Data-Driven Strategy Formulation

When I removed targets from my team, the first question every sales leader asked was... “How did you stop everything turning into chaos?” The answer was simple but not easy. We replaced top down targets with something far more powerful… A personal success blueprint for every rep. If you’ve never used one, here’s exactly what it is and how it works. It's a structured, data informed plan the rep co creates with their manager. It defines the inputs, activity levels & funnel metrics they need to achieve THEIR definition of success. It becomes the foundation for coaching, accountability & weekly 1:1s. Here’s how we built it. Step 1️⃣: Start with what top performers actually do... We pulled the data from our best reps. Things like... Discovery calls per week Discovery to qualified Opps created per month Opps to close Average deal size Sales cycle Etc....you get my point. This became our baseline blueprint. Not a rule, more like a map of effective execution inside our reality. Step 2️⃣: Understand the rep's intrinsic motivators... Because a blueprint only works if the rep is building toward something they care about. But first we needed to model the openness we sought from them. I shared my personal manual for working with me; a meaty guide that included lots of personal info, including my drivers & motivations. Then we found out what drove them... Some wanted a promotion. Some had a clear earning goal. Some wanted to rebuild confidence. Some wanted to be at the top of the leaderboard. Once you uncover the driver, you can build a plan that actually means something. Step 3️⃣: Build their personalised blueprint grounded in data... This is the coaching conversation where real change happens. It sounds like… “If your goal is £X and your deal size is £Y you will need around Z deals…” “Your win rate is X%, top performers sit at Y% percent…where could you realistically get it to?” “With your discovery to qualified at X%, how many discovery calls per week do you need?” The manager questions. The rep thinks. Together they build something ambitious but believable. And everything is rooted in their personal motivator...e.g. a clear path to promotion. The rep signs off. The manager commits to coaching to it. Step 4️⃣: Contract for accountability... This is where most leaders fall. We asked every rep… “When you fall behind, how do you want me to respond?” Some wanted a Slack nudge. Some wanted a short problem solving session. Some wanted it raised in weekly 1:1s Different reps need different triggers. Agreeing this upfront turns accountability into partnership. Step 5️⃣: Use the blueprint every week... Every 1:1 followed the GROW model. Goal, Reality, Options, Will. What’s working, what's not, what options do you see and what will you commit to this week? It keeps the conversation grounded in reality and solution focussed. 5 simple steps but success is driven by the quality of the coaching. That'll be my next post...

If your social strategy isn’t supporting the business, you don’t have a strategy… you have posts. Here’s the framework I use ↓ (save this for your next planning session) 1. Business Goals → What does leadership care about? Revenue, adoption, retention, reputation? → Social isn’t an island. Your piece has to fit the larger puzzle. 2. Marketing Goals → What’s your CMO or Boss laser-focused on? → These outcomes ladder to the business, but marketing defines the campaigns, events that you also have to support. 3. Social Goals → What are your team goals? (growth, publishing volume, share of voice) → Define the role social plays (awareness, consideration, conversion, loyalty) 4. Tactics + Content → Choose formats + channels that ladder up to the above.  → Speak their language. Show leadership why your wins are brand wins. → Use social as a testing ground—experiment, adapt, repeat. At Kickstarter, this framework shaped everything from “pre-campaign” hype posts to creator-driven launches to product announcements. Every post should ladder up the chain. If it doesn’t? Don’t post it. Read my full breakdown for Social Media Strategy today in Hootsuite's Blog: https://ow.ly/ESUs50Xeim7 #HootsuitePartner

