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Examples of dashboards and systems that operations teams use every day.

Redesigned versions of projects Wolkee has already put into production in customer service, sales, finance, energy and custom software. Switch views, hover over the charts and see what each one answers. In Power BI or HTML/CSS, all of them can run in a portal with your brand.

Power BI · HTML/CSS · PythonRefreshed every 15 minPortuguese, English and Spanish
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Customer service · contact center

Queues, agents, IVR and satisfaction on one screen.

Dashboards connected to Avaya, Genesys, Five9, Nice and WhatsApp, refreshed every 15 minutes and deployed within 15 days.

01 · Customer service

Contact volume

When customers reach out and where the queue breaks.

  • Agent scheduling by each channel’s real peak
  • Abandonment at the critical hour, not the daily average
  • Voice, chat and WhatsApp on the same scale

Sources · Avaya · Genesys · WhatsApp

Contact volume by day and hourLast 4 weeks · Avaya, Genesys and WhatsApp
Contacts this month80.7k
Peak hourMon 10h
Abandonment at peak6.8%
MonTueWedThuFriSatSun7h10h13h16h19h22h
fewermoreabove 93% of peak
Illustrative data

02 · Customer service

Agent activity

Who is handling, who is on break and how busy the operation is.

  • Real-time status by team
  • Hourly occupancy vs target
  • Out-of-pattern breaks trigger alerts

Sources · Avaya · Five9 · Nice

Agent activityStatus by team and occupancy by hour
Agents logged in128
Occupancy74%
Average break0:42
Insurance
72%
Health
66%
Retail
75%
Retention
61%
HandlingWaitingBreakOther
Occupancy by hour
target 80%8h10h12h14h16h18h20h Illustrative data

03 · Customer service

IVR journey

How much the IVR solves on its own and where customers give up.

  • Self-service measured option by option
  • Menu and queue abandonment split
  • A basis to redesign the menu

Sources · Avaya · Genesys

Customer journey in the IVRClick a path to open the next level · September
31% self-service

Entry

Menu

Option chosen

Self-service

Agent queue

Cancellation: 5% are solved in the IVR and 95% go to an agent. In the queue, 7.6% give up before being answered.

Illustrative data

04 · Customer service

Satisfaction and NPS

What customers thought of the service, the resolution and the brand.

  • Survey built into calls and chat
  • Promoters, passives and detractors per question
  • Monthly trend of the average score

Sources · Post-contact survey

Satisfaction and NPSPost-contact survey · score 1 to 5
NPS61average 4.31
4%1
3%2
9%3
27%4
57%5
PromotersPassivesDetractors
Average score by month
JanFebMarAprMayJunJulAugSep Illustrative data

Sales

From order to customer, with the same number for every department.

SAP and CRM data modeled in Microsoft Fabric, with views by country, state, channel and customer.

05 · Sales

Sales outlook

Whether the year will hit plan, month by month.

  • Actuals, plan and forecast in one chart
  • Value and volume in one click
  • Expected close before year end

Sources · SAP HANA · Microsoft Fabric

Sales outlook · Invoiced × Plan × ForecastActuals to September, forecast October to December
Invoiced YTD187.9M
Annual plan260.8M
Expected close262.6M
Attainment100.7%▲
17.4Jan17.9Feb19.6Mar20.0Apr19.9May20.8Jun23.0Jul25.3Aug24.1Sep26.2Oct27.1Nov21.4Dec
NFForecastPO (plan)
Illustrative data

06 · Sales

Customer matrix

How many customers buy, how many came back and how many are at risk.

  • Active, new, reactivated and at risk
  • Comparison by quarter and year
  • Customers and revenue side by side

Sources · SAP · Salesforce

Customer matrix by quarterBuying customers and revenue (R$ M) by status
Q1Q2Q3Q4Total
Active20247026436106552,610
20256886717037242,786
2026745762781—2,288
New202458644971242
202566728177296
2026849188—263
Reactivated20242218251984
202527243128110
2026332936—98
At risk202461667258257
202555494652202
2026443941—124
Illustrative data

07 · Sales

Opportunity funnel

Where the deals that close come from.

