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How to Use SKU & Inventory Tracking in Etsy: Step-by-Step Tutorial

By MCP Analytics Team | Published , updated

Category: Etsy Analytics

Introduction to SKU & Inventory Tracking on Etsy

Running a successful Etsy shop requires more than just creating beautiful products. You need to understand which items sell fastest and when to restock before you lose sales to stockouts. SKU (Stock Keeping Unit) tracking turns raw sales data into inventory decisions.

In this comprehensive tutorial, you'll learn how to implement a professional SKU and inventory tracking system for your Etsy shop. We'll cover everything from setting up your SKU codes to calculating optimal reorder points, which helps reduce the risk of stockouts. The method rests on assumptions about demand and supplier lead times, stated where they are used.

Whether you're managing 10 products or 1,000, this guide will help you answer critical questions like:

By the end of this tutorial, you'll have a complete understanding of how to track, analyze, and optimize your Etsy inventory using data-driven methods. Let's get started.

Worked Example: Should a Shop Reorder Now?

Question: Should a shop place a new order now?

Inputs: Simulated daily demand, lead time, stock on hand, stock already on order, and backorders. The script uses independent Poisson draws at a fixed rate and keeps days with no sales in the daily record.

Method: Use the sample standard deviation of daily demand, including zero-demand days. Estimate safety stock as z times that spread times the square root of lead time, and round up. Add safety stock to mean demand during lead time for the reorder point. Compare that point with inventory position: stock on hand plus stock on order minus backorders.

Complete Python script (needs numpy):

import numpy as np
from math import exp, factorial, ceil, sqrt

SEED = 17
DAYS = 30
DAILY_RATE = 1.2
LEAD_DAYS = 7
Z = 1.645
CYCLE_TARGET = 0.95
ON_HAND = 13
ON_ORDER = 4
BACKORDERS = 2

rng = np.random.default_rng(SEED)
demand = rng.poisson(lam=DAILY_RATE, size=DAYS)
mean_daily = float(demand.mean())
sd_daily = float(demand.std(ddof=1))
base_lead_demand = mean_daily * LEAD_DAYS
safety_stock = int(np.ceil(Z * sd_daily * np.sqrt(LEAD_DAYS)))
reorder_point = int(np.ceil(base_lead_demand + safety_stock))
inventory_position = ON_HAND + ON_ORDER - BACKORDERS

print(f"Simulated data, seed {SEED}")
print(f"Inputs: {DAYS} daily demand values incl. zero-demand days; "
      f"simulated Poisson rate={DAILY_RATE} units/day; "
      f"lead time={LEAD_DAYS} days; z={Z}; "
      f"approx. normal cycle-service target={CYCLE_TARGET:.0%}; "
      f"on hand={ON_HAND}; on order={ON_ORDER}; "
      f"backorders={BACKORDERS}")
print("Cycle service: chance of meeting all demand during lead time "
      "without a stockout.")
print("Daily demand:", ", ".join(map(str, demand.tolist())))
print(f"Mean daily demand: {mean_daily:.3f}")
print(f"Sample SD of daily demand: {sd_daily:.3f}")
print(f"Base lead-time demand: {base_lead_demand:.3f}")
print(f"Safety stock: {safety_stock}")
print(f"Reorder point: {reorder_point}")
print(f"Inventory position: {inventory_position}")
print("Decision:", "reorder now"
      if inventory_position <= reorder_point else "do not reorder now")
print("Limit: The simulated daily values are independent draws from "
      "one fixed rate; real demand or lead time may change or move "
      "together.")

# Built-in check: the data were drawn at DAILY_RATE, so the true
# lead-time demand is Poisson with this mean.
def poisson_cdf(k, mu):
    return sum(exp(-mu) * mu**i / factorial(i) for i in range(k + 1))

