Maven Challenge – Net Promoter Score and Likert Analysis – Airline Survey

Another Maven challenge, another blog post!

However, rather than a capturing a summary of a particular strategy, this one is on the development of a set of visuals that are well suited to represent the results of survey data and find the categories with the largest proportions of positive and negative attitudes.

The Challenge

The overall challenge was to analyse a survey response data set from around 130,000 passengers who have travelled on Maven Airlines to determine what areas were contributing to the satisfaction rate falling below 50%, and what data-backed steps could be taken to make improvements.

The Data

The data contained various personal information on each passenger, as well as details about their journeys and class of travel.

In addition there were a number of questions which ask to rate particular services on a scale of 1 to 5, with 1 being the worst service and 5 the best (see below extract from the data dictionary).

This type of survey query is typically known as a Likert Scale survey question, named after Renis Likert, a famous social scientist. Here a 5 number scale has been used, but you may also encounter 7 or 10 number scales. You may also come across non-numeric versions (e.g. “very likely” to “not likely at all”).

These types of scales allow for a little more nuance in the sentiment surrounding a survey response where the answer may not be a binary yes/no.

Analysis of Likert Scales

I had read about Likert scales and survey data when working on the remote working challenge. Although I didn’t fully use it there, I was now keen to learn more about it and look at ways it could be visually represented.

One method I had previously come across in my endless trawling of Youtube tutorials and browsing other challenges such as Workout Wednesday and Makeover Monday, was to show the results in a horizontal bar chart, and show divergence between positive and negative results.

Below are a few different ways of presenting data using the scales. This ranges from a simple stacked bar chart, to basing it around a neutral zero value, to extracting neutral values, and all the way to a full deconstruction.

Each method has it’s own particular advantages depending on what you are trying to present, but they are effective at showing the relative distribution of a spectrum of responses.

Net Promoter Score

A child of the Likert scale is the Net Promoter Score (NPS). Strictly speaking, this is used on survey results with scales of 1-10 (see below), and it is calculated by subtracting the % of promoter scores (9-10) from the % of detractor scores (0-6).

If you return a positive result, then people are more enthusiastic about that aspect of your brand or company, whereas if it is negative, that could be damaging to your company.

What I wanted to show

Taking this into account, I wished to show a version of the bar chart type analysis of the Likert data shown above, along with the corresponding NPS for each category – basically a tidied up version of the below.

However, in this case we only have a scale of 1-5. Therefore I opted to re-scale the analysis based on the following:

  • 1-2 = Negative
  • 3 = Neutral
  • 4-5 = Positive

Again, strictly if I strictly followed the NPS chart it probably should be 1-3 as negative, 4 as neutral and 5 as positive, but I would maybe argue we are looking for “satisfaction” rather than “evangelist promoters”, so seeing 4-5 as positive is good enough for me.

Research on building the Likert bar chart and NPS

I did some “research” ( aka Google and YouTube searching), on how to put together these charts, specifically in Power BI. Although I didn’t search for hours, I didn’t manage to find anything useful as a guide.

Step into the rescue, two invaluable sources of information:

  1. The Big Book of Dashboards (Wexler, Shaffer and Cotgreave)
  2. Andy Kriebel’s Tableau YouTube video

The book provided some great advice on visually what does and doesn’t work in these types of charts, and the video provided an overview of the steps and measures to allow me to build the charts.

Apply the techniques to Power BI

Now it was a matter of translating the textbook advice and Tableau tutorial over to Power BI desktop. This was done in several steps.

1. Power Query Work

First I imported the dataset into Power Query, then created a duplicate of the table for this particular work.

Next, I selected the column headers for all the columns containing the Likert categories. After that, I navigated to the Transform ribbon at the top, then selected “Unpivot Columns”, and then “Unpivot Selected columns.

This resulted in the below layout. There is a column called “Attributes”, which relates to all the Likert categories, and another column called “Values” which are the corresponding Likert scale numbers – effectively all the categories and their corresponding values are in two columns.

While I was in Power Query, I created a “sentiment score”, which was -1 for “neutral or dissatisfied”, and +1 for “satisfied”. I never used this in my final report, but was using it in some experimenting small multiple visuals shown below, effectively showing the 1-5 rating in each category, but mirroring the satisfied and unsatisfied passengers.

I found it showed some interesting patterns, but it took quite a close inspection to glean those patterns, therefore I discarded it for my final report, in favour of the stacked bar chart.

2. Measures

Now that the rejigging of the data was done in Power Query, I moved on to start creating the measures that would help me build the below draft visual. This included the:

  • Count the positive scores (4-5)
  • Count the negative scores (1-2)
  • Count the neutral scores (3) – this was done in two parts as they straddled the zero line
  • % positive
  • % negative
  • % neutral
  • NPS

Counts

The fairly simple count DAX measures were as follows:

Note that the below calculation is multiplied by -1 so that is will appear in the negative side of the y-axis.

