Analysis Plans – The Underdog of Market Research

Analysis Plans – The Underdog of Market Research

Often when I recommend that a research team prepare a formal analysis plan the first response I hear is, “Why? The analysis isn’t due for weeks and I have too many other things to do.”

An analysis plan is not extra work; it’s work that makes all the other project tasks flow efficiently. It will help you produce on-time project deliverables. Typically, you develop an analysis plan in parallel with your research instrument (RI) or questionnaire. Like the questionnaire the analysis plan is tied back to the goals and objectives of the study. In addition to the obvious purpose of an analysis plan, producing a plan serves to improve the RI and manage project scope, these benefits alone will pay you back for the time you devote to creating it.

The RI is referenced in an Analysis Plan (AP) and while there are no hard or fast rules and no one right way to structure an AP we can offer some guidelines. The approach described briefly here is as good as any and better than most for quantitative studies.

The first step in this process will be familiar to those of you who have read AtHeath publications from the Market Research Resource Center (MRRC). Specifically, research has the greatest chance of success when the objectives are clearly stated and that is where we begin. Use these five (5) straightforward steps.

1. State the key study objectives clearly at the beginning of the analysis plan (AP) and refer to them throughout the process.

2. Describe the major comparisons for the analysis (e.g., major cross tabulations for the study such as: Customers versus Non-customers, Companies by size, Customers that are Satisfied, Neutral, or Dissatisfied).

3. State how each question is used to answer a specific objective of the study either on its own or in combination with other data points. Think through how you expect to present the results from each question. What statistics, if any, will you use in the analysis? Identify the independent and dependent variables.

4. Write a clear justification for including the information from the question in the study and perform a section by section “So what” litmus test.

5. When the analysis plan is finished, go back and make sure each key study objective has been addressed.

These five steps are the basic approach to the AP template (see it is straightforward). The key is to focus on objectives and think critically about how to execute on the primary goal of the study.

Next, let’s dive a little deeper inside the Analysis Plan. Two of the five steps of an Analysis Plan (i.e., steps three and four) are repeated for each section of the questionnaire. Combined, these two steps provide the question-by-question detail of your analysis plan. First, each section of the questionnaire is described in a brief outline format. Next, the analysis requirements are described for all questions in the section. Finally, a ‘justification’ is written for why the questions in this specific section of the questionnaire are needed. This is the “So what” litmus test. The example below may help to demonstrate how steps three and four are implemented.

Example Analysis Plan Steps 3 and 4
Section Q of the Questionnaire:

Topic – Accessibility of information and mechanisms to access information (e.g., data):
a. Website features and functions customer depend on and/or like best
b. Perceptions and preferences for push versus pull tactics for receiving information from host firm

Q10, Q11, and Q12 – All focus on the website features and functions customers and prospects value most and the vendors that do the best job of implementing these features and functions. Conduct a feature/function prioritization analysis – multiple response analysis. To optimize the data, recode open-ended questions (Q12) and conduct analysis to classify the best websites.

Q13-16 – For these questions explore the general frequency of website use and specifically respondents’ use websites to make purchases. These are primarily descriptive analyses with comparison by the major cross tab groups already outlined. In addition, we are likely to use these data in a segmentation analysis, which we will describe later.

Q20-40 – Capture data on “touch” issues e.g., pushing information to clients, how frequently and in what ways. Basic descriptive analysis [possible segmentation variable] and cross tabs with significance testing will be applied.

Justification – The first part of this section provides us with competitive information, but more importantly points us to specific implementations that are considered “best in class” by clients and prospects – a very powerful tool for prioritizing and implementing features on our current website and any redesign work we decide to undertake.

The second part gives us general frequency of website use and purchasing, which is nice to know info, but may not be as actionable as other data. It tells us the relative importance of the website across our customers (by type perhaps) and if we build-in ecommerce functionality, how much it might be used. However, I doubt we would decide to provide or not provide ecommerce functionality based on the study results (optional information).

The touch information is highly actionable and can help guide our efforts and inform decisions on the level of investment to make in these activities. End Section Q.

As you can see from the example a thoughtful description of the analysis work and the value of the results, along with the justification provides a roadmap. Time well spent

For a more detailed description of how to develop an Analysis Plan see Analysis Plans Made Easier, an AtHeath publication.

You can also contact Carey for more information. He is an executive level research professional and brings over 20 years of experience to the research community. He holds two advanced degrees in market research related disciplines. Principal and Founder of AtHeath, LLC Mr. Azzara is a consultant, author and a highly respected researcher. Two of the most important features of AtHeath are its Market Research Resource Center (MRRC) and the Expert Community that supports the MRRC.

