Customer Experience & Data Analytics Take Center Stage

Improving customer experience has been reported as one of the top priorities for businesses in 2012. Some companies are even adding a new C-level position to their rosters, Chief Customer Officer (CCO).

Data Analytics

A big driver of this focus on customer experience can be attributed to the gold mine of information gleaned from data analytics. There is so much information available about consumers’ likes and dislikes that companies can analyze the data to build a better experience.

While all this information is great, companies must be cautious in how they extract and use the data. If companies extract only the information which validates their decision making, the customer takes a backseat and all the progress from this data analytics will be lost.

Customer Needs

The bottom line is that the customer should always be the top priority for the business. Yes, businesses should be making solid, innovative products. However, if the customer isn’t interested in buying the product, then the product will have no audience. Before you consider launching a new product, you should listen to what your customers are saying and identify their needs.

Brand Experience

In addition to offering solutions to customer needs, it’s important to ensure the customer has a great experience with the brand. If the customer, again and again, has a bad experience in your store, or with your product, or with your sales force, you can bet that your product will have limited reach. The customer experience should be top-of-mind from production to manufacturing, and from distribution to sales & support-it is paramount in cementing a good relationship between the consumer and your brand.

Determining Demand with Statistical Demand Analysis

Today we’re going to do some thinking and learn about statistical demand analysis.  Why is statistical demand analysis important? Because estimating product demand is essential to reduce the risks inherent with pricing, production and inventory decisions. A failure to accurately estimate demand can lead to over or under production, and if priced incorrectly can negatively affect profits.

How is demand determined?

We’ll talk about two types of demand: Consumer demand and Market or Industry demand.

  • Data on consumer demand is usually obtained through consumer surveys by asking how much they would purchase at a given price point (predictive) or how much they have purchased in the past (retrospective).
  • Market Demand is looked at through a series of price changes over time and analyzing the subsequent change in purchasing activity.  Finally, and the point we will focus on, using naturally observed data on price and quantity and subjecting them to statistical demand estimation.

Statistical Demand Analysis using Liner Regression Model

Recall that regression analysis is a statistical technique for estimating the relationship between variables.  In this case, Demand as a result of price vs quantity sold, city, production, consumer demographics, etc.  Demand, being the dependent variable, is shown in relation to and linked to various parameters (aka assumptions) explaining the relationship.  This is shown by the following linear regression model:

Q = a + bP + cM + dPR +u
Q = quantity demanded (observed) and is the dependent variable
P,PR,M = price, price of related goods, income (observed) = independent variables.
u = random error (not observed)
a,b,c,d, = parameters – unknown and estimated.

Using regression analysis the analyst first obtains a set of survey data (regression data) for Q, P, Pr and M.  Next an estimation of a, b, c, and d is used.  These estimations are difficult and rely on experience, industry knowledge and internal data. When done, you test for significance of the parameter values and using the estimated values of the parameters for analysis, are able to predict the future value of the dependent variable, e.g. demand.

There are three types of regression data: Time series, Cross section and Panel data.  Time series is an observation made of different time periods while Cross section is for different observations at the same point in time.  Panel data is a combination of both.  These are the Data for Q, P, Pr and M.

Specify the Demand Model

Here we’re talking about the unknown or estimated parameters. It is up to the analyst to determine if she will rely on consumer behavior, price, income, price of related goods, size of market, tastes, price expectations etc.  Then, choosing a functional form, usually a choice between liner form and power function.

Linear form looks like this:

Qx – a + bPx + cPr + dM + eN + u

The parameters in this linear function tell the change in Qx for 1-unit change in the associated variable.  It looks like this”

b = ?Qx/?Pr ; c = ?Qx/?Pr; d = ?Qx/?M; e = ?Qx/?N

Determining demand is not an exact science and the accuracy of any prediction is based, in large part, on the experience, knowledge and industry expertise of the analyst.  This is true because of the assumptions the analyst must make in determining the unobserved and unknown variables present in the linear regression analysis.

Linear regression analysis for statical demand gives a more accurate picture of future demand rather than relying on crude projections based on past sales.

We did not cover the power function which is used frequently if the analyst believes demand is non linear.  If anyone can tell me how the power function would look, feel free to comment.  See you next time in Making Molehills out of Mountains University, Market Analysis 101.

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.

The Business Impact of Data Visualization

What is Data Visualization?

Data visualization is the study of data being communicated visually through graphical means. Bar charts, graphs and maps are illustrations of basic data visualizations that have been utilized for quite a few years. Over time, visualizations have become more intricate to the extent of having animations that illustrate changing data over a period of time. There is virtually no limit to the translation of information into an image.

As the visualization designer, you can ascertain which visual components, such as color and shape, symbolize and communicate particular data points.  For example, images could be 2D or 3D, dynamic or fixed, or more importantly they could allow user interaction.

Visualizations provide a unique way to communicate trends and correlations that can ultimately lead to important marketing breakthroughs. Visualizations that represent a large amount of information allow you to see patterns that may normally be hidden in unconnected data sets.