95% of go-to-market teams are drowning in data, and 0% know what to do with it!   That’s the brutal gap we uncovered yesterday in our live session with Davide Grieco (Head of Growth at Clay). I will get straight to it. Here are 6 things the most advanced teams are doing (and you should be copying in 2026):   1️⃣ Leads → pipeline, only when routed intelligently. Webinar signups, demo requests, product usage...all of it lands in Slack or your CRM. But without prioritization, reps don’t know where to focus, and leads go cold. Top teams auto-score and route leads based on fit, behavior, and deal potential, so sales only acts when it matters. 2️⃣ Intent doesn't always trigger action. A single website visit doesn’t trigger a sequence. Clay’s team proved that orchestration only works when fit + behavior + value are layered before making a move.   3️⃣ The 3-dimensional scoring system. Leading teams score every account across these 3 core dimensions👇 1. Account Fit Score: Tech stack, GTM motion, sales team size. 2. Engagement Score: Product usage, events, LinkedIn activity. 3. Contract Potential: Will this account be worth $5K or $500K?   4️⃣ Every account gets routed into one of these 4 tiers: - Low fit, low engagement → Ignore or self-serve. - High fit, low engagement → Educate & warm up. - Low fit, high engagement → Monitor with product nudges. - High fit, high engagement → Full GTM firepower: sales + ads + content.   5️⃣ Go-to-market teams are in sync Sales, Marketing, and RevOps work off the same live dashboards (powered by Clay) where scores, tiers, and signals update in real time.   6️⃣ Everything maps back to revenue. Every live stream, post, and paid ad is tracked, not just by clicks, but by which ICP accounts engaged. Engagement score goes up → campaign triggers fire → pipeline grows.   Is this just another GTM framework? ❌ No, This is what Clay, a $100M ARR company (with a $3.1B+ value) is doing internally, and it’s working. I'm sharing the full slides from the session, because I believe every GTM team should have this playbook! Comment SLIDES and I’ll DM you the full deck. + Link to the full webinar video in the first comment. LFG🔥

Teaching sequential decision analytics VI – How we make decisions   Humans use complex processes for making decisions, but when we transfer this responsibility to a computer, we have to be precise. *Anything* we do on a computer can be translated to mathematical notation and equations, so we should be able to translate the process of making decisions into formal mathematical statements.   Decisions that are made over time (which covers virtually all decisions) are made with methods that can be described with almost 50 words in the English language (see graphic below) in the right context, but the most common in the research literature is “policy” which is simply stated:   Definition: A policy is a method … any method … for making a decision.   There are two broad strategies for making decisions, each of which can be divided into two classes, producing the four classes of policies:   Strategy I: The policy search classes – These are methods that are tuned to work well over time, but which do not explicitly plan into the future. These include:   1)   Policy function approximations (PFAs) – These are analytical functions (typically parametric) that map information in the state variable to a decision. Examples are order-up-to inventory policies, buy low, sell high policies, linear models, even neural networks. 2)   Cost function approximations (CFAs) – These are parameterized versions of (typically) deterministic approximations. Examples might be a simple sort (with bonuses for uncertainty) or a parameterized linear, integer or nonlinear program.   Strategy II: The lookahead classes – These make decisions now using approximations of what might happen in the future. These can be organized into two additional classes:   3)   Policies based on value function approximations (VFAs) – Here we use an approximation of the value of transitioning to a state to identify the best decision now. 4)   Direct lookahead approximations (DLAs) – These plan explicitly into the future, typically over some horizon. These come in two types: a.    Deterministic lookaheads – These use point estimates of the future, as is done by Google maps. b.    Stochastic lookaheads – The best example is decision trees.   My big claim: These four classes (including hybrids) are *universal* - they include *any* method for making decisions. This includes any method in the research literature, anything used in practice, even methods that haven’t been invented yet!  

