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I’m going to assume that because this topic is asking about lead magnets that you already know what it is and what it’s for. So, I’ll go ahead and dive right in by saying that lead magnets, quite simply, work. In fact, when you do it well and manage to give your audience a clear idea of what they’re giving their contact details for, your opt-in rate can increase by almost 85%.It almost goes without saying that lead magnets should be used wisely. You have to be strategic and thoughtful when implementing this kind of marketing technique. Here are some tips that have worked for me:1. Get to know your customersAny marketing effort should begin with your audience. Right now, you have the potential to reach a massive audience online. However, it would be unrealistic to assume that you could reach and be relatable to everyone.Global digital population as of July 2019 (Image Source)No matter how well you craft your copy, how well you design your pages, it won’t matter if you’re not presenting it to the right audience. If they don’t see your offer as relevant or useful, they won’t pay attention. So, you can only imagine how much harder it will be to get them to access gated content if you’re pitching something that doesn’t have value to them.That said, consider first who your customer is. What do they want and need? What are their interests? What do you have that can address these?Lead magnets that work offer something that adds value or answers a need—that’s what draws them to your offer and compels them to sign up.2. Test!I’m a big advocate of always testing your marketing materials. Your lead magnets shouldn’t be exempted from that. You want to understand what copy, design, format, and offer actually elicits the response you want from your audience. A simple split test or A/B test can help you understand audience feedback and behavior to better optimize your lead magnets.3. Explore different content as a lead magnetLead magnets take many forms. Here are some that have proven to be most effective for me:Cheat sheets – These are short tips, lists, and worksheets that focus on a specific challenge your audience usually encounters. They’re concise and easy to write, especially if you’ve gone through the same challenge yourself. However, I would advise that you have it professionally designed so it’s presented in a visually appealing way to add value.Templates – Free downloadable templates are a great resource. Create one that you know actually works for you. For example, if you use a content calendar, you have this template available as a lead magnet for download.Free training – I often offer this in the form of a podcast, webinar, or training video. A quick 10-minute clip or session where audiences can learn something new is a great draw.Whitepapers, ebooks, and tool kits – These are a little more complicated to put together. They’re longer and more comprehensive, but they’re considered high-value content that often compels users to opt-in.There you have it. Hope this helps! If you have any more questions about this topic, or if you want to continue this conversation, go ahead and reach out to me at Leadspanda. I’d be more than happy to answer any of your questions.

A step-by-step guide on how to design a website without code, from brainstorm and research to building prototypes, testing the usability, then setting up to promote it.To design a great website may be a daunting challenge, but you will find this process interesting and enjoyable by keeping the basics in mind. Undoubtedly, there is more to it than just looking good, and good design can not only make the website stand out, but also boost the site traffic & conversion rates indirectly. In the following, we will show you how to design website without code, with general guidelines and one-by-one steps to help keep people coming back.Step 1 – Get design inspirationsWhether you’re encountering a design bottleneck or afraid that you don’t have many inspirations to accomplish the entire process, it’s best to study and learn from others. You will get insights and ideas by comparing and analyzing others’ work, and here are 6 recommended visually-catching design examples for you to refer to in below.1. Product Hunt (Web, Community)2. Shopify (Web, E-commerce)3. So Stereo (Web, Music)4. StyleXstyle (Web, Fashion)5. edX (Web, Education)6. Fedena (Web, Software)Step 2 – Build web prototypesInsights of the industry-leading UXers and PMs in ChinaUXer TalksIn the early stage of inspiration discovery, various of means will be used from time to time to track and document a myriad of creative ideas, in the form of low-fi prototypes like sketches, charts and papers. After that, we need to refine the work and strategies, and the page prototype or the framework design can be the core part of this process. This is the best way to simulate user scenarios and communicate with PM/web development engineers.The above examples are made with Mockplus, a fast and easy web prototyping tool. It’s code-free with comprehensive functionality to meet most of your design needs. Following are some typical design scenarios:1. Visualized interactions with multiple interactive triggers & commands* Hover to show the contentThe mouse-over is a widely-used element in web design, and Mockplus gives full support for it. To click the Flashicon on the properties panel next to the Color, Border and Textwill help you set the interactions when MouseHover or ClickOn.