Today, on your smartphone you have an app store where you can find a dozen apps to have food delivered to your door, but we don’t have an app store where a large enterprise, like a bank, can find an app for income verification, or where an insurance company can find an app for processing claims

Instabase

Testcontainers in ASP.NET Core provides a handy way to get our (potentially) complex dependencies set up to be tested against. It allows dotnet developers to easily spin up small, disposable containers to simulate dependencies required in testing where we might otherwise just stick to mocking out in unit tests. Testcontainers makes it easy to manage dependencies and eliminate the need for mock objects, giving us the flexibility to write different kinds of tests for our applications. Using Testcontainers in ASP.NET Core testing is an important step towards achieving a more efficient development pipeline — and with better test coverage! By automating test dependencies, you can reduce the overall testing time, leading to faster and more efficient development processes. The purpose of this article is to provide you with some insights about Testcontainers in ASP.NET Core tests. Let’s dive into it and see how we can write better tests! Overview of ASP.NET Core Testing When it comes to software development, you cannot neglect testing. It’s such a key part for us to be able to deliver high-quality software and even have the confidence to iterate on our changes. In ASP.NET Core, there are a bunch of ways that we can test. I’ve written about and . However, testing with ASP.NET Core can also pose a few challenges, such as managing dependencies and ensuring tests run consistently. using xUnit to test ASP.NET applications using NUnit for the same thing To help overcome these challenges, we can use Testcontainers! This is an awesome tool that allows for containerized testing of applications and the associated dependencies. Containers provide a lightweight environment for testing with the dependencies of your code which can help ensure tests are run consistently across different environments. After all, getting these dependencies up and running once can be tricky… but doing it in a repeatable fashion across different systems and test runs is a whole different world of “fun”. Testcontainers in ASP.NET Core can help streamline testing by allowing us to create these repeatable and consistent testing environments. This can be particularly useful in ensuring integration tests work as expected by providing the same environment on each run. By using Testcontainers, we can also reduce the possibility of discrepancies between tests by working with dependencies in the same containerized environments every time. Getting Started with Testcontainers in ASP.NET Core To get started with Testcontainers in ASP.NET Core, you’ll need to begin with installation and configuration. Here’s a brief guide on how to set up Testcontainers in your ASP.NET Core project: Installation of Testcontainers in ASP.NET Core The first step is to install Testcontainers into your project. This can be done using your project’s package manager, such as NuGet. Simply search for “Testcontainers” and install the appropriate package. Testcontainers is built on top of the popular Docker platform, so you’ll also need to have Docker installed on your machine before starting. Depending on which dependencies you need for your tests (i.e. are you working with MySQL, MongoDB, Postgres, SQLite, etc…), you will need to pull in other dependencies. I cannot provide an exhaustive list in this single article, but I can at least call this to your attention so that you can pull in the other necessary pieces for your use case! Basic Configuration and Setup Once you’ve installed Testcontainers and you’ve got any additional packages pulled in for dependencies you’ll need, we need to ensure that we create the dependencies in code. We’ll go through an example of this later in the article to see how it looks. Depending on which testing framework you’re using, you’ll want to consider things like: When should I spin up my dependencies? Can I and should I share dependencies as containers across tests? Is there a risk of shared mutable state? When should I clean up my dependencies? We’ll see this in more detail in the upcoming sections. Creating and Starting Containers We call Testcontainers from code to create and start our containers. This can be done easily using the Testcontainers API simply by creating a new container object, configuring it, and then starting the container. Once the container is running, you can use it for your tests as needed. The APIs available to us in Testcontainers often provide an easy mechanism to know which ports we can talk to on the container to access the services that are running inside the container. Overall, getting started with Testcontainers in ASP.NET Core is relatively straightforward, and gets us that consistent testing environment for our applications. Getting setup and configuration code for our dependencies organized in a common spot allows us to quickly begin using Testcontainers in ASP.NET Core tests that we want to write. If you’re interested in learning more about testing with Testcontainers, : check out my affiliate link to this course on Dometrain Testing with Testcontainers in ASP.NET Core When writing tests with Testcontainers in ASP.NET Core, there are a few things to keep in mind. First, it’s important to understand how to create and manage your containerized testing environments using Testcontainers. Fortunately, this is relatively easy to do using the Testcontainers API. Once you have your testing environment set up, you can begin writing tests using Testcontainers. When you’re writing tests with Testcontainers, there are a few different things to consider. For example, you can simulate dependencies between your code and external services using Testcontainers. This can be particularly useful for testing your application’s integrations with other services, such as databases or API endpoints. To give you an idea of how to write tests with Testcontainers, we’ll go through this (hopefully) simple test using Testcontainers! How to Write a Simple Test Using Testcontainers To write a simple test using Testcontainers, you’ll first need to create a container object. In this example, we’ll assume you’re testing an ASP.NET Core application that requires a PostgreSQL database. First, create a new container for the PostgreSQL database using the Testcontainers API: using System;
using DotNet.Testcontainers.Containers;
using Testcontainers.PostgreSql;

