Suggestions for Visuals to Reveal Hidden Insights in Power BI Body

[maverickf17]

Hi Community,

I’m working on a business dashboard where stakeholders have a specific requirement:

• They don’t want to see the “obvious insights” (like sales increasing in a certain region).

• Instead, they want visuals that reveal hidden patterns and insights that are not immediately visible.

• The focus is on customer segmentation and visits (e.g., 1st visit, 2nd visit, 3rd visit).

Currently, the dashboard uses standard visuals (bar, line, column), but the business finds them a bit “boring.”

Can you please suggest some advanced/custom visuals or approaches in Power BI that are effective in uncovering hidden insights?

2) Business tables :

Table Name- Day level burn with Promo Cat

date

actual burn

bonus burn

overall burn

Promot_Category

Table Name - July KPI Data

Card_Barcode

Transaction_Date

Location_Id

Total_Spend

Total_Actual_Spend

Total_Bonus_Spend

First_Swipe

Last_Swipe

Duration_Seconds

Duration_Minutes

Duration_Hours

Total_Recharge

Total_Actual_Cash

Total_Bonus_Amount

Total_Gift_Amount

Table Name- July KPI data can be done in backend

Burn bucket

cust category

avg repeat duration

Count of Customer

Table - July Kpi transaction

Location  id

Game id

Game_ML_id

Game_descrption

Game category

Transaction type

Car barcode

Trasaction _amount

Bonus_Amount

Ticket Amount

Date

Time

revenue

The game category should be

Blue

Green

Red

The cust category should be

Minimalist

Economy

Regular

Elite

-----------------------------------
Their previous mockup that was changed to my dashboard visuals.

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Business metrics

1	Cash Burn Effect On Promotions	Trend Chart Day Level, cashburn as a metric & promotion as a filter.
		Promo Users, Vs Non Promo Users
2	Customer Categories & cash Burn	Bar chart showing customer catgeories & burn percentage across actual & bonus
		Customer cat, burn percetage of V1, V2, V3. Table also include Avg Spend.
		V1, V2,V3 how many customers are we considering in theses
3	Game Categories	Line graph of days, each line a game category/(new category slow or fast) that represent spend per hour
	Categories the games as fast & slow	Game cat, games, customers leaving within 10,20,30,40 as cols,
4	Retention
	group cash burn percentage (100%, 70%, <50%)	show each bucket & number of customers across cust categories(Multi stacked bar chart)
		Matrix of buckets vs average duration between visits(2,3,4,5,6) show the number of customers
5	Operational
	How to cal downtime - gowtham

-----------------------------------------
My ask is that can anyone please do enhanced visuals on top of my visuals and provide me?

Also the last visual matrix is not sorted -> 100% 4% 50% 70%
Please sort that also.

Note: Please do not use your own metrics use the metrics which the visuals is alrady connected to
i.e dont change x axis, yaxis, legends etc. If it is not available pelase mock up the data.
Power bi file link: https://www.dropbox.com/scl/fi/jpnxt3z7br7l332d1cq2c/Cash-Burn-Final-Arjun-IRL-291-6.pbix?rlkey=k7dw... 

Please refer page name : Duplicate of Duplicate of Final Dashboard Cash burn 

-----------------------------------------------------------------------------------------

I have attached the power bi file link.

I request the  Power BI community assistance on the above ISubject.

Thank you,
Maverick

[littlemojopuppy · Accepted solution]

@maverickf17 see comments in red

I’m working on a business dashboard where stakeholders have a specific requirement:

• They don’t want to see the “obvious insights” (like sales increasing in a certain region).  This is reporting the news...everyone would produce a column/line chart for this 🙂

• Instead, they want visuals that reveal hidden patterns and insights that are not immediately visible. I like showing what drove change.  Revenue could change based on changes in volume, price or mix.  This could be represented with stacked column (changes due to price, volume and mix each as it's own stack)

• The focus is on customer segmentation and visits (e.g., 1st visit, 2nd visit, 3rd visit).  Check out the INDEX function to create measures for first/second/third visits.

