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Gizmodo

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Being an entrepreneur means you must keep a lot of things under control. You have to always know what is going on in sales, marketing, finance, inventory, and other aspects of your business.

Moreover, you probably use a myriad of tools to run them. Switching between a CRM, marketing automation software, Google Analytics, Accounting System and your eCommerce back-end won’t provide a clear picture of how your business is doing. 

Wouldn’t it be great to have one screen showing the most important information about your business? eCommerce dashboards are here to give you this opportunity.

What is a dashboard and why do you need it?

eCommerce dashboard is a visual representation of up-to-date data (metrics and KPIs) important for the business. It helps to analyze the main indicators to improve the results. Dashboards are different from other analytics tools because they give insights on the most vital data. Thanks to dashboards you can check the vitals of your business any time you want and not just during your monthly reports.

This becomes possible because they accumulate real-time (or near real-time) data. With the help of a well-tuned dashboard, you can quickly spot and fix problems and see if your company is effective or not. It takes less time and effort to check a dashboard than to dig into monthly reports and collect info manually. 

Dashboards provide statistics on different areas of your online store, including, but not limited to monthly sales, website traffic, and marketing campaigns. You can compare data by any period of time: days, months, or even years. 

Let’s look at the most popular examples of dashboards for eCommerce.

5 eCommerce dashboards examples

We’ve prepared five examples of dashboards that will help you run your store. Keep in mind that there are no universal solutions that can fit everyone. The best solution for you depends on the complexity and scale of your business: are you going to create a marketplace or a small online store? How many product categories and markets are you going to cover? All of this will impact the types and number of KPIs you will have to track. 

Store overview

The overview dashboard shows the performance of the store across all major areas of the business like sales, marketing, inventory, and others. This type of dashboards enables you to see the most important metrics of your business on one screen. 

Goal: to get a snapshot of your store performance.

KPI examples: 

• Total sales
• Sales by product, marketing channel, geography, etc.
• Traffic
• Average order value
• Conversion rate
• Repeat customer rate
• Customer Lifetime Value

Dashboard Example: 

Google Analytics for eCommerce

Google Analytics is the number one tool for analyzing website traffic. It also provides a lot of information which makes it difficult to digest. That is why you need a Google Analytics eCommerce dashboard that will show only the most meaningful data for your online store. 

Goal: to check your website’s most current statistics.

KPI examples:

• Unique visitors
• Average session duration
• Bounce rate 
• Time on site
• Goal completions 
• Traffic sources
• Top pages
• Top keywords

Dashboard Example: 

Sales

eCommerce sales dashboard shows KPIs like total sales, as well as sales by product, marketing channel, location etc. This is a functional strategic tool that allows getting an instant overview of your sales to pinpoint any problem as soon as it occurs. It organizes the most precise and recent data related to commercial success.

Goal: to see current sales performance and compare it to your sales target.

KPI examples: 

• Total sales
• Sales target
• Average order value
• Lead conversion rate
• Up-sell and cross-sell rates
• Profit margin per product

Dashboard Example: 

Marketing

eCommerce marketing dashboards present vital KPIs in digital marketing to track channel and campaign results in real-time. These KPIs include traffic sources, macro and micro conversions, leads, ROI of marketing channels and campaigns, etc. The information is collected from such channels: Google Analytics, email marketing tools, CRM, social media, and others. 

Goal: to see how effective your marketing is in real-time. 

KPI examples: 

• Traffic
• Leads
• Sales
• Conversion rate
• Conversions by channel
• Customer acquisition cost (total and by channel)

Dashboard Example: 

Finance

This dashboard focuses on your budget and analyzes existing assets, profit and expenses, cash flow, income, and other financial indicators. It allows you to see the current figures and financial details to get helpful insights and increase the cost-efficiency of your business.

Goal: to see the current financial state and overall profitability of the business.

KPI examples:

• Revenue
• Net profit
• Gross / Net profit margin
• Cash balance
• Working capital
• Cost of goods sold

Dashboard Example: 

Wrapping up

To sum up, eCommerce metrics dashboards are useful tools that help track your business performance on a daily basis. They can either provide an overview of the entire business or may cover different areas from SEO to finances. 

