5 top customer analytics software tools to use in 2024

In the age of user-centricity, product-led growth means understanding and adapting to your customers’ changing needs and experiences.

Customer analytics tools are key, giving organizations deep, actionable insights into behaviors, preferences, and journeys across various touchpoints.

Here’s how Gartner’s 2024 Market Guide puts it: “For organizations prioritizing customer experience, customer journey analytics and orchestration technologies are now a crucial investment.”

But not all customer analytics tools are created equal. 

In this article, we’ll break down the must-have capabilities for modern customer analytics and review the top platforms in 2024. We’ll show you how to select the right solution for your needs to get a unified view of the full customer journey and drive product optimization, communications and messaging, and sustainable growth.

Types of Customer Analytics Tools: The Evolution Towards Customer 360

Traditionally, customer analytics tools have been categorized based on their focus areas. For example, marketing analytics tools like Google Analytics primarily focus on understanding the effectiveness of campaigns across marketing-specific channels like paid search, social media, and email.

In contrast, first-generation product analytics platforms like Mixpanel and Amplitude track user behavior within the product itself, providing valuable insights into feature usage and user engagement and pinpointing opportunities for improvement. 

In the early days of analytics, just getting visibility into customer flows, engagement, and cohorts felt revolutionary.

But these first-generation tools create a data black-box, showing you instrumented product usage events in a silo from the rest of your customer information. This limited view fails to capture the full context of the customer journey, making it challenging to understand how product interactions relate to broader business outcomes like revenue, retention, and customer satisfaction.

Here’s how John Humphrey, VP of Data Science and Engineering at Mozilla, reflected on his own experience with analytics software:

What we realized is with these tools, you just don’t have a complete view. Yes, what goes on in the product is a lot of my relationship with the customer, but it’s not the entirety of my relationship with the customer.
I kept going through these implementations and especially noticing that the bigger and more complex the business or customer relationship, the more these holes in the big picture started to emerge.

John gives the example of retention — a crucial customer analytics metric. If half your customers are canceling their subscription by phone with your customer support team, but you’re only analyzing data for those who churn within the product, your retention curves will be completely skewed.

One possible solution is copying instrumentation data into SQL and BI tools, in order to bring together different data sources and explore connections between product and business outcomes. But this requires costly, time-intensive data transformations and duplication — and BI tools just aren’t equipped to handle high-volume time-series events.

Luckily, new technology has developed to meet the urgent need for a more holistic approach to customer analytics. 

The rise of the modern data stack, with the data warehouse serving as the central repository for all customer data, has laid the foundation for a new generation of warehouse-native customer analytics tools, like NetSpring. By pointing directly at your composable CDP warehouse, these next-generation tools empower teams to slice, dice, pivot, and explore data across the entire customer journey. That gives you a 360 perspective of your customers (C360) across product usage, marketing touchpoints, sales interactions, support engagements, and key business metrics. 

Must-Have Customer Analytics Software Features

So how can future-focused companies choose tools that maximize visibility across the customer journey? 

We’ve broken down the key features to look for in a customer analytics platform. 

1. Cross-channel analytics capacities

A modern customer analytics solution should make it easy to unify data from multiple sources, including product usage, marketing campaigns, sales interactions, and customer support touchpoints. Look for tools that offer flexible data modeling capabilities, allowing you to combine, analyze, and visualize data from various channels in meaningful ways.

2. Granular segmentation, data visualization, and reporting

Your data analytics platform should allow you to create dynamic segments based on user attributes, behaviors, and interactions across multiple touchpoints, enabling targeted analysis and customization. You’ll also want to make sure you pick a platform that lets you easily build out charts, dashboards, and reports so you can visualize complex data patterns and communicate insights to stakeholders effectively.

3. Truly self-service approach

Choose software that empowers your team to analyze the full customer experience without leaning on data engineers and waiting for reverse-ETL data transformations or complex SQL queries. Prioritize tools that offer no-code data visualization, dashboards, path/funnel/cohort analysis
andad-hoc exploration of the data. Look for a clean UI and strong onboarding and support to ensure ease of use.

4. Security and compliance baked in 

Customer data is sensitive. With an increasing focus on data privacy and security, you’ll need to make sure your tool offers robust security features, such as role-based access control, data encryption, and compliance with industry regulations like GDPR and CCPA. Warehouse-native solutions support strong data governance, as your data stays in your secure enterprise environment, meaning you don’t introduce risks by having to duplicate or move your data.

5. Performance at scale

As your business grows and data volumes increase, your analytics tools must be able to scale accordingly. Look for platforms that can handle large datasets efficiently, ensuring fast query performance and minimal latency even as your customer base expands. You’ll also want to take pricing into account — if your tool charges by data/event volume, you could end up with skyrocketing costs as you scale.

Tools with these core capabilities will give you the analytical firepower to understand your customers’ 360 experience and rapidly iterate based on that understanding to maximize business impact. We’ve put together our top five choices for product, marketing, and sales teams.