var container = new PostgreSqlBuilder()
    .WithDatabase("myTestDatabase")
    .WithUsername("testUser")
    .WithPassword("testPassword")
    .WithExposedPort(5432)
    .Build(); Once you’ve created your container object, you can start it and use it in your tests. Here’s an example of a simple test that uses the PostgreSQL container from : an xUnit Test using DotNet.Testcontainers.Containers;

using Microsoft.AspNetCore.Mvc.Testing;
using Microsoft.EntityFrameworkCore;

using Testcontainers.PostgreSql;

using Xunit;

//
// This code goes into your main web app project
//
public sealed class YourWebApp
{
    // ... assume this is a type from your web application
    // that you want to be testing
}

public class MyDbContext(DbContextOptions options) :
    DbContext(options)
{
    public DbSet<Item> Items => Set<Item>();
}

public sealed record Item
{
    public int Id { get; set; }
    public string Name { get; set; }
}

//
// This code goes into your test project
//
public sealed class MyWebAppTests
{
    [Fact]
    public async Task TestAddingItemToDatabase()
    {
        // Arrange
        var container = await StartContainerAsync();
        await SeedTestDataAsync(container);

        // create a new web application factory to get an HTTP client
        // that can talk to our app!
        var webApplicationFactory = new WebApplicationFactory<YourWebApp>();
        var client = webApplicationFactory.CreateClient();

        // Act
        // TODO: perform your test action
        
        // Assert
        // TODO: assert the results
    }

    private async Task<IDatabaseContainer> StartContainerAsync()
    {
        // Create a new container
        var container = new PostgreSqlBuilder()
            .WithDatabase("myTestDatabase")
            .WithUsername("testUser")
            .WithPassword("testPassword")
            .WithExposedPort(5432)
            .Build();

        // Start the container
        await container.StartAsync();

        return container;
    }

    private MyDbContext OpenDbContext(IDatabaseContainer container)
    {
        // Connect to the database
        var connectionString = container.GetConnectionString();
        var options = new DbContextOptionsBuilder<MyDbContext>()
            .UseNpgsql(connectionString)
            .Options;

        return new MyDbContext(options);
    }

    private async Task SeedTestDataAsync(IDatabaseContainer container)
    {
        // Ensure the database is created
        using var dbContext = OpenDbContext(container);
        await dbContext.Database.EnsureCreatedAsync();

        // Add an item to the database
        var newItem = new Item
        {
            Id = 1,
            Name = "Test Item"
        };

