Constellation Research highlights Magnify.io's Agentic AI Assistant for SaaS growth

We're revisiting our ICP definition(s) again this year and using AI to help us understand our ICP better. Specifically, we're trying to narrow down our focus a bit more this year. Here's the rough process we're following: 1. Narrow down our ICP (using data to identify our highest LTV customers, brainstorming pros and cons of focusing more on one segment over the other). 2. Evaluate the strengths and weaknesses of the companies that compete against us for our ICP's business. 3. Identify objectives (ways we can improve) that can help us better serve/sell to/market, etc our ICP vs those competitors. (We're basically dogfooding our own Predictable Scale course: https://lnkd.in/eScK5VX9) One of our main ICPs is marketing agencies (40% of our revenue). In the past, we would listen to random calls and rely on one person to go and research their needs and one person to do competitive analysis. With AI, we can all be involved. The tool I'm loving most is Four/Four. It ingests all of our recorded customer calls, chats, etc and allows us to ask specific questions of all of the calls/chats with specific types of customers. When I asked it what the "goals and aspirations" of our marketing agency customers are, it spit out this: - Automate and centralize analytics for faster, more reliable reporting to showcase value to clients, boost client retention, and scale efficiently - Be seen as strategic, AI-first partners delivering actionable insights, advanced reporting, and benchmarking to improve client outcomes - Offer seamless, branded, executive-level dashboards and reporting that impresses clients, streamlines communication, and reduces client cancellations - Upskill teams to be competent with dashboarding, analytics, and data storytelling so they remain competitive and deliver best-in-class service - Efficiently onboard new clients and scale reporting/services without bottlenecks, errors, or excessive cost Of course, I know most of this. But, each one of these bullets has a "show more" button next to it which leads to actual customer conversations. It doesn't replace having one (or multiple people) interviewing ICPs to figure out what we should build for them, how to best market to them, etc. But, it's really helpful to inform decisions and empower a broader group of people to do "ICP research."

Predictive AI is making waves across direct sales, and its just the beggining. We’re proud to share our feature in Direct Selling News, where our CEO Yoav Shpringer and Head of Data Science Doron Lahav explain how Predictive AI moves companies beyond static dashboards and delayed actions: to smarter, real-time decisions that actually drive growth. For years, CRM systems have told companies what already happened. Predictive AI changes that, helping them see what’s coming next and act before it’s too late. From churn prevention to distributor onboarding and product recommendations. Predictive models empower companies to retain more customers, engage their field better, and scale smarter. It’s not just about data, it’s about turning insight into action. 👉 Read the full article: https://lnkd.in/dn8HvBVD #PredictiveAI #DirectSelling #AIinAction #ApptorAI #CustomerRetention #FieldEnablement #DSN

𝗪𝗵𝘆 𝘄𝗲 𝗱𝗼𝗻'𝘁 𝘀𝗽𝗲𝗻𝗱 𝘄𝗲𝗲𝗸𝘀 "𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀." 𝘔𝘰𝘴𝘵 𝘢𝘨𝘦𝘯𝘤𝘪𝘦𝘴 𝘴𝘵𝘢𝘳𝘵 𝘸𝘪𝘵𝘩 𝘥𝘪𝘴𝘤𝘰𝘷𝘦𝘳𝘺. 10 calls. Lots of research. Industry deep dives. We skip all that. Here's why. You already know your business better than we ever will. You know: → What questions customers ask → Which objections come up most → What makes your product different → How your sales process works We don't need to become experts in your domain. We just need to understand what you need built. Then we pick the right tool from our stack. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘄𝗲 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗲𝗰𝗵: Simple automation → Zapier/Make When you just need to connect A to B No conversation needed, just trigger and action Basic chatbots → Manychat/Botpress FAQ handling, lead capture, simple flows Works great when paths are predictable Conversational AI → Voiceflow Multi-step conversations, context handling When users need to feel heard, not just processed Complex workflows → n8n Multi-system orchestration, data transformations When one trigger needs to hit 5 different systems Custom AI agents → Langchain/Langgraph Advanced reasoning, multi-agent systems When the agent needs to think, not just respond Custom builds → Python + FastAPI When nothing off-the-shelf fits Complete control, proprietary logic The principle is simple: Use the simplest tool that solves the problem. Not the fanciest. Not the most "cutting-edge." The one that actually works. 𝗥𝗲𝗮𝗹 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: Client: "We need an AI chatbot for customer support" Most agencies ask: "Tell us about your company history, your mission, your values, your target audience demographics..." We ask: "What are the top 10 questions customers ask you?" They give us the list. We build it in Voiceflow. Done in 3 days. 𝗔𝗻𝗼𝘁𝗵𝗲𝗿 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: Client: "We need to analyze sales leads and prioritize them" Most agencies: "Let's do a 2-week discovery phase on your sales methodology" We ask: "What makes a lead high priority for you?" They explain their criteria. We build a data agent with Langchain. It analyzes their CRM data and gives recommendations. 𝗧𝗵𝗶𝘀 𝘄𝗼𝗿𝗸𝘀 𝘄𝗵𝗲𝗻: ✅ You know your business process ✅ You can explain what you need (even roughly) ✅ You value execution speed ✅ You're okay with "good enough" over "perfect" 𝗧𝗵𝗶𝘀 𝗱𝗼𝗲𝘀𝗻'𝘁 𝘄𝗼𝗿𝗸 𝘄𝗵𝗲𝗻: ❌ You need someone to figure out your business model ❌ You want the "most advanced" tech regardless of fit ❌ You prefer long strategy sessions over building 𝗛𝗲𝗿𝗲'𝘀 𝗼𝘂𝗿 𝗽𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆: Don't over-engineer. Don't use Langraph when Voiceflow works. Don't build custom when n8n exists. It's just different. Sometimes simpler is smarter. 𝘞𝘩𝘢𝘵 𝘈𝘐 𝘱𝘳𝘰𝘫𝘦𝘤𝘵 𝘩𝘢𝘷𝘦 𝘺𝘰𝘶 𝘣𝘦𝘦𝘯 𝘱𝘶𝘵𝘵𝘪𝘯𝘨 𝘰𝘧𝘧 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘢𝘨𝘦𝘯𝘤𝘪𝘦𝘴 𝘸𝘢𝘯𝘵 𝘵𝘰 "𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘺𝘰𝘶𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴" 𝘧𝘰𝘳 𝘸𝘦𝘦𝘬𝘴 𝘧𝘪𝘳𝘴𝘵? Drop it in the comments.

