The Benefits of Customer Analytics

By Jeff Sauro

The primary benefit of customer analytics is that better decisions are made with data. These decisions lead to a number of tangible benefits, such as the following:

  • Streamlined campaigns: You can target your marketing efforts, thus reduce costs.

  • Competitive pricing: You can price your products according to demand and by what customers expect.

  • Customization: Customers can select from a combination of features or service that meets their needs.

  • Reduced waste: Manage your inventory better by anticipating customer demands.

  • Faster delivery: Knowing what products will sell when and where allows manufacturing efforts to anticipate demand and prevent a loss of sales.

  • Higher profitability: More competitive prices, reduced costs, and higher sales are results of targeted marketing efforts.

  • Loyal customers: Delivering the right features at the right price increases customer satisfaction and leads to loyal customers, which are essential for long-term growth

Multidisciplinary

The realm of customer analytics crosses departments, skills, and traditional roles. It’s multidisciplinary and typically involves input from and output to:

  • Marketing: This encompasses the messaging, advertising, and the customer demographics and segments.

  • Information Technology (IT): The IT department usually has access to the databases of customer transactions and data.

  • Sales: Front-line contact with customers, knowledge of pricing, revenue, transactions, and reasons for lost customers are included here.

  • Product development: This includes product features, functions, and usability.

Multimetric

No single metric can define customer analytics. It requires a combination of both behavioral and attitudinal data. Some common ones include:

  • Revenue: Simple enough, this is your top line and you’re probably tracking this for your accountant already.

  • Transactions: How many transactions are you completing in a given time frame? Digging deeper into the data, transactions become important for finding patterns.

  • Customer Lifetime Revenue: The total top line revenue a customer generates over some “lifetime,” which can be days, months or years.

  • Future intent: Will your existing customers buy from you again?

  • Likelihood to recommend: How likely will customers recommend your company and products?

  • Product usage: Which features are your customers actually using?

  • Website visits: Are potential customers finding your website and doing what you expect — finding information or buying a product?

  • Return rates: How many products are being returned due to dissatisfaction?

  • Abandonment rates: Did a customer start a transaction and then quit before completing?

  • Conversion rates: How many potential customers do you convert into actual customers?

  • Satisfaction: Are customers satisfied with your product, company, and service?

  • Usability: Do customers have problems using your products?

  • Findability: Can customers find the features they’re looking for in your products, or find what they’re looking for in your website?

Multimethod

No single method defines customer analytics. Some common methods, most of which are discussed throughout this book, include:

  • Surveys analysis: This involves collecting, analyzing, and posing decision questions directly to your customers.

  • Customer segmentation: Not all customers have the same backgrounds, goals, or buying patterns; grouping your customers into similar patterns helps identify opportunities for better marketing and product development.

  • Customer journey mapping: Understanding the process customers go through as they engage with a service uncovers pain points and opportunities for improvement.

  • Transactional analysis: This examines the purchase frequency, amount, and the type of products purchased together for patterns and predictions.

  • Factor analysis: This statistical technique helps identify clusters of similar customers and similar response patterns from survey results.

  • Cluster analysis: Similar to factor analysis, this statistical technique groups customers together into clusters and identifies the best labels for customers to find items in website navigation.

  • Regression analysis: This statistical technique identifies the key variables that have the biggest impact on customer satisfaction and customer loyalty.

  • Neural networks/machine learning: Advanced software programs can adapt to patterns learned from data mining and better predict customer needs.