* Auto-load animations when switching pagesThis kind of interaction is pretty common also in App design. Taking the Starbucks App as an example, there will be mini-animations after page switching, which can be easily achieved by setting the interaction trigger to “OnLoad” in Mockplus.2. A wealth of built-in components and favorite library* Hamburger menuThis can be made by using the Popup Panel with editable components included. Here is a typical example of user directions on the top.* Floating navigation bar & display horizontallyWith the popularity of card-style design, more and more websites have started to use a number of pictures, and the horizontal scrolling of pictures can be quickly fulfilled via the Horizontal scroll of Scroll Box component.3. Reusable elements* Repetitive layout and imagesThis is quite common especially in the news and food design, and we can use the Repeater and Auto Data Fill of Mockplus together to get that done. It’s time-saving and satisfying.4. Rich UI icons and templates* Icons from social, multi-media to nature, sports and brand, etc.Furthermore, there is a complete set of page templates and design resources of UI Kitsfor free download and use. All those are meant to get you started on web design effective and efficiently.Step 3 – Conduct usability testingThough usability testing is not the ultimate goal of prototyping, it’s one of the most crucial parts of web design. We should and have to attach importance to the testing functionality and final results especially at present where the user-centric and adaptive web design has been getting rising popularity. There are up to 8 different ways to test and preview the prototypes in Mockplus, more details are available in the following video.Step 4 – Set up your personal websiteCurrently, there are all kinds of premium or free CMS tools on the market that help both designers and backend developers get work done with least efforts. Here are the recommended website design software without code and steep learning curve.1. CloudPressURL:http://www.cloud-press.net/Suitable for personal use.This tool helps to create responsive WordPress websites without writing any line of code or development skills. There are more than 80 tutorials published by the CloudPress team, covering every detail in page design, from font color, page layout to various sizes and animations.2. BubbleURL:https://bubble.is/Suitable for personal and enterprise use.Bubble can be used to create web and mobile apps, just drag and drop the elements to the interface to start your design online. Those elements include text, maps, video, buttons and other various types of elements.3. XPRSURL:https://xprs.imcreator.com/Suitable for personal use.If you want to build a website without complicated, confusing and expensive development process, then this can be your tool of choice. It allows you to make design with polydoms (smart content blocks) with endless customizations. It’s very much like paying the Legos online.4. HTML to WordPressURL:https://htmltowordpress.io/Suitable for personal and enterprise use.As its name implies, this tool can transform your static HTML into a WordPress them for your web design. It’s a top choice for anyone who wants to update or migrate website without coding. Just upload your HTML to generate your own website.Wrap UpGenerally, there are 4 main steps in designing a great website, but things are often more complex and require collaborative efforts when we actually do it. It requests multi-user editing and online markups in the shared prototypes, and Mockplus has the team collaboration features ready for you already. It’s said that the Enterprise version is just coming on the way, which will allow the enterprise and big team to manage the team with different admin permissions and options.

Updated Aug 2018The following sections will outline five skills that will help you further a career as a Data Analyst:Data Exploration via Excel/Google SheetsData Extraction with SQLData Visualization via TableauData Automation via PythonData Analysis/Science with Python + Stat librariesWho this is for - College students, new graduates, career changers, and new analysts will probably benefit most from this article. It assumes you have minimal analytics, programming, or work experience. This article should help you build a foundation so you can begin or further a career in data analytics.Who I am - I’m a self-taught analyst who has worked at various companies (Netflix, CNET, Zynga) in a variety of analytical roles (Marketing, Finance, Social, Growth) for over a decade.Two notes before proceeding:This article will not outline how to become a data scientist or data engineer (read more about the differences), which generally require degree(s) in statistics or computer science respectfully.While you can learn these in any order, you’ll probably progress most seamlessly by starting with #1 and #2 before #3–51. Data Exploration via Excel / Google SheetsAt most organizations, Microsoft Excel and/or Google Sheets are the most broadly used data applications. While many tools perform a specific function very well (such as Tableau for visualization), few can enable most lightweight data tasks as easily as a spreadsheet. Not