The company name “AtHeath or At-the-Heath” is the point at which forest and grassland meet – a metaphorical point of transformation or transition. Our blog “The Research Playbook” may also be of interest

Why Not Take A Free Website Analysis?

Why Not Take A Free Website Analysis?

A few years ago, as a successful online presence or business was not possible without Search Engine Optimization; today, target oriented Search Engine Optimization is not possible without proper website analysis. In order to cater to this new demand of the market, many of the SEO companies offer free website analysis to attract more customer satisfaction as possible. When looked at from SEO point of view, Free Website Analysis has two fold benefits for both of the parties; customer gets an idea of the services offered by the company and the SEO team has a far more clear and workable SEO plan.

There are two main parts of the free website analysis services:

1. Free SEO Analysis
2. Free Web Design Analysis

Free SEO analysis of the web helps track the trouble in the existing SEO pattern applied. It also enables to discard things that are no longer useful. During free SEO analysis, keyword density, keyword type, keyword location, link type, link location and availability are checked. Since both of these are dynamic entities that keep on changing over time, they need regular replacement and check. One approach is worth mentioning here. Some people undermine Free SEO analysis services provided by the SEO companies. They suspect the results they reach. But, they forget that nobody would like to risk his first impression and not to reduce the burden of the SEO geniuses. However, a free website analysis is incomplete without the free web design analysis.

The appearance of the web; your online business office, is the foremost thing that attracts or loses the attention of the customer. Web design has also very far reaching impact on the user experience which in turn affects the customer conversion rate directly, leading to drastic consequences. Free web design analysis enable analysts to point out the designing blunders and suggesting better alternatives. Free website analysis includes the expert analysis of your web’s layout, the page loading time and issues, processing time of user request and queries and accessibility of the visitor to the information most relevant or demanded by him. As a consequence of free web design analysis, web analysts may recommend a complete re-design of the web and change in the server side technology and approaches.

All of the above observations are given a shape of a report. The results and suggestions are not merely observatory or based on assumptions; rather, they analysts use complicated quantitative approaches to reach them. So, it almost impossible to challenge a web analysis report for quality and reliability.

Free website analysis helps both the SEO companies and the customers. A complete web analysis includes free SEO analysis and free Web Design Analysis. Free SEO Analysis focuses the SEO strategy, indicates the loop holes and suggests remedies. Web design analysis focuses the user experience and the reasons of low customer to conversion rate. All of the observations and results take the form of the website analysis report, suggesting accurate and effective SEO strategy.

Top 10 Ways to Guarantee a Precise Color Analy

Top 10 Ways to Guarantee a Precise Color Analysis

From online color tests to cosmetic color consultations to in-studio color analysis with a certified analyst, options for color analysis abound. Guaranteeing that colors are done properly, though, is an entirely different matter. The accuracy of a color analysis is determined by the tools and surroundings used, as well as the analyst’s skillful eye.

Personal color analysis is based on a concept called simultaneous contrast. This means that when two or more colors are seen simultaneously, each is directly affected, and indeed altered, by the color or colors next to them. In color analysis, various color coded drapes are placed around the face and neck. The colors surrounding a person will produce a color effect, either positive or negative, and those observations are used to place him or her into a certain category of ideal colors. This is precisely why it is imperative to conduct an accurate color analysis in person, as opposed to online.

When deciding who will perform the color analysis, here are the top 10 ways to guarantee the most accurate and complete results:

1) Natural sunlight or full spectrum artificial lighting must be used. Special light bulbs are available for this purpose, and the accuracy of the analysis depends on the use of this lighting. Natural daylight or its equivalent is always best because it holds a balanced blend of all colors in the visible spectrum, and thus enables the analyst to observe the optical effects of colors next to the face. Any other lighting drastically alters the appearance of skin tone and the testing drapes, which in turn creates inaccurate results.

2) The walls and surroundings must be a neutral grey, one that will not detract from or compete with the test drapes used in the analysis. Color is seen most accurately when surrounded by neutral grey.

3) Both hair and clothing must be covered using a neutral grey cap and gown. The color analyst should wear a neutral grey cape, as well, to avoid visually competing with any of the test drapes used during the analysis.

4) The test drapes themselves should be selected by a company that scientifically measures color using an accurate standard, such as the Munsell system. The Munsell system, widely recognized as the worldwide standard for color, measures color in a complete, three-dimensional way. The results of a color analysis are only as accurate as the test materials themselves.

