Practical case-study
Let's see some practical cases of how data can boost profitability in the real-world business environment.
Hardware store maximizing profits by 20% by streamlining operational efficiency
A hardware store has been collecting sales and inventory data for years. Through the CEO Masterclass, the store’s CEO learns how to organize and analyze this data to drive profitability. Here’s how the CEO can increase profitability by about 20% through optimizing inventory management. (Retailers are estimated to loose 15-30% of profits due to poor inventory management).
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Step 1: By going beyond eye-level analysis, the CEO can begin analyzing sales trends and customer preferences using powerful tools like monday.com and Google Data Studio. The store can identify how long each item sits on the shelf before being sold which allows for optimizing stock levels and reducing waste.
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Step 2: With insights from above, the CEO can now adjust to inventory based on real demand and optimize capital allocation efficiently helping the store avoid overstocking of Low-Demand Item or Stockouts of High-Demand Items.
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Step 3: Using tools like Monday.com, the store can continue to collect real-time data and create a data-driven culture leading to the desired 20% increase in net profitability through reduced storage costs and increased targeted marketing budget from the capital freed up by optimising stock levels.
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A Home decor company wantsto maximize ROI from social media Ads
The company is running a Facebook ad campaign targeting various age groups and locations to promote their services.
The CEO gets the report with key metrics:
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Impressions: The number of times your ad was shown.
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Clicks: The number of times users clicked on the ad.
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Budget: How much money was allocated to each segment.
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Leads (if available): The number of conversions, such as sign-ups or inquiries, generated by the ad.
In our Masterclass, the CEO has learnt about analyzing marketing data;
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Step 1: Click-Through Rate (CTR) per age-group and by location: Measures how often people who saw your ad clicked on it.
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Step 2: Calculate Conversion Rate - The percentage of people who clicked the ad and completed a purchase.
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Step 3: Calculate Return on Ad Spend (ROAS) - Measures the revenue generated for each shilling spent on the campaign.
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Step 4: With all the data, identify high-performing segments: Those with high CTRs, low CPLs, high conversion rates and high ROAS.
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