        dbContext.Items.Add(newItem);
        await dbContext.SaveChangesAsync();
    }
} In this example, we’re using Testcontainers to create a container for a PostgreSQL database, and then using that container to run a test that adds an item to the database. We take care of all of that inside the “Arrange” portion of our test setup. But it’s also important to note that because we’re dealing with a web application that we want to test, we can take advantage of . using a WebApplicationFactory to be able to hit the APIs of our ASP.NET Core app For completeness, I also included some example code at the top of the snippet just so you can see a dummy DB context as well as an entity type that we can play around with. Of course, your application will look different, but I wanted the code example to highlight the necessary pieces to minimize confusion about where things are coming from. Testing in Parallel with Testcontainers When it comes to testing our code, one approach we can take to get better performance is to test in parallel. With Testcontainers in ASP.NET Core, we can greatly increase the efficiency and speed of our testing process if we are not spinning up and tearing down resources non-stop. It’s an additional layer of complexity to consider, but if we can do it properly, we can save a significant amount of time. In this section, I’ll cover the advantages of testing in parallel and show how Testcontainers can help with parallel testing. Benefits of Testing in Parallel Testing in parallel has several advantages. First, it can greatly reduce the amount of time it takes to run tests. Rather than running simultaneously in multiple containers, greatly reducing overall testing time. When we only have a couple of tests, sitting back and waiting a minute for them to run after spinning up and tearing down containers may not feel so bad. But if this time continues to increase as we add tests, this starts to become a huge burden. tests one at a time, tests can be run Testing in parallel can help increase efficiency by allowing for quicker feedback on code changes. If we can run tests more quickly, we’ll be able to diagnose and fix issues more quickly, leading to faster development cycles. Ultimately, we’re after high confidence in our changes and getting that feedback as quickly (and as accurately/consistently) as we can. Finally, testing in parallel can help with scalability. As an application grows and tests become more complex, running tests in parallel can help ensure that tests continue to run effectively and efficiently. I hinted at this already, but this may not be a problem for you currently… But when you reach a point where you don’t want to add more coverage because you don’t want to wait, it sort of feels like it’s too late. Introduction to Parallel Testing with Testcontainers To run tests in parallel with Testcontainers, we can make use of the parallelization functionality provided by popular test frameworks. xUnit is one of my preferred testing frameworks, so I’ll be speaking about it here… But there’s no reason these concepts cannot be applicable to other frameworks like NUnit and beyond. In Xunit, tests can be run in parallel by using the attribute to group related tests together, and then using a test runner like to run tests in parallel across collections. To run tests in parallel with Testcontainers specifically, we can use an ASP.NET Core interface to manage container lifecycle on a per-test basis. This allows tests to be run concurrently in different containers while still maintaining separate database and service contexts. Collection xunit.console IAsyncLifetime Implementation and Configuration of Parallel Testing To configure parallel testing with Testcontainers, you should first choose a test runner and test framework that supports parallelization. If you’re working solo, pick whichever one you’re comfortable with. If you’re working on a team, either use what’s in place already or sit down with the team to make a decision together as a group. Next, you’ll need to set up a Testcontainers management system that allows for parallel creation and management of test containers. If you’re working with xUnit like me, one option for this is to use the interface to of containers. However, a lot of the time we have the flexibility to pick different ways to do this. We just need to consider what hook points we have in our tests to set things up and tear them down. If you understand the lifecycle of your tests, then you can make decisions about where to tap into them. IAsyncLifetime manage the creation and lifecycle You should also make sure that their code is properly architected to support parallelization — which is an easy one for us to gloss over here. I can’t speak to how you’ve written your application or your tests… so this might involve refactoring code into smaller, more modular components that can be tested in parallel if they don’t work that way already. You may want to lean on dependency injection to ensure that test objects are created independently of one another and manage lifetimes of things this way to stay better organized. Using Testcontainers in Continuous Integration and Deployment Continuous Integration and Continuous Deployment (CI/CD) pipelines are essential for maintaining a streamlined development process. By incorporating Testcontainers into your CI/CD pipeline, you can ensure that your application is fully tested in an environment consistent with production before any code is deployed. Testcontainers can also help automate the creation and management of test environments, making the process more efficient and effective. One of the really cool things about using Testcontainers locally and in our CI/CD environments is that we can largely set up our dependencies the exact same way. Having things run on-box vs on-server for your tests and ensuring they’re as identical as possible is of incredible value. Imagine trying to debug issues in your tests but the issue only happens locally (or on your CI/CD server) but not the other way around. With Testcontainers, we can generally get all of our dependencies spun up the same way which is a huge benefit. Integration with popular CI/CD tools and platforms Testcontainers is compatible with many popular CI/CD tools and platforms, including Jenkins, GitLab, and Travis CI. By integrating Testcontainers into your pipeline, you can ensure that your code is tested consistently across multiple stages in the development process by leveraging container technology to create and manage testing environments. You’ll also want to consider how to establish best practices for utilizing the CI/CD technology effectively with Testcontainers. These best practices may include creating separate container instances for each test job, utilizing container caching to speed up tests, and automating the management of test environments using infrastructure as code. Again, all things that need to be considered as you roll this sort of thing out for usage. Wrapping Up Testcontainers in ASP.NET Core Using Testcontainers in ASP.NET Core offers several benefits and advantages when it comes to building tests that give you confidence. It allows you to easily spin up and tear down testing environments in a matter of seconds, reducing the overall time and effort required for testing. I truly can’t remember how difficult and complex it used to be to try and set up environments for this sort of thing prior to having something like Testcontainers at my fingertips. Entire testing scenarios used to just go missing, or we’d be investing heavily into maintaining more complex testbed. Testing is a critical part of software development — if you care about quality — and using Testcontainers can greatly streamline the process. Supporting more complex end-to-end style functional, integration, and system-level tests allows you to get a great deal of confidence. By reducing the risk of bugs and errors, you can build trust in your software and instill confidence in your users. I encourage you to try out Testcontainers in your ASP.NET Core tests! You don’t need to ditch your unit tests, but you can layer these in! If you found this useful and you’re looking for more learning opportunities, consider and check out my ! subscribing to my free weekly software engineering newsletter free videos on YouTube Also published . here

