How Sales Managers can Use Data

Microsoft’s wake-up call to Sales Harry Stebbings shared in a tweet today the reality of sales in 2025: “Microsoft’s layoffs weren’t a ‘replaced by AI’ story; they were a ‘replaced by better people’ story.” Facts… Microsoft (and many other companies) can’t afford sending generalist sales reps to get deals done. Especially big deals. Doesn’t work. They kept the solution engineers who knew the product inside and out, and partnered with strategic sales professionals. This isn’t about technology taking your job. It’s about your job evolving faster than you are. Too many AE’s get blindsided because they thought showing up with a feature dump and a smile would still work. That approach now carries massive risk. High chance of no deal, and if you do win, it’s probably a tiny one with a high probability of churn. Not likely you’ll achieve your quota that way. The AEs who survived operate differently - They stopped slinging software and started studying their prospects & customers business. The markets they play in. The ones still standing are: • Reading customer 10-Ks and earnings calls (not relying on legacy CRM notes) • Understanding business constraints, not just pain points • Engaging with the entire team of stakeholders-business group, operations teams, and executives - not just their primary contact • Partnering with SEs as strategic advisors, not just demo drivers. Sales is the “why”and “why now” - in business terms SEs show the “how it’s done” - in business terms • Speaking ROI and business outcomes, not feeds and speeds Here’s where AI plays a key role. Top AEs leverage technology to: • Analyze customer financials faster than ever • Prepare for calls with deeper insights • Research stakeholder backgrounds and priorities • Generate business case scenarios based on customer and market data • Free up time for the high-value conversations that matter Sales leaders are using these tools to: • Identify which AEs are engaging strategically vs. just staying busy. • Lead scoring and routing • Automating low-value tasks • Team composition That visibility is the foundation for optimizing GTM. Provides opportunities to coach and help mitigate risks. Transforms savvy managers into strategic leaders. (Follow Kyle Norton and Kevin "KD" Dorsey - bosses on how to leverage AI). Your job isn’t safe if you’re still operating like it’s 2019. But it’s (more) secure if you’re willing to become what customers actually need: A business advisor who happens to sell software, not a software seller who happens to know some business. The generalist spray-and-pray approach just got expensive. Microsoft proved that. The question isn’t whether this shift is coming to your company. The question is: Are you ready? What did I miss? What are you seeing in your market? Are the fundamentals of selling really changing this fast?

"𝐌𝐚𝐤𝐢𝐧𝐠 𝐒𝐚𝐥𝐞𝐬 𝐁𝐞𝐭𝐭𝐞𝐫 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚 📊" After digging into R-Square's role, let's crack open another topic: regression. Stay with me; it's not the same ol' stats class lesson. We're mixing in some real-world flavor this time. Imagine you run a shop and your sales are going down. You might think about dropping your prices. But it’s not always about being cheaper than the other guy. What if you’re just not reaching the right people with your ads? This is where regression comes in. It helps us look at different things (we call them 'features' in machine learning) like how much you spend on ads, who sees them, and more. It's like detective work to find out what really matters for boosting sales. For example, maybe after looking at the data, you find out it’s not just lowering prices that matter. Maybe your ads are reaching folks who don’t really care about your products or have already switched to a different brand. So, you might find through data that targeting your ads at a different group of people could actually make them more effective. Once we understand what's affecting sales and how promotions can give them a bump, we use regression to predict future sales using different variables, with promotion being one of them. This helps us not just understand but also forecast the impact of our strategies. But here’s the tricky part - how do you explain this to the people making decisions? You could say: “Hey, our model shows if we tweak our ads, we could sell more.” Or, “Our ads are missing our best customers. If we aim them at people who are more likely to buy, we predict sales will go up by $20.45 for every hundred dollars we spend on ads.” Simpler, right? That’s the power of using regression in retail. It’s not just about finding what might work; it’s about explaining it in a way that makes sense to everyone. So, what’s your experience with using data to make better business decisions? Ever had a moment where the numbers opened up a new strategy? Drop your stories below. 👇 Next time, I'll try to dive into A/B testing and how it can help test our ideas. #DataScienceInRetail #Regression #MakeDataWorkForYou

