Message Relevance and Personalization: the Name of the Game

culture of engagementMessaging has become not only a personalized way of communicating with your customer, but when it’s relevant, becomes a necessity to understanding the needs and wants of your customer. Relevance and personalization with messaging allows you to go a step further in taking care of your customer, because you can target them with specific information. Continue reading

Reading between the lines with your survey data: what aren’t they telling you?

Reading between the lines with your survey data should not be necessary, but it is a reality that every business must face. Even with the most well developed surveys there is always the unfortunate reality that customers are not filling them out accurately to provide you the information you need to improve your business and generate increased success. Continue reading

Building Solid Business Strategy Through Market Research

Market Research

As a leader within your organization, are you leading with data driven decisions? Or are you basing your overall business strategy on your “gut” or your previous experience? Don’t do that. It’s better to build a solid business strategy through market research. Here’s why: Continue reading

Improve Your Customer Surveys

It is vitally important for you to take every aspect of your business seriously. If you don’t, the product you provide will decline, your customers will look for other companies and you’ll eventually fall out of business.

Because of this, it is important to know every inch of the company. Every aspect that is both visible to the customers and visible to only those working for the business. Although there are internal checks you can perform, the best way to understand how outsiders perceive the business is through customer surveys. Continue reading

Organizing and Interpreting Complex Sets of Survey Data

Large surveys make the task of data analysis much more complex. In order to uncover meaningful information from your large survey efforts, you will need to define a logical process for organizing and interpreting survey data at the start. While there are numerous techniques you could employ, the following offers a series of reliable techniques for working with complex sets of survey data. Continue reading

3 Ways to Measure Customer Loyalty

Customer loyalty is a tricky sentiment to track.  It is difficult to measure customer loyalty because proof of loyalty, the state of being loyal, is most often shown after an action occurred that indicates a person’s loyalty.  However, past action often indicates but doesn’t guarantee future loyalty.

So how do you measure something that hasn’t happened yet?  You can look for patterns when analyzing responses to survey questions designed to measure specific indicators that, when taken in context by the analyst, have varying degrees of certainty as to future action.  Bob Hayes, author of Measuring Customer Satisfaction and Loyalty, breaks it down into 3 measurements: Retention, Advocacy and Purchasing.

Retention as an Indication of Loyalty

Retention is a reflection of a customer’s willingness to remain with a particular company’s service or products and is useful to measure customer loyalty.  Questions designed to determine loyalty are often based on the “How likely are you…” model to predicate future behavior.  Among wireless or other service provider companies, Retention is most often asked by the question, “How likely are you to switch?”  This question is an indication of the relationship the customer has with the company and may be an indicator of overall satisfaction. Although, the smart analyst should be aware that the question alone, without corroborating evidence, may be an indication of a deeper dissatisfaction with the competition rather than satisfaction with their current company.

This least of all evils attitude is often found in service industries such as cable/internet providers, wireless companies and banking.  To be helpful, retention questions should be supported by an investigation of the second measure, Advocacy.

Measure Customer Loyalty by Measuring  Advocacy

“How likely are you to recommend…?”  or How likely are you to purchase other products from us?” and ” How satisfied are you with…?” are typical advocacy questions.  They are related to retention because the assumption is that a customer that is a cheerleader for or satisfied with your organization is likely to remain with you.  They relate to the customer’s perception of the company’s image that they are doing something right.  Determining what that “right” something is requires additional investigation.  It may be related to a single experience or simply to an overall – but general – impression.

There is overlap between Advocacy and Retention but they are distinctly different.  Advocacy requires less action on the part of the customer, because to advocate does not mean purchasing.   Whereas Retention requires the costumer to engage with your company through the basic transaction of making an additional purchase (or renewing a service) which in itself is a strong indication of customer satisfaction.

However, the strongest indication of customer satisfaction is related to Purchasing.