only are Gsheets/Excel the Swiss Army knives of data exploration, they also have a relatively shallow learning curve, which make either a great tool to learn first. If you’re dead-set on other analysts skills, don’t spend too much time here--but don’t make the mistake of not becoming familiar with a spreadsheet program either. Many data questions can be answered and communicated with a spreadsheet faster than with other technologies.Start by learning the following:FormulasGeneral Formulas. Once you’ve downloaded the data, see if you can enhance it with some formulas. The IF statement, boolean logic (AND, OR), and VLOOKUP functions are the most common formulas used across spreadsheets. Afterward, graduate to learning text-based formulas like MID, LEFT/RIGHT, SUBSTITUTE, TRIM. Experiment with the date formulas--such as converting a date (in any format) to the components of a date (year, month, day).Formula References. You should know the difference between an absolute and a relative reference as well as how to input either via editing a formula using the keyboard (F2) as well as toggling either (F4) via the keyboard.Aggregation Formulas. These formulas help you find conditional summary level statistics: SUMIF(s) , COUNTIF(s), and SUMPRODUCT, which are good to learn for reporting purposesInterested in learning more formulas? See this article.Data Filter. The data filter is a key feature which helps end users, sort, filter, and understand a sample from a large data set. Memorize the keyboard shortcut for creating one--you’ll use this often.Pivot Tables. Pivot tables allow an end users to easily get summary level statistics for a given dataset. Learn how to create a pivot table, and scenarios in which to place fields or metrics in the row, column, filter, or value section. Learn how to create formulas at the pivot table level, and understand how creating them on a pivot table is different than at the data table level. Finally, learn the GETPIVOTDATA function, which is especially useful when creating dashboardsCharting and Pivot Charting. Lean how to create bar, line, scatter, and other charts in Excel. Formatting charts is relatively easy--when you want to change something click on it (or right click), and in general the Excel Ribbon or the right click menu will allow you to modify the look and feel of a chart within the ribbon or or menu.Keyboard Shortcuts. As you begin to get more comfortable, begin mastering the keyboard shortcuts rather than use the mouse. Start by learning the basic shortcuts for tactics like find and replace and paste special. Then move onto to navigating using the keypad. Experiment with selecting rows and columns by using a combination of shift and control. You should eventually learn how to add rows/columns, hide rows/columns, delete rows/columns--all by using the keyboard.Excel Dashboard Design. Learn the Data → Pivot → Presentation pattern, in which one separates the source data from summarized data, and summarized data from the viewable dashboard. This pattern will allow you to easily update a report as more data comes in as well as hide complexity from those who just want to see the most important learnings. How? The first tab contains your data, which you should ideally not change. The second tab contains one or many pivot tables that calculate summary statistics needed for the report. The third tab is a dashboard with one or many visuals or data tables that source data primarily from the second tab (and not from first tab). You’ll present just the third tab to end users, but hide the first and second tabs. When displaying summary level statistics, you’ll likely leverage GETPIVOTDATA—instead of using other summary formulas—will has a faster runtime than the summary formulas. This article explains how to create a dashboard using GETPIVOTDATA such that an end user can select various input options and see a visualization change---Some notes:Excel or Google Sheets? Google Sheets performs best with smaller datasets (<10k rows). It’s also free. Out of the box, Gsheets is also more collaborative, and a good solution if your dataset will be viewed or modified by multiple stakeholders. For larger datasets, spreadsheets with lots of formulas, or the use of esoteric features, Excel is usually the preferred optionDon’t learn Excel VBA. If you’re interested in programming, skip to the Data Programming section and consider Python instead.2. Data Extraction with SQLExcel allows you to slice and dice data, but it assumes you have the data readily available. As you become a more seasoned analyst, you'll find that a better way to get at data is to pull it directly from the source, which often means authoring SQL.The great news about SQL is that unlike a procedural based programming language like Python, SQL is a declarative language. In most cases, instead of writing step-by-step syntax to perform an operation, you describe what you want. As a result, you should be able to learn SQL faster than learning most programming languages.I’m not going to outline all of the flavors of data storage solutions (to start, learn about relational vs non-relational databases) but instead focus on what you’re most likely to encounter--a relational database which supports some flavor of SQL.Start by learning the big six reserved keywords:SELECTFROMWHEREGROUP BYHAVINGORDER BYNext, you’ll want to