5) The color analysis system used must account for the possibility of neutral skin tones. As is the case with other sciences, the field of color analysis has evolved. Popularized during the 1980s, color analysis originally offered four categories, two for warm undertones and two for cool undertones. While this method served us well for what we knew at the time, it is now understood that up to two-thirds of people fall into a neutral color category that is neither fully cool nor fully warm. A modern analysis builds upon what was done in the 1980s, and creates even more precise and complete results.

6) No makeup should be worn during an analysis. Makeup both improves and alters the look of the skin, which makes it difficult to see any optical changes that take place during an analysis. For accuracy’s sake, it is vital to see how a colored drape affects, be it positively or negatively, the look of the skin. The right color creates a visual face lift, and imparts a look of vitality, youth and brightness to the skin. The wrong color visually emphasizes fine lines, wrinkles, and imperfections, yellows eyes and teeth, drags down the face, and makes the skin appear unhealthy. These effects cannot be seen in their fullness if makeup is worn.

7) No facial tanners should be used for at least one week prior to the consultation. Some facial tanners will shift the skin tone too drastically and render an analysis ineffective. Likewise, no colored or tinted contact lenses should be used, as they prohibit the analyst from observing the optical effects on one’s natural eye color.

8) Consider how many choices are included in a personal color palette. The palette should have at least 60 colors that all mix and match seamlessly. This provides a breadth of color to work with and facilitates easy wardrobe planning.

9) A personal book of color should be provided as part of the consultation. Ideally, the book should be printed on special canvas that can be compared accurately against any fabric or cosmetic colors. Some books of color contain fabric squares, which are extremely difficult to match against other clothing fabrics seen in stores. Matching fabric to fabric is complex, even for master colorists, because fabric has a sheen that changes appearance as light reflects off of it. The most accurate personal books of color, when printed on archival canvas, are not only guaranteed to last a lifetime and will not fade, but they serve as a constant against which various fabrics can be compared.

10) The analyst chosen should be fully trained in the field of color analysis. Ideally, he or she should have been trained in person by a company that specializes in the field of color. There is simply no substitution for receiving hands-on training, despite the many online training programs available today. The art and science of color analysis take practice to understand and time to perfect.

Not all color analysis is created equally, so research options well before making the final choice. The more accurate the results are, the more slenderized, rejuvenated, polished and put together one will appear. Why go for looking okay when one can look fabulous? After all, the analysis will determine if and how to color treat hair, as well as what makeup, clothing and accessories to choose.

Conducting Survey Analysis

Conducting Survey Analysis

Questionnaire and sampling design in a survey research are not independent of other modules in the process like data analysis. While a survey research executive is designing research plan and questionnaire, she needs to keep in mind the type of analysis to run afterwards so as to attain the research objective. Same applies to types of conclusions to be drawn from the survey research study. The research, sampling plans and questionnaire design are totally dependent on this. If one fails to do this, you risk collecting an inappropriate form of data for your analyses, or neglecting important contextual questions. Data analysis in a survey research can be categorized into two – qualitative and quantitative analyses.

Quantitative and qualitative analysis

The more frequently deployed technique of data analysis happens to be quantitative data analysis. As the name suggests it is more about number crunching. The quantitative data analysis tries to throw light on more of ‘macro’ issues. It is often expressed in numbers. For example:

a. Almost 70% of the target population elements have expressed positive opinion towards alternate fuels

b. More than 40% the target population elements have shown interest in the new flavor of ice cream

These examples clearly illustrate the prevalence of quantitative data analysis. The terms sampling, statistical treatment, mathematical expressions, etc are the natural derivatives of quantitative data analysis. Quantitative data analysis can be enjoyed most through the difficult route of statistics application.
Quantitative data analysis is used in most of the studies except obviously for qualitative studies like focus groups or clinics. In short, any numbers are seen, make a note than it is quantitative data analysis.

Qualitative data analysis, on the other hand, is all about finding and reporting insights into a subject matter. Qualitative data analysis doesn’t at all deal with any of the statistical treatments or mathematical inference. These examples will illustrate the difference:

a. Three persons have expressed their fear about easy availability of alternate fuel

b. One person has reported too sweet taste of the new flavor ice cream

The examples (taken from live studies) in quantitative and qualitative data analyses clearly show the difference between the two. Quantitative data analysis is about correct application of a statistical or mathematical tool while qualitative data analysis is correctly picking up a stray voice and visualize its impact on the whole analysis. For example, only one person has reported sweet taste of ice cream. Now, is it true for all or only for one person? This must be validated through another set of analysis or study.