Next, as mentioned, for the neutral scores, these straddle the zero line, therefore the calculation is split into two, one for the positive side, and one for the negative side.

All Selected Attributes

In order to transform these count measures into % values, I need a denominator. Typically, you could use an ALL() DAX function so that you are measuring as a portion of the total of a given column, but as I have created an unpivoted table with multiple categories, it requires something a little more refined.

Instead of ALL(), I opted for ALLEXCEPT() with several columns referenced – see below:

Percentage Values

Now this was done, all that was left to do was to create a few simple DIVIDE() functions to finalise the percentage values – simply dividing each count by the All Selected Attribute:

again, remember we need two values for the neutral (positive and negative).

NPS

Finally, after all those measures were created, I can now come to finalising the NPS. If we relook at the below calculation, I recreated a version using my measures. (Note that I add my positive to negative scores, as my negative are actually negative numbers)

Rank NPS – (Bonus Calculation)

I almost forgot, there is a bonus calculation that I needed to order my Likert chart by the NPS. I used the RANX function to rank the attributes by the NPS is descending order, as shown below. I will show you a little trick on how to apply this in the next few paragraphs.

3. Building the Visuals

Ok, now onto the applying all those measures into something visual.

First I select the stacked bar chart. Then I add Attribute to the X-axis. Following that, I add the 4 % measures in the order shown below. It looks a little strange, but the add the negative value first, going from the least negative to most negative, then add the positive values after that.

Finally, drop the Rank NPS in the tool tips.

By adding the Rank NPS to the tooltip, it allows you to select it as a sorting option for your chart. To do this, select the ellipsis (…) at the top right of the chart, then select the sort axis as shown below. This will allow you to rank both your Likert chart and NPS chart by the NPS scorings, and this allows you to have common y-axis categories across both charts.

The NPS chart is simply put together, adding the Attribute in the Y-axis and NPS Grouped score in the X-axis, as shown below.

Once the charts have been built, it is a matter of applying your preferred colour palettes, fonts and formats. in the formatting pane.

4. Final product

Once I was happy with my general visual content, I brought them into my final dashboard, and applied final touches to axis labels, legends and covering titles shown below.

I then applied this set of visuals twice; one time for analysing business class passengers, and another for analysing economy and economy plus passengers. This formed my most detailed analysis in the report, allowing me to drill down in detail from those initial large percentage numbers to finding the issues that were affecting passenger satisfaction.

Let me know in the comments below, or feedback on LinkedIn if this kind of content was of use, or if there were other ways you may have gone about executing this.

Thanks for reading.

Maven Challenge – Remote Work – Strategy Execution

Before we start….

There was quite a bit to this challenge, so I am covering it in one blog and one video. This one will look at the strategy and execution of the planned steps I laid out in a LinkedIn post and in the below photo shot; the separate video looks at the technical aspects of cleaning and manipulating the data, as well as assembling the visuals.

For anyone who has stumbled across this blog accidently, and is not familiar with the challenge, details can be found here.

So let’s begin!

As well as Maven providing the raw data from the remote work survey and some background on the contents, the below is what I took as the brief for the challenge, with the crux purposely highlighted in bold by the Maven team.

For this challenge, your task is to assess the productivity and morale implications of working remotely and outline an ideal policy for the post-pandemic future, presented in the form of a single-page report or dashboard.

Maven Remote Challenge Blog

I used this as the basis for my overall plan for the challenge, which I quickly drafted up using the old school pen and paper method.

Now that was done, it was time to put the plan into motion….

Part A – Considering Outcomes and Goals

When reviewing the brief, I asked myself a few questions that would dictate my outcomes and goals.

  • Why am I actually creating this report / dash?
  • Who is it for?
  • What decision needs to be made as a result of it?
  • What can I do by way of design to facilitate that decision process?

Apart from wanting to compete in the Maven challenge, I am creating this report to summarize a large amount of survey data and analyze how remote work impacts productivity and morale among workers.

In terms of “Who?” and “What decision?”, I considered my key stakeholder to be the senior management at a generic company, and anticipated that they would require a high level assessment with a summary of the positive and negative affects of remote working on mainly productivity, but also morale, and how this would shape how employees felt about remote work. This would in turn help the company decide on what plans they would need to enact in a post-COVID world.