learn common sql functions, such as the CASE statement, boolean operators (AND, OR, NOT), and IFNULL/COALESCE. Next, learn string functions such as INSTR, SUBSTR, and REPLACE.As you begin to write summary level queries which use the GROUP BY keyword, experiment with the aggregate functions such as SUM, COUNT, MIN, and MAX. Following that, learn how to join to other tables. Know the difference between an inner and outer join.Next, take a break from writing SQL and invest in learning more about how relational databases are structured. Know the difference between a fact and dimension table, understand why database indexes (or partitions) are leveraged, and read about why traditional database adhere to 1st, 2nd, and 3rd normal forms. If someone says they have a high cardinality dataset, a snowflaked schema, or slowly changing dimension--you should know what they mean.As you work with larger datasets, you’ll discover that more involved SQL queries require issuing several SQL queries in sequence. For example, the first query may create a table; the second one will insert data into that table; and the third will extract such data. To get started here, read more about temporary tables. Then you’ll want to learn about column data types as well as how to create traditional database tables and indexes/partitions to support more performant querying.---Some notes:SQL Bolt has a great interactive tutorial to help you learn SQL by doingToptal’s top SQL interview questions can help you get your next job that requires knowing SQLThis section only covered data extraction. As you become more senior, you’ll need to know how to build intermediary tables for analysis, or even construct source tables to store non-temporal data. Read more about SQL DML and DDLIf you’re interested in learning more about dimensional modeling, purchase Kimball’s The Data Warehouse Toolkit, which was originally published in 1996 but still relevant for traditional relational databases today.Try creating your own database locally by downloading and installing mysql or postrgres. Or do so via google cloud.This section only covered relational databases. See this article to learn more about non-relational databases3. Data Visualization via TableauIn the past decade, Tableau has become the leading enterprise tool for visualization. If you’re familiar with pivot tables, you’ll find that creating lightweight visualizations and dashboards with Tableau is relatively easy. To spreadsheet users, Tableau feels like working with an enterprise version of Pivot Tables and Pivot Charts. While keeping your analyses private requires a purchased Tableau Desktop license, Tableau public--which stores any saved analyses to the publicly accessible Tableau portal--is free and a great way to get started learning.Let’s start with Tableau Public--begin by creating an account and downloading the software, and then import a dataset into Tableau. Next, learn more about the panels within the tool. You’ll see the data you’ve added broken up into Dimensions and Measures. Try dragging a given dimension into the columns shelf, and a given measure into the Rows shelf. Tableau will analyze the structure of your data, and automatically generate a visualization (without you selecting one). You can easily change the visualization displayed by changing the type, or by shifting the data between Rows and Columns.After you’ve created a couple of different visualizations across multiple worksheets, create a dashboard. A dashboard can contain one or many views (worksheets) and also allow an end user to manipulate such a view via buttons, filters, and other controls. Start by adding one view to your new Dashboard. Then, add a Filter for a given measure or dimension. Once added, you can change the nature of each filter. For example, you can create a slider to change the range of dates included, or add a radio form to allow an end user to select a given measure. Once you have a functional dashboard, feel free to save it to Tableau Public so you can both view it as an external user would as well as modify it later. For inspiration, see some existing dashboards.From here, there’s a lot more you can do and learn. Tableau’s learning curve quickly steepens as you produce more advanced visualizations and deal with more complex datasets. If you want to continue learning, your best bet is to watch Tableau’s series of free training videos.---Some notes:While Tableau is the current Enterprise visualization market leader, it may not be five years from now. Tableau started as a desktop application and then grew to support web-based reporting, and now many upstarts are producing Tableau-like tools that are 100% browser based (See alternatives to Tableau), responsive by default, and built to work in the cloud as well as integrate with other sources.4. Data Programming via PythonNow you can source data from a database with SQL, manipulate it with a spreadsheet, and publish visualizations via a Tableau dashboard. A next natural step is to learn a programming language. Python is the most utilized programming language in the data community as well as the most common language taught at universities. With it you can achieve a number of data-related tasks such as extracting data from a website, loading said data into a database, and emailing the results of a SQL select statement to a set of stakeholders. If