The problem with qualitative analysis is that the analytical tools are limited. The main tool is “content analysis”, which involves reading the full text and recoding it according to a number of categories determined by the researcher. This more than often proves to be time-consuming and subjective.

Retail Data Analysis

Retail Data Analysis

Analytics play a pivotal role in the data flow scheme within a retail organization. A typical retailer generates more than thousands of data points through POS machine. It is difficult for a retailer to make strategic decisions based on this raw data.

A typical retailer has large amount of sales data stored in their systems. The new technologies have the ability to use these historical data to improve retail productivity. To create sustainable advantage over competition, retailers are trying to enhance their product offerings, service levels and pricing models. To prevent value attrition and to protect margins, retailers are trying to reduce their cost-to-serve per customer and thereby making sure that the total cost of ownership of a customer over time is reduced. Managing promotional plans is another critical area for retailers to focus on and target customers more effectively and efficiently.

Small and midsize retailers are facing problem with limited analytical resources to read the pulse of their business processes. Retailers are not able to follow up with day to day sales analysis, category analysis and brand share analysis for all the products.

Most retailers collect every transaction from every store, track every movement of goods and record every customer service interaction. Hence there is no shortage of data, but how does one translate all that data into actionable information? How this information can be used to make better decisions? The main objective of a retail store IT department is to convert the raw data into valuable and useful information.

Business analytics helps to get insights from the structured data, such as sales and productivity reporting, forecasting, inventory management, market basket analysis, product affinity, customer clustering, customer segmentation, identifying trend, identifying seasonality and understanding hidden patterns for loss prevention and store administration.

Analytical techniques such as statistical analysis, data analysis and analytical tools help in understanding patterns and trends within large databases. When we use them for creating analytical models, they provide the edge to decision making. While descriptive analysis helps to identify issues and examine causes, predictive analytics enhances the accuracy and effectiveness of decision making process.

Some analyses applicable to retailers are:

1. Reporting and Sales Analysis
2. Predictive Analysis
3. Inventory Management
4. Promotion-Effectiveness Analysis
5. Demand Forecasting
6. Brand and Category Analysis

Predictive analytics helps a retail organization to enhance its decision making powers by looking at the future with analytical rigidity. Predictive analytics holds the key to taking advantage of these opportunities such that retailers can increase their ability to forecast their customer’s behaviour and plan accordingly. Data analytics capabilities cover a number of possible analyses, using statistical software such as SPSS, SAS, Excel and Minitab.

Data analysis helps in decision making process with operational efficiency, saves costs by providing high quality solutions, facilitates flexible working models and state of the art data security. A well trained analytical team can help in the automation of data cleansing, processing and recurring reporting.

Part II

In the constantly changing competitive business environment, informed and intelligent decisions are the centre stage for every business organization. Data analytics and statistical techniques help to make business decisions and provide valuable insights to an organization.

Data Analytics is the science of playing with sales numbers to arrive at logical decisions by slicing and dicing the data to understand patterns and correlations that could give the company a competitive edge.

Retailers need to analyze various strategies surrounding merchandizing, pricing, promotion, markup and markdown to be able to make the right decision. Statistical and mathematical techniques are used to analyze current and historical data to make predictions about future events. The patterns found in historical and transactional data is used to identify risks and opportunities.

Data analytics gives a summary on top performers, bottom performers, key value items, sales performance, forecasting, trend and seasonality. Inventory management analysis helps a retailer to keep minimum inventory without running out of stock. Analytical team use the power of advanced statistical software, super computers and sophisticated mathematics to give actionable insights to the customer. Advanced mathematical techniques, formulas and statistical methods are used to predict the future demand of a product. This analysis considers the impact of holiday, seasonality and trend effect.

Retail data analysis helps a retailer to target their customers more effectively by campaigns, to improve response time to market changes, to increase employee productivity and to improve customer service at stores. Analytic models examine a customer’s recency, frequency and monetary value of customer visits along with purchase behaviour and provide a customer’s attrition probabilities on which retailers can take corrective action to reinforce loyalty. Some of the key analyses are:

o Customer profitability analysis
o Market basket analysis
o Opportunities for up selling or cross-selling
o Customer satisfaction analysis
o RFM Analysis

An analytical process can take care of data preparation, modelling activities and generates reports. Analytics provide actionable and powerful decision insights. These decisions are required for developing and maintaining profitable customer relationships. Retail reports give powerful insights and actionable analytics to the desks of retail managers and analysts in real time.