Trending December 2023 # Adding Product Insights To Customer Service Dashboards In Power Bi # Suggested January 2024 # Top 21 Popular

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When you want to add more insights to customer dashboards in Power BI, you need to be able to select products.

You need to know what products are going well this year versus last year and the 2 years before that.

Drag the Product Name, Total Sales, Sales LY, and Sales 2Yrs Ago into the canvas. Then, turn it into a clustered bar chart visualization. Change the sort order of the table.

You can see a significant growth in Product 11. It wasn’t much two years ago, but then it increased last year and now it’s the highest selling product. If you select a product, the visualization also changes. It’s a great insight.

The next thing is to calculate your total profits this year versus last year.

All you need to do is copy and paste the other measure, call it Profits LY, and put the Total Profits inside CALCULATE.

Next, create a table, place the Date column in it and get rid of the hierarchy. Also, place Total Profits and Profits LY in the table. Turn the table into a clustered column chart.

This could potentially identify if there’s a sharp increase in profits over a time period versus another. You can also reduce the time frame and change the interaction to get better insights in Power BI.

If you want to see what individual transactions contribute to your profits versus the ones that detracted to it, you need to create a table. To make this easier, utilize the measures and tables that you already have.

Get your City, Product Name, and Order Date and put them in the table. Throw in the measures Total Sales and Total Profits.

Create another measure and call it Profit Margins. Use the function DIVIDE to divide the total profits by the total sales. After that, change the format to percentage.

Drag that measure into the table and you’ll then have your table of information.

If you select a product, you can look at its sales along with all the different sales areas and cities that it was sold.

For this next step, you have to work out another logic around the profit margins. Place the Date into a table along with the Profit Margins and then turn them into a visual. These are your profit margins through time.

However, this needs to be more insightful because you’ll need data and answers about the profit margins that you are selling. You need to know why some are high, above average or below average.

To extract those insights in Power BI, you only need to calculate your total profit margins without using any time frame. You begin by creating another measure.

Call the measure Lifetime Profit Margin. Calculate the profit margin and then use the ALL function to disregard any day that might be in it. It will only look at everything through time. Next, turn the format into a percentage.

If you drag this down into the visualization, you’ll see there’s a line. This represents the average of the profit margins.

Change the visual into an area chart.

You can now see your sales in the visual. Some are below the line, some are above.

The date August 16 has 16% while the total average is 38%. If you want to look at the individual transaction you could bring in your product or salesperson dimension. This is a great way to find insights in Power BI quickly.

There’s even more you could do to improve this dashboard. You could break down and evaluate all the dates that were under the average margin and only narrow in on those.

To finish things off, occupy the space on the side with key metrics and by using the calculations that you have already created.

Create and set up a few cards in the canvas, and then copy and paste it. Set the cards value to Total Sales, Total Profits, Sales LY, Profit Margins, and Profits LY.

There are a few things you can do here. Try to become familiar with the visualization and how you would incorporate them in your models.

Another thing you could do is create a measure for Sales Growth. Divide your total sales by your sales last year and then subtract it by 1. Don’t forget to set the format to percentage.

Next, change the Profits LY card to Sales Growth. You can see there’s a significant sales growth.

If you change things here, all of these metrics change. That’s why these cards are great because they drill into specific information based on the context of the calculation.

By adding product insights in your customer dashboards, you’re able to further drill down to specific pieces of information that may be valuable in understanding your organization’s performance.

The dynamic capabilities of this added technique allows you to arrive at different results that reflect the selections you’re making.

If you select a different subset of customers, you’ll also see how the product’s financial performance change with it.

All the best,

Sam

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Small Multiples Chart In Power Bi: An Overview

In this tutorial, we’ll talk about the small multiples chart, which is a new preview feature introduced by Microsoft. This is also one of the best features for visualization in Power BI. We’ll also be discussing some of its limitations when it comes to visualization.

A small-multiples chart is a data visualization that consists of a series of similar graphs or charts arranged in a grid. It uses multiple views to show different partitions of a dataset. It is often used to compare the entirety of the data. For scenarios with a wide range of data presentations, small multiples are the best design solution.

This is what a small multiples chart looks like.

In order to apply the changes and use this feature, you need to restart the Power BI application.