you’re interested in building web application, you could use Python and Flask to create an API as well as create a website leveraging the Flask HTML templating engine Jinja2. Or, you can leverage Python Notebooks for iterative development, the PANDAS library to see the results of a model you’re building as you develop it.The best way to build a strong programming foundation is to start by learning computer science fundamentals. For example, I was introduced to many computer programming concepts via the book Structure and Interpretation of Computer Programs (SICP) at university. Although originally authored in 1979, the book’s concepts are still relevant today and are still leverage today used at UC Berkeley to teach introductory computer science. Once you learn many of the fundamentals, you should be able to apply them to learn any computer programming language. However, learning the fundamentals can take a lot of time--and the content in SICP is academically dense (this review describes it well). Sometimes the better tactic to get started is to learn by doing.I learned python syntax years ago via Learn Python the Hard Way. The online course costs $30 now--and there are plenty of other free alternatives--but when I took the course (at the time it was free), I found it to be one of the better tutorials for learning the Python syntax. If you’re looking for a free option, head to Learn Python or Code Academy.You will have covered python basics when you’re familiar with python variables, control-flow, data structures (lists, dictionaries), classes, inheritance, and encapsulation. A good way to solidify your knowledge is to think of a project you’d like to implement and begin developing—this site has a couple of datasets that you can use to get started.Now that you have the basics down, you’ll want to learn more about how to become a more productive programmer by improving your development environment. The next three sub-sections will cover how to save/share/iterate your work using Github, author Python scripts using Jupyter Notebooks, and make changes to projects using the command line.4a. Learn version control using GitHub/git.GitHub allows you to host, update, document, and share your projects easily online. You’ll soon discover that GitHub will likely be where you end up when you’re discovering new programming libraries. Start by creating a GitHub account (almost all developers have one). Then spend time iterating through the GitHub tutorials, which will outline all of the capabilities of git. Once complete, you should be familiar with how to git clone an existing repository, how to create a new repository, git add files to a commit, prepare a set of changes with git commit, and push changes to a branch via git push. As you invest time in any project, make a habit of committing it to github to ensure that you won’t lose your work. You’ll know that you’re progressing with git once you feel comfortable using the above commands for both managing your own projects as well as cloning other projects to augment your development efforts.4b. Author Python scripts using Jupyter Notebooks As you’re learning Python, you’ll discover that there are multiple ways to author python code. Some developers will use IDEs built specifically for programming such as PyCharm, others elect rich text editors with a focus specifically on coding such as Sublime, and a small minority will edit code exclusively through a shell using VIM. Increasingly, data professionals are gravitating toward using notebooks--specifically Jupyter Notebooks--to author scripts in a web browser for exploration purposes. A key feature within notebooks is the ability to execute code blocks within each notebook rather than all at once, allowing the developer to gradually tweak a data analaysis. Moreover, since the output is in the web browser versus a shell, notebooks can display rich outputs, such as an annotated datatable or timeseries graph beneath the code that generated it. This is incredibly helpful when you’re writing a script to perform a data task and want to see the progress of our script as it executes without leaving the browser.There are a variety of ways to get started with Notebooks. One way is to download Jupyter and run an instance on your local machine. Another option is to use Google’s free version of notebooks or Microsoft Azure Notebooks. I prefer to use notebooks hosted on pythonanywhere, which is the same service I use to host python-based web applications. The free service will let you create your own python apps but you can’t run notebooks--the most affordable tier is $5/month.A good way to learn some of the key value adds of developing with Notebooks is to explore a dataset using the Python Data Analyst library, PANDAS. This site has a great getting started tutorial. Start by importing a dataset and print it out. Learn more about the data-frame storage structure, and then apply functions to it just like you would with another dataset. Filter, sort, group by, and run regressions. Try leveraging seaborn, a statistical visualization library which leverages matplotlib to explore your datasets visually. You’ll quickly discover that the framework allows for repeatable data operations with option for data exploration against a moderate cardinality dataset. Notebooks are often the preferred prototyping interface for