(RE)Purchasing is a strong Customer Satisfaction Indicator

Purchasing questions like, “How likely are you to (continue)(increase)(purchase different) products from X Company?” are the best indicators of growth through customer loyalty.  They seek to determine if the amount spent per existing customer will increase or decrease based on additional purchases within or across product lines.  It is distinguished from the retention question of how likely are you to switch because a switching question may me a repeat of the same revenue (0 growth) rather than an increase in spending (positive growth)

Use All 3 To Measure Customer Loyalty

All three customer satisfaction indicators are closely related in that they measure costumer intent.  negative responses to these types of questions usually indicate a loss of that customer.  Either they will re-up, purchase additional products or feel good about your company/product/services — or they won’t or don’t.  It is fairly straight forward to develop relevant survey questions to receive the data.

What becomes difficult is providing the context for analyzing the survey data into a meaningful construct that can be used by decision makers.  That is the job of the analyst to rely on his or her experience, knowledge and expertise to put the data into perspective.

Measuring Customer Engagement (Part 2)

In the first article of this series we discussed common characteristics of an engaged customer. In this article we’ll look at measuring customer engagement.

Measuring customer engagement is a process that begins with a clear objective followed by determining measurement metrics, then data gathering and of course, analysis and reporting.

Defining Engagement Measurement Goals

The goal is simple: Is what we are doing working?” Answering this question requires an examination of internal customer and financial data to profile the engagement characteristics of profitable, repeat (loyal) customers and compare them to customers that are less engaged.

Define the Metrics for Engagement

Examine the channels of customer involvement: POS, Website, Call centers, trials and demos etc. engaged customers utilize.  These channels, when measured, contain the data  points used for measuring customer engagement and present the frame work for establishing a measurement goal.   To minimize the complexity of the measurement and to reduce the cost in dollars and time, less is more.  It is better to closely examine several key metrics than to inundate the analysts with reams of data from every touch point or interaction.

Gather the Data

For many companies data gathering possesses challenges related to data ownership and data reporting channels.  The specialized software and tools used to gather web analytics, marketing campaign management and loyalty club data typically belong to Marketing, while call center, agent analytics, IVR analytics and contact center platforms belong to operations or IT.  Consolidating this data for cross analysis with financial data is a keystone for successfully measuring customer engagement.


In the simplest sense, what to do with the information gleaned from a consolidated data analysis is straightforward: get the analysis into the hands of the decision makers.  But that is only half the reporting story.  The data must be fed back into the data pool for action by the various touch point channels in the form of recommended action so that, after implementation, the result of the action is compared with prior data to determine the efficacy of the change.  This process, repeating itself constantly, provides a substantial  customer engagement level and an engagement strategy success picture on an ongoing basis allowing for continuous adjustment to maximize return on the engagement strategy investment.


Isn’t There a Single System or Process?

If measuring customer engagement seems a bit convoluted and anything but straight forward it’s because it is convoluted and anything but straightforward.

Some engagement metrics are extremely difficult to obtain and quantify.  On line data is much easier to capture than off line data.  It’s not easy to capture data that isn’t typically recorded; like time in store, number of items reviewed or compared on a shelf before a purchase decision, etc.  It is also difficult to correlate some data types to a specific customer.  Anonymous data such as that obtained from surveys, cash purchases or web site visits do little to enhance the customer engagement profile.

There is no single methodology or formula to plug into your business model to measure customer engagement. The complexity of measurement requires individualized, company wide programs tailored to fit the needs of your business.


Knowing the profile of an engaged customer in your organization and having the ability to measure customer engagement are but two of the three legs required to successfully create fully engaged, loyal and repeat customers. The third leg is creating a customer engagement culture, bottom-to-top, top-to-bottom, so that your customers are effectively engaged at every stage of interaction with your company, before, during and after the sale, and the next sale and the next….

The final post in this discussion explores some prevalent methods for creating a culture well suited to customer engagement.

