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      An Overview of Open Source Data Visualization Tools


      Updated by Linode Contributed by Mihalis Tsoukalos

      Creating graphic visualizations for a data set is a powerful way to derive meaning from vast amounts of information. It provides a way to extract meaningful relationships between different aspects of your data depending on how the data is mapped and which graphic representations are chosen. Data visualization is a common practice in many sectors, including various scientific disciplines, business settings, the government sector, and education.

      There are many open source tools available to create sophisticated data visualizations for complex data sets. This guide will provide an introductory exploration of data analysis and 2D graphics rendering packages that can be used with R, Python, and JavaScript to generate data visualizations.

      In this guide you will complete the following steps:

      Before You Begin

      Ensure you have the following programs and packages installed on your computer:

      1. The R programming language
      2. The RStudio Desktop application
      3. Python 3.7.0 or higher
        1. pandas Python package
        2. Matplotlib Python package
        3. wordcloud Python package
      4. The Golang programming language

      Assumptions

      This guide assumes you have some basic familiarity with the following concepts and skills:

      1. Basic programming principles and data structures
      2. Are able to read code written in Go, Python, HTML, CSS, and JavaScript

      Create Your Data Sets

      In this section, you will create a data set using the contents of your Bash history file and optionally, your Zsh history file. You will then create a third data set using a Perl script that will extract information from the first two data sets. In the Create Visualizations for your Data section of the guide, you will use these various data sets to create corresponding visualizations.

      Data Set 1 – Bash History File

      A Bash history file stores all commands executed in your command line interpreter. View your 10 most recently executed commands with the following command:

      head ~/.bash_history
      

      Your output should resemble the following:

        
      git commit -a -m "Fixed Constants links"
      git push
      git diff
      docker images
      brew update; brew upgrade; brew cleanup
      git commit -a -m "Cleanup v2 and v3"
      git push
      git commit -a -m "Added examples section"
      git push
      cat ~/.lenses/lenses-cli.yml
      
      

      Create a new directory named data-setsto store your data and copy your Bash history file to the directory:

      mkdir data-sets && cp ~/.bash_history data-sets/data-1
      

      Data Set 2 – Zsh History File

      If you are using the Zsh shell interpreter, you can use its history file as a second data set. Zsh’s history file format includes data that you will need to exclude from your data set. Use AWK to clean up your Zsh history file and save the output to a new file in the data-sets directory:

      awk -F ";" '{$1=""; print $0}' ~/.zsh_history | sed -e "s/^[ t]*//" -e "/^$/d" > data-sets/data-2
      

      Data Set 3 – Perl Script

      To create your third data set, you will use a Perl script that categorizes the contents of your data set files. The categorization is based on the word count for each line of text in your data files or, in other words, the length of each command stored in your Bash and Zsh history files. The script creates 5 categories of command lengths; 1 – 2 words, 3 – 5 words, 6 – 10 words, 11 – 15 words, and 16 or more words.

      1. Create a file named command_length.pl in your home directory with the following content:

        ~/command_length.pl
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        #!/usr/bin/perl -w
        
        use strict;
        
        my $directory = "";
        my $filename = "";
        
        my $CAT1 = 0;
        my $CAT2 = 0;
        my $CAT3 = 0;
        my $CAT4 = 0;
        my $CAT5 = 0;
        
        if ( @ARGV != 1 ) {
           die <<Thanatos
              usage info:
                 Please use exactly 1 argument!
        Thanatos
        }
        
        ($directory) = @ARGV;
        opendir(BIN, $directory)
            || die "Error opening directory $directory: $!n";
        
        while (defined ($filename = readdir BIN) ) {
            # The following command does not process . and ..
            next if( $filename =~ /^..?$/ );
            process_file($directory."/".$filename);
        }
        
        print "Category1tt$CAT1n";
        print "Category2tt$CAT2n";
        print "Category3tt$CAT3n";
        print "Category4tt$CAT4n";
        print "Category5tt$CAT5n";
        exit 0;
        
        sub process_file {
            my $file = shift;
            my $line = "";
        
            open (HISTORYFILE, "< $file")
                || die "Cannot open $file: $!n";
        
            while (defined($line = <HISTORYFILE>)) {
                chomp $line;
                next if ( ! defined($line) );
                check_category($line);
            }
        }
        
        sub check_category {
            my $command = shift;
            chomp $command;
            my $length = length($command);
        
            if ( $length <= 2 ) { $CAT1 ++; }
            elsif ( $length <= 5 ) { $CAT2 ++; }
            elsif ( $length <= 10 ) { $CAT3 ++; }
            elsif ( $length <= 15 ) { $CAT4 ++; }
            else { $CAT5 ++; }
        }
            
      2. Run the Perl script. The script expects a single argument; the directory that holds all data files that you want to process. The file’s output will be saved in a new file command_categories.txt. The command_categories.txt file will be used later in this guide to create visualizations using R.

