Explore FAQ's

Using Boolean Logic in a Crosstabbing Environment

What is Boolean logic?

Boolean logic is a form of algebra in which all values are reduced to either TRUE or FALSE. In a crosstabbing environment, Boolean logic helps to define and refine data sets by combining multiple conditions, making it easier to analyze specific segments or patterns.

Which are the main Boolean operators used in crosstabbing?

The primary Boolean operators are:

AND: Returns TRUE if both conditions are true.
OR: Returns TRUE if at least one of the conditions is true.
NOT: Returns TRUE if the condition is not true.

How can I use Boolean logic in a crosstabbing tool like Explore?

When setting up your crosstab, you can use Boolean operators to filter or define specific data segments. For instance, if you want to analyze data for individuals who are aged 18-25 AND are college graduates, you would use the AND operator to combine these criteria.

Can I combine multiple Boolean operators?

Yes, you can use a combination of AND, OR, and NOT operators to create complex criteria. However, always ensure the logic is clear and coherent to avoid any data confusion. Using parentheses can help organize and prioritize operations.

Why is Boolean logic useful in a crosstabbing environment?

Boolean logic streamlines data analysis by allowing users to quickly segment and filter data based on multiple criteria. It provides flexibility and precision, ensuring that the generated crosstabs are relevant and insightful.

How can I avoid common mistakes when using Boolean logic in crosstabbing?

Always double-check your conditions to ensure they're logically sound. Using clear parentheses and testing the conditions on a smaller data set can help you identify any errors or inconsistencies.

Are there any limitations to using Boolean logic in crosstabbing?

The main limitation is the complexity. As you combine more and more conditions, the logic can become harder to track and understand. It's essential to keep your criteria clear and straightforward, ensuring that the resulting crosstab truly reflects the data segment you're interested in.

What is "autocoding" in Explore?

When using Explore, "autocoding" means that after you've placed your codes in a column or row, the system seamlessly combines your coding strings using Boolean logic, streamlining the process for you.

Manual Coding in Explore

How can I manually edit the code in Explore?

Explore's Visual Code Builder lets you manually adjust the code to your liking:

Linking Operands:
Simply drag and drop from the visual code builder codebook
Editing Linking Operands:
Simply click on the dropdown menus located between each code to change how they link.
Renaming the Code Title:
Clear the text in the 'Coding statement title' field and input your desired title. This method will only alter the title of the row or column you've selected.
Manual coding:
Simply click to switch to manual coding and type in your coding statement.

Exporting Data in Explore

Where can I find the export option in Explore?

The export option is conveniently located in the upper right corner of your screen. Look for the export icon, typically symbolized by an arrow or outward-facing square.

What file formats are available for exporting crosstab reports?

Crosstab reports in Explore can be exported in three primary formats: Google Sheets, XLXS (Excel), and CSV (Comma-Separated Values).

I have a chart I'd like to share. Which formats can I export charts into?

Charts from Explore can be exported in several formats, including Google Slides, Google Sheets, XLXS (Excel), and PPT (PowerPoint).

Is there a specific format for exporting data directly from the reports dashboard?

Yes, from your reports dashboard, you have the option to export data in JSON format. To do this, click on the three dots symbol (or fly-out menu) adjacent to your desired report, and select the export option.

Are there any tutorials or guides on how to use the export feature efficiently?

The interface of Explore is designed to be intuitive. However, if you need further assistance, you can refer to the help section or contact our support team for detailed walkthroughs.

Sharing a report

What's the process to distribute my report to team members?

Every report you create is stored in your personal drive by default. For sharing, you can follow either of the two methods detailed below:

Option 1: Locate the 'save as' icon situated at the upper right-hand section of your display. Tapping on this icon will launch a 'Save as' dialog box, providing an option to relocate your saved report to the shared team drive.

