Today’s global marketplace is more competitive than ever before. Companies across industries are finding that there is not enough product fit to market. To succeed, they must provide an exceptional customer experience at ** every touch point **. Customer data analytics enable corporate organizations to do this.

By analyzing millions of data points in real time, sources of friction and various factors affecting the customer experience can be identified. Armed with this information, you can take the actions necessary to improve the experience and ultimately increase revenue, reduce customer churn and optimize profitability. In this article, we explore how to use customer data analytics, data-related business intelligence, and how to establish customer paths.

What is customer data analysis?

Customer data analytics is a data-driven approach that simplifies the customer experience. It involves tracking and analyzing customer behavior at each stage of the channel and how customers interact with the organization using the various channels. Customer data analytics (also known as big data customer analytics) can help answer the following questions:

  • What does the customer want to accomplish?
  • How do the customer’s goals align with those of the company?
  • What caused the customer to behave in this particular way?
  • What series of customer behaviors contribute to this problem/challenge/consequence?
  • What are the various approaches that customers take? Do these paths share similarities by segment?
  • How can we simplify payment paths?
  • How can we deliver more value to our customers at specific challenge points?

How to use customer data analysis

With a clear understanding of each customer’s path, you can better understand how to reduce friction, increase the number of leads, improve conversion rates, close deals faster, and identify upsell, cross-sell, and additional buying opportunities.

Mining customer data is one of the most effective ways to increase customer life cycle value, increase customer loyalty and drive revenue growth. Specifically, customer data analytics allows you to:

  • Find out where and how customers interact with the business
  • Helps determine whether the current phase in the path is optimal or even in the right order
  • Look at existing marketing and sales processes from the outside out
  • Identify gaps and opportunities to drive transformation

Let’s look at some examples.

  • ** Finance: ** The finance team can use customer data analysis to identify common causes of late payments. They may look at common characteristics or behavior between accounts, often defaulting for 30 days or more, and then implement policies to prevent the impact of potential causes.
  • ** Sales: ** Sales teams may see that people are participating in new account promotions online, but no new accounts have actually been created. They can identify the most common starting points, test new strategies, and even create new consumption paths based on the insights they gain.
  • ** Marketing: ** Marketing teams may track individual customers’ past purchases and then recommend specific products based on previous purchases when the customer next visits the site.

Third, the customer path stage

Before you can improve the customer path, you need to have a thorough understanding of the customer path phases and how they look to the customer base. There are five main stages: awareness, education, evaluation, purchase and advocacy, usually corresponding to certain touchpoints, as shown below.

Awareness:

In the cognitive phase, customers may not even realize they need your product. Social media ads, display ads and other types of advertising help build brand awareness and create awareness of the need.

Education:

During the training phase, customers have realized they have a need and are looking for a solution to the problem. They may ask friends or colleagues for information, or perform searches to find educational content, such as blogs and videos.

Evaluation:

Once customers have identified the solution they need, they start evaluating different products and vendors. They start looking at case studies and recommendations, and may explore demos of the product.

Buy:

In the purchase phase, the customer conducts final research on the product and provider under consideration, viewing sales slips, pricing pages, sales recommendations, etc.

Adoption and advocacy:

After the customer makes the purchase, they implement the product, spreading the word to their friends and colleagues.

How does BUSINESS intelligence adapt

Customer path analysis is a particularly challenging form of business intelligence because of the large amount of data required and the diversity of data sources. Deloitte research shows that companies have, on average, 16 different technology applications that leverage customer data, and an average of 25 different data sources available to generate customer insights and engagement. This data is often isolated, and the sheer volume of data makes it difficult to deal with traditional data warehouses and analysis tools.

However, with cloud data platforms, large data sets can be loaded and ready for analysis in seconds. Analytics software enables the business team to look at various front-end interactions, such as page views and product usage of back-end data. This means that you can connect the dots between the BI solution and the rest of the CRM software – no coding knowledge required!

How to establish customer path

Customer analytics allows you to see the entire relationship cycle of a company with a customer in every interaction with a brand. Building customer analytics based on data will help you better understand the customer’s path and generate more accurate insights.

Identify touch points at each stage of the journey:

Before you can measure and optimize the efficiency of the customer journey, you need to have a clear understanding of the current customer touch points. The simplest approach is to map each potential interaction to a specific stage in the customer’s journey. Start with a broad perspective, and then go deeper as you go deeper.

Identify the source of customer data:

Next, identify all customer data sources. Look at resources at all stages of a customer’s journey, from recognition to sale to repeat purchase to churn. Consider the following:

  • What platforms do potential customers use to discover brands?
  • Where do potential customers interact with the company first?
  • What tools do they use? How long does it last?
  • What is their behavior on your site and/or application? How long do they typically stay in the session, which pages do they view, etc.?
  • Where do companies store data about each type of marketing? Sales? Customer service?

Consolidating data into a single location across sources:

Data sources are integrated so that all data can be brought together. Cloud data warehouses allow you to easily store, manage, and analyze large amounts of real-time data from multiple sources in a centrally managed centralized repository. Ensure that all data sources are connected to CDW. A pre-built data connector can help with this process.

Once you have a customer path, you have a solid foundation for customer path analysis. Choosing a cloud native data analytics tool that can process millions of generated data points and allow non-technical users to perform their own analysis will provide the team with the insight it needs.

6. Drive revenue growth through big data customer analysis

Generating insights into customer journey data is one of the most effective ways to increase customer lifecycle value, increase customer loyalty, and drive revenue growth in an organization. Big data customer analytics is an area where companies will compete in the modern marketplace.