        ./command_length.pl data-sets > ~/command_categories.txt
        

        Note

        Your Perl script must be executable in order to run. To add these permissions, execute the following command:

        chmod +x command_length.pl
        

        Open the .command_categories.txt file to view the categorizations created by your Perl script. Your file should resemble the following example:

        command_categories.txt
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        "Category Name" "Number of Times"
        Category1 5514
        Category2 2381
        Category3 2624
        Category4 2021
        Category5 11055
            

      You now have three sources of data that you can use to explore data visualization tools in the next sections.

      • ~/data-sets/data-1
      • ~/data-stes/data-2
      • ~/command_categories.txt

      Create Visualizations for your Data

      In this section you will use the data sets you created in the previous section to generate visualizations for them.

      Visualize your Data with R and RStudio

      R is a specialized programming language used for statistical computing and graphics. It is especially good for creating high quality graphs, like density plots, line charts, pie charts, and scatter plots. RStudio is an integrated development environment (IDE) for R that includes debugging and plotting tools that make it easy to write, debug, and run R scripts.

      In this section, you will use the command_categories.txt file created in the Data Set 3 – Perl Script section and RStudio to create a pie chart and simple spreadsheet of your data.

      1. Open RStudio Desktop and create a data frame. In R, a data frame is a table similar to a two-dimensional array.

        DATA <- read.table("~/command_categories.txt", header=TRUE)
        
        • This command will read the command_categories.txt file that was created in the Data Set 3 – Perl Script section of the guide and create a data frame from it that is stored in the DATA variable.

        • The header=TRUE argument indicates that the file’s first row contains variable names for column values. This means that Category Name and Number of Times will be used as variable names for the two columns of values in command_categories.txt.

      2. Next, create a pie chart visualization for each column of values using R’s pie() function.

        pie(DATA$Number.of.Times, DATA$Category.Name)
        
        • The function’s first argument, DATA$Number.of.Times and DATA$Category.Name, provides the x-vector numeric values to use when creating the pie chart visualization.

        • The second argument, DATA$Category.Name, provides the labels for each pie slice.

        RStudio will display a pie chart visualization of your data in the Plots and Files window similar to the following:

        Pie Chart

      3. Visualize your data in a spreadsheet style format using R’s View() function.

        View(DATA, "Command Lengths")
        

        RStudio will display a spreadsheet style viewer in a new window pane tab.

        Spreadsheet

      Explore R’s graphics package to discover additional functions you can use to create more complex visualizations for your data with RStudio.

      Create a Word Cloud using Python

      Word clouds depict text data using varying font sizes and colors to visually demonstrate the frequency and relative importance of each word. A common application for word clouds is to visualize tag or keyword relevancy. In this section you will use Python3 and your Bash and Zsh history files to generate a word cloud of all your shell commands. The Python packages listed below are commonly used in programs for data analysis and scientific computing and you will use them to generate your word cloud.

      1. Create a file named create_wordcloud.py in your home directory with the following content:

        ~/create_wordcloud.py
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        #!/usr/bin/env python3
        
        import pandas as pd
        import matplotlib.pyplot as plt
        import sys
        import os
        from wordcloud import WordCloud, STOPWORDS
        from random import randrange
        
        path = sys.argv[1]
        
        if os.path.exists(path):
            print("Creating word cloud for file: " + path)
        
            data = pd.read_table(path, header = None, names=["CMD"], encoding = "iso-8859-1")
        
            skipWords = []
            skipWords.extend(STOPWORDS)
        
            words = ''.join(data['CMD'])
        
            w = WordCloud(
                    stopwords=set(skipWords),
                    background_color='gray',
                    width=4000,
                    height=2000
                    ).generate(words)
            filename = "word_cloud_" + str(randrange(100)) + ".png"
            print("Your wordcloud's filename is: " + filename)
        
            plt.imshow(w)
            plt.axis("off")
            plt.savefig(filename, dpi=1000)
        
        else:
            print("File" + path +  "does not exist")
            
      2. Run your Python script and pass the path of one of your data set files as an argument. The script will read the contents of the file using panda’s read_table() function and convert it into a data frame with a column name of CMD. It will then use the data in the CMD column to create a concatenated string representation of the data that can be passed to wordcloud to generate a .png wordcloud image.

