Every business activity, from marketing to product design to customer support to pricing and packaging, should all be crafted with a singular goal: delighting your customers. This helps drive positive word of mouth, creates repeat customers, and maximizes the customer lifetime value. It’s why customer analytics remains a top trend in analytics, despite economic uncertainty and budgetary pressures.
But how do companies know what customers actually want?
In our modern, hyperconnected digital world, customers are already letting you know exactly what they want and need - through data. Customers interact with a company’s brand, products, and services in so many ways, creating an abundance of data. Through all these different touchpoints, you can get data on the entire customer lifecycle. But that raw data isn’t useful in and of itself. It’s only meaningful when it generates an insight that leads to action. That requires customer analytics.
Just as your customers shape every element of your business, so, too, should customer analytics. Everything from how much money to spend on marketing campaigns to where to improve the sales process can - and should - benefit from customer analytics. Given customers have more choices and options than ever before, it’s critical for businesses to understand exactly what customers want because if they don’t, their competitors will.
In this piece, we’ll cover what customer analytics is, the benefits of using it, real-life examples of businesses using customer analytics, and best practices to follow and how you can leverage it in your organization to surprise, delight, and keep customers coming back for more.
What is customer analytics?
Customer analytics helps you identify and examine patterns in customer behavior to find opportunities to improve business outcomes. This deep connection to customer outcomes makes customer analytics one of the most valuable fields within business analytics. Companies collect vast amounts of data from their customers, but separating the signal from the noise is challenging. With customer analytics, organizations can make sense of this complex data, providing valuable insights into customer demographics and preferences, marketing effectiveness, product and pricing preferences, and more. Many companies utilize data analytics tools to collect, process, and analyze customer data across departments.
5 Benefits of customer analytics
No one wants to be treated like an average or a demographic. We all want to engage with personalized content, offerings, and products specifically tailored to our preferences. Customer analytics is a powerful tool that enables businesses to gain valuable insights into their customer base. By leveraging data and analytics techniques, businesses can unlock a wide range of benefits that drive growth and improve customer experiences. Let's explore the key benefits of customer analytics:
1. Understanding customer behavior
One of the primary benefits of customer analytics is gaining a deep understanding of customer behavior. By analyzing customer data, businesses can identify meaningful patterns, anomalies, outliers, and trends in how customers interact, engage, and buy from your business. Armed with this information, businesses can uncover insights into why customers make certain choices, what shapes their decisions, and how they can influence these decisions . Understanding customer behavior allows businesses to align their strategies, products, and services to meet customer expectations effectively.
2. Improving customer segmentation
Customer segmentation is crucial for targeted marketing and personalized experiences. Customer analytics enables businesses to segment their customer base more effectively. By going beyond simple demographics, businesses can hyper segment customers based on various attributes such as intent, purchase behavior, related interests, and preferences, businesses can tailor their marketing efforts and offerings to be highly relevant, which benefits businesses in every industry. For example, retailers can use this targeted approach to improve common retail KPIs such as conversion rates, average order value, and enhanced customer retention.
3. Enhancing personalization and customization
Personalization has become a key differentiator in today's competitive market. Customer analytics empowers businesses to deliver personalized and customized experiences to their customers. By leveraging customer data, businesses can create tailored product recommendations, personalized marketing messages, and individualized promotions. This level of personalization enhances customer engagement, fosters customer loyalty, and drives customer satisfaction. And this is only going to accelerate with the rise of generative AI, powered by large language models and transformer architectures, that will take personalization to new heights, delivering massive business value in the process.
4. Optimizing marketing campaigns
Marketers are some of the most data-driven professionals in today’s market, specifically because of the ability for data to dramatically improve results and ROI. That requires a deep understanding of their customers. With customer analytics, businesses with data-driven insights can optimize their marketing campaigns around their customers. By analyzing customer data, businesses can identify the most effective marketing channels, messages, and strategies for different customer segments. This enables businesses to allocate their marketing resources more efficiently, reach the right audience, and deliver highly targeted and relevant campaigns. And with what was once considered the future of AI already here today, businesses will be able to further optimize these campaigns with individually tailored experiences. The result is improved campaign effectiveness, higher conversion rates, and a better return on investment.
5. Identifying upselling and cross-selling opportunities
By analyzing customer purchase history and behavior, businesses can identify products or services that are frequently purchased together or are complementary to a customer's previous purchases. For example, Canadian Tire leveraged customer analytics from ThoughtSpot at the beginning of the pandemic to identify cross and upsell opportunities in their pets department, ascertaining who were new pet owners from purchasing behavior, and then being able to help these customers buy related products. This kind of knowledge enables businesses to create targeted upselling and cross-selling strategies, increasing revenue per customer and maximizing profitability, like Canadian Tire did, growing sales 20% despite 40% of their brick and mortar stores temporarily closing
Real-life examples of how customer analytics helps businesses
Customer analytics can feel abstract without examples, so let’s dive into a few to demonstrate how customer analytics works in practice.