  • Source × outcome in one flow
  • Win rate and sales cycle
  • Open pipeline by channel

Sources · Salesforce · Dynamics 365

Opportunity funnelSource × outcome · 1,492 opportunities in 2026
CRM · Salesforce
Win rate33.4%▲ 2.1 pp
Average dealR$ 84.6k▲ 6%
Sales cycle47 days▼ 5
Open pipelineR$ 30.5M
Referral 412Site 365Events 238Outbound 296Partners 181Won 498In negotiation 361Lost 633 Illustrative data

08 · Sales

Churn and customer gains

Whether the customer base is growing or leaking.

  • New and lost on the same axis
  • Monthly net and net retention
  • In customers or revenue

Sources · SAP HANA · CRM

Churn and customer gainsNew (above) × lost (below) and monthly net
Monthly churn2.1%▼ 0.4 pp
Net this year+186
Net retention95.8%
JanFebMarAprMayJunJulAugSep
NewLostNet
Illustrative data

09 · Sales

Sales by state

Where the company sells most and where there is room to grow.

  • State map by revenue or volume
  • Ranking of top states
  • A basis for regional targets

Sources · SAP HANA

Sales by stateRevenue (R$ M) and volume this year

Top states

  1. SP61.2
  2. MG22.4
  3. PR17.8
  4. RS15.1
  5. SC12.6
  6. RJ11.9
Illustrative data

Finance

P&L, expenses and budget with no spreadsheets in between.

Financial close straight from the ERP, expenses vs. budget with automated readouts, margin bridge and allocation across countries, in local currency and US dollars.

10 · Finance

P&L with account drill-down

Where the period result comes from, from group down to entry.

  • P&L structure with each group’s share
  • Click to open accounts and entries
  • Comparison with last year and automatic reading

Sources · SAP HANA · Microsoft Fabric

P&L · account drill-downClick a group to open its accounts · compared with last year
2,277 entries
StructureShareValuevs last yr
Contribution margin26.7%37.70M▼ 29.7%
EBITDA12.5%17.70M▼ 31.2%
Net income10.4%14.70M▼ 3.6%
Period readingRevenue 24.6% below last year; expenses fell 28.3% and contribution margin was 26.7% of revenue.
Illustrative data

11 · Finance

Expenses against budget

How much the company spent, on what and how close it is to the budget.

  • Payroll, operating expenses and share of revenue
  • Monthly pace against budget
  • Ranking of areas with each budget

Sources · SAP HANA · Power BI

Expenses · executive viewJanuary to September · actual against budget
Total spendR$ 18.6M▼ 27.4% vs PO
PayrollR$ 11.9M▼ 31.2% vs PO
OperatingR$ 5.4M▼ 22.6% vs PO
Share of revenue13.8%▼ 0.7 pp

Monthly pace · actual × budget

2.02.53.03.04.05.0JanFebMarAprMayJunJulAugSepOctNovDec
ActualPOSavings

Where each real goes

TotalR$ 18.6M
  • Payroll64%
  • Operating29%
  • Payroll allocation4%
  • Taxes3%

Top areas · tick = budget

  1. 1FINR$ 2.41M▼ 39.1%
  2. 2ITR$ 2.07M▼ 16.9%
  3. 3MKTR$ 2.01M▼ 38.5%
  4. 4RHR$ 1.59M▼ 39.1%
  5. 5LOGR$ 1.36M▼ 41.4%
Illustrative data

12 · Finance

Automatic insights

What changed in the period, written in one sentence, without reading the chart.

  • Year-end projection
  • Concentration, seasonality and accounts that changed most
  • Text that updates with the filters

Sources · DAX · Power BI

Automatic expense insightsReadings generated on every refresh · follow the filters
4 readings

1Year-end projection

At the current pace, the year closes at R$ 28.0M, 6.7% below budget.

PO R$ 30.0M

Ceiling to close on budget: R$ 3.0M per month.