55% of sales leaders witnessed increased lead conversions with intent data, a stat that marks a new era in the art of sales and marketing. 🔍 A Personal Tale: From Data Jungle to Targeted Strategy 🔍 I once partnered with a client who was overwhelmed by a deluge of intent data from Bombora. Picture navigating a dense jungle without a map. The data was vast but unstructured, not effectively mapped to accounts. I was reminded of Craig Rosenberg's words - "The key on intent is fit comes first." 💡 Turning Complexity into Clarity: The Role of Context Our quest was clear: to cut through this jungle and find a path. We initiated a meticulous cleanup, aligning intent data with specific accounts. Then, we took a pivotal step further by focusing on contextual intent data. 🧭 Unlocking the ‘Why’ Behind the Data Contextual intent data is like a compass in uncharted territory. It goes beyond identifying interested accounts; it's about grasping the reasons behind their interest. This deeper understanding enabled us to tailor our approach, addressing the specific needs and challenges of each account. 🌈 The Outcome: Precision-Driven Sales and Marketing Success The transformation was remarkable. Sales dialogues became more focused and resonant. Marketing campaigns struck a chord, addressing the unique context of each account's journey. 🛤️ A 5-Step Blueprint to Mastering Contextual Intent Data Data Harvesting: Collect intent data with an eye for the underlying context of each interaction. Intelligent Mapping: Align this data with specific accounts, illuminating your path through the data forest. Tailored Tactics: Customize your outreach based on the nuanced context of each segment. Adaptive Campaigns: Launch dynamic, context-sensitive campaigns that connect deeply with each account's narrative. Strategic Refinement: Continuously evolve your strategies, responding to the ever-shifting landscape of intent signals and contexts. 📈 Beyond Just Data Points: Contextual intent data isn't merely a collection of information; it's a storytelling tool. It's about transforming raw data into compelling narratives that not only reveal who is ready to buy but also why they are on this journey, creating more meaningful and effective sales and marketing engagements. Step into the world of contextual intent data and watch your sales and marketing narratives change from abstract data points to stories that connect and convert. #ContextualIntentData #SalesInnovation #MarketingTransformation #DataDrivenDecisions #BusinessGrowth #B2Bmarketing #ABM #accountbasedmarketing #METABRAND #IndustryAtom

Had lunch with an old friend over the weekend. A former Software Developer, he is now the GM of a successful Jaguar Land Rover India dealership. It was interesting to hear how he uses data to improve sales outcomes. When he switched careers, being a "data guy", he quickly realized that the automotive business has a TON of data that they are not using at all. Further, he felt strongly that they were relying on the WRONG data. His thesis was that using ACTUAL customer data, collected live from real humans, was way more valuable than what the industry pundits and profiteers in the back office were telling him to rely on. He started by having his Sales Team record the first 3 questions that anyone who came to the dealership asked, jot them on a clipboard and aggregate The top 3 questions in the first 2 months: "How cold does the A/C blow"? "What trims does this come in"? "What's the best price you can do?" He built answers to all 3 questions directly into his sales playbook. Here is how he did it: 1. Before anyone went for a test drive he would go start the vehicle, turn the A/C on full blast so that when people got into the car, it was already cold. So they would say "Wow, that A/C really blows great. This will be great in the summer. (Dealership is in a hot climate) 2. The Reps would mention as they walked out to the car, "this vehicle comes in three trims, I'm gonna show the base trim and go up from there. You will get to see all three available trims today. Does that work for you?" 3. The Reps would close with, "If you like this model or any of the trims you see you today, then we can go back to my desk and work together to get you the best possible price for that vehicle" Easy, right? The results were remarkable. These small changes lead to customers asking less questions, asking different questions, and a 12% increase in new vehicle sales by month 3. He has since continued to iterate on this model and they are now one of the top producing Land Rover dealerships in the United States as a result. Listen to your customers. Incorporate those learnings into your business processes. #customerexperience #data #problemsolving #automotive #sales

