Data Analytics for Events

Imagine turning your Excel spreadsheet into a live calendar, interactive map, and connected dashboard, all in a secure web app Event-driven analytics often hinge on two questions: When do activities happen? and Where are customers located? Excel can capture both dimensions, but presenting them visually and refreshing data from external systems usually requires extra pivots, exports, or BI integrations. In Excel, you can prepare event frequency and clustering insights with formulas: ·    1. Record event dates and participant counts in columns. ·    2. Use =𝗠𝗢𝗡𝗧𝗛(𝗔𝟮) to extract the month number for grouping. ·    3. Build a pivot table that aggregates event totals by month. ·    4. For customer clustering, store zip codes in separate columns. ·    5. Use =𝗟𝗲𝗳𝘁(𝗭𝗶𝗽𝗖𝗼𝗱𝗲) at a character interval of either 1, 3, or 5 to group customers into clusters by location. ·    6. Summarize clusters with pivot tables to spot regional concentrations. This process works, but it is manual and requires refreshing pivots or exporting to other tools for mapping or API integration. In Sheetcast, Calendar, Map, and API Pages extend the same dataset into fully interactive and connected views. A 𝗖𝗮𝗹𝗲𝗻𝗱𝗮𝗿 𝗣𝗮𝗴𝗲 automatically places each event on its scheduled day. A Map Page instantly shows clusters as you zoom and pan, no formulas, no extra exports, just your data brought to life. And with an 𝗔𝗣𝗜 𝗣𝗮𝗴𝗲, you can connect directly to an external system, for example, pulling in live weather forecasts for event dates or syncing your data with your team’s calendars. For example: ·    1. Keep event date and customer location fields structured in Excel. ·    2. Configure a 𝗖𝗮𝗹𝗲𝗻𝗱𝗮𝗿 𝗣𝗮𝗴𝗲 bound to the Event Date column — events appear on the correct day without pivots. ·    3. Add a 𝗠𝗮𝗽 𝗣𝗮𝗴𝗲 linked to the zip code fields from the customer dataset. ·    4. Use 𝗙𝗜𝗟𝗧𝗘𝗥𝗥𝗘𝗙𝗦(𝗔𝟮:𝗔𝟭𝟬,𝗕𝟮:𝗕𝟭𝟬="𝗔𝗰𝘁𝗶𝘃𝗲") so only active customers display on the map. ·    5. Create an 𝗔𝗣𝗜 𝗣𝗮𝗴𝗲 that lets you connect your Calendar Page with an external system, keeping them both in sync. With this setup, analysts move seamlessly from spreadsheets to interactive calendar and map dashboards, with the added benefit of live external data integration. Excel is still where you build the magic; Sheetcast just makes it easier for everyone to see, explore, and act on it.

HubSpot has just released the ability to associate marketing events with more of the CRM data model. Why this matters: For what seems like forever, marketing events data was isolated (to put it kindly). You could track registrations, attendance, and engagement, but it was harder to connect that activity to anything meaningful with customization or an app. Now teams can connect event activity to: • 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 to understand account-level engagement • 𝗗𝗲𝗮𝗹𝘀 to see which events are influencing pipeline • 𝗣𝗮𝘆𝗺𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗢𝗿𝗱𝗲𝗿𝘀 to tie event activity closer to revenue outcomes • 𝗖𝘂𝘀𝘁𝗼𝗺 𝗮𝗻𝗱 𝗮𝗽𝗽 𝗼𝗯𝗷𝗲𝗰𝘁𝘀 to fit the way their business is actually modeled in HubSpot    That means better answers to questions like: • Which events are influencing the pipeline? • Which accounts are engaging most with our event strategy? • How do attendees convert into customers? • What downstream revenue impact are our events creating?    A small but important detail: this also becomes much more tangible on the record level. In the screenshot here, the association is showing directly on the company record under Marketing events, with the label “Influencing event.” This along with an update allowing us to modify enrollment to Events on the Contact Record (earlier in the year) and some awesome Campaign association updates (March 26), and I feel like both Events and Campaigns are getting some much needed love.

