Chapter 1
Introducing Customer Analytics
In This Chapter
Discovering the benefits of customer analytics
Combining methods and metrics for customer insights
Sorting through big and small data
The purpose of business is to create and keep a customer. This statement was made by Peter Drucker, the acclaimed 20th-century management consultant. A simple statement that reveals in just a few words that the long-term viability of a company is not just about maximizing revenue and minimizing costs. Long-term viability is about understanding what it takes to attract customers by continuing to meet and exceed their physical and psychological needs.
Good customer management comes from good customer measurement. Metrics are numbers assigned to everything from website visitors, same-store sales, and profit margin to call-center wait times. Analytics are metrics plus the methods that drive meaningful decisions. You can think of metrics as “informational,” while analytics are “strategic.” So while not all metrics are analytics, all analytics come from metrics. You need metrics to get analytics.
Increasingly, decisions are made on numbers. If you know the numbers better, and can articulate what those numbers mean and how you can differentiate a product, organization, or brand, you can distinguish yourself from your competitors.
Defining Customer Analytics
Although it might not be called customer analytics, chances are, you’re already familiar with some form of customer analytics. The efforts and activities of product development, marketing, sales, and services are driven to anticipate and fulfill customer needs. That is, you can’t sell a product unless someone has a need for it.
Customer analytics is a new term and is broadly used, but it generally includes the following actions and activities:
- Gathering data: Pull together customer purchase records, transactional data, surveys, and observational data at all phases of a customer’s journey.
- Using mathematical models to detect patterns: There are many number crunching, statistical analysis, and advanced modeling techniques that help turn raw data into more meaningful chunks.
- Finding the insight: From the patterns of the data come insights into causes of customer behavior.
- Supporting decisions: Understanding past behavior helps predict future customer behavior from data instead of relying on intuition.
- Optimizing the customer experience: Detect problems with features, purchases, and the product or service experience.
- Mapping the customer journey: From considering, purchasing, and engaging with products and services, mapping the touchpoints and pain points helps identify opportunities for improvement.
Customer analytics is different from many of the other metrics within an organization. The four critical ingredients of customer analytics are:
- Customer focused: The first word in customer analytics is customer. This means that the metrics collected need to come from customer actions or attitudes, or are derived in some way that’s connected to customers.
- At the individual customer level: You need access to the lowest level of customer transaction data, not data rolled up at the product or company level.
- Longitudinal: Customer analytics involves looking at customer behavior over time.
- Behavioral and attitudinal: You need a mix of what customers do and what customers think. Although customer actions (purchasing, recommending) are ultimately what you care about, attitudes affect actions — so measuring and understanding customer attitudes helps to predict future behavior.
The benefits of customer analytics
The benefit of customer analytics is that better decisions are made with data, which leads to a number of tangible benefits:
- 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
In the following sections, I go more in-depth about the data you collect with customer analytics.
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 (see Chapter 6).
- Future intent: Will your existing customers buy from you again (see Chapter 11 and Chapter 12)?
- Likelihood to recommend: How likely will customers recommend your company and products (see Chapter 12)?
- Product usage: Which features are your customers actually using (see Chapters 10 and 13)?
- Website visits: Are potential customers finding your website and doing what you expect — finding information or buying a product (see Chapter 10)?
- Return rates: How many products are being returned due to dissatisfaction (see Chapter 11)?
- Abandonment rates: Did a customer start a transaction and then quit before completing (see Chapter 10)?
- Conversion rates: How many potential customers do you convert into actual customers (Chapter 10)?
- Satisfaction: Are customers satisfied with your product, company, and service (Chapter 9)?
- Usability: Do customers have problems using your products (see Chapter 15)?
- Findability: Can customers find the features they’re looking for in your products, or find what they’re looking for in your website? I discuss findability in Chapter 15.
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 ...