true_mu = DAILY_RATE * LEAD_DAYS
true_safety = ceil(Z * sqrt(DAILY_RATE) * sqrt(LEAD_DAYS))
true_rop = ceil(true_mu + true_safety)
print(f"Built in: true rate {DAILY_RATE} per day, so lead-time demand "
      f"averages {true_mu:.1f} units")
print("Chance that demand in lead time stays within this run's "
      f"reorder point ({reorder_point}): "
      f"{poisson_cdf(reorder_point, true_mu):.1%}")
print(f"With the true rate the reorder point would be {true_rop}, "
      f"covering {poisson_cdf(true_rop, true_mu):.1%}")
runs = 2000
good = 0
for s in range(runs):
    d = np.random.default_rng(s).poisson(lam=DAILY_RATE, size=DAYS)
    rp = int(np.ceil(d.mean() * LEAD_DAYS
                     + np.ceil(Z * d.std(ddof=1) * np.sqrt(LEAD_DAYS))))
    good += poisson_cdf(rp, true_mu) >= CYCLE_TARGET
print(f"In {runs} reruns of the 30-day estimate (seeds 0 to "
      f"{runs - 1}), the reorder point truly reached "
      f"{CYCLE_TARGET:.0%} in {good / runs:.1%} of runs")

Printed output:

Simulated data, seed 17
Inputs: 30 daily demand values incl. zero-demand days; simulated Poisson rate=1.2 units/day; lead time=7 days; z=1.645; approx. normal cycle-service target=95%; on hand=13; on order=4; backorders=2
Cycle service: chance of meeting all demand during lead time without a stockout.
Daily demand: 1, 1, 0, 1, 2, 0, 1, 2, 2, 0, 0, 1, 3, 0, 2, 2, 2, 0, 2, 1, 1, 1, 1, 2, 1, 1, 0, 0, 0, 0
Mean daily demand: 1.000
Sample SD of daily demand: 0.871
Base lead-time demand: 7.000
Safety stock: 4
Reorder point: 11
Inventory position: 15
Decision: do not reorder now
Limit: The simulated daily values are independent draws from one fixed rate; real demand or lead time may change or move together.
Built in: true rate 1.2 per day, so lead-time demand averages 8.4 units
Chance that demand in lead time stays within this run's reorder point (11): 85.7%
With the true rate the reorder point would be 14, covering 97.5%
In 2000 reruns of the 30-day estimate (seeds 0 to 1999), the reorder point truly reached 95% in 79.3% of runs

Result: The run gives mean demand of 1.000 units per day (the simulated rate is 1.2), a daily sample standard deviation of 0.871, safety stock of 4 units and a reorder point of 11 units. Inventory position is 15 units, so the result is do not reorder now. The 30-day mean came out below the simulated rate, so the reorder point of 11 covers lead-time demand 85.7% of the time, not the 95% the formula aims for; with the true rate it would be 14. Across 2,000 reruns of this 30-day estimate, the reorder point truly reached 95% in 79.3% of runs. A 95% target is what the formula aims for, not what a 30-day sample delivers.

Limit: This example uses simulated demand at a fixed rate and a fixed lead time. Real demand or lead time may change or move together.

Sources

Prerequisites and Data Requirements

Before diving into SKU and inventory tracking, ensure you have the following prerequisites in place:

Required Access and Tools

Data You'll Need to Collect

For effective SKU and inventory tracking, you'll need to gather the following information:

Understanding SKU Basics

A SKU is a unique identifier for each distinct product variant in your inventory. For Etsy sellers, a good SKU system might look like this:

CATEGORY-PRODUCT-VARIANT-SIZE
Examples:
MUG-FLORAL-BLUE-12OZ
TOTE-CANVAS-RED-LARGE
RING-SILVER-AMETHYST-7

If you haven't set up SKUs yet, now is the time. Consistent SKU naming makes analysis infinitely easier and helps you scale your business professionally.

Step 1: Set Up Your Etsy SKU System

The foundation of effective inventory tracking is a well-organized SKU system. If you already have SKUs assigned to all your products, you can skip to Step 2. Otherwise, follow these guidelines:

Creating a SKU Naming Convention

Your SKU system should be:

Here's a recommended structure for Etsy sellers:

[CATEGORY]-[PRODUCT]-[VARIANT]-[SIZE/OPTION]

Breaking it down:
- CATEGORY: 2-4 letter product category code (e.g., MUG, TOTE, RING)
- PRODUCT: Specific product line or design (e.g., FLORAL, GEOMETRIC, CUSTOM)
- VARIANT: Color, material, or style (e.g., BLUE, SILVER, COTTON)
- SIZE/OPTION: Size or other options (e.g., SMALL, 12OZ, 7)

Real examples:
MUG-FLORAL-BLUE-12OZ
TOTE-GEOMETRIC-CANVAS-LARGE
RING-CUSTOM-SILVER-8

Adding SKUs to Your Etsy Listings

Add a SKU to every listing and to each variation that you track. Etsy changes Shop Manager, so check Etsy's current help for the exact steps.