🎃 The Surprising Truth That Arrives with Fall: Your AI problem is an ICP problem. I see this exact failure pattern every single week with long-time HubSpot customers. They desperately want the promise of AI—the lead-gen hyper-personalization, the sales velocity—but they're still fundamentally operating on traditional, manual processes. They secure the budget for a spectacular AI Sales Assistant or expensive agency, yet their RevOps system is built on a pre-2020 Data Model. The C-suite mandates "AI for everything," but the ground teams don't trust the data the AI is using. The result isn't a competitive advantage; it’s amplified inefficiency. The AI consumes the inconsistent, siloed data and generates perfectly personalized, yet contextually useless, outputs. Marketing, Sales, and CS teams quickly revert to manual work because they cannot trust the signals generated. You don't need a new hammer. You need to carve a better foundation. To make personalization and AI work efficiently, you must execute a mandatory architectural retreat and secure GTM Alignment across these pillars: ICP & Buyer Personas: Is your Ideal Customer Profile current? A vague or outdated ICP means the AI will target everyone vaguely. This is the critical starting point for your Data Pipeline cleanup. Buyer Journey: Map the new, non-linear reality of your customer’s path. The context an AI Sales Assistant needs is only as good as the journey steps it’s trained to recognize. Brand Voice: Standardize the voice globally. Your AI-generated content must speak with one authentic, consultative tone. The scalable growth differentiator in 2025 is the pragmatic choice to prioritize a solid, trustworthy data infrastructure. What’s the single most outdated part of your GTM foundation right now? #HubSpot #RevOps #DataModel.

Sales teams worldwide are pouring billions into AI tools that predict customer behavior before competitors catch on. Industry analysts project this market will hit $240 billion by 2030, with telecom giants and banks already deploying these systems for everything from spotting high-value prospects to forecasting demand spikes. Mid-sized companies now access the same cloud-based AI capabilities that once belonged exclusively to tech titans. The real differentiator emerging? Businesses that can explain their AI decisions to customers are winning more trust than those chasing pure efficiency.

If you’re hopping from one tool to another ‘just because ‘someone recommended you’. Umm, not a great idea. Because which tool to use depends completely on…. Which stage of business you’re at. In short, Your Business Growth = AI Maturity It breaks down into 3 stages, called the ‘3 P’s of AI Maturity’ 1. Problem-Market Fit: → What this means: At this stage, a company is just starting to explore AI. They pick one or two small problems to see if AI can actually help. → Goal: Show that AI can solve a real problem, even if it’s small. Like helping you find the ‘pain points of your TG’ → Example: Let’s say your customer success team is overwhelmed with support tickets. You decide to test an AI chatbot (like Intercom’s Fin or ChatGPT API) to handle basic customer queries automatically. You launch it for a few clients and see if response times improve. CONCLUSION: Pick one pain point → Test an AI tool to fix it → Measure if it adds value. 2. Product-Market Fit: → What this means: Now that you know AI can work, it’s time to bake it into your actual product or daily operations not just as a side experiment. AI becomes part of how your team and your customers use your SaaS every day. → Goal: Make AI to do more than just finding a solution to ONE particular thing. Something that gets you measurable results. → Example: Your SaaS offers a CRM tool. You decide to embed AI-powered lead scoring directly into the product so users can automatically see which leads are most likely to convert. You might use tools like OpenAI API, Cohere, or AWS Comprehend to build this feature. CONCLUSION: Move from “AI tests” to “AI in action.” → Integrate AI into your core workflows or product features. → ️Focus on adoption and impact, not just innovation. 3. Platform-Market Fit: → What this means: AI works for your product, now makes it work for everyone. Use it across teams so it becomes part of how your company runs. Like, marketing, sales, customer success, finance, engineering. Slowly, AI becomes part of your company’s infrastructure, a shared system everyone benefits from. → Goal: Create a complete unified AI strategy, data layer, and ensure quality, and scalability. → Example: Now your company builds a central AI platform (e.g., using Databricks, Snowflake Cortex AI, or AWS SageMaker) that supports all teams: Marketing: Uses AI to generate personalized campaigns. Product: Uses AI to suggest new features based on user data. Customer Success: Predicts churn automatically. Finance: Uses AI models to forecast revenue. CONCLUSION: Build a shared AI platform. → ️Connect all teams through data and insights. → Scale AI as a business-wide capability, not just a tool. Result? AI maturity for SaaS = Start small → Embed into product → Scale across the company. That’s when AI stops being a “feature” and starts becoming your competitive advantage.