The Small multiple visual is only available in column charts, bar charts, line charts, and area charts. It’s not available in pie charts and any other charts.

First, let’s use a line chart and resize it as shown in the image.

Let’s utilize the Total Defects measure, and place it into the Values field.

For this example, we’ll analyze the total defects by vendor. Therefore, we need to add the Vendor to the Axis field.

Right now, you can see that it’s just a descending line chart.

To enable the small multiples chart visual feature, we need to bring in some data over the Small multiples field. So, let’s place the Vendor into the Small multiples field.

Then, within the Axis field, let’s change the Vendor to Date.

As you can see, we now have small multiple visuals in our visualization.

Let’s turn off the Title and the Background under the Formatting tab.

Then, change the color to yellow under the Data colors.

Right now, we can’t see the title on our small multiple visual because its color is dark. So, let’s change the title color to white. Just change the Font color under the Small multiple title.

Then, let’s change the font size of the title.

Let’s also change the alignment of the title horizontally and vertically by using the Alignment (for horizontal) and Position (for vertical) settings. The best title alignment I found for line charts is positioning them at the bottom. So, let’s change the Position to bottom.

The output should now look like this.

The most important section for a small multiple visual is the Grid layout. The Grid layout setting sets the amount of small multiple visuals that we can display on our rows and columns.

For example, if we increase the Rows and Columns to 6, it’ll display 6 items for rows and columns as well.

However, the downside of this is that we can only increase the rows and columns up to 6.

To make this look better, let’s just use 4 rows.

As a result, we can now see the lines more clearly. Let’s then remove these categories.

To do that, just turn off the Title for both X and Y Axis.

Then, turn off the X and Y axis as well.

We can also hide or display the grid lines by enabling or disabling them on the Y and X axis. For this example, let’s leave this turned on as it defines the borders around the visual and makes it look better.

Note that we currently don’t have conditional formatting for line charts. But you can certainly try doing it by using bar or column charts.

The small multiples visual can also handle secondary values. For example, let’s add another measure in the Secondary values field.

As you can see, it can handle secondary data which makes it a great feature for visualization.

We can also change the color for the second measure (Total Downtime (Hrs)).

Another cool feature is that we can analyze our data by using column charts.

And this is how it looks like if we convert our line visual to a bar chart.

For bar charts, it would be better to change the Axis to Month & Year instead of Date.

Just remember that if we want to make it look better, we can play with the various settings that are available in Power BI. For example, we can reduce the Rows and Columns on the Grid layout to make the bar chart look better.

For the bar height, we can just edit the Inner padding.

Don’t forget to check the other settings in the X and Y axis as well.

As for the sorting, we can only sort them by categories and not by values.

We can also use the area chart for our small multiples visual as shown in the image below.

To sum up, we’ve seen how the small multiples chart allows the viewer to focus on changes in the data rather than on changes in graphical visualization. We’ve also discussed the limitation of this visual when it comes to sorting and the limited number of options for rows and columns. Hopefully, they can make it better in the future.

It’s a relatively new feature introduced by Microsoft Power BI. You can play around with the different visuals that you can use with the small multiples chart.

Until next time,

Mudassir

Showcase Qoq Sales Using Time Intelligence In Power Bi

In this tutorial, we’re going to cover how to calculate quarter on quarter sales differences using time intelligence in Power BI. You may watch the full video of this tutorial at the bottom of this blog.

We’re not just going to do it at a granular level- we are going to try and analyze trends based on quarter on quarter sales.

Sometimes when you are looking at something from a very granular level, your visualizations on a whole will become very busy.

If you can smooth out the results that you’re looking at, it enables you to produce a much more compelling visualization which shows something more meaningful than a busy chart, which shows every adjustment or change in your result through time.

It’s a two-fold example that I will run through here. Not only are we going to run through how to visualize time calculations around different time periods, one quarter versus another quarter, we’ll also be analyzing the difference.

I want to show you how to create Quarter on Quarter Sales or how you can compare one quarter’s results to another quarter.

Then I will also show how to keep it dynamic, and how you can utilize the data model to discover the difference between the two quarters.

This is an example from a recent workshop that I ran by way of the Enterprise DNA webinar series. What we’re trying to do here is to analyze how our sales have fared on any one quarter and then compare it to a prior period.

