Through customer data analytics, you can collect and analyze your clients’ data to get customer insights about their needs, wants, interests, and other valuable factors that affect their relationship with your brand. Using that information, you can build an informed marketing and sales strategy to maximize your engagement with your customers while increasing your sales.

Types of Customer Analytics

There are 4 different types of analyses you can use to sift through your customer data. All of them are extremely useful, so the best strategy is to combine them to better understand your clients.

• Descriptive Customer Analytics: A process that explores historical and real-time customer data to understand a particular phenomenon that has happened or that is currently happening. Example: Checking a website’s traffic and its sales in a particular month.
• Diagnostic Customer Analytics: An analysis focused on understanding why a particular customer-related problem happens using a combination of techniques. Example: Delving into the website data points while combining with other customer data (e.g., data from sales and marketing teams) to understand the website’s traffic and its sales.
• Predictive Customer Analytics: A technique to anticipate potential changes in customer behavior, which often uses data mining and machine learning to make estimations based on historical and real-time information. Example: Analyzing the customer feedback from previous months and years, comparing it to current data, and extrapolating averages and medians to offer estimations on possible future scenarios.
• Prescriptive Customer Analytics: A method to lay out potential courses of action based on the estimations of possible future scenarios made with a predictive analytics tool. Example: Suggesting adjustments to the website to prepare for a projected increase in traffic due to seasonal spikes. Example: Analyzing the customer sentiment data from previous months and years, comparing it to current data, and extrapolating averages and medians to offer estimations on possible future scenarios.

How To Collect and Store Customer Analytics Data

Thanks to the widespread digitalization of business processes, companies of virtually every industry can quickly gather and store customer data from a wide range of sources. However, having so many ways of collecting data can be daunting, which means you’ll need a proper strategy to make sure you’re gathering relevant information in the most efficient way possible.

How can you build that strategy? By taking into account the following suggestions.

#1 Define the type of data you’ll collect and the channels from which you’ll gather it

Though it might seem like collecting tons of data is the best course of action, the reality is that doing that can lead you to an overwhelming amount of useless information. That’s why you need to define 3 essential things before you start collecting data:

• Customer Data Origin: Customer data has 3 main origins. First-party data is the information you collect through your own channels and customer analytics tools. Second-party data is the information that comes from another organization’s first-party data. Third-party data is the information you might buy from a data aggregation service. You need to decide which combination of the 3 is best for you, depending on the data you’re able to collect yourself, the partnerships you have with other companies, and the data availability in your industry.

• Customer Data Type: There are plenty of different types of data you can collect: website traffic, total sales, leads, social media engagement, answers to surveys, clicks on buttons—the list goes on and on. Before collection, you need to define what data type will better inform your customer analytics solutions.

• Customer Data Sources: If you end up choosing first-party data as your information’s origin, then you need to determine which sources are best to gather your selected data type. Thus, you might need to collect information from your website, POS system, social media, market research, review sites, surveys, in-store interactions, or apps, among many others.

#2 Lay out your collection process.

After identifying the data you’ll be collecting, it’s time to choose the tools that will help you gather it. There are plenty of customer analytics software tools available in the market, so which ones you end up using depends on the type of data you’ll be focusing on as well as your existing digital infrastructure.

For instance, you can analyze website traffic through Google Analytics, social media engagement through native analytics tools provided by the social platforms themselves, and marketing efforts through marketing platforms like HubSpot or specialized services like SurveyMonkey.

You should also consider adopting a customer analytics platform (CDP), a tool that allows you to centralize different types of data coming from different sources and origins. In that way, you’ll have easier access to relevant information and will gain the ability to work with it in a single place.

#3 Store the data properly

Since you’ll be collecting a lot of data, knowing how to store it correctly is a must to avoid bottlenecks, availability issues, and data losses. That’s why you need to understand that data storage involves more than just dumping data into a database. You need to define the storage format, determine if you need to apply some sort of standardization before storing, and build the right infrastructure to gather it all.

You could say that a customer analytics tool is enough to centralize all data, but things aren’t that simple. While a CDP can certainly help you, picking the right one can be tricky. That’s because you need the best CDP, one that can seamlessly integrate with your data sources and orchestrate them in ways so that you won’t lose data in the migration. Another option is adopting data warehousing services, but that’s a complex solution that’s best suited for big companies.