Using Data Visualization to Immediately Observe Key Points

Data visualization does not occur in isolation. Therefore it is important that you understand the context that you are communicating. When planning your communication, make sure it meets these key points:

  1. Precision. Could it be accurate with regard to proper quantitative assessment?
  2. Versatility. Can the creation conform to assist numerous wants?
  3. Aesthetically Pleasing. The data visualization must not insult the reader’s senses; a great example of this is moire patterns.
  4. Efficient.  Is your visualization simple to understand?  Will the viewer “get it” with ease?
  5. Effective. You need to be sure you display the information; evaluate regardless of whether you need to increase or even reduce data-ink percentage; additionally think about brase non-data-ink as well as brase repetitive data-ink.

The Importance of Making Data More Usable

In recent years, a variety of methods have been proposed for analyzing the consequences of data effectiveness.  During a study conducted by the University of Texas, researchers found that if a company increased the accessibility of data for the user, by only 10%, the result could increase revenue drastically and create new jobs. The researchers analyzed accessibility, mobility, quality, and usability to come up with these findings. Overall, improving your company’s data usability will likely benefit business and should not be overlooked.

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.

Establishing Deeper Connections with Customer Engagement

This article is the first in a three part series exploring the twin concepts of customer engagement and the engaged customer.  Some CEOs, especially in the SaaS arena describe an engaged customer and a non-engaged customer like describing the difference between a Van Gogh and overstock office art , “I don’t know much about art, but I do know what I like.”

What is an Engaged Customer?

An engaged customer is engaged by your company on two fronts: (i) before, up to, and including the point of sale and (ii) during the life cycle (use) of the product or service.  Recent research has shown that an engaged customer exhibits three specific traits that, if identified, promoted and supported within your organization, translates directly to a better bottom line.

Common Attributes of an Engaged Customer

Customer Loyalty

A hallmark of an engaged customer, loyalty is an essential trait for survivability in the current slower paced and more costly market place.  It costs less to maintain (for repeat business) a current customer than to identify, attract and convert a new one.  The slower pace refers to the lower rate of business transactions rather than the frenzied pace of product development.

Customer Interaction

Customer interaction refers to the customer’s relationship with your company.  Interactions occur on line and off line, before and after the sale.The type, quality and frequency of these interactions have a huge impact on maintaining an engaged customer.

Customer Feedback/Input

Providing a means for customers to provide feedback and input about your product or service creates a sense of involvement in the process of determining what the consumer wants.  If your average customer’s input has an identifiable impact on your product offering, they are more likely to buy the product or update once it is available.

The Point Is…

The methods of engagement vary but most commonly involve an interaction before there is a problem or complaint.  On line; product reviews, product ratings, surveys and live chat are recent customer engagement examples.  Check-in calls to customers, special events at retail outlets, unique purchasing opportunities and loyalty point programs are examples of off line engagement.

Regardless of the method, the goal of engagement is to create a deep connection with customers. Your customer’s involved relationship and emotional brand attachment are competitive advantages that are difficult for competitors to overcome.  This leads to the question, “How do you measure customer engagement?”  That is the topic of the 2nd article in this series.

Turning Survey Data into Actionable Insights

What is most important to your customer base, and what drives their overall satisfaction? Undoubtedly, it can be difficult to know what customers are looking for from your business. Many factors go into whether or not a prospect will browse your products, make a purchase and, most importantly, return for future business. With proper survey development and survey data analysis, you can accurately uncover these key drivers.

To identify what drives customer satisfaction, the first step is to ask your customers. The information gathered from surveys can pinpoint where to focus and will help with the strategic planning process.

Keep the following objectives in mind as you create the questions for your surveys:

  • Find the Top Attributes. A variety of elements could be important to any given customer, but with a wide-spread survey you can discover which are important to the largest number of people. These top attributes will be the key drivers that you’ll want to focus on improving and maintaining. If you run a shop, you may find the most people desiring “Pleasant atmosphere,” “Good return policy,” or “Large selection.” If you’re an online company it may instead be “Easy to navigate” or “Availability and helpfulness of customer service.”
  • Get a Rating. Once you know what it is that your customers deem important, you’ll also need to know how well you’re doing in each aspect. Have your customers rate how well they find your service in each attribute. The most important ones to focus on are the top-rated elements that your customers have found lacking from your business. That way you don’t have to pour money and resources into every aspect of your company. Instead, you can target specific points that your customers actively desire, and potentially even move money away from the unwanted attributes.
  • Calculate Importance. While everyone in your survey may say that they want “Good customer service,” that may not be of greatest importance to them. It may not, for instance, affect whether or not they return to your business. Make sure that your survey asks customers to list desired attributes by their importance, so that you have the most specific information available.

Driver analysis surveys can be very simple or complex; however, analyzing the results is an intricate process.  As a result, many organizations struggle to understand how to turn survey data into actionable insights. You don’t need to spend a fortune trying to identify what makes your business lucrative and desirable to customers. The availability of survey data analysis software has made it possible to quickly visualize data in a useful, easy to understand way. These tools offer an affordable solution to glean the most important data from the people who use your services and make well informed business decisions.