Data Analytics In Sales

Sales Leaders please remember this. Data doesn’t tell you what’s wrong. It just tells you where you need to look. It is still surprising to me how non data driven a lot of sales managers. VPs and CROs are But even with the ones that are they tend to stop too short. Ok close rates are low? Why? Now we need to go see where in the cycle the are getting kicked out. Ok most aren’t making past POC. Ok now we need ti go review the POC process. Listen to those calls. Review those emails. NOW we are getting closer to the cause. Show rates are bad? Ok we need to go review how far our they are being booked, what the calls sound like, what lie confirmation process is, etc If data told the whole story we wouldn’t be needed as leaders. Data tells you where to look. We bring the context from what we find there. Dig deep this next quarter. Pick ONE MAJOR METRIC and diagnose the living you know what out of it. Find the WGLL Then 4D it. Define Document Demonstrate Deliberately Practiced Go get it.

Last week, the CRO of a $36M ARR SaaS turned to us. They missed their Q4 forecast by 28%. The board wasn't happy. Here's the playbook we used to fix it. 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 I talk to dozens of sales leaders every month. This CRO is not an exception: • Inaccurate forecasts due to poor visibility • Poor visibility due to missing CRM data • No clear process & accountability 𝗛𝗲𝗿𝗲'𝘀 𝗮 𝗽𝗿𝗼𝘃𝗲𝗻 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝘁𝗵𝗮𝘁 𝗵𝗲𝗹𝗽𝗲𝗱 𝟭𝟬𝟬+ 𝗕𝟮𝗕 𝗦𝗮𝗮𝗦 𝗖𝗥𝗢𝘀 & 𝗥𝗲𝘃𝗢𝗽𝘀 𝘁𝗲𝗮𝗺𝘀 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲𝗹𝘆: ✅ 𝗙𝗶𝘅 𝟭: 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 The cornerstone of effective deal reviews and visibility into pipeline & forecast health. 1️⃣ Activity data: WHY IT MATTERS: • Emails/meetings not logged = unclear deal velocity • No engagement = high risk of deal slippage • Use activity data, not gut feel E.g. • Last activity date • Next meeting date • Email reply rate  .. SITUATION: The CRO & RevOps team faced 3 issues: 1. Reps forgot to log activities 2. Auto-logging failed (poor opp & contact role mapping) 3. Most opps lacked contact intelligence (who’s involved, decision-maker, multi-threading) No activity/contact insights = no visibility. SOLUTION: Auto-capture emails & meetings with a solution that identifies contact roles. Ideally with an Outlook Add-In/Google Extension to improve opp mapping (e.g. Weflow does this). 2️⃣ Salesforce data entry: WHY IT MATTERS: • Often missing key fields (e.g. MEDDIC) • Bad CRM data = poor deal reviews & forecasts SITUATION: 76% of their MEDDIC fields were not populated. Reps hated updating Salesforce. = managers lacked deal visibility. SOLUTION: An AI notetaker that auto-extracts and updates MEDDIC fields in SFDC from call transcripts (e.g. Weflow). ✅ 𝗙𝗶𝘅 𝟮: 𝗙𝘂𝗹𝗹 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻𝘁𝗼 𝗗𝗲𝗮𝗹 & 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗛𝗲𝗮𝗹𝘁𝗵 WHY IT MATTERS: To improve deal reviews & forecasts, managers need leading indicators of deal health: • Push count • Configurable warnings • Multi-threading & velocity  .. A pipeline coverage dashboard (CQ, Q+1) creates extra visibility. SOLUTION: Embed insights in Salesforce or use revenue intelligence/forecasting tools (like Weflow). ✅ 𝗙𝗶𝘅 𝟯: 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝗶𝗲𝘀 & 𝗠𝗼𝗱𝗲𝗹𝘀 SITUATION: They ... 1. Only forecasted new logos (ignoring expansions/renewals) 2. Used weighted forecasts + spreadsheets (highly inaccurate) SOLUTION: • Opp record types for expansion/renewals • Auto-create renewal opps upon closed-won • Combine models: 1. Deal-by-deal submission & review (+ auto roll-up) 2. Dynamic weighted 3. ML-based (They now run this in Weflow) 💭 𝗖𝗹𝗼𝘀𝗶𝗻𝗴 𝗡𝗼𝘁𝗲 I didn't touch upon revenue cadence/process due to character limits (but we helped fix this too). WHAT WOULD YOU ADD? 👇 ____ PS: We built Weflow to help B2B SaaS revenue teams forecast accurately. Take a product tour (on desktop): https://lnkd.in/eXHt-i6q