To come up with these insights, I first grabbed my Dates field and turned it into a filter (right), and then grabbed the Date column and turned it into a table (left).

If we calculate the total of anything (e.g. Total Sales, Total Profits, Total Costs, etc.), these are what I call core calculations. These calculations are very easy to do because they are just simple sums or simple aggregations.

First, I’m going to drag the Total Sales into the table.

Now, if we want to compare on a quarter to quarter basis, we need to use time intelligence calculations. My favorite time intelligence calculation is the DATEADD function so I highly recommend familiarizing yourself with how to use the DATEADD function inside the CALCULATE function as you can see in this formula:

In this calculation, we referenced the initial core calculation, which is our Total Sales. We used the DATEADD function so we can jump back to any time period.

Since we wanted to do a quarter-on-quarter sales, all we had to do inside of DATEADD is to specify that we want to jump back one quarter.

This is my favorite function to use when it comes to time intelligence in Power BI because of all the variability and flexibility that you can put in this formula.

In this case, we’re just going to look at it from a quarterly perspective. Once I finish writing down this formula, I’ll drag it into the table.

You can see the Total Sales is being calculated from the current context, which means we’re calculating for whatever the particular day is.

However, the Sales LQ is calculating 1 quarter or 3 months ago from this day.

What’s so great about this calculation is how reusable it is. I’ll copy and paste the table I just made, grab my Quarter & Year measure, and drag it into the second table I have created.

Now, we are getting the true Quarter on Quarter calculations, and the timeframe or window we’re looking at is being determined by the filter we have in place.

We can drill into any grouping of quarters and make a comparison of our Total Sales and our Sales Last Quarter.

We can also work out what the changes are by creating a new measure. The formula I’ve used is to deduct the Sales LQ from the Total Sales.

I’ve subtracted the time intelligence calculation we created using DATEADD from our initial core calculation. This gave me the absolute Quarter on Quarter Sales Change.

There’s so many different ways that you can you can utilize these techniques. We’ve honed in on quarter on quarter here, but you can do your calculations for month on month or year on year.

If you’re just starting out with time intelligence in Power BI, this is a really good technique to practice and get you going. You’ll understand how context and measure branching works, and how to use time intelligence calculations. Once you implement them well, you can ultimately create Power BI reports that look compelling and showcase really good insight.

For many more time related insights that you can discover and illustrate with Power BI, check out this detailed course module at Enterprise DNA Online.

Time Intelligence Calculations

I hope you enjoy this tutorial as much as I have.

Sam

The Difference Between Sum Vs Sumx In Power Bi

There is still a lot of confusion about the difference between SUM vs SUMX in Power BI. This is key knowledge that users have to master because both functions can be used across different scenarios, but there are cases where one is more efficient than the other. You may watch the full video of this tutorial at the bottom of this blog.

I’m going to focus on one example here that would show the distinction between the two. But before I jump into that example, it is important to understand the difference between an aggregating function and an iterating function.

When it comes to DAX, there are two types of calculation engines – the aggregators and iterators.

Aggregating functions include SUM, AVERAGE, MIN, MAX and COUNT. Iterators, on the other hand, are functions that have an X at the end, like SUMX.

Iterating functions go through every single row of a table to add logic to each of these rows.

Aggregating functions look at the entire column left over after the context is placed in a formula. From there, a single aggregation is done for the entire column at a single time.

How is SUM used as an aggregator?

In this example, I’m going to compute for the Total Revenue in the sample data given.

The context is always important here. In this case, each specific date is the context of each specific result.

If I dig deeper into this table, it will show that there is a direct relationship flowing from the Date going into the Sales table.

Then if I look at the data working underneath this model, this is how everything fits together.

So the relationship is linked to the Order Date column here. Once specific dates from this column are filtered, the corresponding results are shown under the Revenue column.

From there, the SUM would just do one big calculation of the filtered results.

Now, I’m going to use SUMX on the same sample data so that you can see the difference. I can actually calculate for that Revenue without touching the Revenue column.

When the SUMX function is used, it will always ask for a table. Note that either a physical table or a virtual table can be used here.

To come up with the Revenue, I’m going to choose the Sales table. Then, I’ll place an expression, which can be a measure or a specific column from that table into this formula so that it can start running logic on every row. The expression, as explained here, returns the sum of an expression evaluated for each row of the table.