2Concentration

12 of 24 areas account for 80% of spend.

80%

Bars = spend by area · line = cumulative %.

3Seasonality

February was the costliest month: 20.4% above average.

Dashed line = monthly average.

4Accounts that changed most

Trade shows grew the most against last year.

  • Trade shows+R$ 164k
  • Training+R$ 139k
  • Fuel+R$ 88k
  • Salaries−R$ 2,020k
  • Consulting−R$ 747k
  • Marketing−R$ 654k

Compared with the same period last year.

Illustrative data

13 · Finance

Budget-to-actual bridge

What explains the gap between budget and actual spend.

  • Savings and overruns by expense group
  • Budget execution by area
  • A reading ready for the results meeting

Sources · SAP HANA · Power BI

Budget-to-actual bridgeWhat explains the gap between budget and actual spend
R$ 9.3M under budget
27.90MPO
−5.62MAdmin payroll
−4.07MSales payroll
−2.41MAdmin expenses
−0.31MSales expenses
−0.14MTaxes
+3.25MAllocation
18.60MActual

Reading: admin payroll accounts for 60% of the savings; payroll allocation was the only group over budget.

Execution by area · tick = budget · % spent

  1. FinanceR$ 5.31M67%
  2. SalesR$ 3.98M59%
  3. TechnologyR$ 2.71M65%
  4. PeopleR$ 2.12M62%
  5. LogisticsR$ 1.62M60%
Illustrative data

14 · Finance

Margin bridge

Why margin went up or down: volume, price, mix, FX or cost.

  • Effect of each lever in percentage points
  • Consolidated and by country
  • Local currency and US dollar

Sources · SAP HANA · Microsoft Fabric

Margin bridgeWhat drove the gross margin change, in percentage points
28.4%Margin 2025
+1.2 ppVolume
+2.1 ppPrice
−0.8 ppMix
−1.4 ppFX
−0.6 ppCost
28.9%Margin 2026
30.1%Margin 2025
+0.9 ppVolume
+2.6 ppPrice
−0.5 ppMix
−0.3 ppFX
−1.1 ppCost
31.7%Margin 2026
25.2%Margin 2025
+1.8 ppVolume
+1.2 ppPrice
−1.3 ppMix
−2.9 ppFX
−0.4 ppCost
23.6%Margin 2026

Reading: price added 2.1 pp and FX took 1.4 pp off the consolidated margin.

Illustrative data

15 · Finance

Allocation across countries

How much of each shared expense belongs to each country.

  • Rules by percentage or headcount
  • Cost leaves the payer and lands on the user
  • Country P&L with the real cost of operations

Sources · SQL · Python

Expense allocation across countriesPayroll paid by Country A and allocated to each country project
ProjectPaidRuleAllocatedResult
Country AR$ 10,00070%R$ 7,000−R$ 7,000
Country B—15%R$ 1,500−R$ 1,500
Country C—10%R$ 1,000−R$ 1,000
Country D—5%R$ 500−R$ 500

The cost leaves the country that paid and lands in the project that used it. Each country P&L shows the real cost of the operation.

Illustrative data

Solar energy

Generation, auditing, utility bills and billing for more than 1 GW of plants.

Forecasts from solar irradiance, inverter-vs-utility audits, AI bill reading and the invoice the customer receives, all generated by the same system.

16 · Energy

Generation forecast

How much each plant will generate and whether it is generating as it should.

  • Forecast from INMET solar irradiance
  • Tolerance band to flag deviations
  • A basis to bill and plan credits

Sources · INMET · Python · Power BI

Generation forecastIrradiance (kWh/m²/day, INMET) and generated × forecast energy
Jan6.1
Feb5.9
Mar5.4
Apr4.9
May4.3
Jun4.0
Jul4.2
Aug4.8
Sep5.3
Oct5.8
Nov6.0
Dec6.2
Generated this year1.06 GWh
Forecast accuracy96.8%
Next month+9% generation▲
JanFebMarAprMayJunJulAugSepOctNovDec
GeneratedForecast±8% range
Illustrative data

17 · Energy

Energy audit

Whether the energy the utility reported matches what the inverter measured.