        ./create_wordcloud.py ~/data-sets/data-1
        

        You should see a similar output from the Python script:

          
        Creating word cloud for file: /Users/username/data-sets/data-2
        Your word cloud's filename is: word_cloud_58.png
            
        
      3. Open the word_cloud_58.png image file to view your word cloud.

        Word Cloud

      You could use a similar process to create a word cloud visualization for any text you want to analyze.

      Visualize Data using D3.js

      D3.js is a JavaScript library that helps you visualize JSON formatted data using HTML, SVG, and CSS. In this section you will us D3.js to create and embed a pie chart visualization into a web page.

      To convert your data set into JSON, you will create a Golang command line utility that generates JSON formatted plain text output. For more complex data sets, you might consider creating a similar command line utility using Golang’s json package.

      1. Create a file named cToJSON.go in your home directory with the following content:

        ./cToJSON.go
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        package main
        
        import (
            "bufio"
            "flag"
            "fmt"
            "io"
            "os"
            "regexp"
            "sort"
        )
        
        var DATA = make(map[string]int)
        
        func lineByLine(file string) error {
            var err error
            f, err := os.Open(file)
            if err != nil {
                return err
            }
            defer f.Close()
        
            r := bufio.NewReader(f)
            for {
                line, err := r.ReadString('n')
                if err == io.EOF {
                    break
                } else if err != nil {
                    fmt.Printf("error reading file %s", err)
                    break
                }
        
                r := regexp.MustCompile("[^\s]+")
                words := r.FindAllString(line, -1)
                if len(words) == 0 {
                    continue
                }
        
                if _, ok := DATA[words[0]]; ok {
                    DATA[words[0]]++
                } else {
                    DATA[words[0]] = 1
                }
        
            }
            return nil
        }
        
        func main() {
            flag.Parse()
            if len(flag.Args()) == 0 {
                fmt.Printf("usage: cToJSON <file1> [<file2> ...]n")
                return
            }
        
            for _, file := range flag.Args() {
                err := lineByLine(file)
                if err != nil {
                    fmt.Println(err)
                }
            }
        
            n := map[int][]string{}
            var a []int
            for k, v := range DATA {
                n[v] = append(n[v], k)
            }
        
            for k := range n {
                a = append(a, k)
            }
        
            fmt.Println("[")
            sort.Sort(sort.Reverse(sort.IntSlice(a)))
        
            counter := 0
            for _, k := range a {
                if counter >= 10 {
                    break
                }
        
                for _, s := range n[k] {
                    if counter >= 10 {
                        break
                    }
                    counter++
                    fmt.Printf("{"command":"%s","count":%d},n", s, k)
                }
            }
            fmt.Println("];")
        }
            
        • The utility expects file paths to your Bash and Zsh data sets as arguments.
        • It will then read the files and find the 10 most popular commands and output it as JSON formatted data.
        • Several Golang standard library packages are used in the utility to perform operations liking reading files, using regular expressions, and sorting collections.
      2. Run the command line utility and pass in the paths to each command history data set:

        go run cToJSON.go data-set/data-1 data-set/data-2
        

        Your output should resemble the following:

          
        [
        {"command":"ll","count":1832},
        {"command":"git","count":1567},
        {"command":"cd","count":982},
        {"command":"brew","count":926},
        {"command":"unison","count":916},
        {"command":"gdf","count":478},
        {"command":"ssh","count":474},
        {"command":"rm","count":471},
        {"command":"sync","count":440},
        {"command":"ls","count":421},
        ];
            
        

        You are now ready to create your pie chart visualization and embed it into a web page using D3.js

      3. Create an HTML file named pieChart.html and copy and paste the following content. The DATA variable on line 31 contains the JSON data that was created by the cToJSON.go script in the previous step. Remove the JSON data in the example and replace it with your own JSON data.