Since the sample data includes the Order Quantity, I’m going to use that here to get the Total. I’m also going to use the Unit Price.

Once I drag that formula into the report, the results are exactly the same.

Of course, they’re both showing the same results because they are both deriving data from the same two columns – the Order Quantity and the Unit Price.

Why use the SUMX if it yields the same result as the SUM anyway?

With the SUMX, the logic is applied not just to an entire column, but to every single row within that column. In fact, I could delete the Revenue column and still be able to retrieve specific results.

So imagine that logic being applied at every row. It multiplies the Order Quantity and Unit Price for the 1st row then saves that into the memory. It does the same thing to the 2nd row and all the other rows after that, saving each individual result.

This means that at the end, what’s being used to calculate the SUMX is not the physical data on the table, but the results saved in the memory.

Hopefully I was able to explain the main difference between SUM vs SUMX in Power BI, especially to those who are still getting the hang of what Power BI can really do.

SUMX will also be useful in cases where you have thousands to millions of rows. As long as the tables and columns referenced in your measures are there, using iterating functions would make the process more efficient.

All the best,

Sam

How To Create Compelling Power Bi Color Palette

In this blog tutorial, I’m going to go over some design and color ideas when creating a Power BI color palette. You may watch the full video of this tutorial at the bottom of this blog.

These are some of the more questions people ask me. How do you create your color palettes in Power BI? How do you get such a coherent set of colors on your report? So, I devised this development technique or strategy for Power BI reports. I’m big on colors and I want to make sure that my reports are visually appealing.

In this example, I’m using a report that I went through during an Enterprise DNA Learning Summit session.

This interesting color palette in this report is certainly not a standard one that you can find from the built-in color palettes inside of Power BI.

First, I’m going to show you how I easily get this grouping of colors that look good on a page.

I’ll show you a couple of websites that allow me to create this compelling Power BI color palette. The first one is Palette FX.

In this website, I can place any picture in here, and then get the colors that are within that picture.

I found this random picture online of this group of colors. I just typed in purple and gold and this is one of the images that popped up in Google.

I downloaded this image and I placed it into the Palette FX website.

Sometimes, you can’t get enough colors to make up an entire palette for Power BI. You’ll need more than five colors to create a palette inside Power BI.

I put the format in a simple text file to set things out. Then, I change the hex values, which will make up my palette. You’ll see here that I have nine that I need to make up.

What I personally do to complete the palette is I go to this website called chúng tôi ., which is free to use.

I can create a palette based on the core colors that I choose. I simply input the hex values here, and then it creates the palette with those colors.

Once I get that done, I input the hex values into my Data Colors, and then import my theme.

And that’s how I create this grouping of colors that works well together.

Another big tip that I’d like to share with you is that, even though I have this great color palette, I still use only a few of the colors, specifically the top line. These colors on top are the main colors that come out of that palette I created.

I don’t recommend using a range of different colors on report pages. It will overwhelm the report visuals.

In this brief tutorial, I share my best practices and method of creating a Power BI color palette to make reports visually compelling.

I’m big with colors, as they make key information in the report stand out. The report becomes pleasant to the eyes of the users as well. However, I highly recommend that you keep it simple and not overdo the report by using too many different colors. Give this a try and just be creative.

All the best!

Sam

Top 4 Helpshift Alternatives For Customer Service

Customer service software is an indispensable asset as 90% of customer service leaders report customer expectations are higher than ever. With the rising expectations and the need for efficient and omnichannel support, selecting the tool that fits business needs best can be challenging and time-consuming for companies.

There are several common features of popular customer service software such as:

Ticketing system to manage and track customer queries.

Multichannel support that allows customers to reach out to the support teams through various channels, including email, phone, social media, live chat, and more.

Automation that enables streamlining the customer service process.

Analytics and reporting that provide insights into the performance of the customer service team, common issues faced by customers, resolution times, and more.

Knowledge base and self-service portal that enables customers to find solutions to their issues themselves with FAQs, guides, and forums.

When a company decides to implement customer service software, it’s important to consider its unique needs, customer base, and the channels most frequently used by its customers. However, it can be difficult for companies to distinguish among popular tools as each customer service software has its own strengths and special features.