data scientists, and thus worth learning how to use if you’re interested in learning more about statistics.4c. The Command Line - using shells and editing with vimIf you’ve read this far, you’ve probably already used a shell, a command-line based user interface for interacting with a computer. You’ve likely used shells to execute python code, download code libraries, and commit changes to git. Knowing how to execute a file, navigate within a shell, and monitor an active process will help you become a stronger data analyst. A great place to learn more about shells is following this interactive tutorial. You know that you’re becoming more proficient with shells when you can easily navigate within a directory, create aliases, change file permissions, search for files and/or contents using grep, and view the head/tail of a file.VIM is a unix-originated command-line text editor which is run in a shell. It’s especially useful when you want to view or edit a file—such as a log or a data output—on a remote server. Initially, you’ll likely find that learning VIM is a bit cumbersome because you primarily interact with the application without a mouse. However, over time you’ll begin to develop the muscle memory needed to toggle between edit-mode, view-mode, and executing commands. A great place to get started with VIM is to go through this interactive tutorial. You’ll know that you’re becoming more comfortable with VIM once you can easily navigate between input and edit mode, go to a row by a number, add or delete a row or character, search and replace text, and easily exit and save files you’ve edited.5. Data Analysis/Science with Python + Stat librariesWhile the goal of this article is not to describe how to be a data scientist--that typically requires a undergraduate and/or graduate level education in statistics--having a solid foundation in statistics will help any analyst make statistically sound inferences from most data sets.One way to get started is to take an online course in descriptive statistics--such as this free one from Udacity--which will teach you how to communicate summarized observations from a sample dataset. While you may be tempted to jump to other hotter industry topics such as machine learning, start with the basics. A solid foundation in descriptive statistics is a prerequisite for machine learning as well as many other statistics applications. After going through Udacity or other tutorials, you should be able to describe various types of distributions, identify skews, and how to describe central tendency, variance, and standard deviation.Next up, graduate to learning inferential statistics (such as Udacity’s free course), which will enable you to draw conclusions by making inferences from a sample (or samples) of a population. Regardless with the learning path you take, you should learn how to develop hypothesis as well as become familiar with tactics for validating such hypothesis using t-tests, understand when to leverage different types of experiments, as well as compute a basic linear regression with one more more dependent variables.The two most popular languages for applying statistics are R and Python. If you’re just getting started, I’d recommend using Python over R. Python is generally considered an easier language to learn. Moreover, Python is typically understood by most teams who build data products. There are more libraries available in Python that can be applied to a wider set of data applications--such as deploying a website or creating an api. This means you can often start an exploratory analysis in Python and easily append a few more libraries to deploy a tool / product leveraging such data, which can reduce the time to release. Finally, data applications continue to gravitate to Python over R as the preferred applied statistics language, so by learning the statistical libraries on Python you’ll be riding this latest adoption trend.Regardless of which language you choose, both Python and R can be executed via Jupyter Notebooks, which allow for more easy visualization and communication as you’re getting started.Next, try learning more about machine learning (Udacity’s free ML course is here). Following any course you should be more familiar with how to differentiate a supervised vs unsupervised learning, understand bayes theorem and how it’s used in ML applications, and outline when decision trees are leveraged. Once you’ve learned the concepts, try cementing your understanding by implementing one of these 8 machine learning projects.Finally, Python has a wealth of free libraries commonly leveraged by data scientists. One way to become more familiar with data scientist tactics are to try experimenting with data science libraries. For example, scikit-learn provides standard algorithms for machine learning applications, and NLTK is a library which can help you process and analysis text using NLP.Wrap UpNow you can write a python script to extract data (#4), store it in a database with SQL (#2), build a model to predict future observations with a python data science library (#5), and share what you learn via a spreadsheet (#1) or a Tableau Dashboard (#5). During that process, you may have committed your code to git, authored in a Jupyter Notebook, and published it on your python-hosted server. Congratulations! You’re well on your way to becoming a data analyst.