After mulling over these questions and answers, it was fairly straightforward in my mind that my overall outcomes would include:

  • Outlining the basis for an ideal future remote working policy for a company
  • Consider any implications (positive or negative) on worker morale and wellbeing
  • Measure how productivity was impacted during remote working
  • Summarize the above in a high level explanatory report

These aspects, as well as the fact the requirement was for a single page static report/dash, meant that firstly, I would be analyzing at a high level rather than a granular level. The only reason to go to a granular level would be to analyze a specific industry or demographic, which I did not consider part of my brief (rightly or wrongly).

Secondly, I would also be looking to produce and explanatory set of visuals rather than an exploratory set.

What’s the difference?

Exploratory vs Explanatory

The storytelling with data blog will define this more eloquently than me, but basically an exploratory report would allow the user to manipulate data through filters and slicers and search for their own answers to questions they may have. For explanatory reports, someone has already done the “exploration” on the data, and is presenting what are hopefully the key points with supporting data and visuals.

Part B – Metrics and Comparisons

Now a general approach has been defined, I looked at potential comparisons and metrics that would be used to determine company policy. I needed to keep in mind that if a company is going to base their policy on these, they need to be robust enough to allow a decision to be made.

You may think it’s strange as I haven’t mentioned looking at the data yet, so how would I know what metrics or comparisons I will be able to make?

While this is true, I believe it is good to have an idea of what the most appropriate metrics could be in order to fulfil your brief, even before you look at the data in any kind of detail.

Productivity

I know I have two data sets, one for 2020 and one for 2021. The obvious metrics would be to try and measure productivity in each separate year, and then do a year-over-year (YoY) comparison. If data is also available, I could also look at a further comparison with “normal working” productivity. This will tell me whether remote working has had a positive or negative effect and whether that was sustained between 2020 and 2021.

Morale

Similar to productivity, I would look at changes in any issues that may affect morale or working conditions between “normal work” and the periods of remote work in 2020 and 2021. Again, if possible, I would like to see how the stats change over time.

General Opinion

I am guessing that the survey will ask people’s opinions of working remotely, and whether they are generally in favour of it and whether they would like to retain it as a benefit long term. If so, I could measure any change over time, or the ratio of people in favour versus those not in favour, and this could also feed into the overall future policy to be developed.

Part C – Reviewing the Data

With ultimate goals and an idea of the type of metrics I feel would fulfil the brief, it was time to look at the data itself.

This was a little dauting at first. It wasn’t the fact that each survey had around 1,500 respondents; rather it was that the 2020 survey had 73 questions, and the 2021 survey had 109 questions. Add to that, many of the questions were quite lengthy, and were the headings of each column. This made it quite difficult to know exactly what you were looking at!

Quick solution

My quick solution for reviewing? Copy and transpose the head row from each file into a new table.  This makes it much easier to read, and easier to see how many of the questions are actually grouped together.

It also allowed me to add a few columns to each table aligned to the metrics. As I reviewed each years’ questions I could tick whether I thought particular questions may be related to assessing morale, productivity or an overall policy. Having this list also came in very handy as a reference when the data was eventually loaded into Power BI.

As an example of the transposed table, below is a sample of a few questions from the 2020 and 2021 files that would touch on productivity.

2020 survey – productivity
2021 survey – productivity

Also, to assist anyone that may try out the challenge, I have added a copy of a blank file that could be used to conduct this exercise.

Once I reviewed and listed out all the questions I thought were applicable to my goals, I was happy to move onto the next step and start some provisional review and analysis of the survey results.

Part D – Pull in Data and Develop Plan

There were two distinct csv files for the surveys, and I loaded them into Power Query and Power BI. The respondents in each survey are not linked, therefore there was no immediate need to link the files via a model.

On reviewing the questions and answers for each data set, I narrowed my focus to a limited set that I could build a plan around. This allowed me to create a storyboard and then further detail each aspect – all of which is laid out below.

Storyboard

I usually try to sketch together a outline plan or storyboard when I have reviewed the data and have a general idea of what I want to show and how I want it to structure and flow. There are usually a few changes along the way, as you will see in the final product. However, you will note here that I wanted to show a main headline to capture productivity, morale and the policy, and then expand on each aspect in separate sections.

Productivity – Self Assessment

I wanted to calculate the productivity ratings in 2020 and 2021 and compare. Both surveys had a similar question to ask how the employee rated their own productivity when compared to working in the workplace. As such, I could use it as an effective comparison.

The answers were categorised in a large number of % values displaying degrees of more, less or the same productivity. I decided to combine and reduce the number of categories to 5, as shown below in my sandbox draft version. I also wanted to show the % change for each category to emphasize the increase in productivity between the two years, also shown below. Text could be further added to further summarize and explain the point.

Productivity – Management Assessment

As the above were self-assessments, I wanted to develop the next level of narrative and to see if managers saw the same impacts on productivity. I was able to find a survey question directed to managers, to see how they rated the productivity of their own employees.