It's just as important to define the measures to quantify insights as it is to create visualizations for them and it's unlikely you'd be able to create a visual without defining the appropriate measures.  Another thought: play around with the key influencers visual (probably not the actual name of it) and there's a cool tree visual that could also help here.

Hope this helps!

[v-echaithra]

Hi @maverickf17 ,

May I ask if you have resolved this issue? Please let us know if you have any further issues, we are happy to help.

Thank you.

[v-echaithra]

Hi @maverickf17 ,

I hope the information provided is helpful. I wanted to check whether you were able to resolve the issue with the provided solutions. Please let us know if you need any further assistance.

Thank you.

[v-echaithra · Accepted solution]

Hi @maverickf17 ,

As you mentioned, the business now seeks more non-obvious insights those that go beyond traditional charts and KPIs and truly surface hidden behaviors, patterns, or exceptions. With that in mind, here are several visual enhancements, techniques, and UX design recommendations to elevate your dashboard and meet those expectations.

Use Power BI's built-in anomaly detection feature on line charts to automatically identify unexpected spikes or dips in metrics such as actual cash burn or total spend. This is ideal for highlighting behavioral shifts that may not be evident from trendlines alone.
The Key Influencer visual is excellent for surfacing hidden relationships in your data. For example, it can help identify which customer segments, promotion types, or game categories are most responsible for high bonus burn rates. This visual is interactive and guides users toward understanding causality.
Decomposition Tree visual allows users to dynamically break down a measure (e.g., cash burn) by several dimensions such as promotion type, customer category, and site. It supports root cause analysis by letting users drill into whichever aspect is contributing the most to the KPI variance.
Rather than cluttering your main page, you can enrich it by designing tooltip pages that appear on hover. For example, hovering over a burn bucket could show customer retention, promo code used, or average spend. Drill-through pages can take the user from high-level KPIs down to transactional or customer-level detail for deeper investigation.
Create heatmaps to visualize high and low engagement times by hour and day. This can reveal when customer dwell time is longest, or when bonus spend is the highest. Calendar heatmaps can help identify patterns across months or promotional periods.
These are powerful for visualizing relationships between multiple variables. For instance, you could plot dwell time vs spend per swipe, with bubble size representing bonus usage. This can reveal clusters of customer behavior, such as high-spend low-duration users or vice versa.
Keep visual overload in check, use interactivity (drill, tooltips, toggles) to reduce clutter. Validate that any added visuals or DAX logic do not compromise performance, especially on large datasets. Maintain consistency in formatting, color usage, and labels to enhance readability.
To meet your stakeholders' need for deeper insights, shift focus from static bar and column charts to dynamic, layered, and interactive visuals. Tools like anomaly detection, decomposition trees, scatter plots, and smart narratives can turn a descriptive dashboard into a diagnostic and exploratory one.

Hope this helps.
Chaithra E.

[littlemojopuppy · Accepted solution]

@maverickf17 see comments in red

I’m working on a business dashboard where stakeholders have a specific requirement:

• They don’t want to see the “obvious insights” (like sales increasing in a certain region).  This is reporting the news...everyone would produce a column/line chart for this 🙂

• Instead, they want visuals that reveal hidden patterns and insights that are not immediately visible. I like showing what drove change.  Revenue could change based on changes in volume, price or mix.  This could be represented with stacked column (changes due to price, volume and mix each as it's own stack)

• The focus is on customer segmentation and visits (e.g., 1st visit, 2nd visit, 3rd visit).  Check out the INDEX function to create measures for first/second/third visits.

It's just as important to define the measures to quantify insights as it is to create visualizations for them and it's unlikely you'd be able to create a visual without defining the appropriate measures.  Another thought: play around with the key influencers visual (probably not the actual name of it) and there's a cool tree visual that could also help here.

Hope this helps!