Tips for keeping customers satisfied

culture of engagementIn business, you know the customer is always right – even if they’re wrong – and should be your top priority. Running a company that keeps customers coming back time and time again is the ultimate goal; with these tips for keeping customers satisfied, you can accomplish that goal on a daily basis. Continue reading

Ways that Survey Data Can Be Used to Make Big Improvements

Survey Data Collection

Survey Data Can Lead to Big Improvements

Survey data is one of the easiest forms of data to obtain to rate overall customer experience and satisfaction so you can make beneficial changes to your business. Whether your business is brick and mortar, online, or both, you can gear your customer surveys to ensure that you gain the data you need to improve product or service quality, improve customer service, maintain market share through competitive offerings, and improve the overall level of the customer experience. Continue reading

Consumer Insight from Panel Research

If knowledge is power, than survey data analysis may be one of the most powerful weapons for marketers and market research groups. Whether we’re talking about social or consumer market research, doing this work through the use of panels really helps cull the field and find out what people are thinking, feeling, and how they would react to decisions and product launches from different groups. Continue reading

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.

Creating a Customer Engagement Culture (Part 3)

This post, Creating a Customer Engagement Culture, is the last installment in the customer engagement trilogy.  In it, I examine the recommendations of the authors of the article, Creating an Engagement Culture, published in Chief Learning Officer Magazine.

“Improving employee and customer engagement is hard and there are few models to guide leaders on how to achieve it.” – Leimbach, Michael & Roth, Tim, (2011) Creating an Engagement Culture, Chief Learning Officer Magazine.

The primary lesson I took away from the article was that focusing solely on customer engagement strategies ignores your employees who, after all, are the people engaging your customers.  If your employees have not made an emotional choice to be loyal to your company they’re not as effective when engaging your customers.  According to the authors there are 5 areas leadership and their managers must strategically align to create a customer engagement culture: Opportunity, Personal Accountability, Validation, Inclusion and Community.

As the quote above says, creating a customer engagement culture isn’t easy.  It’s also not impossible.

5 Key Components to Creating a Customer Engagement Culture

  1. Opportunity – Focus on potential rather than on loss.  Focus on growth rather than survival.  Create an environment where employees feel they are engaged in a process that recognizes personal contribution as necessary for company success.
  2. Personal Accountability – Set, Communicate and Measure behavioral expectations that support company values.  This task is about aligning what you do (task specific) with how to behave while doing it (action specific). Achieving this objective may require manager communication training to reinforce, support and clarify expectations.
  3. Validation – Acknowledge and encourage everyday performance, not just the top performers.  When leadership validates its employee’s efforts they send a signal that employees matter.  It is the daily affirmation, a note, a kind word, or a gesture that says, “Hey, employee, you matter and we notice.” that makes employees feel personally supported and valued.
  4. Inclusion – Change is difficult but when Leadership engages employees in the change process they achieve buy in when change decisions are made inclusive. Imposing change from above creates resistance but effective dialogue resulting from leadership listening to employees, incorporating the best suggestions into the change process and regular, positive communication creates a sense of community and trust that flows upward from employees to leadership.
  5. Community – A term I often hear regarding business cultures is “silos”. Departmental, informational, operational and other silos of isolation contribute to “not my department” or “not my job” attitudes. Seeking a high engagement culture in your company means tearing down the silos and building community engagement halls where information, goals, success stories and failure challenges are shared and acknowledged; a place where collaboration is encouraged.

Stating the obvious, there is nothing said here about creating systems to encourage customer engagement as a cultural value.  Instead, the authors focus on what your company can do to align executive and management leadership around the values of an engaged culture.

I agree with the author’s that engagement is a choice made by your employees and customers.  It is not something that can be imposed.  They conclude that a culture of engagement is one where the conditions under which engagement can occur have been met, thereby providing your employees – and by extension your customers – an opportunity to choose to be fully engaged with your company.

I think most of us would agree that it’s not easy but it’s also not impossible.