Productivity – Occupation

To complete my narrative on productivity, I wanted to go slightly more granular. The results so far were showing generally improved productivity, but for balance I wanted to see if there were particular occupations that were more likely to experience lower productivity. This could allow management to then potentially request further analysis if their companies had employees that had occupations with low rates of productivity.

Morale – Barriers

There were no questions in the surveys which explicitly dealt with morale; however, I noted that in 2021 there were a set of questions related to potential barriers to remote working, and whether people had improved, worsened or largely the same experiences for each barrier when compared to normal working. These barriers included issues such as:

  • motivation
  • isolation
  • health
  • collaboration
  • poor management

I took these as being related to morale. I consolidated these queries using an unpivot technique in Power Query, and again consolidated the ratings to a simple three ratings of, “worsened”, “same/no barrier” or “improved”. This allowed me to quickly see how each issue affected employees in 2021.

To supplement this, I noticed there were additional related standalone questions related to isolation, health and wellbeing. My plan was to bring these all together in an extended visual with a supporting narrative (see below draft result). It was also an opportunity to try out Gestalt’s principle of connection by boxing around some of the bar chart graphics in an attempt to connect them.

Sandbox sample graphs for morale

Morale – Working Times

From looking at the data and seeing that people were more active and feeling better, I thought then next level would be to examine the typical working day to see what impact remote work was having on people’s general activities.

There were a set of questions in 2021 related to hours people were assigning to daily tasks. I note there were similar questions in 2020, but there were significant errors in the arithmetic, which meant I could not make an equivalent comparison, and therefore discarded them.

I again performed an unpivot of columns in Power Query to consolidate the data, and found an average significant drop in time required for commuting (expected), and an increase in time for personal and family time, which you could potentially relate to increased wellbeing and morale. This was in spite of people actually working slightly longer workday on average.

Policy – Employee Expectations

Finally, I thought the policy section should provide the story arc to bring the productivity and morale factors together. I came across a question that I thought could knit it together – This was how much time employees wished to work remotely in the future.

Again, the answers were categorised into % values in intervals of 10, with some text answers also. I decided to consolidate these into day values to give a more simplified and easier to read view. This would show management that there was significant appetite from employees to work remotely, but that there was also a large number of people who wanted very minimal or no remote work. I highlighted this in red to purposely draw attention, and show there was a potential need for flexibility.

Policy – Retention and Attraction

Finally, and to again supplement the above with closely related data, there were questions to managers as to whether they believed offering remote working would improve employee retention and also attract new talent. Instead of using charts or graphs for these types of supplements, I decided to just use aggregated numbers and supporting text.

Stitch it all together

Once I had completed all my visuals in separate sandbox tabs, I copied them into a combined report/dash, which resembled the sketch I had shown earlier as my initial storyboard.

My initial draft was complete, and I added some summary headings to each section to highlight the key take-aways from each set of charts.

I was reasonably happy with the draft, but I wasn’t quite finished just yet.

Part E – Review Outcomes and Goals

I felt that individual components of my goals had been achieved by each of the visuals I had included, as well as how they were grouped.

However, as part of my review, I took a tip from storytelling with data and Maven’s thinking like an analyst course, and used the opportunity to get feedback from a member of the Maven community, and also one of my subscribers to this blog (Marjolein Opsteegh).

After working with the data on and off for a few days, the intention was to step back and get a set of fresh eyes to briefly study the collection of visuals and see if it was relatively understood, and whether it flowed naturally and captured the brief.

Marjolein was generous enough to take the time to give some constructive feedback and criticism, which is a real learning process I actually enjoy. There were some general layout and formatting tips, but the crucial comment was to bring the overall outcomes and policy to the fore, and put them top and centre of the dash, rather than at the bottom which I had in my draft.

Part F – Fine Tune and Submit

After the review, I rearranged the running order of the dashboard, which made the summary and overall policy the first thing you see. I then followed it by productivity and morale, with some slight formatting on the headers.

Overall, I was quite pleased with the result, and was happy to apply some of the techniques I had been reading about in Cole Nussbaumer Knaflic’s storytelling with data book, a highly recommended read.

One change I might like to make after having the chance to look at it for a few days however, might be to increase the overall size of the visual, and provide a little more white space to give some of the visuals a little more breathing space from each other.

[UPDATE: As noted at the intro – I opted to support this blog with a walkthrough video of creating each of the visuals, especially those that used the unpivot and consolidation functions in Power Query] – for further work on Maven Challenges, stay tuned on LinkedIn, follow my twitter account which is notified when I write a blog, or alternatively just follow this blog.

Any feedback or queries, please drop them in the comments below – Thanks!