One company that leverages customer analytics to the extreme is Albertsons. One of the largest and most well known grocers, Albertsons sells thousands of products, has both brick and mortar locations and an ecommerce business, and operates in different communities, serving diverse customers. With so many different touchpoints, Albertsons leverages customer analytics from ThoughtSpot to both track products down to the universal product code (UPC) and store level, and segment their customers. This customer data is then enriched with third party data to give an incredibly holistic view of their customers.
Another company that is known for its laser focus on customer analytics is Amazon. Amazon tracks browsing data, email data, ad data, review data, sales data, and more to see what products customers prefer. From there, they created a strong recommendation engine so that whenever a user comes to their site looking for something specific, the best possible product appears.
This makes the buying decision much easier, converting more prospects to loyal customers. And once Amazon learns the habits of a typical customer in a certain segment, they use lookalike modeling to find other similar prospects who are likely to turn into high LTV customers. As a result of these customer analytics strategies, Amazon’s growth has skyrocketed over the past decade.
Best practices to follow when using customer analytics
To ensure the success of customer analytics initiatives, it's important to follow best practices that optimize the process and maximize the value of the collected data using data collection methods. Here are five best practices to follow to get the most out of your customer analytics:
1. Define clear goals and objectives
Before diving into customer analytics, it's crucial to define clear goals and objectives. Determine what specific outcomes you want to achieve through customer analytics, such as improving customer satisfaction, reducing customer churn, or optimizing marketing campaigns. Clear goals provide focus and guide the entire analytics process, ensuring that the data analysis aligns with the desired outcomes. This often means bringing business stakeholders in at this phase to ensure you’re building a solution that solves the real challenge faced by these customer facing roles.
2. Collect and consolidate data
To derive meaningful insights, it's essential to collect and consolidate relevant data from various sources, often into a cloud data platform. This includes customer transaction data, website analytics, CRM systems, social media interactions, and any other relevant data points. For companies who have already adopted a modern data stack, gathering this data into a single place is easier than ever. Plus, companies can take advantage more seamlessly of third party data, enriching their proprietary data and gaining an even more robust view of their customers.
3. Ensure data accuracy and quality
The accuracy and quality of the data used for customer analytics are critical, which is why data quality metrics are an essential component of effective customer analytics programs. Dirty or incomplete data can lead to incorrect conclusions and ineffective decision-making. Regularly clean and validate the data to eliminate duplicates, errors, and inconsistencies. Implement data governance processes to maintain data integrity and ensure data privacy and compliance with applicable regulations. Investing in data quality measures will result in more reliable insights and better data driven decision-making.
4. Utilize advanced analytics techniques
To uncover deeper insights from your customer data, it's important to leverage advanced analytics techniques. These techniques include augmented analytics, predictive modeling, machine learning, and data mining. By applying these techniques, businesses can discover hidden patterns, identify trends, segment customers more effectively, predict customer behavior, and optimize inventory levels. Advanced analytics empowers businesses to make accurate sales forecasts and informed decisions based on data-driven insights.
5. Empower business users to leverage
Once you’ve developed a robust foundation for your customer analytics program, you’re ready to put it into action. The most effective put these customer analytics in the hands of frontline decision makers who are engaging with your customers day in and day out. According to a study from Harvard Business Review, more than 80% of companies that empower these decision makers with self-service BI are improving customer satisfaction and product and service quality, which is having a direct impact on revenue. Leaders are growing revenue 10-30% more than their laggard peers.
5. Continuously monitor and iterate
Customer behavior and market dynamics are constantly evolving, so it's important to continuously monitor and iterate your customer analytics efforts. Regularly review and analyze the data to identify emerging trends, changing customer preferences, and new opportunities. This iterative approach allows businesses to stay agile and responsive, making necessary adjustments to strategies, campaigns, and experiences based on the insights gained from customer analytics.
Start your customer analytics journey here
Happier customers, better products, and growing revenue all starts with more effective customer analytics. The impact of these efforts scale with the number of people in your organization who can effectively use them to glean insights and take action.
With ThoughtSpot, everyone in your organization can engage with data the same way they chat with a colleague or friend. ThoughtSpot delivers AI Analytics, powered by natural language processing, so your entire company can operationalize customer insights in a self-service manner, anytime, anywhere. ThoughtSpot has cutting-edge AI technology to find new populations to target or forecast how current customers may behave in the future. Not only that, ThoughtSpot also makes it easy for users to embed analytics and build interactive data apps that integrate with other cloud tools like your CRM, ERP, and product analytics software, so your customer insights are right in your existing workflows, without even having to log in to a business intelligence platform.
Get started on your customer analytics journey by signing up for a 14-day free trial of ThoughtSpot today.