Last year, I was working with the manager of a national staffing agency during their Q1 internal review. They had a branch that was off track from the start. After just three months, the branch was significantly behind its revenue and margin targets, and they were worried. We pulled up the dashboard to analyze the situation. On the surface, everything looked decent with the recruiting performance meeting the benchmark. But when we dug deeper, we found the problem: not enough job orders were available to meet their financial targets. Even if they filled 100% of the existing orders (which, as we know, is unlikely), it simply wouldn’t be enough to hit the goal. Next, we broke down where the orders were coming from. The branch was on track with their existing client growth plan, but they didn’t have enough new clients bringing in new orders. Initially, the manager wanted to create a new sales plan that increased the number of prospects and activities the sales rep was responsible for....but this would have been the wrong decision. That’s when we turned to the Sales Activity Tracker. The numbers immediately jumped out: The in-market sales rep was already maxed out with prospecting activities, meetings, and pipeline management. Based on the first-year value of a new client, it became clear that this sales rep literally couldn’t do enough to hit the branch's goal. At that moment, the manager made the tough decision to revise the revenue target and rework the budget to maintain profitability for the year. They also placed the sales rep on a performance improvement plan to focus on improving conversion rates through the sales cycle. In the end, the sales rep didn’t work out, but here’s the silver lining: By identifying this early, they were able to pivot quickly. Rather than holding on to an underperforming sales rep and risking a loss at the end of the year, they made the tough call, restructured, and ended up with a profitable branch. The key takeaway? Know your numbers, track your metrics, and use the insights to make data-driven decisions. It’s better to adjust early than wait until it’s too late.

I wrote an article for Forbes. Not because we needed PR but because most GTM teams are still stuck in 2015. You can now dynamically convert live web data into sales signals capturing real-time deep hiring activities, shifts in technology/gtm preferences and even subtle product pivots. But we’re still relying on static snapshots: - Crunchbase for funding - ZoomInfo for contacts - BuiltWith for tech stack - Apollo for bulk lead lists Reasoning Agents are here. How we consume the internet has changed. So should prospecting. Instead of blindly hitting the same list of accounts as your competitor, modern GTM requires systems that monitor and reason from live company behavior and surface signals from the accounts that are actually displaying buying behaviour. Here’s a simple 5-point framework to shift from outdated lead scoring to signal-based GTM: Monitor broadly: job boards, social posts, product docs, ad libraries, company updates. Capture nuance: look for subtle changes: new strategies mentioned, sudden hiring spikes for users of your product. Prioritize signals: decide what actually signals buying intent Maintain history: stack and log signals over time to build context and spot real patterns. Make it usable: plug these insights into your CRM or Slack so sales can act fast. Business leaders can make meaningful improvements to their GTM strategies without requiring significant investment More details in the article here: https://lnkd.in/g4YxCfhR Would love your thoughts.

Hey sales leaders: When evaluating the ROI on your current sales stack, don't just look at usage. Look at performance insights you can extract. You're likely missing out on juicy data. You probably bought your Sales Engagement Platform so that SDRs can use multi-touch sequences/cadences to do prospecting. You probably bought your Conversation Intelligence tool to be able to listen to call recordings. You probably bought your Sales Content Management tool so that you have a central repository for all internal and external sales content. That totally makes sense. After all, those uses cases are pretty much why those tools were created. But a savvy sales leader knows that every rep-facing sales technology is capturing sales activities and behaviors in some way -- often both the seller's activities and the buyer's activities. And the insights from that data can be extremely valuable. I'm mostly not talking about volume -- # of calls, # of emails, # of meetings. Those can be important from a management perspective. But from a sales performance improvement perspective, I don't care a whole lot about those. What I do care about are things like: --Average # of meetings in won/lost deals --% of people on the customer's side who spoke during meetings throughout a sales cycle --Average discount rate on quotes Those kinds of metrics can help me uncover potential performance gaps, and areas for coaching interventions. And guess what: You can only get this kind of data because you invested in a sales technology that was created for another purpose. See the attached screenshot for some examples. This is not at all comprehensive, of course. Pro tip: This is an advanced play, and you'd need a solid ops team. But whenever possible, I recommend NOT requiring your managers to log into 10 different sales tools to see their data. Instead, ingest their API directly into your CRM or data visualization tool. That way you can see all the reporting and insights you want in one place. Happy selling. #heysalesleaders #salesexcellence