key activities and additional metrics which we could hold the team accountable to, that we knew would correlate towards greater success across the team. How easy was that? :)

Coming into an organization as the first sales hire puts a lot of responsibility on your shoulders. You are basically in charge of proving the validity of this function within the company. There are a few things that I would consider and act on in this position: Start with the short term. As a first hire in a sales organization, you are required to deliver results that have a very immediate impact that meets the business needs. This means focusing on some low-hanging fruits in order to deliver results within a short time frame. Build a framework. As a first hire within the team, you should make sure you document your work, and create clear guidelines and processes, with the expectation of adding additional members to the team in the future. This will ensure a smooth expansion of the team while positioning you as a thought leader and a pivotal member of this function. Go beyond your scope. As a junior sales hire, never underestimate the power of tenacity. I always invite my team members to push the boundaries and look for additional scope and responsibilities whenever they feel capable. This is a very strong signal that you are willing to take on more, and when management will face a new task at hand, they will know they can count on you.

In today's world it is relatively quite easy to make a very well informed decision when assessing a new role in a different company. These are some of the things I look at and advise my mentees to do too : * If it's a public listed company, look at their financials to figure out their year on year growth. * Check out websites like RepVue which give you a very good understanding about how sales reps in that company are doing * On LinkedIn, check out the trend on their headcount,especially in sales, is it increasing consistently or not. * Check out analyst reports and websites like g2.com to see how the product is rated by their customers. * Check websites like Glassdoor to understand about the culture of the company you are considering * If possible speak with a few customers and partners of the company as well. 

Our top reps excel at spotting potential opportunities. Since we're focused on run-rate tactical deals in the form of additional license, new initiatives, and pilots, our main stakeholders are typically at the VP level or below. The key here is mastering the art of tailoring conversations - distinguishing between "above the line" and "below the line" discussions ensures that our messaging aligns with the audience. In larger enterprises, industry trends and company direction are often widely available. It's crucial to delve into these trends, gaining a deep understanding of industry challenges and directions. This forms the basis for crafting a unique perspective that resonates not only with the business but also its key players. Once everyone's on the same page and the problem is crystal clear, we can then propose a forward-thinking solution that drives them towards their desired outcomes. What sets this approach apart is its emphasis on discovery. Rather than leading with a product pitch, we invest time in understanding the company's priorities. This makes our interactions much more aligned with the client's needs, creating a more genuine and less "sales-y" experience. This method ensures that our solutions aren't just one-size-fits-all, but tailored to fit the precise needs of each client we engage with.

Be clear on your sales methodology and discovery best practices, there is several simple tools to use from BANT ( Budget Authority Need and Time ) to Situation, Pain, Impact, Critical Event, and Decision. Generally, a sales rep needs to have a conversation with the customer to move the lead from an MQL to SQL and should have identified Pain points to be able to sell them a solution, identify the criteria for a effective discovery call and use training tools to review with your reps if this is not a SQL and not just " happy ears " 

SDR leaders have a plethora of data to work with, but sometimes it is hard to know where to look. A good starting point is to map out your funnel and ensure you have a metric that measures both quantity and quality to determine which steps of the action are below expectations. Take cold calling as an example. In general, the key metric to evaluate the success of your cold calls is the number of cold calls required to book a demo or the percent of calls that result in a demo. Say you are seeing that it takes you 100 calls to book a demo, but you know your industry average is half that. You might be tempted to jump in and do some mock call scenarios, but what if the problem is not your team’s live cold call execution? Your training may not improve your team’s success rate. You must first map out the steps of successfully sourcing a demo from a cold call. 1. Place the call 2. Have the prospect answer 3. Have the prospect agree to a demo 4. Have the demo occur Each of these steps will have its own benchmark that you can compare your rate to. Say you see that you have a very low answer rate, but convert one in three answered calls to a demo. In this case, mock call training won’t effectively raise your demo booked rate. Instead, you may want to train your team on how to mark good numbers and avoid multiple calls to known bad numbers, how to call at effective times, or even look at better lead data options. When looking at any metric, make sure you are gauging both quantity (are you making enough cold calls) and quality (are those calls being answered and are those conversations turning into live deals).

Begin by baselining critical metrics for new seller success. It could be things like: time to first deal, deal velocity, time to pipeline, etc. Then: * Divide new sellers into 'cohorts' * Measure their metrics against more tenured seller cohorts * Understand trends that support or refute your hypotheses * Draw correlation where you see it to infer if your programs are working Causation is near impossible but with large enough data-sets, you can find correlations & trends that will help explain and direct future learning.

I see 2 different places that sales is headed: 1. Do more with less. Brute force (hiring more) used to be the answer to growing sales. Now the strategy is to load up the top reps, keep them busy, and get the best from them before we even think of hiring someone else. Sales efficiency is key from a profitability standard which is where the market is right now. 2. AI. Not saying the robots are coming for our jobs just yet, but with technology now that automates so many manual processes, is it so far fetched that in the next 5 years technology can tell us who to sell to, how to contact them, and write the email itself? What about AI images that talk and you don't realize that they are not actual people? Does it feel so futuristic to believe AI can dial or Zoom someone, respond based on what is said, overcome objections, negotiate pricing, and automatically send out order forms? There will always be a place for sales in some industries, especially emerging technologies where everything is new.