Analytics aren’t just numbers; they’re your roadmap to publishing growth. Data isn’t power, it’s potential. For publishers, the real value lies in transforming raw metrics into repeatable growth strategies that drive audience retention, revenue, and #SEO performance. Too often, publishers collect vast amounts of data but fail to extract meaningful takeaways. The key is understanding what content resonates, how audiences engage, and where opportunities for growth exist. Collecting data is easy; extracting insights is not. Without clarity, metrics like pageviews and bounce rates become distractions. For example, a 40% drop in returning visitors isn’t just a traffic issue—it’s a retention red flag. By using the right tools and refining strategies based on real data, you can turn numbers into growth. Here are actionable strategies to turn data into action: 1. Know Your Audience Beyond Pageviews Pageviews alone don’t tell the full story. Instead, track return visitors, time on page, and scroll depth to measure true engagement. Tools like Google Analytics 4 (GA4) and Parse.ly provide deeper insights. Cohort analysis can reveal trends, millennials may prefer video, while Gen X engages more with newsletters. For example, if mobile traffic spikes by 20% after 8 PM, push breaking news via mobile notifications to capture that audience in real-time. 2. Optimise Content Performance with Behavioural Data Understanding why some content performs well helps you replicate success. Use @Google Search Console and Semrush to analyse search visibility and Hotjar Digital Marketing Company to track user interactions. For example, if "AI in media" gets 3x more shares than "content trends," double down on AI-related content. Additionally, A/B test headlines (e.g., “5 Growth Hacks” vs. “Proven Tactics”) to see what improves click-through rates. 3. Track Conversions, Not Just Traffic Traffic alone doesn’t guarantee success—conversions do. Set up goals in GA4 to measure newsletter sign-ups, paid subscriptions, or product purchases. Identify which referral sources drive the highest conversion rates, and adjust your strategy accordingly. For example, premium subscribers from "how-to guides" tend to have a 15% higher lifetime value than general news readers, meaning content type matters when driving long-term revenue. To scale what works, automate reporting with Power BI Visualization or Looker Studio to save 10+ hours per month. Analytics only matter when they drive actions. The biggest mistake any publishers can make is to treat data as a report card instead of a playbook. Start by auditing one content category this week, setting up a conversion goal in GA4, and A/B testing a headline. Data doesn’t lie, but it won’t work unless you do something. What analytics tools are you using to grow your publishing efforts? Share your go-to platforms in the comment below. #DigitalPublishing #SEO #ContentStrategy #AudienceGrowth #DataAnalytics

Outbound isn’t a department. It’s a workflow. Most sales teams still treat outbound like it exists in a vacuum. - The SDR team does its thing. - Marketing runs separately. - Data and RevOps sit in the background. But outbound works best when it’s fully integrated into the way a company sells. The best teams aren’t running outbound as a siloed function. They’re embedding it into marketing, sales, and RevOps, so outreach happens at the right time, to the right person, with the right context. A prospect doesn’t just wake up one day and decide to book a meeting. Before they ever get an email, they’ve likely: - Engaged with a LinkedIn post - Clicked on a competitor’s ad - Visited a pricing page - Researched the problem on Google Most outbound teams ignore these signals and just blast sequences. The best ones track them and reach out at the perfect moment. Here’s what that looks like... Outbound, when it’s fully integrated: - Marketing & outbound are synced. - SDRs don’t chase cold lists, they re-engage leads who showed intent but never converted. - Someone clicks an ad? That data triggers outbound. - Someone interacts with a LinkedIn post? That person moves into a sequence. Tools like Vector 👻, RB2B, and Trigify.io surface these signals automatically. Data & ops power outbound decisions. Before a prospect gets an email, they’re enriched, verified, and scored. If they don’t meet the criteria, they don’t get contacted. This happens inside Clay using Findymail. Outbound is multi-threaded. It doesn’t stop at email. SDRs reach out on LinkedIn, phone, and social, all timed around buying signals. When a lead is ready, they’re surfaced in Salesforce or HubSpot for follow-up. Outbound isn’t just about sending emails anymore. It’s about tracking behavior and responding at the right time. The tech stack that powers it: To make this work, outbound teams need to be as data-driven as marketing. - Website & ad intent tracking: Vector 👻, RB2B - Buyer engagement tracking: Trigify.io, Clay, LinkedIn Sales Navigator, Teamfluence™ - Data enrichment & verification: Clay, Findymail - Automated outreach & sequences: Instantly.ai, HeyReach - CRM & SDR workflow automation: HubSpot, Salesforce, Pipedrive The teams winning in outbound today aren’t the ones sending the most emails. They’re the ones embedding outbound into every part of the GTM motion. Where does outbound sit in your GTM motion? Siloed, or fully integrated?