        Note

        In this example, your JSON data is hardcoded in pieChart.html for simplicity. Web browser security constraints restrict how a document or script loaded from one origin can interact with a resource from another origin. However, you may consider using the d3-fetch module to fetch your JSON data from a specific URL.
        ~/pieChart.html
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        <!DOCTYPE html>
        <html lang="en">
          <head>
            <meta charset="utf-8">
            <title>History Visualization</title>
        
            <style type="text/css">
              * { margin: 0; padding: 0; }
        
              #chart {
                background-color: white;
                font: 14px sans-serif;
                margin: 0 auto 50px;
                width: 600px;
                height: 600px;
              }
              #chart .label{
                fill: #404040;
                font-size: 12px;
              }
            </style>
          </head>
        
          <body>
            <div id="chart"></div>
          </body>
        
          <script src="https://d3js.org/d3.v3.min.js"></script>
          <script type="text/javascript">
        
          var DATA = [
              {"command":"ll","count":1832},
              {"command":"git","count":1567},
              {"command":"cd","count":982},
              {"command":"brew","count":926},
              {"command":"unison","count":916},
              {"command":"gdf","count":478},
              {"command":"ssh","count":474},
              {"command":"rm","count":471},
              {"command":"sync","count":440},
              {"command":"ls","count":421}
              ];
        
            var width  = 600;
                height = 600;
                radius = width / 2.5;
        
            var pie = d3.layout.pie()
                        .value(function(d) { return d.count; })
        
            var pieData = pie(DATA);
            var color = d3.scale.category20();
        
            var arc = d3.svg.arc()
                        .innerRadius(0)
                        .outerRadius(radius - 7);
        
            var svg = d3.select("#chart").append("svg")
                        .attr("width", width)
                        .attr("height", height)
                        .append("g")
                        .attr("transform", "translate(" + width / 2 + "," + height / 2 + ")");
        
            var ticks = svg.selectAll("line")
                           .data(pieData)
                           .enter()
                           .append("line");
        
            ticks.attr("x1", 0)
                 .attr("x2", 0)
                 .attr("y1", -radius+4)
                 .attr("y2", -radius-2)
                 .attr("stroke", "black")
                 .attr("transform", function(d) {
                   return "rotate(" + (d.startAngle+d.endAngle)/2 * (180/Math.PI) + ")";
                 });
        
            var labels = svg.selectAll("text")
                            .data(pieData)
                            .enter()
                            .append("text");
        
            labels.attr("class", "label")
                  .attr("transform", function(d) {
                     var dist   = radius + 25;
                         angle  = (d.startAngle + d.endAngle) / 2;
                         x      = dist * Math.sin(angle);
                         y      = -dist * Math.cos(angle);
                     return "translate(" + x + "," + y + ")";
                   })
                  .attr("dy", "0.35em")
                  .attr("text-anchor", "middle")
                  .text(function(d){
                    return d.data.command + " (" + d.data.count + ")";
                  });
        
            var path = svg.selectAll("path")
                          .data(pieData)
                          .enter()
                          .append("path")
                          .attr("fill", function(d, i) { return color(i); })
                          .attr("d", arc);
          </script>
        </html>
        
      4. Navigate to your preferred browser and enter the HTML file’s absolute path to view the pie chart. For a macOS user that has stored the HTML file in their home directory, the path would resemble the following: /Users/username/pieChart.html

        JS Pie Chart

      Next Steps

      Now that you are familiar with some data visualization tools and simple techniques, you can begin to explore more sophisticated approaches using the same tools explored in this guide. Here are a few ideas you can consider:

      • Create a new data set by extracting all git related commands from your history files; analyze and visualize them.
      • Automate some of the techniques discussed in this guide using Cron jobs to generate your data sets automatically.
      • Explore the Python for Data Science eBook’s data visualization section for a deeper dive into using pandas.

      Find answers, ask questions, and help others.

      This guide is published under a CC BY-ND 4.0 license.



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      The 8 Best Web Management Tools for Small Businesses in 2019


      From site design to social media and everything in between, managing your web presence is integral in today’s market. There are several reasons why you need a strong online presence: it reinforces your brand, boosts your marketing efforts, and most importantly, helps you connect with your customers.

      But what if you’ve got a local clientele or are a brick-and-mortar operation that’s not really interested in doing the whole e-commerce thing? Do you really need a small-business website?