Maven Challenge – Superbowl Commercial – My Plan and Strategy

After submitting my Power BI report on LinkedIn as part of the Maven Super Bowl Challenge, I was messaged by a few people asking if I could put together an article or video blog on how I created it. So, here I am!!

Rather than jumping onto the snapshots of the Power BI platform and going through the process of putting the visuals together, I thought I would take a few steps back, and go through the plan and strategy I now go through before attempting these types of challenges, as without that you can often create a confused message. I pick up new lessons with every challenge, and find it an excellent form of self-development.

Final Submission

My Key Steps in Planning and Strategizing

For this particular challenge, I broke my process down into the following steps, but they are equally applicable to other work I do:

  1. Read the Brief
  2. Consider the End User
  3. Summarise Scope and Objectives
  4. Review the Raw Data
  5. Consider available metrics
  6. Develop a story or flow
  7. Sketch your layout and structure
  8. Execute, Review and Polish

Step 1 – Read the Brief

The challenge noted the following:

We’ve just added a brand new data set to the Data Playground, containing data from Super Bowl commercials for 10 popular brands this century

and

For this challenge, you’ll be assuming the role of Marketing Analyst at Maven Motors, an up-and-coming US car manufacturer looking to make a splash in the market. They have approved the budget to run a TV spot during the 2022 Super Bowl, but need you to analyze historical data to help guide the creative direction.

Your task is to recommend a data-driven strategy for the Maven Motors Super Bowl spot, and present it in the form of a single page report or dashboard.

They are asking you for two things, namely to “analyze the historical data to help guide the creative direction…” and to “….recommend a data-driven strategy for the Maven Motors Super Bowl spot.”

So, we need to keep these two points in the forefront of our mind as we progress.

Step 2 – Consider the End User

Perhaps the most important step and question to ask yourself – who am I creating this report for and how do I want them to feel when they look at it? This will dictate the style, format and presentation.

For example, the Harry Potter challenge or the Jordan vs LeBron challenge would encourage the use of colours, graphics and specialised visuals to engage the general viewer and add an element of fun and excitement.

My Harry Potter Magic Submission

However, as this challenge is aimed at the decision makers within a large corporation, the style and format should be very different. For this, if I imagined being in their shoes, I would like to see the following type of summary report or dashboard:

  • Structured – easy to read and flows well
  • Professional – the product should align with the standards and values of the company
  • Related to the brief – no need for superfluous information that distracts or wastes time, something I don’t have a lot of!
  • Comprehensive but to the point – only pick and present the key findings
  • Able to invite a decision – use the data and recommendations to enable an easy decision to be made

Step 3 – Summarise Scope and Objectives

Following Project Management 101 techniques, I then documented my scope and objectives. This may seem like a duplication of the above, but is helpful as a reference to always come back to. For this reason, I usually write this out and have it sitting to the side to glance at as I work on the final solution:

Scope Notes

In case you can’t read my writing:

Scope

  • Provide a 1 Page Report
  • Analyze Historical Data
  • Provide Recommendations for a data-driven strategy

Objectives

  • Enable board members, who are “time-poor” people, to have sufficiently succinct information in order to make a key decision on the direction of the marketing strategy
  • Be clear, concise and professional
  • Enable board members to be confident in your analysis (i.e. structure, methodology, analysis all appear credible, therefore we can put a measure of trust in the recommendations).

Step 4 – Review the Raw Data

It’s only now that I actually come to open the data files and look to see what is in there.

I found that, having performed the above steps, when I started looking at the data I was immediately considering whether this particular type of information or column was relevant to my brief or objectives.

I will try and briefly go through the my main thoughts when reviewing the data (below snapshot for 2000 and 2001), and how it related back to the scope, and what I would look at for more detailed analysis.

I would note at this point that this usually takes a few days, as I often like to let it ferment a little while I sip on a flat white or two.

2000-2001 data snapshot

Data Not Considered Relevant

Here’s point number oneNOT ALL DATA WILL BE RELEVANT.

Therefore, it was important to put aside any items that are not particularly useful in relation to the scope of works. Looking at the above table, these are highlighted in light blue.

Individual Brands

You may argue that the brand is important, but I would argue otherwise.

What’s the difference between Coke and Pepsi, or Bud and Bud Light in the context of this data? I don’t think this data set is going to tell you especially with the individual sample sizes.

What may give insight is the type of brand (food, cars, alcohol, etc. which I will address later). So for me, I took the decision to ignore individual brands.

The links themselves have no data analysis value for me here. Their only use may be to look at the actual ads, or provide a link in your dashboard or report. Therefore, I ignored these.

