Using AI for Customer Insights

I'm an AI novice but finding great value in using ChatGPT* and Bard to analyze both my and reps I coach calls. Gong recently added some AI into their Highlights tab. It's okay and sure to get better, but I still copy/past transcripts into Bard for more insights. Here's what I'm doing. To the power users reading this, please provide your recommendations in the comments (looking at you Marcus Cauchi). 1. Paste call transcript into Bard. Ask: Please provide a summary; what problems the customer has that were explicitly spoken; what questions I (or rep name) asked that got the best results; what questions could I/he have asked to get at problems? 2. Review and ask follow-up questions to Bard. Examples: What are the impacts to the customer of changing to our service that were talked about? Impacts of not changing? Considering the company and role of the customer, what problems and impacts may they have that we didn't discuss? What questions can I ask to get to those problems and impacts? How urgent did the prospect seem? Who else is involved in purchasing? ... Was this discussed? ... Who should be included? ... How can I ask that next time? And on and on. Ask it both about what was discussed and what should have been discussed. Ask it to analyze similar buyers in the industry and their challenges and apply to this meeting; what did I miss? Bottom line: use the AI to analyze and coach your calls. *The free ChatGPT has a character limit but Bard doesn't seem to right now so it's why I've been using Bard for these analyses. #sales #salestips #chatgptprompts #bardprompts

What are the best current cases? Believe it or not, they are NOT the known problems. This is the mistake that a lot of people make. They go after the basic things that they heard another company has done in a similar industry. I have seen too many CFO''s get caught up in the AI equivalent of "keeping up with the Joneses." They heard about another company doing something magical in AI, and they want it. Sure, there is a benefit in learning what others have done, but these successes are not really generalized. So in manufacturing, we may look at, for example, the idea of doing more preventative maintenance, but understand, this is something we've been looking at for decades, if not longer, and AI doesn't add as much to the equation as some may think. These standard problems and solutions are not where you will see a lot of value from AI. It will be those special use cases that are part of your secret sauce that will gain you the most value. For example, I had a client who had a lot (and I mean a lot!) of data in a survey. It was all non-structured free text. They had a team of five people that would be going through this data all day long, trying to interpret it to extract things that they wanted to know about their customer's behavior. I challenged the method with a simple experiment. If I took five different questions and gave the responses to all five people, at least three out of the five of them would have vastly different interpretations. By using AI, we were very quickly able to extract the data and had it convert this unstructured data to structured and pull out the insights in a much more objective fashion to make much better decisions. That isn't one of those standard use cases that you hear about, but it was game-changing to their retention rate since they understood their customers better. So, I want you to consider what are all those special use cases specific to your business that are out there. This is where you'll see your most value right away out of what we do in AI today. #manufacturingtechnology #manufacturinginnovation #manufacturingtech #artificialgeneralintelligence #artificialintelligenceforbusiness

Want to know what customers think about your brand and your competitors' brands? In just 15 minutes, you can uncover key insights from data that might be right under your nose with the help of AI. Unsure where to start? Begin with the resources you already have: ► Customer Feedback - Reviews, social media comments, and survey results (e.g., market research, CSAT, NPS) ► Customer Interactions - Transcripts from interviews, calls, and advisory board meeting notes ► Published Content - Case studies, customer quotes in articles ► Other Relevant Data Sources Note: If you're dealing with sensitive information, remember to redact and anonymize it prior to using AI. Here’s a practical example using Asana and its publicly available customer reviews on Capterra: Review the screenshots in the carousel below to see how my team and I used ChatGPT (GPT-4) to extract: ► Asana pros and cons with word clouds plus ideal customer profiles ► Table summarizing previous vendors, reasons for switching, and alternatives considered ► Tables highlighting potential customer inquiries at different funnel stages, value propositions (old way, new way, quantification ideas), and objection handling FAQ This process involved simply copying and pasting customer reviews into a document, uploading a PDF version to ChatGPT, and providing clear instructions on the insights I wanted to extract. The entire process took about 15 minutes and the results are grounded in insights from the customer reviews. Expanding your analysis: While this example focuses on reviews, consider analyzing other types of customer data to gain a comprehensive understanding of your customers and competitors. Remember: The quality and diversity of the data you provide will directly impact the insights generated. To ensure a well-rounded understanding, consider using a wide range of data sources that cover different aspects of the customer experience. AI can quickly help you start, but it’s just the beginning. Human oversight is crucial to guide, challenge, double-check, and collaborate with AI for the best results. After reviewing the AI-generated insights, you might identify gaps or areas needing deeper analysis, which you can then explore further. Have you used AI to gain customer or competitive insights? What worked and what didn’t? Share your experiences and tips in the comments below. For collaboration on inspiring your teams with applied AI in sales and marketing, feel free to reach out to me, Tahnee Perry, or Daniel O'Neill. #CustomerInsights #CustomerReviews #CompetitiveAnalysis #AIAnalytics #ResponsibleAI #GrowthPathPartners

