Product teams want to deeply understand their end-users. Emerging startups want to facilitate this by synthesizing qualitative and quantitative feedback from customers. Advances in artificial intelligence are revolutionizing software — product teams are becoming better equipped to anticipate & predict customer behavior. We are in a new era of product-led growth.
The Demand for Customer Intelligence Software
Founders, Product Managers, Designers, Customer Success Managers, and other adjacent teams are always looking for the best tools to understand their customers. Customers are your biggest advocates behind the scenes. What they say, how they feel, and what they do with your product are absolutely critical. You are delivering a product that solves their needs. If you can solve your customers’ needs well, it can help your product gain traction with other customers. Customers are at the core of every framework that teams use. Retaining customers, having them adopt your product, and creating a customer base that refers other potential customers are imperative to your company’s success.
“Product-led growth (PLG) is an end user-focused growth model that relies on the product itself as the primary driver of customer acquisition, conversion, and expansion” (Source).
Growing a customer-base can be modeled through the AARRR Framework — it’s a process of acquiring customers, activating customers, retaining customers, creating referrals, and generating revenue.
“We’re customer obsessed. We start with what the customer needs, and we work backwards” (Source).
A common theme from product teams is that feedback from customers is often qualitative. Designers and PMs can conduct user research, log that information, and prioritize based on an internal framework that works at their companies. This isn’t always perfect. Successful teams listen to their customers, but there are challenges to this. Prioritizing customer problems is time consuming, deciding the most important problems to solve is subjective, and tracking feedback from customers doesn’t have one clear path. New software tools are emerging that quantify the qualitative feedback from customers. Startups are emerging that allow companies to use artificial intelligence to predict customer behavior, offer deep learning models to detect patterns between customers, and integrate several 3rd party APIs to offer an end-to-end solution for companies.
Market Overview and Growth Potential of the Modern Data Stack:
The Big Data market is projected to grow at a CAGR of 9.5%, from $50.61 billion in 2020 to $97 billion in 2027 (Source). The Modern Big Data Stack focuses on a few key areas: Data Ingestion & ETL, Warehousing, Analytics & BI, Orchestration, Transformation, Monitoring & Observability, and Deployment. (Diagram Source).
The Analytics & Business Intelligence software market is one of the most rapidly changing and thriving spaces within Big Data. Tools are emerging that are geared towards helping product teams understand their customers. A few intriguing areas include: the automation of customer intelligence, feedback capturing, and analytics gathering that are driven by applications of artificial intelligence.
There is a fast-growing trend of automation around customer feedback and understanding this deeply. Automation enables product teams and business segments to understand qualitative data from customers — and quantify it in the form of actionable insights. Customer intelligence is crucial to understanding how to build the right product, as well as sell it. Further, it’s important to understand this in order to scale any SaaS company that needs to understand customer analytics at a deeper level.
The Rising Pre-Seed and Seed Companies:
1. Enterpret| Stage: Seed | Backed by: Kleiner Perkins, Sequoia Capital, Unusual Ventures, Vinay Hiremath, Cristina Cordova, Curtis Liu, Melisa Tokmak, Badrul Farooqi, Paige Costello, and more.
Enterpret helps product teams quantify and organize customer pain points. They enable filtering for feedback, creating flexible models that adapt to your product, understanding event analytics & feature flags, differentiating churned & power users, identifying trends in feedback, logging customer feedback, having automatically detected alerts for customer issues, and connecting to several integrations to centralize customer feedback across a diverse range of tools (customer support, surveys, reviews, communication platforms, product analytics, notifications, social media, forums, and more). (Source)
2. Viable| Stage: Seed| Backed by: Craft Ventures, Javelin Venture Partners, and Bossa Nova Investimentos
Viable uses AI to understand qualitative feedback from customers. They have feedback analysis reports, insights with context, customizable reports (by features, customer segments, markets), a Q&A tool that uses natural language to deliver answers on customers, and integrations with several services to sync data (Source).
3. Adaptive Pulse| Stage: Pre-Seed| Backed by: Seed Round Capital, Startup Boost 2020 Spring Toronto
Adaptive Pulse focuses on “[predicting] customer churn” and “[boosting] net revenue retention.” They achieve this by honing in on both qualitative and quantitative customer intelligence. Adaptive Pulse has three core platform features: DataHub, AI Engine, and Retention Intelligence (Source). In addition to the core features, they have several integrations across different categories, such as CRM, support, product, conversational, data & BI tools, and more (Source).
4. Deep Talk | Stage: Pre-Seed | Backed by: Start-Up Chile
Deep Talk “[transforms] text into powerful data” using “chats, emails, surveys, [and] social networks” to help inform teams. They offer features such as topic detection, trained deep learning models to find detections, analyze text in real time, and segment audiences (Source). The software has an end-to-end dashboard solution that provides visualizations and analytics on customers & feedback in real time, allowing teams to make actionable decisions. Deep Talk also has several integrations with CRMs.
Enterpret — an intriguing Customer Feedback Intelligence company:
“Enterpret is emerging onto the scene with its take on building analytics on natural language, so that people building the product can learn directly from customer feedback without relying on another company department to provide the information.” (Source).
Enterpret is an emerging customer intelligence company that is leading the charge for changing how teams are approaching Product-Led Growth. Enterpret’s level of aggregation with third-party service providers is what makes their business model interesting. They are not only creating filtering tools, models, and decision-based features internally — but also integrating with other top software providers externally to create the ultimate data machine for customer intelligence and feedback. Enterpret closed their seed round with backers such as Kleiner Perkins, Sequoia Capital, and Unusual Ventures. They have a highly technical founding team, a strong track record with customers, and the support of investors to propel to the next level as a customer intelligence company.
Entrepreneurs to look out for in the Product-Led Growth and Customer Intelligence Ecosystem:
These are a few of the individuals who are at the forefront of creating customer intelligence tools for Product teams and revolutionizing how teams approach Product-Led Growth.
- Varun Sharma and Arnav Sharma (Enterpret)
- Daniel Erickson (Viable)
- Jennifer Huynh and Johnson Phanyaseng (Adaptive Pulse)
- Valeria Aynbinder and Victor Aynbinder (Deep Talk)
These are four of the top emerging startups in this ecosystem. Feel free to reach out if you want to talk about anything on software, design, product teams, or early stage investing!