Collecting data – be it voice of the customer or otherwise – requires a plan. Details of the plan should include what data to collect, how to get the information, where the information will come from and so on. Before any of these details are defined, however, the first step is to identify what a company wants to learn from the data.
When collecting voice of the customer (VOC), there are generally two types of data: reactive and proactive. Understanding these categories enables companies to understand what information VOC data can offer. The following descriptions of each data type will help practitioners determine which approach is appropriate for a given improvement effort.
Businesses receive reactive data after a customer has used or experienced the product or service. Many times businesses get reactive data – whether they want it or not – through complaints, returns and credits.
Sources of reactive data are customer complaints, technical support calls, product returns, repair service hits, customer service calls, sales figures, warranty claims, website hits, surveys and the like. Most businesses make it a point to track this information and make it available to process improvement teams. As a result, this data is often easy to obtain and can help define defects and how frequently they occur.
Reactive data can be used to find out what aspects of the product or service cause problems for customers, what customer needs are not being met, and what the customers expect from the business in the future (new services, products and features). For example, if a business receives repeated calls to its support center that customers are not receiving orders on time, then it might begin the process of tracking down the root cause of late deliveries.
Although often thought of as negative in nature, reactive data can be positive and reinforce an existing business course. A drop in customer complaints regarding late deliveries, for example, can affirm that action to correct this problem has been effective.
The danger with reactive data is that some customers will “tell” the business about a problem by not buying from that business again. This problem can sneak up on an unsuspecting organization. A business should never assume that they have all pertinent reactive data.
Directly engaging the customer is one way of learning the untold story. Employees of a retail store, for instance, could survey shoppers leaving the store without any purchases. Automotive dealerships do this type of surveys with some success – success being an adjustment in processes that satisfy customer critical-to-quality concerns as a result of what they learned.
The other approach to overcoming the deficiencies of reactive data is by collecting proactive data.
Data that is collected before the customer experiences their first, or subsequent, encounter with the business’ product or service is proactive data. Examples of proactive data include data collected in a market research effort regarding potential new products or services, shopper perspectives on store and parking lot layouts, and getting comparative information across your competition’s stores.
Sources of proactive data are interviews with potential customers, focus groups, surveys, market research and benchmarking. This type of data can be difficult to obtain, in terms of both validity and the collection process. Customer surveys and focus groups can miss customer segments or ask the wrong questions. Existing market research can be expensive to purchase and at the same time be inapplicable to the company’s particular customer base.
The way to overcome the difficulties surrounding collecting proactive data is through careful planning – developing a data collection plan with clear operational definitions that has been vetted by stakeholders, process owners and customers (current and potential) will result in reliable information.
Many businesses outsource this type of data collection planning to a company that specializes in market research. Once a data-collection plan is in hand, a business can either collect the data themselves or have the third-party company do it for them.
A business can use reactive data to point the way to where proactive data collection will do the most good, focusing those activities on important customer issues. Without such a focus, the business will be shooting in the dark. Consider, for example, asking customers what color of widget they prefer when the sharpness of the widget is their real concern. Not only will dull widgets turn away customers (regardless of color), but asking the wrong questions indicates that the business is out of touch with its customers. Customers might feel that a business is not focusing on their needs (and they would be right in this case) and buy from a competitor instead.
|Proactive Versus Reactive VOC|
|Data Type||Customer Experience||Sources||Uses||Strengths||Weaknesses|
• Product returns
• Sales figures
• Warranty claims
|Documentation of customer experience||Easy to obtain and directly related to a customer’s experience||• After the fact
• Bad experiences have already occurred, making damage control part of the solution
|Proactive||Before||• Focus groups
• Market analysis
|Planning for new products and services, and documenting shifting customer critical-to-quality issues||Precedes customer interaction and potentially avoids bad customer experiences||• More difficult to ensure data validity
• More expensive to collect
Reactive data helps a business to define defects in the customer’s language; proactive data helps to prevent defects before they affect the customer. Further, proactive data helps focus the business on the important issues of the future – from the next customer visit to where the business is investing its research and development dollars. Both data types are important and depend upon each other to meaningfully improve customer satisfaction.