Here’s How We Built a Sales Team That Ops, Product, and Marketing Actually Love. In many organisations, there's a quiet tension between sales, operations, product, and marketing. Sales, in its drive for growth, can sometimes over-promise to close deals. While this might look good on paper, it often creates a real, damaging cost for other teams: - Product gets crushed under unrealistic expectations. - Ops is stuck scrambling to deliver what was never scoped. - Marketing has to explain gaps they didn't create. And ultimately, trust is lost, both internally and with the customer. AI is making us all look hard at how companies sell, shining a spotlight on where these old sales methods are slow, confusing, or just don't work anymore. The best companies know this and are changing how they build sales teams to be fast, talk clearly, and build real trust with customers. At Software Finder, we built our sales team the smart way. Here’s how: 𝐖𝐞 𝐇𝐢𝐫𝐞 𝐁𝐮𝐢𝐥𝐝𝐞𝐫𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐒𝐦𝐨𝐨𝐭𝐡 𝐓𝐚𝐥𝐤𝐞𝐫𝐬: We look for people who love to solve problems, coach others, and do lots of different jobs. They need to be eager to learn and adapt, not just follow old playbooks. This helps us move really fast. 𝐖𝐞 𝐒𝐭𝐚𝐫𝐭 𝐒𝐦𝐚𝐥𝐥 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐁𝐢𝐠: Instead of just one salesperson to test things, we hire 2-3. This helps us quickly figure out if a problem is with the salesperson, or if our product message needs to be clearer. We get solid answers, fast. 𝐄𝐯𝐞𝐫𝐲 𝐂𝐚𝐥𝐥 𝐢𝐬 𝐚 𝐋𝐞𝐬𝐬𝐨𝐧: Our sales team isn't just about closing deals. They're like detectives, gathering clues. We use data from every conversation to understand what buyers truly need and where our product can get even better. This feedback helps everyone in the company learn and improve. 𝐖𝐞 𝐊𝐞𝐞𝐩 𝐓𝐡𝐢𝐧𝐠𝐬 𝐒𝐢𝐦𝐩𝐥𝐞: We don't make things too complicated too early. Our salespeople handle the whole journey: finding leads, showing how our platform works, and following up. This keeps things clear and efficient for everyone. So, why do our Ops, Product, and Marketing teams actually love our sales approach? Because our sales team brings back honest, clear information. They don't just sell; they bring insights that help us all build a better platform for businesses. We use this same data-driven thinking to help other software vendors make smarter choices and truly grow.

How to Use a Risk Matrix to Prioritize What Matters Risk is everywhere—but not all risks are created equal. That’s where a Risk Matrix becomes one of the most powerful tools in a risk manager’s toolkit. By plotting Likelihood (how likely it is to happen) against Impact (how severe the consequences would be), the matrix helps teams: • Identify which risks are critical and require urgent action • Separate low-impact risks from true business threats • Prioritize resources based on what’s actually at stake But qualitative isn’t enough. Enter quantitative analysis. While color-coded matrices provide clarity, quantitative methods take risk evaluation to the next level. By applying numerical values, probability distributions, or simulations (like Monte Carlo analysis), you can: • Reduce subjectivity and bias in risk ratings • Forecast potential financial losses and performance deviations • Compare risks across projects and portfolios • Strengthen business cases for mitigation investments Key Takeaways: • Green = Monitor Low likelihood and low impact — keep an eye, no immediate action. • Yellow = Manage Medium threats — define controls and monitor progress. • Orange/Red = Act Fast High or critical risks — escalate, mitigate, and assign ownership. Why It Matters: A well-used matrix—enhanced with quantitative insights—supports decision-making, improves stakeholder communication, and aligns risk management with corporate strategy. #RiskManagement #ERM #OperationalRisk #Governance #InternalControl #RiskMatrix #QuantitativeAnalysis #MonteCarloSimulation #StrategicPlanning