Option 2: Navigate to your Dashboard and access the 'My reports' segment. Here, a list of all your reports will be displayed. Adjacent to each report, you'll notice an icon with three dots. Clicking on this will expand a menu with various functions. Selecting 'Move to' from this menu will prompt a dialog showing all the drives you can transfer your report to.

Understanding the Affinity Report

What is "affinity" in the context of market research?

"Affinity" refers to the natural relationship or correlation between two variables, often representing the inclination or preference of a specific consumer segment towards a particular product, service, or brand. It measures how closely aligned a consumer group's interests or behaviors are to a certain brand or media content.

How does a Market Researcher use the Affinity Report?

For a Market Researcher, an Affinity Report is a tool that measures the relationship between two variables, such as a brand and a consumer segment. It provides insights into potential target markets, consumer behavior, and purchasing tendencies, aiding in the development of tailored marketing strategies.

Why is the Affinity Report valuable for Media Owners?

Media Owners use the Affinity Report to determine which brands or products resonate most with their audience. This understanding helps them target advertising sales efforts more effectively, ensuring that ad placements result in enhanced engagement and promising better returns for advertisers.

What importance does the Affinity Report hold for an Agency?

For agencies, the Affinity Report is a key tool in media planning and buying decisions. It provides insights into which media platforms or channels will most likely engage with a certain brand or product, ensuring campaigns are placed where they can reach the most receptive audiences, optimizing both effectiveness and ROI for clients.

How can a Consumer Insights Manager benefit from the Affinity Report?

A Consumer Insights Manager leverages the Affinity Report to delve deeper into the relationships between consumers and specific brands or products. The insights guide product development, marketing positioning, and strategic campaigns, aligning them with consumer preferences and thereby fostering a more robust brand-consumer relationship.

How can I produce an affinity report in Explore?

First, designate attributes like gender, income, and social grade in the columns. Then, position your affinity targets (the brands or entities you're aiming to assess compatibility with) in the rows. For instance, if your goal is to identify which media channels align closely with a specific bank's customers, list the bank's name along with different media channels in the rows. When you initiate the report creation, the platform will prompt you to pinpoint your primary target. Once you've done this, Explore will automatically rank the rows based on their Affinity Scores.

How is the affinity score calculated?

The Affinity Score on a given row is calculated as the sum of squared differences between the %Row values of the subject row and the specified base target.

Where do my Google files end up?

Upon your initial export of a report to Google Sheets or Google Slides, you'll be asked to grant Telmar Explore permission to store subsequent reports in your Google Drive. Post authorization, a fresh folder named 'Explore' will be established within your Google Drive, serving as the destination for all future exports.

How to customize the name of my report?

By default, all new reports are labeled as 'New crosstab'. You have two methods to rename them:

Option 1:
On the top left of your screen, click on the edit symbol adjacent to the report name. This will make the text box editable, allowing you to input your desired name.

Option 2:
On the top right corner of your screen, you'll see a 'save as' symbol. Clicking on it will bring forth a 'Save as' window, where you can not only assign a name to the report but also provide a brief description.

How to customize a column/ row name?

There are several ways you can adjust the titles of your rows and columns:

Option 1:
Leveraging the 'Title mode' in the sidebar:

Click the 'Menu' button on the right-hand side.
Choose 'Title mode'.
Opt for the 'Short title' to present only the specific item from your category.
Select 'Long title' if you wish to showcase the complete hierarchical path from the codebook.
Pick 'User title' to display names you've assigned to rows/columns in prior sessions.
Go for 'Data context' if you desire more granularity by highlighting specific codebook layers.
This method will only alter the titles of all rows and columns in your crosstab.

Option 2.
When you select a row or column, an 'Edit coding' symbol becomes visible. Clicking on this symbol will bring up the 'Visual code builder' window. Within this window, clear the text in the 'Coding statement title' field and input your desired title. This method will only alter the title of the row or column you've selected.