Estimated Cost

You may think- Wowwww! How can you ignore cost??? Well, I am not totally ignoring cost, but the total cost provides little insight, as there is the variable of ad length to consider.

The longer the ad, the higher the cost, right?

So, I checked if there is a correlation with a quick cost/time calculation. Guess what? The $ cost per second is constant for each year. Therefore I ignored the total cost, and focussed on the $ cost per second instead.

YouTube Likes

Again, you may wonder why I might be interested in YouTube views, but not likes? Well, this is my personal insight. I watch a lot of YouTube, and I enjoy a lot of what I watch, but I rarely feel the need to click “like” – I would be more likely to subscribe. I heard that YouTube itself does not place too much importance on “likes” these days too, they are more likely to track views, duration, click through, etc.

Additionally, the number of views is not underestimating the number of people who have engaged with the video (even if they only watched for a few seconds), but the number of likes will actually underestimate the number of people who actually like the video, as it is not compulsory to click the like button if you liked it! Again, I knocked that one off my analysis list.

TV Viewers

This may or may not be another surprise to some. If you take the time to look at the TV views for any given year, you will see they are pretty much constant – see below ( I recall there was one outlier). You can read into this that the money you pay to advertise during the Super Bowl is giving you the direct access to everyone that is watching.

As it is constant, the duration, brand, type, etc. are not directly impacting the number of viewers. I would only take this into account if you had a live feed of figures while the ads were being watched.

So, you guessed it, I kicked TV Viewers to the kerb!

TV Viewers – Year 2000

Data Considered Relevant

OK, ok….. so I have pretty much now ignored a large part of the dataset. So what exactly am I actually interested in, and what information relevant to the scope do I think I can get out of it? Well, let’s see now….

Year

An easy one – we want to potentially see how our variables change over time, so we will definitely be keeping Year

Brand Category

With ten brands, its a little difficult to compare. However if we can reduce this to a smaller set of categories, it may be of benefit. In the back of my mind, I was thinking that as Maven Motors is a motor company, it might be useful to look at the performance of Kia, Hyundai and Toyota as a group, rather than as individual companies as it effectively provides a larger single sample size.

Following that, it made sense to group the others into Alcohol, Snacks and Drinks, Sports and Services, and see if there were any trends among those categories.

By the way, I created a YouTube video showing you how to create those categories.

Video Characteristics

It was difficult to glean any real insight into the 7 categories without more detailed analysis. Further, it is part of the brief to influence the “creative direction” of the advertisement. Therefore, it was a no-brainer to retain this data.

Again, I created YouTube video actually showing you how to “unpivot” these characteristics for more effective analysis.

Length

Further, I saw that there were varying lengths of ads, and there may be a potential trend over time which may allow me to impart some recommendation on what the current trend is – so that was kept in too.

YouTube Views

I retained this as one of my key data points. As Maven Motors were already gaining access to the TV network through having a large marketing budget, I believed that a true test of whether an ad was “creatively successful” (viral), was whether people then took the time to seek it out on YouTube to watch again, or felt the need to share it with friends, family and their social networks.

$/s

As discussed earlier, I saw that the $/s for advertisements was constant for each year, which makes sense. I kept and used this data point as I would be able to see the trend in cost over time and potentially give further assurance to the board on likely final expenditure (even though budgets were already approved).

Number of Ads per Brand per Year

One further data point I noticed was that some brands were having more than one ad per year. The below example shows Budweiser using 13 ads over 4 Super Bowl events. Again, this is something I thought would be interesting to look at further to see if there were additional recommendations I could make on the potential number of ads to run.

Budweiser Ads – 2000 – 2003

Step5 – Consider Available Metrics

Considering all of the above, I came up with the following list of data points or metrics I wished to examine and present and make data-driven recommendations on:

The number of ads in a campaign year – What was the average number of ads per year per brand category.

Ad Duration – what is considered a standard ad length, and what is the trend over time telling us we should do this year.

$/second – what is the historical trend in ad cost, and see if we can forecast the range for the coming year.

Average YouTube Views vs Ad Characteristics – analyze the each characteristic and see which is more likely to garner additional views.

Step 6 and 7 – Develop a Story or Flow and Sketch Out

The next steps were to capture the entire process above is a single structured sheet that would form my final report. My story needed to:

  • Set the scene (provide context),
  • Lay out my methodology,
  • Establish the scope of the data analysed
  • Present the key data and insights
  • Give a summary recommendation

Again, I find it useful to sketch this out on a piece of paper (see below), along with even some potential visuals I have in my head.

I note that for this report I included a significant amount of text. This was on purpose as one of my key objectives was to enable the the board members to have confidence in my analysis. I felt that if I laid out the context, methodology and recommendations in clear concise language, that is then further backed up by data, this will provide that level of confidence required in such situations.