𝐇𝐚𝐫𝐧𝐞𝐬𝐬𝐢𝐧𝐠 𝐀𝐈 𝐟𝐨𝐫 𝐚 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐄𝐝𝐠𝐞 𝐢𝐧 𝐅𝐢𝐧𝐓𝐞𝐜𝐡 𝐒𝐚𝐥𝐞𝐬 In the rapidly evolving FinTech sector, Artificial Intelligence (AI) has become a critical driver of strategic advantage, particularly in sales. As a seasoned recruiter with a focus on niche Salesforce talent, I've observed firsthand how effectively integrating AI can revolutionize sales strategies and enhance customer engagement within the FinTech industry. AI’s capabilities extend far beyond automating routine tasks. In FinTech sales, it offers profound insights and fosters a more personalized approach to customer interactions. Here’s how AI is reshaping FinTech sales strategies: 1️⃣ Predictive Analytics: AI-driven tools analyze vast amounts of data to predict customer behaviors and preferences, allowing sales teams to anticipate needs and tailor their offerings. This proactive approach not only enhances customer satisfaction but also increases the likelihood of conversion. 2️⃣ Enhanced Customer Profiling: AI algorithms can sift through data to create detailed customer profiles, helping sales teams understand who their customers are, what they need, and the best ways to serve them. This targeted approach significantly improves the effectiveness of sales pitches and promotions. 3️⃣ Automated Customer Interactions: Chatbots and virtual assistants, powered by AI, handle initial customer inquiries and support, providing timely and consistent responses. This frees up human agents to focus on more complex and high-value interactions, optimizing resource allocation. 4️⃣ Improved Risk Assessment: In FinTech, assessing credit risk and potential fraud is crucial. AI enhances these processes by quickly analyzing risk factors based on historical data, leading to more accurate assessments and reducing potential losses. 5️⃣ Real-time Feedback and Adaptation: AI systems provide real-time feedback on sales performance and market conditions, allowing sales strategies to be adapted quickly. This agility is crucial in the fast-paced FinTech market, where customer needs and competitive landscapes can shift rapidly. As AI technology continues to advance, its integration into FinTech sales strategies will become more profound. Companies that can effectively harness this potential will not only enhance their operational efficiency but also significantly improve their customer engagement and sales outcomes. If you’re looking to integrate AI into your FinTech sales strategy, or if you’re a professional skilled in this area, let’s connect. Together, we can explore how to harness AI for strategic advantages in your sales efforts, driving not just growth but transformation in the FinTech landscape. #FinTechSales #ArtificialIntelligence #StrategicAdvantage #SalesInnovation #JamesHickey