How to perform Trend Analysis

Trend analysis in Explore is a powerful feature that allows users to study changes in market research data over different waves or periods. By comparing and analyzing data from different points in time, users can identify patterns, shifts, and long-term changes in consumer behavior, preferences, and market dynamics.

Why is trend analysis important for market research and analysis?

Trend analysis provides a deeper understanding of market changes, allowing stakeholders to:

Predict future market behavior based on past patterns.
Adjust strategies proactively in response to evolving consumer preferences.
Spot potential challenges or opportunities early.
Differentiate between short-term fluctuations and long-term shifts.

How does trend analysis benefit media owners?

For media owners, trend analysis:

Enables data-driven decision-making regarding content creation and distribution.
Helps in identifying viewer/listener/readership shifts and adjusting content strategies accordingly.
Assists in understanding the evolving preferences and behaviors of target audiences.

Why should media buyers and agencies utilize trend analysis?

Media buyers and agencies can:

Optimize ad placements based on evolving audience behaviors.
Forecast the effectiveness of ad campaigns.
Identify rising platforms or channels.
Tailor messaging based on the changing sentiments and preferences of the target audience.

How does trend analysis in Explore differ from simple crosstab analysis?

While crosstabs provide a snapshot of data interactions at a given point in time, trend analysis focuses on changes across multiple timeframes. By using trend analysis, one can visualize and understand how data points and relationships evolve, whereas crosstabs offer a more static view.

Can I compare data from different syndicated datasets in a trend analysis?

Trend analysis in Explore is optimized for comparing different waves of the same syndicated dataset. This ensures that the data is consistent and comparable across timeframes.

How does trend analysis help in understanding consumer insights?

Trend analysis provides a dynamic view of consumer behaviors, sentiments, and preferences. By tracking these over time, businesses can:

Understand emerging consumer needs.
Recognize shifts in brand perceptions.
Determine the effectiveness of marketing and branding strategies over time.

Is it complex to perform trend analysis using Explore?

Explore is designed with user-friendliness in mind. While trend analysis is a powerful tool, we've ensured that it's intuitive for users of all levels. With a few clicks, you can select different waves of syndicated datasets and view comprehensive trend analyses.

What kind of visualizations can I expect with trend analysis in Explore?

Explore provides a variety of visualizations including line graphs, bar charts, and heat maps, among others. These visuals aid in understanding data trends, making the insights more digestible and actionable.

How do I start performing a trend analysis in Explore?

This is how you start performing trend analysis in explore:

Step 1: Select Your Data Sets

Use the drop-down menus at the top of the survey selection screen to filter and find the data sets relevant to your analysis.
Alternatively, once you have selected a survey for usage, the available survey list will automatically be filtered down to other compatible trending surveys.
After selecting each of the desired surveys, use the arrow in the middle of the screen to move them to your active analysis list, then click "Use selected surveys".
If you have already created a crosstab with one survey, you can use the ’Add trendable surveys' button at the top of your codebook to add more surveys to your analysis.
Each of the selected surveys will be highlighted in a different color in your crosstab
Should you want to exclude a survey from your analysis, you can open the ‘Manage surveys’ dropdown at the top of your codebook and click on the ‘Hide survey’ icon
To effectively compare two cycles from your chosen data sets, you can execute specific computations by clicking on the ‘Add trending calculations’ button at the top of your codebook. The outcomes will be showcased in a new column appended to your crosstab.

Step 2: Define Your Columns

Navigate to the coding screen.
From the available categories, choose the demographic or parameter you wish to analyze.
Highlight the desired categories and drag them to the "Add Column" button. Depending on your needs, you can use the 'OR' function to group categories.
You can further refine the column by adding additional criteria, dragging them onto your existing column.

Step 3: Define Your Rows

Use the filter function to narrow down to the category of interest.
Highlight the items you wish to analyze and drag them to the "Add Row" button.