Report Structure Sketch

Step 8 – Execute, Review and Polish

Once the above it set, it is now a matter of prepping and cleaning any data (categorization, etc.), establishing the measures, then building the visuals into the structure I had envisaged.

One thing I like about Tableau is creating visuals on separate pages, and then constructing a final dashboard. I sometimes replicate that feel in Power BI, where I will trial and build individual visuals on separate tabs before bringing them in. Below are a few samples.

Data Scope and Ad Length Visuals

This gets the concepts down, so I know the type of data I want to show and in what general format.

Options for Ad Characteristics – Phase 1

I will experiment with how things are presented and how well the visual can communicate the point I want to make.

Options for Add Characteristics – Phase 2

Once I am comfortable with the concept, I will look to “polish” it. What does this mean to me?

Polishing Steps

As I said, once I am happy with the story, flow, structure and type of visuals I am using, I will move on to “polish”, which for me usually involves the following:

  • Selecting an appropriate colour theme/palette and applying consistent colours for headings, text and visuals. There are lots of websites that help generate themes and palettes.
  • Apply consistent fonts and font sizes
  • Remove superfluous information:
    • axis titles (if already obvious)
    • axis values (if using data labels)
    • repeated legends, which can be done with use of consistent colouring
    • gridlines, axis lines, etc. unless really needed
    • give concise visual titles
    • any other item that is not conveying
  • Align titles, text boxes, visuals (the eagle eyed will note a few of my visuals are off centre!)
  • Provide consistent spacings

Applying these steps allows me to move from the above visuals to the below final versions, which I believe met my goal of giving clean, concise and professional looking visuals.

If you have made it to the end of this, congratulations!! I apologise that my blogs are maybe not as short and to the point as my dashboard, but I hope this provided some insight into the process I took in developing my challenge submission, and maybe provided you with some tips and tricks for the next one.

Let me know what you think – would you change anything, or do you have a good alternative approach? I would be interested to hear.

Also, If you would like to see a video version of this and maybe some exploration of actually creating the above visuals, leave a comment below, or drop me a message on LinkedIn.

Social Media Dashboard – Power BI

Overview

I have created a walkthrough so that you can use just 6 DAX formula to develop data extracted from your social media accounts into a metrics dashboard like that below, whether that be LinkedIn, Twitter, blog accounts or your own website.

Sample Fictional Data from LinkedIn Corporate Account
LinkedIn Metrics Dashboard developed in Power BI

A Youtube video of this walkthrough is linked here. Some of you may have come here after viewing it – thanks, and welcome! I will get down to what you came here for…

6 Key Metric DAX Formula

From the video, you will see that I used six key DAX expressions or formula again and again to create a comprehensive set of metrics to allow you to develop the dashboard. Here they are in order of development.

Totals

Total LinkedIn New Followers = 
SUM(LinkedIn_Data[LinkedIn New Followers])

You can use SUMX in lieu of SUM if you wish here, noting you will need to provide a table and expression in lieu of a column.

Year to Date (YTD)

LinkedIn New Followers YTD = 
TOTALYTD(
    [Total LinkedIn New Followers],
    LinkedIn_Data[Date]
)

Latest Month (MTD)

LinkedIn New Followers This Month = 
Calculate(
    [Total LinkedIn New Followers],
    LASTDATE(LinkedIn_Data[Date])
)

Previous Month

LinkedIn Followers Previous Month = 
Calculate(
    SUM(LinkedIn_Data[LinkedIn New Followers])
    ,PREVIOUSMONTH(LinkedIn_Data[Date]
    )
)

Month Over Month Difference


LinkedIn Followers Diff MoM = 
VAR CurrentS = Sum(LinkedIn_Data[LinkedIn New Followers])
VAR PreviousS = [LinkedIn Followers Previous Month]
VAR Result = CurrentS - PreviousS
Return
    Result

Month Over Month % Growth

LinkedIn Followers MoM Growth % = 
Divide(
    [LinkedIn Followers Diff MoM],
    [LinkedIn Followers Previous Month]
)

Bonus – Last Date

One simple bonus DAX formula that will extract the latest date in your date table. This is useful for title blocks and banners in you reports and dashs, as it will automatically update when you add your monthly data.

Latest Date = 
LASTDATE(
    LinkedIn_Data[Date]
)

Resulting Data

Once I have developed a set of calculations, I like to test them in a matrix to make sure they have the desired outcome. This is what I have done below. You can then be confident that the cards and visuals you create accurately reflect your data set.

Resulting DAX calculations with verification table showing outcomes

Comments and Feedback

If you have any comments, feedback, or requests, please let me know below or leave a comment on my Youtube channel.