Pro Tip: Create a master SKU spreadsheet that lists all your SKUs with their full product descriptions, costs, and supplier information. This becomes your master record of SKUs.

Expected Outcome

After completing this step, every product variant in your Etsy shop should have a unique SKU assigned. You should be able to export order data that includes these SKU codes for analysis.

Step 2: Export Your Etsy Order Data

Now that your SKUs are properly set up, it's time to extract your sales data from Etsy. This data will form the basis of your inventory analysis.

Downloading Order Data from Etsy

Etsy publishes its own help for downloading your data: see the two Etsy Help pages in Sources above. Etsy changes Shop Manager, so check the steps and the file's columns against Etsy's current help and your own download.

Etsy gives you a CSV file of your order data. Check whether yours includes these columns, which the rest of the tutorial uses (names and availability depend on the export you choose):

Preparing Your Data for Analysis

Once you've downloaded the CSV, open it in your spreadsheet software. You'll want to create a clean dataset focused on SKU performance. Here's how to structure it:

Key columns to focus on:
- Order Date
- SKU
- Quantity
- Item Price
- Order ID (for counting unique orders)

Create calculated columns:
- Week Number: =WEEKNUM(Order_Date)
- Month: =MONTH(Order_Date)
- Revenue: =Quantity * Item_Price

For advanced users, you might want to aggregate this data by SKU. Here's a sample formula structure in Excel or Google Sheets:

To calculate total quantity sold per SKU:
=SUMIF(SKU_Column, "MUG-FLORAL-BLUE-12OZ", Quantity_Column)

To calculate average daily sales per SKU:
Put the first and last day of your export in two cells, for example
B1 = start date, B2 = end date, and the number of days in B3:
B3: =B2-B1+1
(counts both end days and every day with no sales; it is at least 1)
Then, for one SKU:
=SUMIF(SKU_Column, "MUG-FLORAL-BLUE-12OZ", Quantity_Column) / $B$3
Use the same $B$3 for every SKU so that days without sales count as zero.

Expected Outcome

You should now have a clean CSV file with at least these columns: Order Date, SKU, Quantity, and Price. This data is ready for inventory analysis.

Step 3: Analyze SKU Performance and Sales Velocity

With your data prepared, it's time to identify which SKUs are your stars and which are gathering dust. This analysis will drive your inventory decisions and help you focus on what's working.

Calculating Sales Velocity

Sales velocity tells you how fast each SKU is selling, and it drives the reorder calculation in Step 4. Here's how to calculate it:

Sales Velocity = Total Units Sold / Number of Days in Period (both end dates included, zero-sales days included)

Example calculation:
SKU: MUG-FLORAL-BLUE-12OZ
Units sold in 90 days: 180 units
Sales velocity: 180 / 90 = 2.0 units per day

Create a summary table with these columns (illustrative figures for four SKUs; the rank is among all of the shop's SKUs, so the last row is not rank 4):

| SKU                  | Total Sold | Days | Velocity | Revenue  | Velocity Rank |
|---------------------|------------|------|----------|----------|------|
| MUG-FLORAL-BLUE-12OZ| 180        | 90   | 2.00     | $3,240   | 1    |
| TOTE-CANVAS-RED-LG  | 135        | 90   | 1.50     | $4,050   | 2    |
| RING-SILVER-AME-7   | 90         | 90   | 1.00     | $2,700   | 3    |
| MUG-SOLID-GREEN-12OZ| 18         | 90   | 0.20     | $324     | 15   |

ABC Analysis for Inventory Prioritization

ABC analysis categorizes your inventory into three tiers based on revenue contribution. This helps you focus your attention where it matters most:

To perform ABC analysis in your spreadsheet:

1. Sort SKUs by total revenue (descending), with revenue in column E,
   headers in row 1 and SKUs in rows 2 to 100
2. In F2, calculate the share of revenue BEFORE this SKU (cumulative
   share of the SKUs above it), and fill down:
   =(SUM($E$2:E2)-E2)/SUM($E$2:$E$100)
3. In G2, assign the category from F2, and fill down:
   =IF(F2<0.8,"A",IF(F2<0.95,"B","C"))
   Using the share before the SKU means the SKU that crosses a threshold
   stays in the higher category. Example with revenues 960, 20, 10, 6, 4
   (total 1000): shares before are 0, 0.96, 0.98, 0.99, 0.996, so the
   categories are A, C, C, C, C. The 960 SKU is an A-item even though it
   alone is above 95%.