Technical buyers shortlisting customer service vendors will probably come across Helpshift, which is one of the most popular tools within the customer service software landscape. In this article, we’ll examine the potential drawbacks of Helpshift according to user reviews and explore top alternatives to help tech buyers in their decision.

Overview and shortcomings of Helpshift

Helpshift is a digital customer service platform allowing businesses to deliver support across multiple channels, focusing on in-app support for mobile applications. Some of the features it offers are:

It also offers a 30-day free trial with a starting price of $150/month.

On the software review platforms we analyzed, Helpshift is listed on G2, Capterra, and TrustRadius, but not on Gartner Peer Insights and Peerspot. And on the platforms where it is listed, it has the lowest number of reviews compared to the other tools discussed in this article (Table 1). These suggest that Helpshift is not as popular as other tools on the market.

Table 1. The total and average number of reviews of customer service tools

HelpshiftFreshdeskIntercomSalesforceZendesk Total reviews*41669264188370611,001 Average reviews13813858377412200

*Review counts are from software review platforms Capterra, G2, Gartner Peer Insights, Peerspot, and TrustRadius as of 2023/07/04.

On the platforms it is listed, its overall user ratings are:

G2: 4.3/5

Capterra: 3.9/5

TrustRadius: 7.9/10

User reviews highlight two significant areas where the platform could improve:

Analytics and reporting

Multi-channel support

Analytics and reporting

Some users have been dissatisfied with the lack of depth in Helpshift’s analytics and the limited ability to track individual and team performance. They complain about the lack of basic information such as time spent by an agent on a ticket or an agent’s resolution rate per hour. The lack of these crucial metrics restricts managers’ ability to gain actionable insights and improve service quality.

Users also express a need for enhanced search capabilities within the analytics, specifically around tags. Some have resorted to external services like Power BI to compensate for these deficiencies, but even then, the integration with Power BI has been described as challenging to work with.

Multi-channel support

Helpshift’s multi-channel support capabilities are another area of concern for many businesses. Users highlight a lack of support for some text-based communication channels such as WhatsApp, and Apple Business Chat. They also point out missing integrations with popular platforms like Telegram and TikTok.

The absence of these channels forces businesses to rely on separate platforms to cater to their customers, causing fragmentation of resources and focus. This lack of seamless multi-channel support can make managing customer interactions challenging and inefficient.

Top 5 Helpshift Alternatives 1. Salesforce Service Cloud

Salesforce Service Cloud is a customer service and support application that helps businesses deliver customer service across various channels.

Salesforce Service Cloud is built on the Salesforce Customer 360 Platform, enabling seamless integration with other Salesforce products. It also leverages AI through Salesforce Einstein to offer automated suggestions, route cases, predict customer behavior, and provide intelligent insights for increased productivity. Some of its features are:

Intelligent chatbots

Customer feedback and survey tools

Software integrations (Contact Salesforce for details)

30+ supported languages

Self-service options

Facebook, Instagram, Twitter, and YouTube integration

Salesforce Service Cloud’s starting price is €25 per seat/month and offers a 30-day free trial.

Its overall user ratings on different software review platforms are:

G2: 4.2/5

Capterra: 4.4/5

TrustRadius: 8.9/10

Gartner Peer Insights: 4.4/5

Peerspot: 4.1/5

Based on user reviews, here’s how Salesforce Service Cloud addresses Helpshift’s limitations in terms of:

Analytics and reporting

User feedback highlights the platform’s robust and efficient capabilities in providing in-depth reporting and meaningful analytics. Service Cloud allows businesses to track key performance metrics such as response times and customer satisfaction rates, providing valuable insights into customer behavior and helping identify areas for improvement.

Users also commend the accessibility and clarity of these analytics, noting the ease with which they can track the success of sales cadences, configure dashboards, and quickly access crucial data. The reporting and analytics tools of Service Cloud help businesses to make data-backed decisions, thereby optimizing their customer service operations.

Multi-channel support

Service Cloud provides a unified platform where customer service agents can engage with customers across various channels including phone, email, chat, social media, and SMS. Users have acknowledged Service Cloud’s omni-channel support and call routing capabilities for resolving operational issues.

The platform’s design supports multiple channels, enabling greater reach and improved documentation of customer interactions.

Furthermore, Service Cloud offers a mobile-first approach, aiming to cater to customers over their preferred channels.