  • Inverter × utility bill, month by month
  • Gaps above 2% trigger an alert
  • A basis to dispute the utility

Sources · Inverters · Python

Energy auditWhat the inverter measured × what the utility reported on the bill
Inverter (MWh)3,068
Utility (MWh)3,043
Gap (MWh)25
JanFebMarAprMayJun⚠ 4.8%JulAugSep
InverterUtility⚠ gap above 2%

July: the utility recorded 4.8% less energy than the inverter. Dispute opened.

Illustrative data

18 · Energy

Utility bill read by AI

What the utility bill says, with no one typing.

  • Automatic collection from utility portals
  • AI reading with confidence per field
  • Check against the contract allocation

Sources · Python · OpenAI · OCR

Utility bill read by AICollected from the utility portal, read and checked automatically
validated
  1. Consumer unit3002 418 77199.9%
  2. Billing monthSEP/202699.9%
  3. Energy injected41,860 kWh99.7%
  4. Credits offset38,214 kWh99.6%
  5. Bill amountR$ 1,284.3799.8%
  6. Due date18/10/202699.9%
✓Credits match the contract allocation · R$ 0.00 discrepancy
Illustrative data

19 · Energy

Credit allocation

Where each plant’s energy goes and how much credit is about to expire.

  • Allocation by plant and consumer class
  • Banked and expiring credits
  • Billing generated from the allocation

Sources · Python · SQL · Power BI

Credit allocationMWh offset by plant and consumer class · September
412 units
Injected1,297 MWh
Offset96.4%▲ 1.2 pp
Banked46.7 MWh
Expiring in 60 days3.1 MWh!
Planalto PV 612Terra Nova PV 384Serra Azul PV 301Commercial 498Industrial 372Rural 241Public sector 186 Illustrative data

20 · Energy

The bill your customer receives

How much the customer pays, how much they saved and where each amount comes from.

  • Generated and sent by the system every month
  • Savings against the simulated utility bill
  • Carbon avoided and equivalent trees

Sources · Incite · Python

The bill your customer receivesGenerated every month by the system, showing savings against the utility
PDF · e-mail
Illustrative data

Custom systems

Records and rules that replace the shared spreadsheet.

Web apps (CRUD) with single sign-on, access control and a log of every change. The data feeds the dashboards automatically.

21 · Systems

Access management (RLS)

Who sees what in the BI, with no IT ticket and every change logged.

  • Access by country, report, margin and seller
  • Single sign-on with Microsoft Entra ID or Google
  • FX rates and product and customer mappings in the same app

Sources · Python · Flask · SQL Server · Azure

Report access management (RLS)Python web app with Microsoft login · each row defines what the person sees in the BI
CRUD · SQL Server
UserCountryReportsMarginRLSScope
Ana Ribeiroana.ribeiroCountry A and D
Carlos Méndezcarlos.mendezCarlos Méndez
Juliana Pradojuliana.pradoAll
Diego Rojasdiego.rojasDiego Rojas
Marina Costamarina.costaCountry D
Export RLS for all users

Click the fields to change them, then Save.

Illustrative data

22 · Systems

Consumer unit registry

Where the team registers plants, customers and units, and tracks each bill collection.

  • Groups, parent accounts and consumer units
  • Portal logins stored securely
  • Collection status and gap alerts

Sources · Incite · Python · SQL

Consumer unit registryThe registry that triggers bill collection, allocation and billing
CRUD · Python
AddChange groupDelete

Collection status · today

  • ✓UC 3002418771PO 700 · Condo · NeoenergiaSeptember bill collected
  • ✓UC 3002418790Sabin II · Clinic · NeoenergiaSeptember bill collected
  • …UC 1325845Terraço · Retail · EquatorialWaiting for the utility portal
  • !UC 7781204Vega · Industry · CPFLEnergy 4.8% below the inverter
  • ✓UC 5524319Guará II · School · EquatorialSeptember bill collected
Illustrative data

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