      The answer is an internet-troll-style, all-caps, gigantic YES: 97 percent of consumers use the web to search for local businesses. If you want your small business to reach its full potential, you need to have an online presence.

      But where do you start? After all, there are thousands of tools out there that claim they help with web management. So we’ve put our fingers on the keyboard and nose to the grindstone — gross, right? — to research the best web management tools for your small business. Here’s what we found.

      Web Design

      When it comes to web design you’ve got several options. Of course, you can always hire a professional team from a trusted firm to tackle your web design, but experts don’t come cheap. If you’re looking for a more cost-effective method, you’ll be pleased to learn that there are several programs that are easy to navigate, have beautiful templates, and provide easy click-to-edit functionality to make creating a beautiful website easy — even if you don’t know how to code or aren’t a professional designer. Here are some of the best.

      1. WordPress

      If you’re at all web savvy, WordPress is an excellent choice. WordPress is a content management system (CMS) that makes it easy to regularly update your website with fresh blog posts, news stories, and more. WordPress does have a slight learning curve, but it’s relatively easy to tackle and you’ll be pleased with the number of features.

      • Flexibility — WordPress is extremely versatile. You can create a blog, a portfolio, a business website, an online community, an online store, and basically anything else you can think of.
      • Simplicity — Not only is WordPress flexible, but it’s also simple to use. With WordPress, you don’t have to know how to code to get a responsive website up in minutes. Plus, tools like DreamPress are specifically built to make maintaining your site and keeping it secure really easy.
      • Popular — WordPress is one of the most popular web design tools on the market. That means there are thousands of pre-designed themes, useful plugins, and resources to help you build, modify, and update your site with ease.
      • Cost — Since WordPress.org software is free, it’s a budget-friendly option that can scale with your business as you grow and need more features.

      Get More with DreamPress

      DreamPress Plus and Pro users get access to Jetpack Professional (and 200+ premium themes) at no added cost!

      2. Remixer

      Remixer is another top-of-the-line website building tool that was whipped up fresh in DreamHost’s developer kitchen. Here’s why it’s great: if you need a DIY website that doesn’t look homemade, Remixer is a click-to-edit solution. So what else does Remixer have going for it? Glad you asked.

      • Responsive — With Remixer, you can rest assured that your website is 100 percent responsive. This means no matter what device your visitors are using to find your site, they’ll have a great experience. In 2018, a mobile-optimized website is a must for small-biz owners.
      • Easy — Remixer is designed with ease in mind. That’s why the click-to-edit interface makes it easy to import your own images. If you don’t have your own art, the royalty-free image library takes care of that too.
      • Quick — Just a few clicks on Remixer and you can get a website up and running in 10 minutes. Yeah, we timed it.
      • Easy to Export  You can start your site in Remixer and then easily export it to WordPress when you’re ready to add e-commerce or blogging functionality.

      Need a Beautiful Website?

      Design it yourself with Remixer, our easy-to-use website builder. No coding required.

      E-Commerce Software

      If you want e-commerce capabilities, we’d recommend building your website with WordPress — the flexibility and scalability are ideal for online stores. But remember: WordPress doesn’t come with an e-commerce solution out of the box. You’ll have to add that functionality, either via a plugin or an additional platform, to create a digital shopping cart.

      3. WooCommerce

      WooCommerce is a popular WordPress plugin and one of the best e-commerce solutions on the market. And it’s easy to get started! Basic features to help you manage payment, shipping, and other common tasks come baked in. If you need additional options, however, it’s easy to find extensions to add functionality.

      • Budget-Friendly — Both WordPress.org software and WooCommerce are completely free, as are some of the extensions (most of the others range from around $50 to $100). Getting your online store up and running doesn’t have to burn a hole in your pocket.
      • Scalable — WooCommerce makes scaling from small business to larger venture easy. For example, if you need more payment gateways, simply download and install a relevant extension or plugin.
      • Secure — Security is vital for every website but especially if you’re managing transactions. WordPress’ dedication to security and frequent updates will help protect your site. Additionally, customers using DreamHost’s managed WordPress options are protected by a built-in firewall that eliminates the need for additional security plugins.
      • Search Engine Optimized — WooCommerce itself is light on SEO-related features. It’s built using code optimized for SEO, and there are a few relevant extensions you can add, such as SEO Friendly Images. However, if you really want to improve your store’s chances of getting to the first page of Google, you can download a WordPress SEO plugin.