#AICustomerService: Because dealing with humans is so passé.   When it comes to customer engagement, #generativeAI is a game changer for ALL businesses.   Generative AI, powered by LLMs like #ChatGPT, can analyze historical customer data and identify patterns, preferences, and trends. LLM's can tailor your marketing messages, product recommendations, and customer support interactions to meet individual customer needs. They can automate customer engagement processes like chatbots or virtual assistants.   Generative AI can also help process customer reviews that reveal their overall feedback on the brand. This information enables businesses to make #datadriven decisions, develop targeted marketing campaigns, and enhance their products or services based on customer feedback.   Though it may all sound hunky-dory, Generative AI algorithms have limitations. They rely on historical data, which can introduce biases or fail to capture evolving customer preferences. For all we know, LLMs are currently working with data only as current as 18 months ago. It's important to be transparent with customers when you integrate AI into their experiences.   It won’t be one-size-fits-all, for now.

As a researcher, I value the insights from a designated sample. As the saying goes, “you don’t know what you don’t know.” In the world of #CX, one way to learn what you don’t know is by gathering feedback from customers. The top way companies track CX is through post-interaction surveys (66.8%) and one-to-one conversations (50.3%). But a growing method is AI-enabled inferred sentiment (43.7%), which uses AI to assign a value to every conversation, according to Metrigy’s research studies. Only about 30% of consumers will respond to surveys (and this includes the short SMS messages asking them to rate one to five stars), so AI provides a highly accurate gauge of customer sentiment. During my fourth episode of Nextiva's #TheFormula, I talk about the importance of using #AI to look at customer sentiment, along with the results of the interaction, to provide a measurement of customer (and agent) success. My advice for gathering and leveraging customer feedback is: ✅ Use multiple methods for gathering feedback, including surveys, focus groups, and AI ✅ Take action based on the results of that feedback (AI also can summarize and make recommendations) ✅ Use the information gleaned from that feedback to train contact center agents, as well as other employees in the company Watch Nextiva's entire series with experts in CX by clicking the link in the comments below ⬇️ #contactcenter #customerservice #customersentiment #surveys #AICSAT #inferredsentiment

Did you know that you can use AI to turn every sales call into a personalized onboarding map? Done right, this can improve product engagement, customer satisfaction, and retention. Danny Villarreal outlines exactly how to do this. Danny is a seasoned Customer Success leader, most recently as VP at Jungle Scout. The sales cycle and multiple interactions along the way provide a gold mine of data that's waiting to be tapped into. What are their biggest challenges? Objections? Goals? Understanding these points are critical not just close the deal but to set the stage for a highly personalized onboarding experience. 🤖 AI steps in as a game-changer here. By analyzing these conversations, you can guide sales teams with better questions and strategies, tailored to each client’s specific needs and pain points. Post-sale, this data transforms into a 'customer brief,' a blueprint for personalizing their journey further. The true magic happens in onboarding. With insights from AI, you can focus on what the customer values most, addressing their unique challenges head-on. This isn't just about showing them how to use our product; it's about proving its value in their specific context. 💡 The goal? To be the painkiller, not just a vitamin. Aim to provide immediate value, leading to those quick wins and aha moments that convince customers they made the right choice. This early success often leads to upselling opportunities within the first few months. AI allows us to create empathetic algorithms that center around what the customer truly cares about. It's about more than just onboarding; it's about validating their investment in real-time and guiding them towards realizing tangible value faster. For marketers, customer success teams, product, and sales professionals, this approach is invaluable. It not only enhances customer satisfaction but also drives internal decision-making with solid, actionable data. The full episode is in the comments, a ton of insights shared by Danny 👇