I increased my open rates by 17% with these 5 subject line tests: Your subject line is the first impression your email makes. It determines whether your audience opens the email or skips it entirely. Here are 5 subject line tests I ran that actually moved the needle (and why they work): 1. Add Personalization: Instead of: “Improve Your Email Marketing Results” I tested: “Chase, These Email Tips Could Boost Your Revenue” Why this works: Seeing their name feels personal and grabs attention in a crowded inbox. Personalization also shows you’ve tailored the content specifically for them. --- 2. Tap Into Curiosity: Instead of: “Email Marketing Strategies for Your Business” I tested: “You’re Leaving Money on the Table with Email” Why this works: Curiosity compels people to open. But the key is delivering on the promise—your content has to match the intrigue, or you’ll lose trust. --- 3. Create Urgency: Instead of: “How to Improve Your Email Campaigns” I tested: “Last Chance to Fix This Email Mistake” Why this works: FOMO (fear of missing out) gets people to take immediate action, especially when there’s a sense of a ticking clock. --- 4. Go Shorter: Instead of: “Here’s Everything You Need to Know About Email Marketing” I tested: “Better Emails, Today” Why this works: Short, punchy subject lines cut through the noise, especially on mobile where 50%+ of emails are opened. --- 5. Use Numbers or Specificity: Instead of: “Email Tips for Business Owners” I tested: “3 Subject Lines That Boosted Open Rates by 17%” Why this works: Numbers and specificity make your email feel actionable and credible. People know exactly what they’re getting. --- The Big Lesson: Your subject line is your email’s best salesperson. Start testing small variations today—personalization, curiosity, urgency, or brevity. Even a 1% improvement across a large list can make a massive impact. What’s the best subject line you’ve tested?

Just published my new piece in Forbes Technology Council: why predictive AI is becoming the dealership's secret weapon. And even if you are not in the automotive industry - there's a lot to learn here. Less than 5% of dealerships actually use their data to predict customer behavior. They're sitting on goldmines of sales records, service histories, and financing data - yet still operating on gut feel. The auto industry faces unprecedented disruption. EVs are surging (42% growth in Q1 2025). One in four buyers completes purchases entirely online. Supply chains remain volatile. In this environment, waiting for perfect data is a luxury dealers can't afford. We helped one of Israel's largest automotive groups rank past buyers by repurchase likelihood. By focusing on the top 5-10%, they achieved a 6x increase in conversion rates. And the model even revealed patterns their best reps hadn't noticed - like buyers over 50 being far more likely to return. The message is clear: dealerships that predict will pull ahead. Those that guess won't. Start small. Pick one underperforming decision. Test predictive insights against your current approach for 30 days. The results will surprise you. Read my full article: https://lnkd.in/dhRjhcpv 

It’s time to stop thinking like it’s 2005. Correlation may flatter your GTM story, but only causation proves impact. More than 80% of companies missed their sales forecast in at least one quarter over the last two years (Gong, 2024). In H1 2024, 49% of companies missed their revenue goals (GTM Partners Benchmark Report, 2024). At the same time, executives keep putting faith in attribution models that only tell a sliver of the story. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: too often, data is interpreted in ways that confirm existing assumptions rather than test them. Harvard Business Review found that sales leaders are frequently blindsided by overinflated forecasts driven by “all-too-human behavior” (Harvard Business Review, 2019). GTM Partners research shows that poor data quality can cost companies up to 25% of annual revenue, yet 60% don’t even measure these costs. That’s value leakage every CFO cares about. It’s time to fix this. Here are 5 ways to make GTM decisions actually data-driven: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗻𝘂𝗹𝗹 𝗵𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀: Harvard Business Review notes that “consistently accurate sales forecasts are rare because many companies fail to align their sales and marketing departments.” Assume your campaign 𝘸𝘰𝘯’𝘵 work—then try to prove yourself wrong.     2. 𝗥𝘂𝗻 𝗽𝗿𝗼𝗽𝗲𝗿 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆 𝘁𝗲𝘀𝘁𝘀: Compare your marketing results to a control group to see the actual lift your efforts create. MIT Sloan warns that confirmation bias leads us to “interpret ambiguous facts in light of preexisting attitudes.” Stop crediting natural growth to your LinkedIn ads.     3. 𝗕𝘂𝗶𝗹𝗱 𝗿𝗲𝗱 𝘁𝗲𝗮𝗺𝘀 𝗳𝗼𝗿 𝗺𝗮𝗷𝗼𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: MIT Sloan recommends bringing together “different perspectives on the same issue” because organizational biases cloud interpretation. Create space for contrarians—the risks of blind spots are too expensive to ignore.     4. 𝗧𝗿𝗮𝗰𝗸 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝙖𝙣𝙙 𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀: Research shows the average B2B buyer has ~31 touchpoints with a brand before deciding (Dreamdata, 2024). Your last-touch attribution is missing most of the story.     5. 𝗣𝗿𝗲-𝗿𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀: Record in advance your testing methodology and success criteria. This prevents “analysis after the fact” bias and ensures accountability when results don’t fit expectations. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: If your data never challenges you, it’s not science; it’s storytelling. The companies that break through are the ones willing to let the data argue back. What’s the most obvious confirmation bias you’ve seen in GTM? #GTM #MarketingLeadership #causalinference  

