Customer Analytics For Dummies (2015)

Part I

Getting Started with Customer Analytics

Chapter 3

Planning a Customer Analytics Initiative

In This Chapter

Outlining the scope and goals of an analytics initiative

Choosing metrics, methods, and tools

Setting budgets and sample sizes

Launching a customer analytics initiative can be confusing on where to start and which method to use when. In this chapter, I review the best practices for defining, collecting, and analyzing your customer analytics to help make better decisions from your customer data.

There are a number of ways of thinking about goals, methods, and outcomes. Your organization may already have a project scoping process that you can adapt for your customer analytics initiative. In this chapter, I use an approach based loosely on the Six Sigma methodology, tailored to customer analytics. It’s a framework that’s flexible and familiar and works across disparate products and companies.

The basic framework is to define what you want to do, find the right ways to measure it, do something about the measures, and put processes in place to continue using customer analytics to make better business decisions.

A Customer Analytics Initiative Overview

Before you start on your initiative, keep these four things in mind:

· Access to the right data: It’s hard to increase the frequency of customer purchases, conversions, or attitudes if you don’t know what customers are purchasing, when they are purchasing it, or what they are thinking. Be sure you know that the data you need exists, or that you’ll be able to collect and analyze it.

· Customer level data: To do the most with customer analytics, you’ll want to gather data for each customer, not aggregated data at product or company levels.

Because customer analytics is about understanding the customer from past data to predict future data, you need to identify transactions, revenue, and survey data for each customer. You can then roll this lower level customer data up to product or company level summaries as needed.

· Analytics that focus on the customer: The “right” analytics depend on the method. But one thing that all good customer analytics have in common is that they are meaningful to the customer. Just like airlines should care more about on-time arrivals than on-time departures, your analytics should be felt by customers at all phases of their journey.

If you want to improve the customer support experience, customer satisfaction with the call outcome is a better metric than the number of calls answered in an hour. The latter is an example of company centric and the former is customer centric.

· Getting buy-in: Planning, collecting, and analyzing data is only good if something is going to be done about the insights.

All too often, I see organizations spend a lot on research and customer measurement projects but the results stop at the executive presentation meeting. Unfortunately, insights aren’t acted upon because the people who can change the product, price, or experience aren’t involved with the data collection and planning. They are naturally resistant to outsiders telling them what to do. This can happen with both internally and externally collected data. Get buy-in from the people you need to implement your insights and minimize the “not invented here” attitude.

Customer analytics should be shared with not only executives, but also with product development, sales, and support staffers. As part of the planning and getting buy-in, be sure the analytics will cross customer touchpoints and be accessible across the organization.

Not all customer analytics initiatives lend themselves well to the more systematic methodology I include here. So don’t try to force extra steps or complexity just to meet this framework.

A lot of creative thinking goes into making a plan. Don’t feel like you need to fit every project into this process. That’s especially the case if your goals and methods are narrow in scope. The rest of this chapter covers the details of putting together your customer analytics initiative.

Defining the Scope and Outcome

The first stage is goal setting: where you define the scope and outcome of your project.

Don’t overlook or rush through this stage. Collecting customer analytics takes time and money. You can easily exceed your budget if you compiled the wrong data.

1. State the goals of the initiative.

Think in terms of the intended outcome (for example, an increase of 10% in revenue of a product line over the next year). The more specific you can be, the more attainable the endeavor.

2. Write down the questions you want to answer.

Data is meaningless unless it’s collected for a reason. Articulate what business questions you’re hoping to answer. Avoid being vague and large in scope. Start small and specific and itemize your questions.

You want to be SMART: Specific, Measurable, Attainable, Realistic, and Timely.

Some examples of questions customer analytics can answer include:

· Which product feature should I add to this product?

· What is preventing customers on the website from completing a purchase?

· What labels should I change in the website navigation?

· Why are customers not recommending a product and how can I improve positive word of mouth?

· What percent of high-income mothers are aware of the brand and website?

· Who are the most profitable customers?

· How long until a customer makes a repeat purchase?

There’s a good chance you aren’t the first person to collect and use customer analytics in your organization. Look for past initiatives, past projects, and what worked and what didn’t work. The documents, results, and people involved in past initiatives will save you a lot of effort and prevent you from reinventing the wheel.

Identifying the Metrics, Methods, and Tools

During this step, identify the metrics and methods you’ll use to answer your questions and achieve your goals:

· Look for metrics that are meaningful to customers. Think on-time arrivals instead of on-time departures. See Chapter 2 for ideas on the right data to collect.

· Identify what tools you’ll need for data collection.

Consider collecting customer data by surveying existing customers. Even something as simple as surveying customers requires several inputs.

Table 3-1 shows how you can go from question (from the preceding section) to metric to method, and finally to the right tools for the two examples of customer loyalty (see Chapter 12) and findability (see Chapter 15).

You also need to understand your baseline scores. It’s hard to know if you’ve improved anything if you don’t have a baseline measure.

After surveys, customer transactions and purchase data are popular sources for finding baseline data. You need

· Access to customer data: This is often guarded in organizations because it contains both sensitive company and customer data.

· Transaction data at the right level of detail: Total revenue by product is often at too high a level to understand what’s driving purchases. In many cases, you want to obtain customer transaction data at the product level. This way, you can understand who these customers are (demographics and so forth), when they made the purchase, for how much, and how often (for repeat purchases).

Some of the most important insights companies gain from their customer analytics comes from merging survey data with transactional data. One of the biggest challenges is being able to properly match customer survey results with past and future transactions. You may need the help of a database administrator or IT person to be sure you can merge survey data with transactional data.

Setting a Budget

Every project requires a budget — whether it’s large or small. Consider the following as you prepare your project:

· Software: If you have millions of customers and as many monthly transactions, you’ll benefit from sophisticated software that integrates into sales and accounting systems. Products offered from SAS, IBM, and Oracle can cost upwards of six figures to implement and service.

Some of the best insights still come from simple calculations in Excel or a calculator. Don’t think you need to wait to get approval to purchase expensive software to begin making decisions from customer analytics.

· Time: You can spend a lot on the hard costs of software and services as well as the softer costs of employee time. Software packages that can fit the budget for single-person companies to the largest enterprises are available. Throughout this book, most methods and analysis can be conducted with Excel, free web software, or options that don’t require very expensive software.