If your CRM is just sitting there collecting dust and contact info, you’re missing out on its biggest potential: intelligence. With AI-powered features like lead scoring, predictive forecasting, automated follow-ups, and churn prediction, your CRM can become your most powerful sales tool. This blog breaks down six ways to put your CRM to work using AI. Less busywork. More revenue: https://lnkd.in/ezGe7vG9 #CRMTools #BusinessGrowth #MarketingAutomation #ContentStrategy #MarketingInsights

• Most businesses read reviews. But only a few understand what customers are actually feeling. This is where Sentiment Analysis becomes essential. • What is Sentiment Analysis? It is AI that analyzes every customer review, chat message, email, and social media mention and determines whether the sentiment is positive, negative, or neutral. It helps businesses understand customer emotions at scale instead of manually reading hundreds of comments. • Types of Sentiment Analysis 1. Basic Sentiment (Positive / Negative / Neutral) Quick overview of overall customer mood. 2. Fine-Grained Sentiment (5-level rating) Very positive → Very negative level of intensity. 3. Emotion Detection Identifies emotions like anger, happiness, frustration, confusion. 4. Aspect-Based Sentiment Analysis Breaks down feedback into components (e.g., food, service, delivery) to show what specifically needs improvement. • Examples of Real Business Impact 1. Restaurant: - AI discovered complaints about slow weekend service → Staffing adjusted → Rating increased from 4.1 to 4.6. 2. Online Retail Store: - AI identified repeated complaints about sizing → Updated size charts → Return rate dropped 30%. 3. Healthcare Clinic: - AI detected frustration with wait times → Scheduling adjustments → Patient satisfaction scores increased. 4. Marketing Agency: - AI monitored client brand sentiment → Negative spikes flagged instantly → Response time reduced significantly. • Cost & Implementation - Free tools and CRM-built features can start providing insights immediately. - Starter plans range from $50 to $200/month. - Custom-trained models are available when scale or language specialization is needed. Start small with: • Google Reviews • Support email sentiment • Social media comments Then expand into multi-channel automated analysis. • How to Select the Right Tool Look for: - Strong language and slang understanding - Integration with your review/support channels - Clear dashboards and sentiment trend reports - Accuracy with sarcasm and mixed emotions - Scalable pricing Test the tool with real customer data before committing. • The Future - Sentiment AI is evolving into: - Voice tone emotion recognition - Churn prediction - Automated response drafting - Real-time brand health monitoring - This is no longer optional. It is becoming a required competitive capability. If your business wants to improve customer experience, prevent issues early, and make decisions based on insight rather than guesswork, sentiment analysis is one of the most effective tools you can adopt. Follow ExpertBridge for more updates  #SentimentAnalysis #CustomerExperience #SmallBusinessGrowth #AIAutomation #BrandReputation #SMBMarketing #CustomerFeedback #AIForBusiness #BusinessIntelligence #ExpertBridge

Make it easier to hear your customers In this clip, I had the privilege of joining Bill Price, President of Drive Solutions, on a LinkedIn Live to dive into the best ways to hear the Silent Sufferer customer. 🎥 Watch the full conversation here: https://lnkd.in/gSjcVUBn If you’re curious about how AI is redefining customer experience — or want a glimpse into the future of CRM — this is a must-watch. And to see how we’re building that future at TNE.ai, visit: https://tne.ai/ #CustomerExperience #AI #CRM #SilentSufferers #Innovation #DigitalTransformation

Unlock Explosive Growth with AI for Sales Funnel Optimization In today’s fast-moving business world, success relies heavily on AI for Sales Funnel Optimization. From my experience leading digital sales teams, I saw how manual processes slowed growth—until AI-powered platforms changed everything. With automation and real-time insights, sales funnels became smarter, faster, and more effective. In fact, as Ringover notes, 21% of sales professionals already use AI at work—a number that keeps rising. These tools bring a powerful, often underrated solution to sales funnel optimization challenges. For example, AI can customize customer interactions, streamline CRM systems, and even manage entire campaign platforms through smart automation. This doesn’t just improve efficiency; it allows professionals to focus more on strategy, not just maintenance. I’ve seen firsthand how advanced analytics helped us understand behavior changes quickly, adjust in real time, and stay competitive. When we integrated an AI assistant that could answer questions like why a m https://lnkd.in/gwPnsAxs