Title tag: Customer data analytics: Leading tools and best practices

Meta description: Learn what customer data analytics is and how to use it effectively. Explore top customer analytics tools to find software that fits your needs.

How customer data analytics turn scattered customer data into insight

Your customer relationship management (CRM) system records a renewal opportunity, your product tracks declining usage, and your support desk holds an unresolved ticket. Viewed separately, each record tells only part of the story. Customer data analytics connects these records so you can understand the customer’s situation and decide how to respond.

To act on customer data analytics, you need a dependable way to view a customer’s information across systems. Once the records line up, you can investigate behavior and value without rebuilding the customer’s history for every report.

What are customer analytics?

Customer analytics is the practice of collecting and analyzing customer behavior, transactions, and interactions to understand and predict how people engage with a business. The customer is the unit of analysis, whether that means an individual buyer or a business account.

It’s different from general business analytics, which might explain a change in total revenue, for example. Customer analytics examines which customers contributed to that change and what happened along their journeys. A customer dashboard can show purchases and usage, while deeper analysis investigates the patterns behind them. You can rely on a combination of analytics and business intelligence to fully understand the customer journey.

Why customer analytics matters

A complete view of customer relationships provides context for decisions that a single transaction or interaction can’t provide. Customer analytics allows you to achieve the following:

• Identify at-risk customers: Combine declining usage with support history and renewal dates to prioritize accounts for review. A usage drop alone doesn’t necessarily mean that a customer is going to leave.
• Personalize marketing and support: Use purchase history and open issues to decide whether a customer needs an offer or a service follow-up.
• Improve product decisions: Compare the behavior of retained and departing customers to identify features or journey stages worth investigating.
• Increase customer lifetime value: Evaluate repeat purchases and retention together to find opportunities to sustain relationships. In retail, customer 360 analysis identifies these opportunities based on purchasing patterns across channels.

Types of customer data and sources

Different data types answer different questions about a customer:

• Descriptive data: Attributes such as location, company size, subscription plan, or account tenure describe who the customer is.
• Behavioral data: Browsing activity and purchase patterns show what the customer does over time.
• Interactional data: Support tickets, sales calls, and email exchanges document contact with your business.
• Attitudinal data: Customers provide feedback with their opinions about a product or service.

These categories overlap within source systems. A support ticket records an interaction and may also contain feedback about the product. These are some of the systems that provide data for customer analytics:

• CRM systems: These systems store contact details and other customer information, such as transaction history. The information helps to identify renewal opportunities.
• Product tracking systems: Events and timestamps reveal feature adoption, conversion paths, and recurring use.
• Support platforms: Ticket metrics provide useful service context.
• Marketing tools: Campaign responses and channel activity support cross-channel customer analysis.
• Billing and transaction systems: Invoices, payments, refunds, and subscriptions establish financial activity.
• Survey tools: Satisfaction scores and open-text responses add the customer’s stated perspective.

Before joining these records, determine which identifiers connect them. An account may have several contacts, each with multiple product profiles. Treating every profile as a separate customer would distort both customer counts and account-level revenue.

5 leading customer analytics tools

Customer analytics tools don’t all focus on the same metrics. CRM platforms emphasize relationships and commercial activity, product analytics tools investigate usage, and customer data platforms maintain profiles across touchpoints. Choose customer analytics software based on the question you need to answer and the data it requires.

1. Salesforce Customer 360

Salesforce Customer 360 is best suited to organizations coordinating sales, service, and marketing within the Salesforce ecosystem. Salesforce describes Customer 360 as a suite of connected apps rather than a standalone analytics product.

It provides shared customer context across applications with connected workflows, and teams can use the platform for sales and service reporting. You can evaluate the specific apps and data services your analysis needs to decide what to add to your suite.

2. HubSpot

HubSpot works well for organizations that want to bring marketing, sales, and service together on a unified platform. Combining HubSpot data with other sources can help build a more holistic view of your customers. Its dashboard and reporting software supports custom reports across customer records, while advanced marketing reporting covers revenue attribution.

3. Mixpanel

Mixpanel features customer behavior analytics with a focus on digital products. Its core analytics reports include funnels to measure conversion between events, retention reports to examine return behavior, and breakdowns by customer properties or cohorts.

For example, an analyst could compare onboarding completion across acquisition channels, then examine whether those groups return. The usefulness of that comparison depends on consistent event tracking and customer identifiers.

4. Amplitude

Amplitude is well-suited to teams that want to investigate how product journeys relate to engagement and retention. Its analytics chart options include event segmentation, funnel analysis, and retention analysis.

Use these together to identify where customers stop progressing and whether different groups return at different rates. A relationship between feature use and retention is a starting point for investigation, not proof that the feature causes customers to stay.

5. Twilio Segment

Twilio Segment is useful when your main challenge is connecting customer identities and distributing their context to other tools. Its Unify capabilities include identity resolution, a profile explorer, and an API for accessing user profiles. As a customer data platform, Twilio Segment is a different kind of customer data analytics platform from a traditional reporting application. 

Best practices for building a customer data analytics strategy

Regardless of which software you choose, you can make the most of customer data analytics with the following approaches:

• Define the decision and metric: Start with a question such as which accounts need renewal support and define the observation period and signals that trigger review. For acquisition analysis, agree on customer attribution rules before comparing channel performance.
• Centralize relevant touchpoints: Bring the required source records together and document their matching keys. Preserve transaction and event detail so analysts can investigate a customer-level summary without losing history.
• Assign shared ownership: Have sales, support, and marketing agree on definitions such as active customer and churned account. Name an owner for each definition and for the action that follows a flagged result.
• Validate data before interpreting it: Check data freshness, missing identifiers, duplicate records, and join behavior, while restricting access to sensitive fields. If joining support tickets to invoices duplicates invoice rows, aggregate each source to the appropriate level before combining them.

How Fivetran builds a dependable customer 360 for analytics

 A key challenge to customer analysis is the wide range of sources that data comes from, along with the complexity and ever-changing nature of those sources. Fivetran’s prebuilt connectors automate record movement from systems such as Salesforce, HubSpot, and Zendesk into a central destination. This gives analysts a current customer 360 that combines customer data from multiple sources.

Centralization supplies the records, but your models still need to establish which records belong to the same person or account. Fivetran Transformations supports prebuilt Quickstart data models, dbt packages, and custom projects to prepare source data for analysis. Your team applies its identity mappings and business definitions to build customer-level tables.

Integrated scheduling triggers model runs after connection syncs finish. With the movement and modeling steps coordinated, analysts can build metrics and dashboards on maintained tables instead of repeating the same joins for every request. 

Explore Fivetran Transformations to turn customer data into insights faster.

FAQ

What are some key metrics for customer data analytics?

Customer retention rate, churn rate, customer acquisition cost, and customer lifetime value are all useful. Retention measures how much of a starting customer population remains over a defined period. Churn measures customer losses, acquisition cost relates acquisition spending to new customers, and lifetime value estimates value across the relationship. 

What’s the difference between on-premises and cloud customer data analytics?

On-premises analytics runs on infrastructure your organization operates. Cloud analytics uses infrastructure or applications hosted by a provider. The choice distinguishes who manages capacity and maintenance.

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