The fundamental advantage is that you can check your KPIs any time, not just once a month when you prepare your reports. This will help to detect and fix problems almost on the spot. Another advantage is that dashboards are visual tools that allow you to digest information easily. Additionally, they are customizable and can show the indicators which you need the most.

In this article, we shed some light on what dashboards are and why they matter. We also gave examples of dashboards that might be helpful for your eCommerce business. Give these tools a try and get a clearer picture of how your store is doing.

The post 5 best examples of eCommerce dashboards to help you take control of your business appeared first on noupe.

noupe

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I’ve seen this meme posted several times online:

The point is always the same.  It’s not fair that the Red area is represented by eight senators and the green represented by two senators.

So the conclusion they draw is that we must abolish the Senate.

Because the Left already controls the House of Representatives, they need to attack the Senate.

I look at this and come to a different conclusion.

The population density of NYC is 27,000 per square mile.  The average for the United States is 94 per square mile.

New York City is 288 times more dense as the average.

Cities like NYC are unnatural and unsustainable.

They can’t grow enough food to feed their population or have landfill space to dispose of their garbage.

Clearly before we abolish the Senate we have to abolish the megacities.

https://theawesomer.com/photos/2022/01/heavy_metal_gordon_ramsay_t.jpg

Link

(PG-13: Language) Guitarist Andre Antunes is at it again – adding a heavy metal soundtrack to video clips that could benefit from musical accompaniment. This time, he took the angry tirades of Gordon Ramsay berating chefs on Hell’s Kitchen and amped up their intensity to 11.

The Awesomer

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Laravel News

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1Route::controller(PlacementController::class)
2    ->prefix('placements')
3    ->as('placements.')
4    ->group(function () {
5        Route::get('', 'index')->name('index');
6        Route::get('/bills', 'bills')->name('bills');
7        Route::get('/bills/{bill}/invoice/pdf', 'invoice')->name('pdf.invoice');
8    });

1// Returns 'Hello, Claire'
2Blade::render('Hello, ', ['name' => 'Claire']);
3 
4// Returns 'Foo '
5Blade::render('@if($foo) Foo @else Bar @endif', ['foo' => true]);
6 
7// It even supports components :)
8// Returns 'Hello, Taylor'
9Blade::render('<x-test name="Taylor" />');

Laravel News

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Laravel News Links

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The Pandas DataFrame/Series has several methods to handle Missing Data. When applied to a DataFrame/Series, these methods evaluate and modify the missing elements.

This is Part 13 of the DataFrame methods series:

• Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
• Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
• Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
• Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().
• Part 5 focuses on the DataFrame methods pct_change(), quantile(), rank(), round(), prod(), and product().
• Part 6 focuses on the DataFrame methods add_prefix(), add_suffix(), and align().
• Part 7 focuses on the DataFrame methods at_time(), between_time(), drop(), drop_duplicates() and duplicated().
• Part 8 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 9 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 10 focuses on the DataFrame methods reset_index(), sample(), set_axis(), set_index(), take(), and truncate()
• Part 11 focuses on the DataFrame methods backfill(), bfill(), fillna(), dropna(), and interpolate()
• Part 12 focuses on the DataFrame methods isna(), isnull(), notna(), notnull(), pad() and replace()
• Part 13 focuses on the DataFrame methods drop_level(), pivot(), pivot_table(), reorder_levels(), sort_values() and sort_index()

Getting Started

Remember to add the Required Starter Code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

Required Starter Code

import pandas as pd
import numpy as np 

Before any data manipulation can occur, two new libraries will require installation.

• The pandas library enables access to/from a DataFrame.
• The numpy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

Feel free to check out the correct ways of installing those libraries here:

• How to Install Pandas on PyCharm?
• How to Install NumPy on PyCharm?

If the installations were successful, a message displays in the terminal indicating the same.

DataFrame drop_level()

The drop_level() method removes the specified index or column from a DataFrame/Series. This method returns a DataFrame/Series with the said level/column removed.