Identifying Trends and Seasonality

If you have more than 90 days of data, look for patterns:

This helps you avoid over-ordering seasonal items. Patterns seen in a few months of data are descriptive; a swing in one month can be ordinary variation, so look for a pattern that repeats before acting on it.

Expected Outcome

You should now have a ranked list of your SKUs with sales velocity, ABC classification, and trend information. You know which products are your revenue drivers and which are underperforming.

Step 4: Calculate Optimal Reorder Points

Knowing when to reorder is just as important as knowing what to order. Reorder too early, and you tie up cash in excess inventory. Reorder too late, and you lose sales to stockouts.

Understanding Reorder Point Components

The reorder point is when your inventory level triggers a new order. It's calculated using this formula:

Reorder Point = (Average Daily Sales × Lead Time) + Safety Stock

Where:
- Average Daily Sales: Your sales velocity from Step 3
- Lead Time: Days from placing order to receiving inventory
- Safety Stock: Buffer inventory to handle demand variability

Calculating Your Reorder Points

Let's work through an example with invented numbers (not from a real shop):

Product: MUG-FLORAL-BLUE-12OZ
Average daily sales: 2.0 units/day
Supplier lead time: 14 days
Desired cycle service: about 95% (z = 1.65, which assumes independent daily demand at a fixed rate and a fixed lead time)

Step 1: Calculate base demand during lead time
Lead Time Demand = 2.0 units/day × 14 days = 28 units

Step 2: Calculate safety stock
For 95% service level with moderate variability:
Safety Stock = 1.65 × Standard Deviation × √Lead Time

If your standard deviation of daily sales is 0.5 units:
Safety Stock = 1.65 × 0.5 × √14 = 1.65 × 0.5 × 3.74 = 3.09, round UP to 4 units

Step 3: Calculate reorder point
Reorder Point = 28 + 4 = 32 units

RESULT: When inventory position reaches 32 units, place a new order

Use inventory position as the trigger. It is stock on hand plus stock on order minus backorders. Record each open order and its amount as stock on order, and record unfilled orders as backorders. The worked example above shows the calculation.

Cycle service is the chance of meeting all demand during lead time without a stockout. Under the example's assumptions, estimate safety stock as z times the sample standard deviation of daily demand times the square root of lead time, then round up. Include days with no sales when you calculate daily spread. This method does not cover demand that changes over time or moves together across days.

Simplified Safety Stock Calculation

If calculating standard deviation feels overwhelming, use this simplified approach:

Safety Stock Rules of Thumb (starting values to adjust, not derived from data):

For A-Items (critical products):
Safety Stock = Average Daily Sales × Lead Time × 0.5

For B-Items (moderate importance):
Safety Stock = Average Daily Sales × Lead Time × 0.3

For C-Items (low priority):
Safety Stock = Average Daily Sales × Lead Time × 0.1

Example for our A-Item mug:
Safety Stock = 2.0 × 14 × 0.5 = 14 units
Reorder Point = 28 + 14 = 42 units

Determining Order Quantities

Once you know when to reorder, you need to know how much to order. Consider these factors:

Simplified Order Quantity Calculation:

Order Quantity = Average Daily Sales × Reorder Cycle Days

If you want to reorder every 30 days:
Order Quantity = 2.0 units/day × 30 days = 60 units

Or round up to supplier case quantities:
If supplier sells in cases of 24: Order 72 units (3 cases)

Creating Your Reorder Schedule

Build a master inventory tracking spreadsheet with these columns:

| SKU        | Current | Daily | Lead | Safety | Reorder | Order | Status       | Days Until |
|            | Stock   | Sales | Time | Stock  | Point   | Qty   |              | Reorder    |
|------------|---------|-------|------|--------|---------|-------|--------------|------------|
| MUG-FL-BL  | 45      | 2.0   | 14   | 4      | 32      | 60    | OK           | 6.5        |
| TOTE-CA-RD | 22      | 1.5   | 21   | 5      | 37      | 50    | REORDER NOW  | -10.0      |
| RING-SIL-7 | 15      | 1.0   | 10   | 3      | 13      | 30    | REORDER SOON | 2.0        |

Illustrative figures. Each reorder point is daily sales × lead time + safety stock, rounded up (tote: 1.5 × 21 + 5 = 36.5, so 37).