The 5 Best Customer Analytics Solutions for Product-Led Companies in 2024

1. NetSpring 

NetSpring is a next-generation, warehouse-native customer analytics platform that stands out from the competition by offering a truly comprehensive, C360 view of the customer journey. By leveraging the data warehouse as the single source of truth, NetSpring lets teams explore data from multiple sources, from acquisition metrics on the website to usage metrics within the product. It’s a powerful self-service tool designed for modern data architectures — it supports that all the major cloud data warehouses including BigQuery, Databricks, Snowflake, and Redshift.

Key Features

• Warehouse-native architecture for seamless data integration powered by a single source of truth
• Flexible data modeling for custom metrics and segments
• Self-service analytics with an intuitive user interface and a rich library of pre-built reports
• Advanced slice-and-dice segmentation across any dimension, anytime
• Robust visualization and reporting capabilities
• Powerful self-service ad-hoc data exploration features
• Enterprise-grade security and compliance features and no data duplication required
• Join, merge, and enrich event streams with additional customer context, including offline data

Benefits

• Provides a holistic, 360-degree view of the customer journey by combining advanced product analytics with business and customer context across touchpoints
• Avoids down data silos and inconsistent, fragmented analytics by working off of the warehouse as a single source of truth
• Reduced total cost of ownership (TCO) as there’s no need for additional ETL or reverse-ETL pipelines and SQL data engineering
• Empowers teams with self-service analytics that enable the data-backed decisions that drive business success
• Warehouse-native approach means you can run real-time analytics off of mutable data, i.e. changes in underlying records are automatically reflected in your reports, visualizations, and models

Limitations

• Requires a modern data stack (data warehouse, composable CDP)
• Slight learning curve for users brand new to the flexible modeling capabilities

Pricing

Simple, predictable seat-based pricing starts at $49/month per user, with no limits on events, metrics, or data volume.

Best for

NetSpring is ideal for future-focused, data-driven organizations with a modern data stack. It’s particularly well-suited for companies looking to empower teams with 360° insights on the full customer journey, including easy-to-use advanced, ad-hoc data exploration. 

What sets NetSpring apart is its warehouse-native ability to provide a truly unified view of the customer experience, without the need for complex data integration or expensive data duplication. That ensures data integrity, security, and compliance, while giving you unparalleled analytics firepower, flexibility, and scalability.

2. Mixpanel

Mixpanel is one of the pioneering first-generation product analytics platforms. It’s a fully integrated, vertically-stacked solution that’s purpose-built for instrumenting and analyzing product usage on a granular level for user-focused customer analytics. 

Key Features

• Granular tracking and visualization of user events, interactions, and behaviors patterns within the product 
• Path analysis illuminating user journeys, conversion funnels, and dropoff points
• User segmentation based on product usage, behavioral patterns, and custom attributes
• Dedicated analytics for A/B testing product changes and monitoring experiment performance
• Built-in direct messaging channels for targeted engagement of specific user segments
• Integrations with several major enterprise tools

Benefits

• Easy to get started with a user-friendly interface and comprehensive template library for basic analytics use cases
• Flexibility to define and analyze custom events tailored to unique product scenarios and requirements
• Capture and analysis of user-level information combined with session and interaction details
• Purpose-built experimentation dashboards for monitoring A/B tests and product experiments

Limitations

• Product data silo means you’ll have to duplicate data outside the platform to analyze customer information outside the product as well as additional business context
• Limited options for freeform, ad hoc visual data exploration beyond the pre-built suite of reports — custom analyses require advanced SQL engineering beyond self-service
• Events have to be manually instrumented which means ramp up is resource-intensive

Pricing 

Event-based model with tiers including a free “Starter” plan up to 20M events/month, a “Growth” plan starting at $24/month for up to 300M events, and a custom enterprise-level plan (pricing on request).

Best for 

Mixpanel could be a good option for companies taking their first steps into basic product analytics without a modern, integrated data stack. However, it may not meet the needs of organizations looking to maximize their customer analytics with advanced exploratory analysis and cross-functional insights beyond core product usage.

3. Amplitude

Amplitude is another prominent first-generation, vertically-integrated product analytics platform that enables businesses to understand customer behavior within the product. With a focus on self-service analytics and collaboration, Amplitude empowers teams to explore data, uncover insights, and make data-driven decisions.

Key Features

• Comprehensive product behavioral analytics powered by granular event tracking and monitoring
• Path analysis visualizing user journeys, conversion funnels, and navigation flows
• Cohort analysis and user segmentation based on product interactions and attributes
• Native A/B testing and analytics quantifying experiment performance
• Customizable data visualization dashboards and reporting 
• Direct integrations for multiple enterprise marketing, sales, product, and data tools for crucial customer and business context
• Collaboration features so teams can analyze data together, share insights, and collaborate

Benefits

• Granular configuration options for tracking even highly nuanced customer product interactions across different journeys and stages
• Advanced analytics and reporting based on custom attributes, giving more flexibility than some other first-generation tools like Mixpanel
• Automated behavior clustering using AI/ML helps you build behavioral cohorts and identify lucrative customer segments
• Proactive data reliability monitoring with anomaly detection and alerts

Limitations

• Not the simplest UX and requires training and upfront schema design and configuration required to predefine events and behaviors to instrument
• Doesn’t natively support open-ended, ad hoc visualization and exploration beyond pre-built reports — this requires data duplication and use of BI tools (note: Amplitude plan to provide some warehouse-based functionality in 2024, but these are limited add-ons rather than a full, integrated warehouse-native approach)
• Event-based pricing can become prohibitively expensive as event volume scales — and you’ll generally end up paying for events you don’t even analyze

Pricing

Multi-tiered model including a free plan, “Plus” paid tier ranging from $49-$2,520/month for up to 3,000 MTUs, and custom “Growth” and “Enterprise” pricing tiers.