Here's my cheat sheet for a first-pass quantitative risk assessment. Use this as your “day-one” playbook when leadership says: “Just give us a first pass. How bad could this get?” 1. Frame the business decision - Write one sentence that links the decision to money or mission. Example: “Should we spend $X to prevent a ransomware-driven hospital shutdown?” 2. Break the decision into a risk statement - Identify the chain: Threat → Asset → Effect → Consequence. Capture each link in a short phrase. Example: “Cyber criminal group → business email → data locked → widespread outage” 3. Harvest outside evidence for frequency and magnitude - Where has this, or something close, already happened? Examples: Industry base rates, previous incidents and near misses from your incident response team, analogous incidents in other sectors 4. Fill the gaps with calibrated experts - Run a quick elicitation for frequency and magnitude (5th, 50th, and 95th percentiles). - Weight experts by calibration scores if you have them; use a simple average if you don’t. 5. Assemble priors and simulate - Feed frequencies and losses into a Monte Carlo simulation. Use Excel, Python, R, whatever’s handy. 6. Stress-test the story - Host a 30-minute premortem: “It’s a year from now. The worst happened. What did we miss?” - Adjust inputs or add/modify scenarios, then re-run the analysis. 7. Deliver the first-cut answer - Provide leadership with executive-ready extracts. Examples: Range: “10% chance annual losses exceed $50M.” Sensitivity drivers: Highlight the inputs that most affect tail loss Value of information: Which dataset would shrink uncertainty fastest. Done. You now have a defensible, numbers-based initial assessment. Good enough for a go/no-go decision and a clear roadmap for deeper analysis. This fits on a sticky note. #riskassessment #RiskManagement #cyberrisk

Which heatmap methodology makes more sense 3x3 or 5x5? RAW@AI: Great question—choosing between a 3x3 or a 5x5 heatmap is something risk managers often debate. Let's first explore your context briefly: Are you using heatmaps because stakeholders or auditors expect something visual and colorful once a year, or are your executives actively making decisions based on these outputs? If it's the former—simply providing a colorful visual to tick an audit or compliance box—either 3x3 or 5x5 can serve that purpose, though a 3x3 is typically simpler and quicker for stakeholders to grasp. If your risk assessment is used for genuine strategic decisions, however, neither approach is ideal. Here's why: The core issue with qualitative matrices like 3x3 or 5x5 heatmaps is that they suffer from subjectivity and inconsistency. Different people interpret "high" or "medium" differently, and there's little meaningful difference between a risk rated as "4" versus one rated as "3". In practice, these ratings seldom translate directly into actionable insights or inform critical decisions clearly. A better approach—and here's where the evolution of your risk management practice comes into play—is to gradually shift towards more quantitative methods that directly integrate into decision-making. For example, rather than trying to fit a complex reality into arbitrary categories, you could use a decision tree or Monte Carlo simulation to quantitatively evaluate actual outcomes and their probabilities. This approach provides clearer insight into how uncertainties directly affect your objectives and shows decision-makers the real-world implications of their choices. Consider this practical example: Imagine you're evaluating the risk of operational downtime in your supply chain. Rather than rating it as "medium likelihood, high impact" on a heatmap, you could model potential downtime scenarios using Monte Carlo simulations. You'd quantify how often downtime might occur, how long it would last, and what its financial impact would be. Decision-makers then receive clear, numeric outputs such as "there's a 40% chance annual losses will exceed $500,000 under our current maintenance schedule." That kind of insight directly informs whether investing more in preventive maintenance is justified. I recall from our previous exchanges that you mentioned the importance of clearly communicating risks to executives and stakeholders. Decision trees, tornado diagrams, and simulations don't just provide clarity—they communicate risk information in the language that executives speak: dollars, timeline impacts, and strategic trade-offs. But I know switching entirely overnight might be challenging. So, perhaps consider a hybrid approach: continue briefly using your heatmap (3x3 for simplicity), while gradually introducing these more quantitative methods on a key project or decision. Over time, stakeholders will start experiencing firsthand the value of more precise and actionable data.