A word of caution

Customer satisfaction data collection is a thorny subject. There are plenty of regulations and norms that govern how you can gather information from your customers. Those normatives aim to protect the privacy of client data, which is why it’s very important for you to have a deep understanding of all applicable data regulations to make sure you’re respecting them at all times. Failing to do so can lead to considerable fines and a loss of customer loyalty, which will definitely impact your reputation.

How Does Your Company Analyze Customer Feedback?

Once you have your data properly stored, it’s time to analyze it. Again, this will depend on the type of data you’re working with and the business insights you’re trying to get from your datasets. Both of those things will determine which kind of customer behavior analytics tool you’ll need from one of the following:

• Customer Analytics Tools: Takes numerous criteria into account to create groups of customers that are then analyzed for highly targeted marketing campaigns.
• Business Intelligence Solutions: Converts raw data into insights that suggest improvements, pinpoint issues, and help streamline operations.
• Digital Experience Platforms (DXP): Uses data to help manage and deliver optimized digital experiences.
• Customer Data Platforms (CDP): Brings data from multiple sources together into a centralized hub that guarantees accessibility and provides analytical features.
• Customer Journey Mapping Solutions:  Dives into data related to the customer life cycle with your company to provide a complete picture of their history with you.
• Customer Journey Analytics Platforms: Uses customer maps to analyze different touch points in search of improvement opportunities.

Best Customer Analytics Tools

We’re living in the golden age of data, where companies can gather customer information from a myriad of sources to gain insights that directly translate into a competitive advantage.

Given that context, it’s not surprising to learn that there are countless tools to analyze customer data. How can you pick the right one? It’ll all depend on your business goals. However, there are a handful of essential tools that you should consider adding to your customer data platform infrastructure.

#1 Google Analytics

Google Analytics is a free tool that offers a free trial and helps you analyze web traffic and user actions on your site. One of the best things about it is that you can customize certain parts of the analyses to better understand customer behavior and different audience segments.

• Features: Web traffic analysis, audience segmentation, user behavior insights, goal setting, and conversion tracking.
• Pros: Free, integrative with other Google services, extensive documentation, and user community.
• Cons: Can have a learning curve, data sampling in larger sites, and some limitations on real-time data.
• Best For: Small to medium-sized businesses looking for a free solution, digital marketers, and SEO specialists.

#2 Microsoft Power BI

One of the most popular business intelligence tools in the market, Microsoft Power BI deeply integrates itself with other products of the Microsoft family to offer visual-based data discovery and augmented analytics through powerful dashboards.

• Features: Data visualization, dashboard sharing, integrations with Microsoft products, and augmented analytics.
• Pros: Deep integration with Microsoft ecosystem, user-friendly, affordable pricing.
• Cons: Some limitations in complex calculations, steeper learning curve for advanced features.
• Best For: Businesses already invested in the Microsoft ecosystem, BI professionals, and data analysts.

#3 Tableau

This tool allows you to connect spreadsheets, databases, and big data tools so you can access comprehensive insights through visual dashboards. It leverages smart algorithms to better organize the data and provide easier access to insights. Engaging with a tableau development company can further enhance this capability, optimizing the integration and visualization process to meet specific business needs more effectively.

• Features: Data visualization, connection to various data sources, smart algorithms, dashboard sharing.
• Pros: Intuitive interface, strong community, and training resources, versatile in data source connections.
• Cons: Can be expensive, and might have performance issues with very large datasets.
• Best For: Data analysts, large enterprises, and researchers.

#4 AWS Data Analytics

This service from Amazon can help you create a data lake where you can unify data and guarantee its governance. You can expand its basic offering through the integration of other Amazon services, such as Redshift’s cloud data warehousing or EMR’s processing platform at scale.

• Features: Data lake creation, data unification, extendable with other AWS services.
• Pros: Scalable, integrates well with AWS ecosystem, robust security features.
• Cons: Complexity in setting up, costs can grow with increased usage.
• Best For: Medium to large enterprises, businesses already on AWS, data engineers.

