Graded projects: 11
Average best score: 83/100
Career level: Analyst
Core tool stack: Excel (10) · email (1)

About

Working through retail scenarios on a graded simulation platform, I have built customer count validations, revenue breakdowns, and acquisition trend charts entirely in Excel. Each task required translating a specific stakeholder question into a formula or PivotTable structure, then presenting the result clearly enough for a non-technical audience to act on it.

Across five Meridian Retail tasks, I applied SUMIFS, COUNTIFS, AVERAGEIFS, and running-sum formulas to real-scale fictional datasets, and paired those outputs with combination charts and formatted summary tables. My average best score across eleven graded submissions is 83 out of 100.

Skills

Excel Formulas

COUNTA, COUNTIF, COUNTIFS for multi-condition row counting
SUMIFS with date-range and status-exclusion criteria
AVERAGEIFS for conditional averages across categories
Running cumulative sum using expanding absolute reference

Excel PivotTables

PivotTable construction with row, column, and filter field placement
PivotTable date grouping by month
Sorting summary tables by a calculated column

Excel Charts & Presentation

Combination chart (clustered column plus line series)
Stacked bar chart from PivotTable data
Excel Table formatting with filter-ready header rows
Naira currency formatting with thousand separators
Multi-sheet workbook organisation by analysis topic

Highlights

Reconciled cross-departmental data across three systems with a 95/100 validation score, resolving discrepancies for a retail investor summary using multi-condition Excel formulas
Designed investor-ready visualizations, including cumulative customer acquisition charts and combination trend plots, maintaining an average project score of 80% across 7 submissions.
Built Q4 financial breakdowns in Excel using PivotTables and stacked bar charts to cross-check gross vs. net revenue, scoring 92/100 and 91/100 for reporting accuracy.
Developed multi-metric channel summaries analyzing customer counts, order frequency, and lifetime value (LTV) using advanced conditional formulas like SUMIFS and COUNTIFS.

Featured work

Retail · Excel

Monthly customer acquisition trend chart with cumulative growth line for an investor slide

Independent grading: 88/100(verify, opens in a new tab)

Problem

Chidi Okafor at Meridian Retail (simulated retailer) needed a customer growth chart as the centrepiece of an investor presentation slide. The chart had to show both monthly new customer counts and a running cumulative total, formatted in Meridian's brand colours.

What I did

The PivotTable used the created_at column grouped by month on rows and a count of customer_id as the value, with is_duplicate set to FALSE in the filters field so only unique customers were counted. A cumulative total column was added beside the PivotTable using a formula that locked the start of the sum range with an absolute reference while allowing the end reference to expand row by row, producing an accurate running total for each month. From that data, a combination chart was built with orange clustered columns for monthly counts and a navy line for the cumulative total, matching the brand palette Chidi specified. Data labels were added to both series so the chart could be read without a data table beside it.

Finding

The PivotTable grand total of 2,331 aligned with the full customer count from the earlier validation task, confirming the data source was consistent across both analyses.

Outcome

Chidi had a chart ready to drop into the investor deck that showed both the pace of monthly acquisition and the cumulative scale of the customer base in one visual.

Next step

Filtering the same chart to show only customers acquired through paid channels versus organic channels would let Chidi show investors the return on acquisition spend, rather than total growth alone.

PivotTable construction with row, column, and filter field placement
PivotTable date grouping by month
Running cumulative sum using expanding absolute reference
Combination chart (clustered column plus line series)

Retail · Excel

Channel performance summary ranking nine acquisition channels by customer count and average value

Independent grading: 72/100(verify, opens in a new tab)

Problem

Chidi Okafor at Meridian Retail (simulated retailer) needed a single table for the investor deck comparing all nine acquisition channels on customer count, average orders per customer, and average lifetime value. The aim was to identify which channels brought the most valuable customers, not just the most numerous ones.

What I did

COUNTIFS, AVERAGEIFS, and SUMIFS formulas calculated each column for every channel row, each formula using the channel field as its condition to restrict the calculation to that channel's customers. A bonus total lifetime value column was added beyond the brief, giving Chidi an absolute scale alongside the per-customer averages. Channels were then sorted by customer count descending so the most-used channels appeared at the top. The result is a flat, formula-driven table rather than a PivotTable, which makes individual values easier to audit by tracing a formula back to its source range.

Finding

Instagram ranked first by customer count and TikTok ranked last among the nine channels, and average orders per customer ranged between roughly 8.1 and 10.4 across all channels.

Outcome

Chidi had a structured comparison across all nine channels in a format compact enough to fit a single investor-deck slide, with both volume and per-customer value visible in the same row.

Next step

Dividing total lifetime value by customer count to verify that the average lifetime value column is consistent with the SUMIFS-derived totals would confirm there are no rounding or filter mismatches between the two columns.