Machine Learning-Powered Demand Sensing: Revolutionizing Real-Time Decision Making In the realm of demand forecasting, machine learning (ML) is reshaping the landscape by enabling real-time analysis for predicting short-term demand with exceptional precision. Unlike conventional methods that rely solely on historical data, ML-driven demand sensing incorporates a wide array of data sources, including sales figures, inventory levels, weather patterns, social media trends, and economic indicators, to swiftly identify fluctuations in demand. For instance, in the context of event management, demand sensing proves invaluable in anticipating attendance variations influenced by external factors such as weather conditions or concurrent events. Through sophisticated ML algorithms, subtle trends like a sudden spike in ticket purchases triggered by social media engagements can be detected, empowering organizers to promptly adjust their strategies related to inventory, staffing, or promotions. This innovative approach not only slashes forecast errors by as much as 50% but also streamlines resource distribution and mitigates risks associated with overbooking or inventory shortages. By translating raw data into actionable intelligence, demand sensing fosters agility and accuracy in navigating dynamic market conditions.

Growth mindset = lots of experiments = a flood of data   Events, properties, customer interactions - they pile up fast   And then the questions start: * Where’s this event going? * What does it track, exactly? * When did we add this property, and why?   To avoid drowning in data chaos, we use a Taxonomy It’s our internal source of truth for all things tracking   What does it cover?   Every single event, with crystal-clear details:   - event name - what we’re calling it - description - what it actually means - firing rules - when it triggers - source - where it fires from (server/client) - destination - where it sends the data - properties - all the juicy metadata and what it tells us   Having this mapped out means: ✅ No guessing ✅ Clean analytics ✅ Smarter decisions   If you’re running growth experiments and don’t have a tracking taxonomy yet, start one today Future you will thank you

Too often, events and congresses are treated as standalone activations, disconnected from the larger engagement ecosystem. But in reality, they should be an integral part of a broader, data-driven engagement strategy, seamlessly integrated into systems like Veeva Systems, Salesforce, and omnichannel CRMs. How, you may ask: • If an HCP asks a specific question during a congress panel, that data should trigger tailored content recommendations in the CRM, instead of a generic post-event email. • Ensure that digestible short-form key learnings from live sessions flow into on-demand content libraries, allowing non-attendees to engage later. • A Veeva-integrated chatbot could automatically send relevant whitepapers, webinars, or advisory board invites based on what an HCP engaged with at the event. • A Salesforce-powered HCP journey map could ensure that a congress attendee automatically receives digital touchpoints (e.g., follow-up emails, LinkedIn discussions, or small-group webinars) aligned with their specific interests. • A company using Veeva Vault CRM (or other CRMs) + Events Management + Salesforce Einstein AI (or Copilot) can dynamically adjust post-event outreach strategy based on how an HCP interacted with content at the congress. • Sprinklr + Salesforce CDP for social listening enables Digital Opinion Leaders (DOL) Activation by tracking and analyzing post-event conversations across various social and digital platforms. The key takeaway is that events and congresses should be fully integrated within the CRM, digital engagement, and omnichannel ecosystem—ensuring that every interaction contributes to a seamless, long-term engagement strategy rather than just a single event touchpoint. Check the full episode with Pierre Metrailler at Onomi / SpotMe on demand: https://lnkd.in/dwg7BYiu And if you want to learn more about our expertise at The Palindromic across "Next-Gen CX Strategy" and "Expert Engagement & HCP360 Enablement" - drop me a note at claude.w@thepalindromic.com

What if you could see how engaged your webinar audience really was…not just guess? We just launched attendee analytics with heatmaps in Wistia’s webinar product, and I have to say: it’s kind of blowing my mind. You’re no longer stuck with just knowing who registered or showed up. Now you can see who actually paid attention. Who kept your webinar open the whole time. Who was clicking, chatting, reacting. Who stayed fully focused for 5 minutes or 50. And here's the kicker: This signal is stronger than what you'd get from an in-person event. At a live event, you can’t tell who’s just being polite versus who’s genuinely tuned in. But this? This shows you exactly who’s leaning in and gives you the data to act on it. We’re talking names, companies, watch time, engagement… all in one place. This is the clearest window I’ve ever seen into audience interest. I made the below video because I was so excited to use the product with real data. Seeing how it works on our webinars made it click for me in a whole new way. Attendee analytics is more than a feature. It’s a new metric. One that gives you real insight into what’s working and who actually cares. You don’t need a bigger audience. You need better signals. And now, we’ve got them.