Thanks

Datasets

5 Quick Tips for next level BAN in Tableau

Everyone loves a BAN (Big Aggregate Number). They are your all important key numbers in your dataset and should be jumping off the screen and and ingraining themselves in the back of your retinas!

But the standard way of creating them in Tableau can be a bit dull and monochrome.

Below I’ll step through the usual way of creating a set of BAN, followed by 5 quick tips to take them up a level in a recent dashboard I created as part of a Maven challenge

Standard BAN Creation

Normally, you will have a relatively small set of categories that you wish to show an aggregate value for (example being “Age Group” below.

We traditionally create this by dragging the category (Age Group) into the columns, then pulling the calculation into Text marks area.


Voila! We have a BAN. Not very pretty, but a BAN all the same.

You can adjust the header and value for font type, size and colour. Normally, that is about as far as most people go.

What’s the alternative?

But hey – what if you want your BAN in a single horizontal or vertical line, or you want to colour code based on the category or value?

What can we do to customise our BAN and make it that little more memorable?

5 Simple Tips to go from “Boring BAN” to “Badass BAN”

1. Orientation

It is easy to change from a single horizontal to a vertical line, by simply dragging the category from column to rows. This allows you to maximise your data real estate depending on how how you are structuring your overall visuals.

2. Headers

We can drop a duplicate category “Age Group” onto the Text marks card. Then right click on the category header and select remove. This will result in the second image below – still not too pretty, but we are on our way.

You can keep the category above or below the BAN by shifting it up or down on the marks card.

However, my preference is to keep it below, as it keeps the focus on your Big Number!

3. Font

Many people may have different views, but my preference is to keep a single font on a dashboard. Having multiple fonts can become an unwanted distraction and give a clunky look.

4. Size

For the numbers, bigger is better!! Make the size of the numbers much larger than any adjacent text to emphasize the contrast.

To do this, select the Text icon in the marks card, and click on the three little dots on the side.


This will bring up the “edit label” input box. Here you can adjust the size and font attributes (bold, underline, italic).
In my example, I chose 36 for the Aggregate Number and 16 for the underlying category.

5. Colour

Adding some colour can help place emphasis on numbers or categories, and help improve the aesthetics and feel of your visual.

Ctrl dropping the category onto the colour mark would allow you to assign distinct colours based on each category, whereas Ctrl dropping the calculations “CNT(Consumers)” onto the colour mark will allow you to assign colour.

You can also maybe apply a quick table calculation. Here I opted to show a % of total, rather than the straight numbers. This gives a good overall perspective. As shown in the below picture, you can right click on the aggregate number and select the quick table calculation.


Lastly, in my example, I opted for adjusting the colour based on the BAN value itself. As I wanted to draw the eye to the highest value, I used a diverging scale from a green (#00aa00) to a white, which was offset at -20%. This enabled my lowest value to still be almost visible, while keeping the focus squarely on the largest number.

Overall

I was pretty happy with the outcome, and was able to apply the same effect to two sets of BAN. This helped maintain the overall important consistency and look when they were brought into the main dashboard.

What do you think?

As always, if there are any questions or comments, please reach out. I am happy to help where I can, and always open to feedback on alternative methods and learning new tricks from the data fam.

Happy Analysing – DG

Maven Challenge – Mexican Restaurant Scouting

It was that time of the month when Maven Analytics set their data playground challenge#mavenrestaurantchallenge. This time it was using 5 separate csv data tables detailing consumer and restaurant data from cities across three states in Mexico during 2012.

The brief was to

  • assume the role of a Lead Analyst that scopes out new restaurant opportunities abroad
  • review and analyse the data provided for interesting patterns and trends
  • develop a single page visual that gives potential investors a good understanding of the market to guide them in making investment decisions
  • post your final proposal on LinkedIn for review by Maven and fellow challengers

My Plan

As the final product was going to be a single page (jpg) being viewed on LinkedIn, it makes sense to keep the visual:

  • Simple and clean with a consistent palette
  • Structured and flowing, telling a progressive story
  • Clear in terms of visibility of graphics and legibility of any text

I landed on asking three simple questions:

  • Who? – who were the key demographic to target in terms of age and smoking/drinking habits
  • Where? – which city or location had the highest ratio of consumers to restaurants
  • What? – what kind of cuisine should a restaurant serve based on popularity and levels of current availability

My Solution

I decided to use Tableau for this challenge to try out some newly acquired skills. The below was my final proposal posted on LinkedIn, as well as on my Tableau Public Account

I will share some tips on how I made a few of the visuals in a follow up blog. If anyone reading has any queries on any part of the visual or it’s development, drop me a comment, and I’ll be happy to provide further detail.

Happy Analysing!