Last week, we tested a small tweak to our outbound emails, and it generated 12 qualified calls. The interesting part? The tweak took under two minutes. We changed our email subject line to "Your LinkedIn profile..." We were testing a psychological hack based on how people scan their inbox. Decision-makers don’t go through every email. Their brain is filtering for two quick answers: 👉 Is this about me? 👉 Is this urgent? "Your LinkedIn profile" hits both. It sounds personal, relevant, and specific. The kind of email you'd get from the platform itself. It also triggers an "unfinished loop." The brain naturally wants to complete a thought, so when it reads “Your LinkedIn profile…” it pauses and thinks, “What about it?” That pause gets them to open the email. If you want to try this, here’s how to make it work 👇 1. Use what's relevant to them. “Your website,” “Your pricing page,” or “Your team” all create relevance. 2. Match it with genuine context inside the email; otherwise, it feels like clickbait. 3. Keep your email specific to them. The pattern is simple, → If the subject sparks curiosity, they open. → If the first line feels relevant, they stay. → If the ask feels simple, they reply. What’s one subject line that’s worked surprisingly well for you? #EmailMarketingHacks #SubjectLineSecrets #SalesStrategy #PsychologyInMarketing #OutboundSales

We built a predictive outbound engine for a $35M SaaS that finds buyers before they raise their hand. This changed their pipeline forever. Before, this is how Outbound worked 👇 You would scrape lists, launch cold emails, and hope someone was “in market.” Most replies--> Not interested/Wrong timing/ Or worse-no reply at all. Now, our process changed that model. Here’s how we do it: 1️⃣ Step 1: Start with intent We use Propensity, Bombora and Warmly, to track which companies are researching relevant topics. No more industry limits, we open the filters wide to spot interest across every sector. 💡 Optional: Add a growth filter if you want to get even tighter, but the main thing is finding those “hidden” buyers. Output: A live list of companies already searching for solutions you offer. These are not cold leads, they are future buyers you catch before the market does. 2️⃣ Step 2: Enrich and qualify, fast. Now we drop those accounts into Clay or BetterContact We pull: → Company size, revenue, funding, HQ → Who they’re hiring (remote, distributed roles) → Decision-makers in HR, Ops, IT, Finance (or whatever is relevant) → Fresh news: funding, layoffs, partnerships, expansions...etc Every bit of context shapes your outreach (No more “saw you have a dog” lines. Each message hits real pain points, in real time. The result? A pipeline full of warm accounts, primed for outbound. Each one shows real signals. The best part: These leads convert. We see up to 4-5% positive reply rates, and half of those turn into booked calls. Outbound is about building systems that spot real buying intent, enrich every record, and make every touch count. Want to see how this works for your team? DM me or drop a comment below and I’ll show you our workflow. #outbound #saas #b2b #workflow #clay

Inflation can erode consumer purchasing power, forcing businesses to rethink their pricing and product strategies. #BigBazaar, one of India’s leading retail chains, turned to real-time sales data to make smarter, faster decisions—and here’s how they did it. 🔍 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: With rising inflation, BigBazaar noticed: ✔️ A decline in premium product sales ✔️ More customers opting for smaller pack sizes ✔️ A shift toward private-label and economy brands Without clear data insights, adjusting to these changes would have been a guessing game. 📈 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: Instead of reacting late, BigBazaar leveraged real-time analytics to track purchasing patterns at the SKU level. This enabled them to: ✅ Identify a growing preference for budget-friendly alternatives ✅ Adjust procurement and stocking strategies to align with demand ✅ Optimize promotions by offering targeted discounts on trending products rather than blanket price cuts 💡 The Result: ✔️ A 12% increase in sales for private-label products (Tasty Treat, Golden Harvest) ✔️ A 9% improvement in customer retention among price-sensitive shoppers ✔️ Reduced excess inventory of slow-moving premium items 🎯 Key Takeaway: In uncertain times, data beats intuition. Businesses that track real-time trends can pivot quickly—ensuring they meet customer needs while protecting profitability. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒏𝒂𝒗𝒊𝒈𝒂𝒕𝒆 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏? #DataDrivenDecisionMaking #DataAnalytics #