A new Deloitte report on how sports teams use data analytics to drive attendance and revenue makes one thing very clear. The most valuable asset a sports team owns is no longer the stadium. It is not the broadcast deal. It is not even the players. It is the data on who is sitting in the seats. Digital tickets, biometric entry, app behaviour, purchasing patterns inside the venue. Teams that are collecting and commercialising this intelligently are building something that compounds in value every season. Personalised promotions that drive repeat attendance. Sponsorship packages built on real audience intelligence rather than estimated reach. The ability to identify and convert non-attendees using the same data infrastructure. Every other major consumer industry figured this out years ago. Sport is catching up fast. The teams that understand fan data as a commercial asset, not just an operational tool, are going to look very different financially from the ones that do not within the next five years. #Sportech #FanData #SportsBusiness #SportsFinance #DataAnalytics #SportsInvestment #FanExperience #SportsTech

Today we're announcing Event Intelligence. A new feature that's going to fundamentally change how companies approach events. Companies spend millions on events every year. But most show up blind. They don't know which prospects are attending. They don't know which active deals are walking the floor. They don't know which customers are three booths away. Popl, at its core, is a data company. And now, with just a conference name as input, our proprietary data engine combines first-party event data, partner datasets, marketing signals, historical attendance patterns, and AI predictions to give you something that's never existed before: a complete picture of who's going to be at an event, before you even book your flight. Here's how it works: - Within any Popl Campaign, you can now request an Event Intelligence dataset for that event. - Our data engine and AI agents generate the full exhibitor list, including booth numbers. - We then run Popl enrichment on each company—data like annual revenue, headcount, industry, and more. - Finally, we cross-reference each exhibitor with your CRM, providing full context and bucketing each into one of four categories: → New Account – company isn't in your CRM yet → Prospect – company exists in your CRM, but no active deals → In-Progress Deal – there are active opportunities being worked → Current Customer – closed-won deals, meaning they're an active customer Each company also gets an AI-generated context summary, so your team has readable intel at a glance. It's time we make events a predictable revenue channel. Not a gamble. Solve event lead capture and get event intelligence all-in-one: https://hubs.la/Q042bz6Z0

As a PMM at HockeyStack I have access to a wild dataset. We see millions of marketing and sales touchpoints, and their impact, across hundreds of mid-market and enterprise B2B SaaS companies. With HockeyStack Labs, we’re turning that data into industry insights. The kind that shift how GTM teams allocate budget, design campaigns, and track performance. Each Labs report starts the same way: 1. Pull real, aggregated performance data from across our customers. 2. Build dashboards directly inside our platform 3. Partner with our data team to surface the patterns that show what actually drives revenue Our latest report dives into event marketing performance. We analyzed 2.6M B2B deals from 200 SaaS companies to uncover which events actually drive pipeline and when they have the most impact on closed won revenue. These are the same insights our customers access every day inside their own dashboards. It’s incredibly exciting to aggregate them and sharing the learnings more broadly. More to come! Read the report here: https://lnkd.in/gYCfmcYS

The best events do the following: 🤝 Make space for in-person connections 🤩 Show off brands in real, tangible ways 📈 Build momentum in a territory  💸 Generate revenue That last one — generating revenue — is so challenging because it’s not something you can track alone; it takes close partnership with sales and ops to figure out reporting and attribution that makes sense. But I’m telling you, this is well worth your time. If you can’t prove the ROI of your events or attribute any deals to event attendance, why should your budget stay the same or increase for the second half of the year? Here are 3 examples on how to track revenue from events: Lead capture and conversion analysis - Method: Use registration forms, badge scanners, and interactive touchpoints to capture leads at events. - Example: Integrate captured leads into your CRM system and track them through your sales funnel to analyze conversion rates and revenue generated. I like to put time around this. Track trade show leads for six months to see where they go. Attribution models and multi-touch reporting: - Method: Assign value to different touchpoints in the customer journey, including event attendance, using attribution models. - Example: Implement a multi-touch attribution model that credits a portion of revenue to events when attendees make a purchase after interacting with other marketing efforts. Post-Event Surveys and Direct Sales Correlation: - Method: Conduct post-event surveys to gather feedback on purchasing intentions and follow up on actual sales. - Example: Send surveys immediately after the event and follow up a few months later to see if attendees made a purchase. Cross-reference survey responses with sales data. You know the impact of events, just be sure you’re proving it to others who don’t.