The syntax for this method is as follows:

DataFrame.droplevel(level, axis=0)

For this example, we generate random stock prices and then drop (remove) level Stock-B from the DataFrame.

nums = np.random.uniform(low=0.5, high=13.3, size=(3,4))
df_stocks = pd.DataFrame(nums).set_index([0, 1]).rename_axis(['Stock-A', 'Stock-B'])
print(df_stocks)

result = df_stocks.droplevel('Stock-B')
print(result)

• Line [1] generates random numbers for three (3) lists within the specified range. Each list contains four (4) elements (size=3,4). The output saves to nums.
• Line [2] creates a DataFrame, sets the index, and renames the axis. This output saves to df_stocks.
• Line [3] outputs the DataFrame to the terminal.
• Line [4] drops (removes) Stock-B from the DataFrame and saves it to the result variable.
• Line [5] outputs the result to the terminal.

Output:

df_stocks

result

DataFrame pivot()

The pivot() method reshapes a DataFrame/Series and produces/returns a pivot table based on column values.

The syntax for this method is as follows:

DataFrame.pivot(index=None, columns=None, values=None)

For this example, we generate 3-day sample stock prices for Rivers Clothing. The column headings display the following characters.

• A (for Opening Price)
• B (for Midday Price)
• C (for Opening Price)

cdate_idx = ['01/15/2022', '01/16/2022', '01/17/2022'] * 3
group_lst = list('AAABBBCCC')
vals_lst  = np.random.uniform(low=0.5, high=13.3, size=(9))

df = pd.DataFrame({'dates':  cdate_idx,
                                    'group':  group_lst,
                                   'value':  vals_lst})
print(df)

result = df.pivot(index='dates', columns='group', values='value')
print(result)

• Line [1] creates a list of dates and multiplies this by three (3). The output is three (3) entries for each date. This output saves to cdate_idx.
• Line [2] creates a list of headings for the columns (see above for definitions). Three (3) of each character are required (9 characters). This output saves to group_lst.
• Line [3] uses np.random.uniform to create a random list of nine (9) numbers between the set range. The output saves to vals_lst.
• Line [4] creates a DataFrame using all the variables created on lines [1-3]. The output saves to df.
• Line [5] outputs the DataFrame to the terminal.
• Line [6] creates a pivot from the DataFrame and groups the data by dates. The output saves to result.
• Line [7] outputs the result to the terminal.

Output:

df

result

DataFrame pivot_table()

The pivot_table() method streamlines a DataFrame to contain only specific data (columns). For example, say we have a list of countries with associated details. We only want to display one or two columns. This method can accomplish this task.

The syntax for this method is as follows:

DataFrame.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True)

For this example, a comma-delimited CSV file is read in. A pivot table is created based on selected parameters.

Code – Example 1:

df = pd.read_csv('countries.csv')
df = df.head(5)
print(df)

result = pd.pivot_table(df, values='Population', columns='Capital')
print(result)

• Line [1] reads in a CSV file and saves to a DataFrame (df).
• Line [2] saves the first five (5) rows of the CSV file to df (over-writing df).
• Line [3] outputs the DataFrame to the terminal.
• Line [4] creates a pivot table from the DataFrame based on the Population and Capital columns. The output saves to result.
• Line [5] outputs the result to the terminal.

Output:

df

result

For this example, a comma-delimited CSV file is read in. A pivot table is created based on selected parameters. Notice the max function.

Code – Example 2

df = pd.read_csv('countries.csv')
df = df.head(5)

result = pd.pivot_table(df, values='Population', columns='Capital', aggfunc=[max])
print(result)

• Line [1] reads in a comma-separated CSV file and saves to a DataFrame (df).
• Line [2] saves the first five (5) rows of the CSV file to df (over-writing df).
• Line [3] creates a pivot table from the DataFrame based on the Population and Capital columns. The max population is a parameter of aggfunc. The output saves to result.
• Line [4] outputs the result to the terminal.

Output:

result

DataFrame reorder_levels()

The reorder_levels() method re-arranges the index of a DataFrame/Series. This method can not contain any duplicate level(s) or drop level(s).