Some of the fastest-growing AI companies are hitting $3-4M in revenue per employee. If your first instinct is to hire more reps to hit your number, you’re already scaling wrong. The traditional mindset goes something like this: → More pipeline needed? Hire more SDRs. → More deals to close? Add more AEs. → Scaling? Build layers of management, RevOps, and enablement. That used to work when markets were inefficient, software wasn’t intelligent, and the only real multiplier was human effort. But that model breaks in 2025. Not because people are worse but because you can now do more with fewer people. Today, the companies that are scaling fastest aren't doing it through headcount. They're doing it by designing systems that process their TAM faster and more efficiently than ever before. Just look at the early standouts: Cursor: $1.7M ARR/FTE Aragon.ai: $1.1M ARR/FTE Bolt: $1.3M ARR/FTE So instead of asking: “How many people do I need to hit this number?” Sales leaders should now be asking: “How can I process this TAM with fewer humans and smarter systems?” Agents win in areas where speed, consistency, and volume matter more than nuance. → Email — consistent, context-aware follow-ups → Social outreach — timing DMs, warming up prospects → Pipeline acceleration — replying instantly, confirming meetings, managing buyer intent queues → High-volume outbound — things that would take a rep 8 hours now take a few seconds This is becoming the new GTM baseline. There are still key moments that require EQ, trust, and creativity and that’s where your reps shine. → Cold calling → Social selling — posting content, building presence, becoming a magnet for inbound → Discovery & deal progression — understanding nuance, navigating internal politics, negotiating complexity The point isn’t to replace people. It’s to redeploy them where they create the most leverage. The best GTM teams of the future won’t be larger. They’ll be smaller, sharper, and system-led — blending humans + agents in one operating model. That means sales leaders need to redesign the org from the ground up: - Define which parts of the funnel are agentic vs human-led - Shift hiring from volume to strategic role design - Rebuild onboarding to include agentic collaboration - Change the way you measure performance — it’s not about how many calls, it's about how fast and effectively you're processing TAM You don’t need 100 reps. You need 10 reps and 1 agent that can do the work of 50, instantly, tirelessly, and without dropping the ball.

Crafting a Data and Analytics Strategy That Really Resonates For many organizations, articulating the tangible value of a data strategy can be a significant challenge. It's common to default to a technology-centric approach, leading to skepticism about solving a "problem" with a "hammer". 🔵 Strategy First, Technology Second Gaining buy-in for your data and analytics vision before diving into the technical details of the operating model. This prevents stakeholders from questioning the need for proposed technology solutions. Communication is key, and it must be segmented based on your audience – whether you're educating or informing (sideways; business partners), persuading (upwards; sponsors), or instructing (downwards; D&A teams). Each approach demands different content, length, and emphasis in your presentations. 🔵 Concise, Outcome-Led Vision Your vision statement should be remarkably concise, ideally 20-40 words, deliverable as an "elevator pitch". It should clearly state how your data and analytics team contributes to the top three organizational goals, identifies the specific stakeholders you aim to help, and outlines three mechanisms for delivering value. This also includes explicitly stating what you won't focus on, ensuring clarity and preventing dilution of effort. 🔵 Align with Business Transformations and Culture To ensure relevance, your strategy must connect with ongoing major business transformations within the organization. Furthermore, addressing cultural barriers to data-driven decision-making is paramount. I suggest framing the culture as "outcome-led" / "value-driven" and "decision-centric" rather than merely "data-driven". 🔵 Broaden The Appeal and Resonate, Wider Incorporate contemporary drivers and trends (e.g. how DA& teams are responding to Generative and Agentic AI), categorizing them as technology, internal, or market/societal factors, to demonstrate your strategy's forward-looking nature. 🔵 Defining Value and Measurable Impact Prioritize your primary stakeholders (ideally three), and for each, define the top three goals your team will help them achieve. For each goal, identify three measurable metrics, creating a "metrics tree" that clearly tracks your contribution to their success. Gartner defines three core value propositions for data and analytics: 1️⃣ Utility: Providing enterprise reporting as a service for common questions. Central team, allocated budget, data warehouse, etc. 2️⃣ Enabler: Facilitating business outcomes through self-service analytics, coaching, and projects based on business cases. 3️⃣ Innovation: Driving new initiatives like AI for decision making and prescriptive analytics. Each value prop requires a different delivery model, from service desks for utility to portfolio management for innovation, and these should be aligned. Collaborating with leaders like CIO, CISO, CAIO is also crucial for innovation efforts. Develop a D&A strategy that demonstrates tangible business value.