#5 Adobe Analytics

This encompassing platform can collect data from multiple sources (including websites, mobile apps, and in-store systems) to provide insights to improve the customer experience. It’s a great tool for hyper-personalized marketing campaigns.

• Features: Data collection from various sources, insights for improving customer experience, and hyper-personalized campaigns.
• Pros: Comprehensive data collection, and robust segmentation capabilities.
• Cons: Expensive, steeper learning curve.
• Best For: Large enterprises, digital marketing agencies, and e-commerce businesses.

#6 Mixpanel

It focuses on event tracking, allowing businesses to understand user behavior on apps and websites. It’s known for its detailed funnel and retention analysis.

• Features: Event tracking, funnel analysis, retention metrics.
• Pros: Real-time data, user-friendly interface.
• Cons: Limited historical data, costs can escalate with increased events.
• Best For: Startups, mobile app developers, and product managers.

#7 Kissmetrics

Similar to Mixpanel, it tracks individuals, groups, and segments with its event-based analytics, especially for funnel and cohort analysis.

• Features: Event-based analytics, funnel analysis, cohort tracking.
• Pros: Deep user insights, and integration with other marketing tools.
• Cons: Pricing can be high for small businesses, similar functionality to other cheaper tools.
• Best For: E-commerce businesses, digital marketers.

#8 Amplitude

A product analytics platform that helps businesses understand user engagement, retention, and conversion. It’s especially popular among SaaS businesses and mobile apps.

• Features: User engagement metrics, retention analysis, conversion tracking.
• Pros: User-friendly, strong focus on product metrics.
• Cons: Limited query capabilities, more expensive than competitors.
• Best For: SaaS businesses, mobile app developers, and product teams.

#9 Heap

Offers event-based tracking and doesn’t require manual coding to track user interactions, making it user-friendly for those who don’t want to involve developers in every tracking decision.

• Features: Event-based tracking, auto-track capabilities, no coding required.
• Pros: Easy to set up, great for non-tech-savvy users.
• Cons: Less granular control, and limited integrations.
• Best For: Small businesses, startups, non-developers.

#10 Segment

Not purely an analytics platform itself, but a data integration platform. Segment collects customer data from various sources and then sends it to other tools for analysis. It ensures that data is consistent across all tools.

• Features: Data integration, data consistency, tool-to-tool data transfer.
• Pros: Simplifies data workflows, and ensures data accuracy across tools.
• Cons: Doesn’t provide analytics itself, costs can be high.
• Best For: Medium to large enterprises, and businesses using multiple analytics tools.

#11 Looker

Part of the Google Cloud suite, Looker is a business intelligence software that allows companies to explore and visualize data, making it easier to derive insights.

• Features: Data exploration, visualization, part of Google Cloud.
• Pros: Scalable, SQL-based, deep integrations with databases.
• Cons: Requires SQL knowledge, learning curve.
• Best For: Data scientists, large enterprises, and businesses on Google Cloud.

#12 CleverTap

This is more of a mobile engagement platform that allows for customer segmentation, messaging, and analytics. It’s particularly useful for mobile app businesses looking to understand and engage their users better.

• Features: Mobile engagement, customer segmentation, in-app messaging.
• Pros: Comprehensive mobile insights, and real-time analytics.
• Cons: Limited web analytics, can become expensive.
• Best For: Mobile app businesses, and marketers focusing on mobile.

Customer Data Analytics Platform Development

While you can always go for an off-the-shelf customer data analytics platform to manage your client’s data, there’s also the possibility of developing your own. Create customer journey maps to visualize the entire process.

While that might seem like an unnecessary option, the reality is that a custom data analytics platform can provide you with even better insights to improve customer retention and boost customer lifetime value.

The benefits of developing your own customer data analytics platform include:

• Seamless integration with your entire digital infrastructure, especially your data sources.
• Highly customizable features to get tailor-made results.
• Increased flexibility regarding updates and upgrades.
• Complete ownership of the data with reduced privacy concerns.
• Most cost-efficient solution in the long run.

It’s true that a custom-built customer data analytics platform isn’t for everyone, as it’s a best fit for companies with complex operations and multiple data sources. Yet, if you’re steadily growing or you are part of an enterprise, then relying on a custom solution can provide you with a competitive advantage that off-the-shelf alternatives won’t.