The syntax for this method is as follows:

DataFrame.reorder_levels(order, axis=0)

For this example, there are five (5) students. Each student has some associated data with it. Grades generate by using np.random.randint().

index = [(1001, 'Micah Smith', 14), (1001, 'Philip Jones', 15), 
         	(1002, 'Ben Grimes', 16), (1002, 'Alicia Heath', 17), (1002, 'Arch Nelson', 18)]
m_index = pd.MultiIndex.from_tuples(index)
grades_lst = np.random.randint(45,100,size=5)
df = pd.DataFrame({"Grades": grades_lst}, index=m_index)
print(df)

result = df.reorder_levels([1,2,0])
print(result)

• Line [1] creates a List of tuples. Each tuple contains three (3) values. The output saves to index.
• Line [2] creates a MultiIndex from the List of Tuples created on line [1] and saves to m_index.
• Line [3] generates five (5) random grades between the specified range and saves to grades_lst.
• Line [4] creates a DataFrame from the variables on lines [1-3] and saves to df.
• Line [5] outputs the DataFrame to the terminal.
• Line [6] re-orders the levels as specified. The output saves to result.
• Line [7] outputs the result to the terminal.

Output:

df

result

DataFrame sort_values()

The sort_values() method sorts (re-arranges) the elements of a DataFrame.

The syntax for this method is as follows:

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)

For this example, a comma-delimited CSV file is read in. This DataFrame sorts on the Capital column in descending order.

df = pd.read_csv('countries.csv')
result = df.sort_values(by=['Capital'], ascending=False)
print(result)

• Line [1] reads in a comma-delimited CSV file and saves to df.
• Line [2] sorts the DataFrame on the Capital column in descending order. The output saves to result.
• Line [3] outputs the result to the terminal.

Output:

DataFrame sort_index()

The sort_index() method sorts the DataFrame.

The syntax for this method is as follows:

DataFrame.sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)

For this example, a comma-delimited CSV file is read into a DataFrame. This DataFrame sorts on the index Country column.

df = pd.read_csv('countries.csv')
df = df.set_index('Country')
result = df.sort_index()
print(result)

• Line [1] reads in a comma-delimited CSV file and saves to df.
• Line [2] sets the index of the DataFrame to Country. The output saves to df (over-writing original df).
• Line [3] sorts the DataFrame (df) on the indexed column (Country) in ascending order (default). The output saves to result.
• Line [4] outputs the result to the terminal.

Output:

Finxter

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Link

Rhys Darby (Murray from Flight of the Conchords) stars in this high-seas comedy adventure series about a wealthy man who abandons his life of privilege to become a pirate. Taika Waititi, the busiest man in Hollywood, does double duty as Executive Producer and performs as Blackbeard. Premieres 3.2022 on HBO Max.

The Awesomer

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Are you ready to head back to Middle Earth? Amazon Studios revealed the title and trailer Wednesday for its highly anticipated prequel to the “Lord of the Rings” series, called “The Lord of the Rings: The Rings of Power.”

The series will debut on Prime Video on Sept. 2.

“The Rings of Power” is set in the Second Age of Middle Earth, thousands of years before the events of J.R.R. Tolkien’s “The Hobbit” and “The Lord of the Rings.”

The series “will take viewers back to an era in which great powers were forged, kingdoms rose to glory and fell to ruin, unlikely heroes were tested, hope hung by the finest of threads, and the greatest villain that ever flowed from Tolkien’s pen threatened to cover all the world in darkness,” Amazon said in its YouTube description for the trailer.

Amazon founder Jeff Bezos tweeted an image of himself holding a big slab of wood with the series title on it. “Can’t wait for you to see it,” he wrote.

IGN has behind-the-scenes details on how the title sequence was created, and it wasn’t with CGI, but rather with molten metal and a “hunk of reclaimed redwood.”

Amazon first announced that it had acquired the rights to adapt Tolkien’s work in 2017.

“’The Lord of the Rings’ is a cultural phenomenon that has captured the imagination of generations of fans through literature and the big screen,” Sharon Tal Yguado, head of Scripted Series for Amazon Studios, said in a statement at the time. 

Tolkien’s book series was named Amazon customers’ favorite book of the millennium in 1999. Director Peter Jackson’s theatrical adaptations included “The Fellowship of the Ring” (2001); “The Two Towers” (2002); and “The Return of the King” (2003). The films grossed nearly $6 billion worldwide and won a combined 17 Academy Awards, including Best Picture for “King.”

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