Using Technology to Enhance Voice of Customer Efforts

For the last 20 years, we’ve built VoC programs around the same formula: send surveys, wait for responses, analyze, react. It’s clean. It’s measurable. I also think it’s wildly out of step with how customers live and interact every day. Over the next several years, I think VoC shifts from interruption-based to observation-based. Passive signal capture from wearables, devices, connected products, in-app behavior. We’ll have a more honest picture of the customer experience than any survey ever gives us. This data will help us predict what’s about to happen and give every brand the chance to act before the customer ever raises a hand. Leading brands will blend passive signals with targeted, active listening. They’ll also give instant value back to the customer for every piece of data they share, whether it’s volunteered or detected. Everyone else? They’ll still be chasing CSAT responses while fewer and fewer customers fill out surveys. On Monday, here’s where to start if I were you: Compare where you think you’re getting feedback to where customers actually express themselves. Document the gaps. Test one new signal source line app behavior, device data, or voice tone in calls, and see how it changes your insight. Identify how you can route every signal into a system that can respond instantly, not just analyze later. Make every piece of feedback, whether active or passive, trigger something tangible for the customer. Build comfort with behavioral data, machine learning outputs, and multi-signal analysis on your team. VoC is about to stop asking questions and start delivering answers. The only question left is: will your program be ready when the shift happens? #customerexperience #voc #surveys

🛑 𝗗𝗼𝗻’𝘁 𝗙𝗹𝘆 𝗕𝗹𝗶𝗻𝗱: 𝗨𝘀𝗲 𝗔𝗜 𝘁𝗼 𝗞𝗻𝗼𝘄 𝗬𝗼𝘂𝗿 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 Having trouble keeping pace with your customers' desires and needs? If you're not leveraging real-time data on customer behavior and preferences, you're essentially flying blind. 💥 This lack of insight can cripple your marketing and sales efforts, leading to ineffective customer engagements and stunted sales growth. Here’s where Voice AI steps in as a powerful ally: ❇️ Real-Time Data Collection: Implement Voice AI to engage with customers directly. This technology collects essential data on preferences, concerns, and feedback as the conversation happens. ❇️ Instant Feedback Loop: Set up your Voice AI to provide real-time feedback to your marketing and sales teams. This means they can pivot and adjust strategies instantly, enhancing the effectiveness of your campaigns on the fly. ❇️ Real-Time Alert System: Integrate a real-time alert system within your Voice AI setup. This can notify team members immediately when it detects key customer triggers, like expressions of dissatisfaction or excitement, prompting swift and appropriate action. By integrating these strategies, you'll not only meet but exceed customer expectations, enhancing engagement and driving sales. How are you leveraging technology to stay on top of customer preferences? Share your strategies below! #innovation #digitalmarketing #technology #bigdata #entrepreneurship #voiceai

It’s AI Voice of Customer time here on day 2 of 30 days of Ignition workflows. Every company is sitting on a mountain of insights. But accessing those insights is incredibly hard. They’re buried in 10,000’s of customer support tickets, sales call transcripts, and seller notes inside your CRM. Easily analyzing that volume of data is hard enough, but it’s also incredibly hard to simply get access to all of it. SO many Product and Product Marketing teams I talk to don’t even have Salesforce licenses to be able to export this stuff from their CRM! With Ignition Voice of Customer, all you have to do is connect your CRM, sales transcription tools like Gong, and support ticket tools like Zendesk... Ignition continuously imports all those customer conversations, auto-tags them with feedback categories, and uses AI to extract key thematic takeaways like feature ideas, product strengths/weaknesses, and marketing language being used by customers. You can even segment your results by customer or tag, to get a more granular view into specific subsets of feedback, and refine it with your own additional context like “analyze all conversations that talk about pricing”. Like all Ignition “Research” features — the insights collected here, also help feed your company’s own personalized LLM which allows you to generate better plans and marketing assets, by refining outputs using your customers’ specific pain points and language. Customers like Worldstrides have told us that in the first week of using it, this feature alone has saved them hundreds of hours, and delivered critical insights that would’ve otherwise gone unsurfaced.