Calculate "Days Until Reorder":

= (Current_Stock - Reorder_Point) / Average_Daily_Sales

A negative value means stock is already below the reorder point; its size is how many days of sales below it you are (the tote above is 10 days of sales under its reorder point).

Expected Outcome

You now have specific reorder points and order quantities for each SKU. You know exactly when to place orders and how much to order to maintain optimal inventory levels.

Step 5: Flag Reorders in Your Spreadsheet

Manual inventory tracking works when you have a few products, but it takes more effort as you scale. Colour flags and a status column make the reorder list quick to read each time you update your stock counts.

Creating Spreadsheet Flags

You can flag SKUs at or near their reorder point using conditional formatting; the flags update when you update the stock counts:

In Excel/Google Sheets:

1. Select your "Current Stock" column
2. Apply conditional formatting rules:
   - If Current Stock < Reorder Point: Red background
   - If Current Stock < (Reorder Point × 1.2): Yellow background
   - Otherwise: Green background

3. Add a "Status" column with this formula:
   =IF(Current_Stock < Reorder_Point, "REORDER NOW",
      IF(Current_Stock < Reorder_Point*1.2, "REORDER SOON", "OK"))

Weekly Inventory Review Checklist

Establish a weekly routine to review your inventory:

  1. Monday Morning: Update current stock levels in your tracking system
  2. Review Reorder Flags: Check which SKUs have hit reorder points
  3. Place Orders: Submit purchase orders for items needing restock
  4. Update Sales Data: Add the past week's Etsy orders from a new export
  5. Recalculate Velocities: Update sales velocity calculations monthly
  6. Review ABC Classification: Quarterly review of product performance

Key Performance Indicators to Monitor

Track these metrics to measure your inventory management effectiveness:

1. Inventory Turnover Ratio = Cost of Goods Sold / Average Inventory Value
   Set your own target from your margins and cash.
2. Stockout Rate = (Days Out of Stock / Total Days) × 100
   Set a lower target for A-items than for C-items.
3. Carrying Cost = (Average Inventory Value × Carrying Cost %) / Revenue
   Use your own carrying cost percentage.
4. Fill Rate = (Orders Fulfilled Immediately / Total Orders) × 100
   Set your own target.

Expected Outcome

You now have a spreadsheet that flags SKUs at their reorder point whenever you update it. You're tracking key performance metrics and following a regular review schedule.

Interpreting Your Results

Now that you've implemented SKU and inventory tracking, let's interpret what your data is telling you and make strategic decisions.

Reading Your Sales Velocity Data

Your sales velocity analysis should reveal clear patterns:

Understanding Inventory Turnover

Your inventory turnover ratio tells you how efficiently you're using capital:

Turnover of 6x per year means:
- Average product sits for about 60 days before selling (365 / 6)
- You cycle through your entire inventory 6 times annually

Low turnover compared with your own earlier periods:
- Capital may be tied up in stock that sells slowly
- Risk of obsolescence increases
- Storage costs eat into profits

Very high turnover:
- Frequent stockouts are possible, so check lost sales
- More frequent small orders can raise ordering costs
- You may be missing supplier volume discounts

Identifying Opportunities and Issues

Your data will reveal actionable insights:

Making Data-Driven Decisions

Use your inventory analysis to inform broader business strategy:

  1. Product Development: Create variations of your fastest-moving SKUs
  2. Marketing Budget: Allocate ad spend to products with highest velocity and margin
  3. Supplier Negotiations: Use volume data to negotiate better terms
  4. Cash Flow Planning: Forecast inventory purchases based on reorder schedules
  5. Pricing Strategy: Test price increases on high-velocity, low-competition items

For more sophisticated analysis techniques, consider exploring advanced machine learning approaches to predict future demand patterns.