COUNTIFS for multi-condition row counting
AVERAGEIFS for conditional averages across categories
SUMIFS with date-range and status-exclusion criteria
Sorting summary tables by a calculated column

Retail · Excel

Reconciling three conflicting customer counts for an investor summary

Independent grading: 95/100(verify, opens in a new tab)

Problem

Chidi Okafor at Meridian Retail (simulated retailer) had a reporting conflict: marketing, operations, and finance each produced a different customer count from the same dataset, and he needed to explain the discrepancy to an investor. My job was to replicate all three counting methodologies in Excel and produce a labelled summary table Chidi could walk through in a meeting.

What I did

Each methodology called for a different Excel counting function. COUNTA captured every row in the customer sheet, representing marketing's total signup figure. COUNTIF then filtered that down to rows where is_duplicate equals FALSE, which is how operations counts after deduplication. For finance's definition, COUNTIFS added a second condition requiring total_orders to be greater than zero, isolating non-duplicate customers who had actually transacted. The three results were placed in a formatted Excel Table with a filter-ready header row and plain-language labels so the logic was self-explanatory without any additional notes.

Finding

Marketing's count stood at 2,331 total rows, operations' deduplicated count came to 2,131, and finance's active customer count was 2,105, with each gap traceable to a specific data rule.

Outcome

Chidi received a single structured table that made the source of each discrepancy visible, removing the need for back-and-forth between teams before the investor meeting.

Next step

A useful next step would be to cross-check whether any of the 26 customers counted by operations but not by finance (2,131 minus 2,105) have an account creation date in the current reporting period, which would tell Chidi whether those are new accounts yet to place an order or genuinely inactive ones.

COUNTA, COUNTIF, COUNTIFS for multi-condition row counting
Excel Table formatting with filter-ready header rows
Multi-sheet workbook organisation by analysis topic

Retail · Excel

Separating gross and net October revenue to cross-check the finance system

Independent grading: 92/100(verify, opens in a new tab)

Problem

Nura Ibrahim at Meridian Retail (simulated retailer) needed the October 2023 revenue figure verified in Excel before presenting it to the finance team. She specifically wanted the gap between gross revenue, which includes all order statuses, and net revenue, which excludes cancelled, returned, and refunded orders, so the impact of those statuses could be quantified.

What I did

Two SUMIFS formulas did the core work, both pulling from the total_naira column and filtering on the order_date column to restrict results to October 2023. The gross figure used only the date range as a condition. For net revenue, two additional criteria excluded the three unwanted statuses by testing the status column against each one. The gap between the two figures was then derived as a simple subtraction and labelled clearly. All three monetary values were formatted with NGN and thousand separators to match Meridian's reporting style.

Finding

Gross October revenue was NGN 56,876,792.48 and net revenue after removing cancelled, returned, and refunded orders was NGN 38,450,382.35, a gap of NGN 18,426,410.13.

Outcome

Nura had a verified, formatted figure to bring into the finance system reconciliation, with the revenue lost to each excluded status visible in the same sheet.

Next step

Breaking the NGN 18,426,410.13 gap down by the three excluded statuses separately would show whether cancellations, returns, or refunds drive the largest share of lost revenue, which would help Nura decide where to focus operational attention.

SUMIFS with date-range and status-exclusion criteria
Naira currency formatting with thousand separators
Multi-sheet workbook organisation by analysis topic

Retail · Excel

Q4 2023 revenue by order status to assess whether October cancellations were an anomaly

Independent grading: 91/100(verify, opens in a new tab)

Problem

After calculating October revenue, Nura Ibrahim at Meridian Retail (simulated retailer) wanted to see whether the level of cancellations that month was unusual or part of a broader pattern. She asked for Q4 2023 revenue broken out by order status across all three months so the trend could be read at a glance.

What I did

A PivotTable placed order status on rows and order_date, grouped by month, on columns, with total_naira as the values field. That structure meant each cell in the table showed revenue for one status in one month, making cross-month comparisons immediate. All seven statuses appeared in the output with no omissions. A stacked bar chart beneath the PivotTable translated the same data into a visual format, with a legend separating October, November, and December so Nura could assess month-to-month shifts without reading individual cells.

Finding

The October grand total of NGN 56,876,792.48 matched the previously verified figure, confirming the PivotTable's data source and aggregation were correct across all statuses.

Outcome

Nura received a single view that let her compare cancellation revenue against other statuses across the full quarter, giving enough context to judge whether October was an outlier.

Next step

Calculating cancellations as a percentage of gross revenue for each month, rather than as an absolute naira figure, would make the trend more comparable if overall order volumes shifted between October and December.

PivotTable construction with row, column, and filter field placement
PivotTable date grouping by month
Stacked bar chart from PivotTable data
Naira currency formatting with thousand separators

Additional projects

Customer Analytics

Complaints volume breakdown by hub and category · Excel · 72/100
Executive summary of complaints landscape for leadership review · Email · 96/100

Data Quality

Duplicate customer detection and name match analysis · Excel · 62/100
Initial data profiling for Meridian sales dataset · Excel · 62/100

Financial Reporting

October 2023 revenue validation and reconciliation · Excel · 92/100

Operations Analytics

Hub performance ranking by delivery rate and complaint volume · Excel · 92/100