You've automated 50% of simple FAQ queries with AI. Good. Now what? Simple search and information retrieval AI assistants can only scratch the surface for reducing customer support costs. Real 10X impact comes from resolving complex ticket issues. Why? Because while simple tickets could save $10-15/ticket, complex tickets cost over $200+/ticket based on complexity, people involved & nuance. But, this is where almost every traditional “AI” chatbot stops performing.  Because your AI support agents need to be adept in 3 things: 🌟 Using knowledge bases  🌟Root cause analysis  🌟Iterative learning Let’s see how these 3 things help:  🌟Knowledge Bases - Your knowledge base is your powerhouse for your support AI agent. You need to provide every source your support agent would need to solve the complex ticket. Like product documentation, tutorial videos, FAQs, website pages, “hidden” stashed documents, forums, and more. Your AI agent should be able to extract information from all these data sources effortlessly & train on them to become as wise as your support agent. 🌟Root cause analysis - One of the things that your support team does is understand customer query, intention and need & do the detective work to identify the root cause. Your AI agent should be able to do the same, but efficiently & with less hallucinations. 🌟Iterative learning - Your best support agents weren’t best when they joined. They learnt on the job by conversing with customers & learning about the product. Your AI agent should do the same. It should become better with more conversations, information & data sources you introduce it to. Everyone can scratch the surface with simple bots. But solving complex tickets naturally is where you’ll gain an exponential competitive advantage. If you’re looking to add such AI support agents for your company, reach out to me. I work with companies looking to improve customer experience with Gen AI assistants that actually work! #ai #aichatbot #aiassistants #artificialintelligence #genai #insurance #sales #support #customerservice #customersupport #generativeai

"Say you'll remember me standing in a nice dress, staring at the sunset." 🎤 “Wildest Dreams” by obviously, you-know-who! Let's throw it back to last week's #Pulse2024... Along with the '80s outfits, high school nostalgia, and amazing shoe styles, I heard from so many attendees one moment that stood out: Human-First AI! 🤖 ✨ Chuck Ganapathi our President and COO, invited his product leads Denise Stokowski, Manu Mittal, Jake Ellis, and Joris Dieben on stage to unveil 13 new Human-First AI-powered features in Gainsight: — Ask Timeline: The CSM job is tough. You're asked to know about every client, feature and use case. Often, we resort to Slack or Teams and say "does anyone know a client who..." Just this morning, I "Asked Timeline" "what customers use Gainsight Success Plans" and got back a great and accurate list. It's like magic!🪄 — Analyze Data: Rather than learning a reporting interface, just ask questions like "who are my biggest clients renewing this quarter with a poor health score."⏳ — AI Follow Up: Never miss a beat (and save a ton of time) with integrated AI that syncs with Zoom and Gong to take summarize and follow up on your meetings.🔃 — AI Intel: Be predictive and stay ahead with AI-driven alerts that are crucial, actionable, and right at your fingertips.🤔 — Enrich with AI: Who loves updating contacts? Automatically populate contact details using AI internal signals.🚦 — Copilot in Gmail: A new seamless integration with Gainsight Copilot within your Gmail to access vital info with no extra hassle.💌 — Copilot in ChatGPT: Leverage Gainsight knowledge directly within ChatGPT for smarter, faster customer insights.📚 — Write with AI: Enhance your communications by using AI to craft emails, polish updates, etc.📝 — Autopilot: End customers can ask questions and receive intelligent responses leveraging community, product, support, and knowledge base insights.🙋🏾♂️ — AI Recommendations: Use AI to find the perfect customer to talk to in the community.🤗 — Knowledge Center Bot Enhancements: Experience stronger AI-powered self-service with our enhanced Knowledge Center Bot.💪 — Community UI: A new look to our Community platform with more modern interactions, feeds, and sidebar navigation designed for you.🚀 — One Customer Hub: A unified web experience for education and community, merging profiles, navigation, and interactions into one seamless journey with AI built-in.🌎 And it was extra special to have Robert McNeely at Workiva and Kari Ardalan at Qualtrics join to share how Human-First AI is helping their CS teams be more effective today. Link to the full video is in the comments.👇 Some say Generative AI is the future of Customer Success, of SaaS and of humanity — I think they’re wrong.💥 Generative AI is the present. It’s here today. To paraphrase Seinfeld, “it’s real and it’s spectacular.” What do you think is the top AI feature/enhancement that will make the most impact on CS teams? What would help the most?