Limitations of Spreadsheets as a Tool for Analyzing Survey Data

Spreadsheets suffer from several inherent disadvantages when it comes to performing large scale tasks such as analyzing survey data. The amount of records spreadsheets can handle at any given time is quite limited in comparison to what is required for survey data, for example. Trend awareness is also a complex task which a spreadsheet is just not suitable to perform.

Recent studies indicate that over 90 percent of all spreadsheets contain errors. This amount of potential inaccuracy alone should preclude you from using spreadsheets as a primary tool for analyzing survey data — a task in which accuracy is imperative. Much of spreadsheet’s shortcomings in terms of accuracy stems from the fact that many of them are derived from older versions of other spreadsheets converted to perform a different function. While this tactic may save time in the short term, resources are lost when errors inevitably crop up down the line.

Types of Spreadsheet Errors

There are three primary types of spreadsheet errors which afflict their users with frustration and ultimately incorrect or inadequate information:

  1. Stealth Errors: As their name implies, these errors are especially problematic because of their difficulty in locating them. Often times, data can look reasonable and accurate at initial glance, but upon closer inspection is found to be wrong. The most hazardous aspect of stealth errors is that many are discovered years after they are relevant or sometimes are never discovered at all.
  2. Outlier Errors: These occur when a spreadsheet appears operational, but the results produced are obviously inaccurate. While these are easily found, fixing the calculations or formulas responsible for the error is a tedious and time-consuming task.
  3. Friendly Errors: Calling these types of errors “friendly” is a result of the spreadsheet software discovering them for you; an error message is displayed identifying the offending formula or calculation.

While spreadsheets have secured their place in today’s market place, utilizing them for the purpose of analyzing survey data often falls outside their scope of usefulness.

If you are interested in going beyond the spreadsheet and venturing in to the realm of powerful yet flexible survey analysis software, then look no further!

Leveraging Data Analytics for Competitive Advantage

When seeking to gain a competitive advantage, data analytics can really fall in to only one of three areas: accurate, misleading or irrelevant. What about inaccurate, you ask? Inaccurate data is misleading and therefore inaccuracy is also not relevant.

Customer data is captured by either passive (click rates, POS data, etc.) or active (customer feedback, surveys responses, etc.) means and is used extensively in business to gain a competitive advantage. Keep in mind that the larger the sample pool and the longer period of time the data is gathered, the more accurate an analysis will be. This article focuses on the benefits of leveraging data analytics for competitive advantage rather than on the types of data gathering techniques.

Types of analytic solutions and models:

Segmentation: divide customers into homogenous groups.
Classification: including decision trees, logistic regression, etc for predicting customer behavior
Forecasting: used to predict future metrics based on historical data, e.g. sales forecasting.
Basket Market Analysis: Customer purchase data used as a basis for segmentation.
Explanatory Modeling: Used to determine why something happens.
Predictive Modeling: Used to classify customers, or prospects
Decision Modeling: Used to guide decision making for a particular situation.
Customer Value Analytics: in conjunction with Segmentation and classification, it is used to identify customer type and priority.

Benefits of Data Analytics

In terms of survey analysis, the following is a list of benefits to using data analytics for competitive advantage along with the analytical solutions and models used:

  • Tailored offerings to fulfill specific customer needs.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling 
  • Develop new products or services or determine product and service features that are important to customers
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, market basket analysis 
  • Better customer relationship management.
    • Customer value analytics, classification, explanatory modeling, customer experience analytics, Exception analysis
  • Find, attract and retain the best customers
    • Customer value analytics, classification, explanatory modeling
  • Distinguish preferred from marginal customers.
    • Customer value analytics, classification, explanatory modeling 
  • Customize marketing plans to specific markets.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, explanatory modeling
  • Identify new market opportunities.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, explanatory modeling
  • Develop insight into changing customer requirements,
    • Customer value analytics, classification, explanatory modeling
  • Target pricing
    • Customer value analytics, classification, explanatory modeling, predictive modeling, market basket analysis 
  • Determine which customers are open to cross-selling.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, market basket analysis