Best for

Amplitude could be a good option for firms that have just started their product and customer journey analytics, enabling path, funnel, and cohort analysis and A/B experimentation. 

However, for companies in an advanced stage of growth, its event-based pricing and constraints around ad hoc exploratory analysis may mean it’s not the best choice to drill down into business or overall customer journey questions in a self-service way.

4. Heap

Heap is another first-generation product analytics platform, but unlike Mixpanel and Amplitude, it takes an automated approach to user behavior tracking. Its automatic data capture capabilities eliminate the need for manual event tracking, meaning businesses can retroactively analyze customer in-product interactions without requiring upfront configuration.

Key Features

• Automatic capture of granular user events and interactions without manual tagging and instrumentation setup
• User-friendly event visualization tools and capabilities for retroactive analysis of historical data
• Funnel analytics for monitoring user conversion paths, dropoffs, and behavioral patterns
• Cohort discovery and user segmentation based on product usage, attributes, and behaviors
• Session replay makes it easy to observe and analyze individual user sessions and journeys
• “Effort analysis” features surface the areas of highest user friction within user flows

Benefits

• Simple to set up and use 
• Automated event capture dramatically accelerates instrumentation and time-to-insight
• Path and funnel analysis pinpoint bottlenecks, dropoffs, and areas ripe for optimization
• Session replay views help foster empathy for real customer experiences and make debugging more efficient
• Out-of-the-box integration with enterprise marketing, BI and analytics ecosystems

Limitations

• Analysis is inherently limited to product usage data — incorporating context from other sources means costly, inefficient ETL and reverse-ETL transformations and SQL querying as workarounds
• Limited depth for custom data extraction or tailored reporting and no advanced exploratory ad hoc analysis 
• Automated capture means large volumes of potentially irrelevant data are ingested, which makes it a challenge to optimize cost and performance
• No experiment and A/B test comparisons

Pricing

Session-based model with a free tier for up to 10,000 sessions, with higher tier pricing not publicly disclosed.

Best for 

Heap’s core strength lies in its simplicity, enabled by automated data capture and intuitive workflows. This makes it an appealing choice for businesses still in the early stages of customer analytics, looking to get up and running as soon as possible. 

However, it lacks analytical depth and flexibility, giving you a product-centred view rather than a full C360 understanding. 

5. Kissmetrics

Kissmetrics is a product and marketing analytics platform focused on empowering e-commerce companies with unified insights into the customer journey. Its mission is to give companies the insights they need to drive better customer experiences and engagement.

Key Features

• Simple, easy-to-understand user journey mapping 
• A/B testing and campaign experimentation capabilities tailored for e-commerce marketing and product use cases
• Automated email journey and lifecycle marketing campaigns for targeted user segments
• Out-of-the-box integrations with the major e-commerce tools 

Benefits

• Intuitive visualization of the customer journey tailored for ease-of-use by non-technical roles
• Multi-channel attribution tracking of user behavior and conversion paths
• Turnkey integration with leading e-commerce platforms and martech stacks

Limitations

• Kissmetrics’ simplicity means tradeoffs in the depth and sophistication of analytics
• Limited support for advanced, custom analytics use cases and no ad-hoc open-ended data exploration
• E-commerce industry-specific focus constrains relevance and adoption outside of e-commerce verticals
• Event-based pricing model can become cost-prohibitive as data scale and complexity increases
• Limited multi-project support, with the lower pricing tiers constraining analysis to a single app or website environment.

Pricing

Flexible pricing tiers ranging from $26/month to $5,000+/month based on event volume.

Best for 

Kissmetrics is a viable choice for early-stage e-commerce brands seeking a unified view across basic product and marketing analytics. However, its narrow vertical focus and lack of advanced analytics and ad-hoc exploration features may not serve the needs of companies with multiple products or within other industries.

To drive sustainable growth and deliver exceptional experiences, companies need to break free from data silos and embrace a unified, 360-degree view of the customer experience.

NetSpring is the clear frontrunner in next-generation customer analytics built off the modern data stack, with a platform that empowers teams with self-service, ad hoc exploration of data across all touchpoints. By tapping into your existing data warehouse as the single source of truth, NetSpring eliminates the complexities and inconsistencies of data duplication and integration, ensuring data integrity, security, accessibility, and governance.

With NetSpring, you can unlock game-changing customer analytics insights that connect the dots between product usage, marketing campaigns, sales interactions, and customer support engagements. That’s the first step to being able to make truly data-driven decisions that optimize the customer experience and drive tangible business impact.