      Your Store Deserves WooCommerce Hosting

      Sell anything, anywhere, anytime on the world’s biggest eCommerce platform.

      4. Shopify

      Shopify is a subscription-based, e-commerce platform that makes it easy for beginners to build and customize an online store in very little time. It also provides a “guru” service to guide you through the creation and management of your e-commerce site.

      • Reliable — Shopify is one of the largest e-commerce solutions on the market. Because it requires a monthly subscription — plans currently start at $29 per month — you can count on Shopify’s rock-solid performance.
      • Integratable — It’s easy for DreamHost customers to integrate a Shopify store with your site. There’s also an app store where you can download add-ons that provide functionality such as social media, shipping, and accounting.
        Support — Shopify provides support through its comprehensive help center, where you can find everything from tutorials to troubleshooting guides. You also get access to 24/7 support via chat, email, or phone.

      Still wondering whether you should opt for WooCommerce or Shopify when building your e-commerce site? Wonder no more. We’ve put together a comprehensive guide to help you parse out which platform might be more suitable for your website.

      Social Media

      Even a small business can have a large social media presence. The social tools below will allow you to expand your web presence all in one integrated interface. Be in several places at once? It’s every small business owner’s dream!

      5. HootSuite

      HootSuite integrates with Facebook, Facebook Pages, Twitter, Instagram, Google+, Google+ Pages, LinkedIn, FourSquare, MySpace, YouTube, and WordPress. It allows you to schedule posts ahead of time, focusing on peak performance times for each platform.

      • Convenience — If you’re sick of switching between your social media profiles just to push out daily updates, then you’re going to love HootSuite. With HootSuite, you can manage all your social platforms from one location.
      • Collaboration — Many small businesses have several people creating content and posting across channels. If you don’t have a cohesive system, you’re setting yourself up for a social snafu. HootSuite gets everyone on the same page.
      • Reputation Management — HootSuite makes it possible to never miss a mention or complaint that your team needs to address.

      6. Buffer

      Buffer supports up to 25 social accounts, allowing you to post across all your platforms from one interface. Here are some of Buffer’s other top features.

      • Trends — Buffer has integrated analytics and insights, making it easier to tweak your social strategy based on data.
      • Scheduling — Buffer allows you to create a custom schedule for each day. You can do it daily, monthly, or annually — basically whenever it’s convenient.
      • Insights — You’ll never have to wonder when you should share your next Instagram pic or Facebook missive. Buffer keeps track of your followers so you know when it’s the optimal time to post.

      7. Everypost

      Everypost makes it easy to curate content from across the web, schedule your posts, and integrates with Facebook, Twitter, Google+, Linkedin, Pinterest, and tumblr.

      • Analytics — You can social smarter with the analytics provided by Everypost. The dashboard is sleek and easy to use.
      • Curation — Everypost helps you curate content directly within the app — no switching around to find shareable content, design content, and post content.
      • Customization — You can maximize the potential of each post you create. This helps you reach all of your audience members, no matter what platform they are on.

      Marketing

      It’s not enough to build your website and forget it; you’ve got to be strategic about getting your brand out into the world — and in front of the eyes of your potential customers — as much as possible. Even if your business is a brick-and-mortar deal, promoting your website and digital offerings will usher local Googlers into your doors.

      8. HubSpot

      HubSpot offers a free marketing tool and WordPress plugin to help keep track of potential customers and assist you with lead conversion — that is, converting a casual browser into a committed buyer.

      • Capture — HubSpot makes it easy to grab website visitors’ email addresses by helping you create a pop-up invitation or another module. It can even pull and track information from any form submission on your website — no matter what tool or plugin.
      • Track — Along with keeping track of visitors’ email addresses and activity on your website (purchases, time spent on page, etc.), you’ll know which other websites your potential customers have visited.  
      • Convert — Analytics displayed on a simple dashboard will help you know what’s working for your site (and what isn’t) and how to target marketing emails to bring your customers back.
      • Simple — The HubSpot WordPress plugin is simple to use, even for the non-techie, and it plays nicely with other tools such as Shopify.

      Want to Keep It Simple?

      Obviously, there are several tools out there to help you effectively manage your online presence. But you don’t have to overwhelm yourself by investing in every option on the market! If you’re brand new to the web, use Remixer to get your website up quickly. Then you can add some of the other options on this list to build your brand online.



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