Data Analysis Tools Created with You in Mind

Today’s data analysis tools are more sophisticated and robust than ever before.  But complexity is not necessarily better.  If a tool is too complex it looses efficiency until it is no longer cost effective to use.  Think Space Shuttle.  What is needed are user friendly data analysis tools.

User friendly data analysis tools are tools that you, the end user, can employ to accomplish analysis tasks quickly, easily and efficiently. They work intuitively.  They do not require extensive training.

For many analysts, data visualization is the best method for understanding and communicating complex data relationships. The human mind identifies and recognizes patterns more intuitively when presented visually through charts and graphs as opposed to presenting data points and lists.

But simply presenting data visually is only half the challenge. Creating those charts and determining which are relevant is a time consuming and manually laborious task that’s prone to input error.

Answering that challenge is what mTAB™ and it’s companion product mTABView™ have been doing for years.

Ready-to-use data

mTAB’s™  sophisticated database compression technology quickly and easily turns raw data into ready-to-use data. This powerful feature means engaging in hands-on analytics without extensive training and without relying on your vendor to do the heavy lifting, setting up and manipulating the data.

In today’s cost sensitive business environment, many company’s seek user friendly data analysis tools that allow analysts to spend more time analyzing and less time parsing raw data, creating charts and preparing reports.  mTAB™ is designed with the spreadsheet user in mind.  Its user interface will be familiar to anyone who has ever used a spreadsheet thereby creating a synergy between the data, the interface and the analyst, resulting in higher productivity and deeper analysis.

Create, update, present

Once the initial analysis is done and it’s time to create a meaningful report, the task of manually generating multiple charts can be daunting to even the most experienced analyst. That’s where mTABView™ comes into play.  It automates the creation and update process by linking the survey data directly with the charts.  Update the data and the charts are automatically updated.  There is nothing more user friendly than automation and the ultimate in ease-of-use is mTABView’s™ one click export to PowerPoint.  With just one click your tables, charts and graphs are loaded into fully customizable PowerPoint slides.

And that is only the surface!

Pivot Tables – what are the alternatives?

Creating concise, informative summaries out of huge lists of raw data is a common task and spreadsheets of old have long troubled our weary eyes and tired minds with endless rows and columns of unserviceable data. Digging deep into the data with a standard spreadsheet required knowledge of spreadsheet formulas, math skills, and a tedious mind. The powers that be created the pivot table to replace this unnecessary work, forecasters everywhere rejoiced and were glad.

Data spreadsheets can contain a lot of information and sometimes it may be difficult to obtain summarized information in a simple fashion. Let’s say you’ve compiled a long list of data – for example, sales figures. A pivot table is a spreadsheet tool that gathers cross tabulation data and allows the user to select exclusively desired information from the columns and rows such as individual products or a sales date. Not only will a pivot table highlight desired information, but it can easily “pivot” variables around its axis to gain a different perspective on the data you are working with. The spreadsheet is now in the power of your hands.

The pivot table you will be working with will look and function similarly to the spreadsheet template, but there are many more functions behind those columns and rows. Once data has been populated in the pivot table spreadsheet, it is time to run the data and view the numbers. Sums, averages, standard deviations, and counts are just a few functions the pivot table can produce instantly! Pivot tables make the data seem easy to understand and they can be used within any level of data mining: nominal, ordinal, interval or ratio. The pivot table will become a good friend in helping prepare for reports since its functionality in a spreadsheet allots for more research time, and perhaps, resting those weary eyes and tired minds.

Essentially, a pivot table is a cross-tabulation report – an analysis type that survey research analysts have long known the benefits of. The few differences between the pivot table and a cross-tabulation report are seen in the pivot table’s deficiency of research power. The pivot table establishes an interdependent relationship between the two tables of values but does not identify casual relationships. Also, the pivot table’s power is indicative to the survey size. For example, a 20 question survey with a sample population of 100 can be effectively analyzed with pivot tables within a spreadsheet but when sample population and survey size increases over time or between markets then a more powerful, specialized survey analysis tool is required.