Step 4: Customize Your Column/ Row Heading (Optional)

Click on the desired column/ row and amend its title to make the data presentation clearer.

Step 5: Visualize the Data (Optional)

Click on the chart tab at the top of your screen. By default, a line chart will appear, which is generally recommended for trend analysis.
You can make any changes to the charts by using either the ‘Global chart settings’ at the top of the chats screen if you wish to apply the changes to all your charts. Alternatively, you can customize individual charts by using the ‘Settings’ button at the top of each individual chart.
The theme and color scheme of the chart can be customized as well.

Step 6: Export the Analysis (Optional)

If you need to present or share your findings, you can directly export your analysis to presentation formats such as PowerPoint.
TIP: For further customization in PowerPoint, right-click on the chart and select "Edit Data". This will allow you to make changes in an Excel format, which will automatically update the PowerPoint chart.

This guide provides a step-by-step approach to creating a trend analysis in Explore. The process can be adjusted based on specific analysis needs and the available data sets.

How do I find my reports?

Once the report is tagged, you can find the report either

By searching for the report name in the search bar on the top
By navigating to it using the drive selector on the right.

How to work with custom audiences

Custom audiences allow you to save codings made on rows and columns to be reused later. Custom audiences let you build out an audience or target and quickly reuse that across other surveys from the same provider.

How do I save a custom audience

Follow these steps to save a custom audience:

Create a crosstab
Add in and code the rows and columns you want
Click the ‘Menu’ button in the top right and select ‘Save audiences’
Assign a filename to the audience
Choose the columns and rows you would like to save to the audience
Choose the drive (either to your drive or a shared drive)
Save the audience

How do I access my saved audiences?

Saved audiences can be accessed from the codebook in the bolded Custom Audiences category. This category will always be the first category in a codebook, regardless of if you have a saved audience in there or not.

Can I edit a saved audience?

By right clicking on a saved audience you can share, delete or rename the saved audience

Working with N-Tiles

N-tiles (also called quantiles) are a statistical concept used to divide a dataset or a distribution into equal intervals or sections. The idea is to partition the data into 'n' equal-sized groups, where 'n' represents the number of partitions you want to create. These partitions are created based on the data's values, usually in ascending order.

How do I create N-Tiles

There are two ways to create N-Tiles.

Method 1:

Click on Crosstab settings button on the top left of the crosstab table, and then click on N-Tiles
The N-Tiles popup appears
Choose the target type (table, rows or columns), targets, where you want to apply the target to (Tables, columns, rows), the N-Tiles type (Tertiles, Quartiles, Quintiles, Deciles, Percentiles) and click ‘Generate’
Then the N-tiles are generated

Method 2:

Navigate to the Coding Grid and click on the Coding grid Menu button
Click on N-Tiles and choose the relevant settings in the N-Tiles pop-up and the N-Tiles will be generated

What do the N-Tiles types mean?

There are different types of n-tiles, they are listed below along with their definitions:

Tertile:
A 3-way split n-tile, also known as a tertile or a tercile, divides a dataset or distribution into three equal parts (n=3). Tertiles are calculated by ordering the data values in ascending order and then finding the two points that split the dataset into three equal intervals.
Quartiles:
These divide the data into four equal parts (n=4). They include the lower quartile (Q1), which is the 25th percentile; the median (Q2), which is the 50th percentile; and the upper quartile (Q3), which is the 75th percentile.
Quintiles:
These divide the data into five equal parts (n=5), with each quintile representing 20% of the data.
Deciles:
These divide the data into ten equal parts (n=10), with each decile representing 10% of the data.
Percentiles:
These divide the data into 100 equal parts (n=100), with each percentile representing 1% of the data.

Why do I not see a survey in my list of available surveys?

Why do I not see a specific survey?

If you do not see a survey it might be because you do not have the relevant permissions to view the survey or the survey is not loaded yet. Please reach out to your customer success representative for more information.