Run It on Your Own Etsy Export

Export your orders from Etsy as a CSV and upload it at MCP Analytics, then ask the question in plain language; the report names the method. Nothing connects to your Etsy account.

Try it on your data

Common Issues and Solutions

Even with a solid system in place, you'll encounter challenges. Here are the most common issues Etsy sellers face with SKU and inventory tracking, along with practical solutions:

Issue 1: SKUs Missing from Etsy Export

Symptom: When you download order data, the SKU column is blank for many items.

Cause: SKUs weren't assigned to listings before orders were placed, or variations don't have individual SKUs.

Solution:

Issue 2: Inconsistent Sales Patterns Make Forecasting Difficult

Symptom: Some days you sell 10 units of a SKU, other days zero, making reorder points unreliable.

Cause: High sales variability, seasonal products, or viral traffic spikes.

Solution:

Issue 3: Supplier Lead Times Keep Changing

Symptom: Your reorder points are based on 14-day lead time, but actual delivery ranges from 10-25 days.

Cause: Supplier inconsistency, shipping delays, customs issues.

Solution:

Issue 4: Running Out of Storage Space

Symptom: Your calculated order quantities would exceed your storage capacity.

Cause: Growing product line without proportional storage expansion.

Solution:

Issue 5: Can't Afford to Order Recommended Quantities

Symptom: Your optimal order quantity is 500 units, but you only have budget for 200.

Cause: Cash flow constraints limiting inventory investment.

Solution:

Issue 6: Etsy Data Export Doesn't Match Sales Reality

Symptom: Your exported data shows different quantities than what you actually shipped.

Cause: Cancelled orders, refunds, or custom orders not properly recorded.

Solution:

Issue 7: Too Many SKUs to Track Effectively

Symptom: You have 500+ SKUs and can't keep up with manual tracking.

Cause: Product line complexity without a system for tracking it.

Solution:

Still Having Issues?

If you're experiencing problems not covered here, the issue might be with your data collection or analysis method. Re-check that the export covers the period you think it does and that quantities add up to what you shipped.

Next Steps with Etsy Analytics

Congratulations! You've now implemented a comprehensive SKU and inventory tracking system for your Etsy shop. But this is just the beginning of data-driven shop management.

Expand Your Analytical Capabilities

Now that you have solid inventory analytics in place, consider these advanced analyses:

1. Customer Lifetime Value Analysis

Identify which products attract repeat customers and calculate their long-term value to your business. This helps you make smarter decisions about customer acquisition costs and marketing spend.

2. Product Bundle Analysis

Discover which SKUs are frequently purchased together. Use this data to create bundles, cross-sell recommendations, and strategic product placement.

3. Pricing Optimization

Test different price points for your products and measure the impact on both velocity and total revenue. Find the sweet spot that maximizes profit.

4. Marketing Attribution

If you're running Etsy ads or external marketing, combine your advertising data with SKU performance. Understand which marketing channels drive sales of which products.

5. Seasonal Forecasting

Build more sophisticated forecasting models that account for seasonality, trends, and promotional periods. Prepare inventory months in advance of peak seasons.

Continuous Improvement Process

Inventory management isn't a one-time project. It's an ongoing process. Implement this quarterly improvement cycle:

  1. Quarter 1: Establish baseline metrics and implement tracking systems
  2. Quarter 2: Optimize reorder points based on actual stockout and overstock data
  3. Quarter 3: Refine product mix, expand winners, eliminate losers
  4. Quarter 4: Improve supplier relationships and negotiate better terms using your data

Resources for Deeper Learning

Continue your journey toward data-driven Etsy success with these resources:

Your Journey Forward

You've taken a major step toward professional inventory management. The difference between successful Etsy shops and struggling ones often comes down to inventory optimization, having the right products in the right quantities at the right time.

With the system you've built today, you're now equipped to:

Remember: Perfect inventory management doesn't exist, but continuous improvement does. Review your metrics regularly, adjust your parameters as your business evolves, and always question whether your current approach is serving your goals.

To run this on your own export, see the Run It on Your Own Etsy Export section above.

Your turn

Bring your own data and the question you actually need answered.

MCP AnalyticsData science by email Send us your data and your question, and we will reply. ds@mcpanalytics.ai

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