A Beginner's Guide to Testcontainers in ASP.NET Core

Get ready for the ride: online shopping is about to be completely upended by AI. Over the past few years, the scale and rate of AI development has been astounding. Also, the release of ChatGPT opened the door for millions of people to experience the power of AI first-hand. This has led to a rapid rise in companies cashing in on the hype – everyone wants to be a leader in AI innovation and dreams of magically co-creating content and dynamic conversations with their users. So the current reality where every company slaps on a bland chatbot devoid of customer context and character is really disappointing. At , we know that our customers sweat the details for the home screens of their apps – after all, they’re the digital front doors for their businesses. They’re carefully curated with findings after customer research, refined and polished through numerous design iterations, and built using end-user profile information to keep content relevant and interesting. Algolia Replacing this digital front door with a blank chatbot without any context strips away all the carefully curated relevance and delight. We’re at risk of turning a familiar, simple process of browsing an app into something less human, just for the sake of technology. But what if this new technology was more attuned to the habits of consumers and didn’t require them to learn new ways to do familiar tasks? What if we empowered them with the power of conversational and generative AI in the behaviors they’re already experts at? To do just that, to your Search and Discovery user experiences. Now, you will be able to strategically deploy AI-powered features that enhance your customers’ journey, inspiring and delighting them, guiding them to find exactly what they need, and, without having to teach them new metaphors or behaviors to use an app. we’re excited to announce a new framework that brings the power of Conversational AI and Generative AI As we started developing our AI framework, we spoke to many customers and technology experts alike to truly understand what they need. Here’s what we learned: : The hype and prospects of AI are exciting, and the speed and rate of change of development are pretty astounding. However, innovation for innovation’s sake is insufficient for Algolia customers – these features should enhance UX and connect business goals to their consumers’ needs. A blank chatbot that lives independently of the carefully curated user journey doesn’t fit these goals and often conflicts directly with them. No Innovation for the Sake of Innovation : In spite of the breathtaking rate of AI development, it’s still early days. Many of us have seen AI products released to large audiences with poor vetting with the effect of generating misleading, incorrect, or even harmful information. This is a direct function of releasing products too quickly to market without a thorough understanding of the end-user and what they’re trying to achieve. Trust and Safety are Paramount “Organizations that don’t manage AI risk are much more likely to experience negative AI outcomes and breaches. Models won’t perform as intended, and there will be security and privacy failures, financial and reputational loss, and harm to individuals. AI that is carried out wrongly can also cause organizations to make poor business decisions.” ~ Avivah Litan, Distinguished VP Analyst, Research Gartner Algolia takes trust and safety very seriously, and our customers expect nothing less. Our Conversational and Generative AI features are designed with stringent guardrails that ensure trust and safety for our customers and their end-users in such a way that it enhances the user experience further. : Cutting-edge technologies give us glimpses of what the future may look like. In order to make them valuable to everyday life, they need to be approachable and usable by a wide variety of people. By using a familiar chat/messaging interface, OpenAI made ChatGPT and its Large Language Models accessible and meaningful to anyone – as opposed to forcing developers who are able to comb through GitHub to install and test an LLM. Today, anyone can login and create queries. In a similar way, Algolia’s AI framework will build on familiar heuristics to enhance the end-user journey, which means less time spent learning how the AI features work and more time spent on tasks that matter to customers. AI needs to benefit from inclusive design (LLMs) This approach to inclusivity is important for Algolia customers too – they’ve spent time, energy, and attention in building and optimizing the best Search and Discovery experience for their users. Our framework is designed to be installed easily and with minimal changes to the customer journey – resulting in less time spent setting up a framework. So what’s in the Algolia conversational AI framework? Our customers’ needs are wide and varied, which means a one-size-fits-all approach to AI is insufficient. The Algolia AI framework will allow Algolia customers to compose and deliver the power of AI based on specific problems and challenges they see in their Search & Discovery journey. To do this, the framework will include two important building blocks – AI Actions and AI Assists: : AI Actions are UI controls that allow end-users to invoke the magic of AI in their experience. For example, a button next to a carousel allows the end user to generate a list of related items. Or, a link next to a shirt in a product detail page can bring up lookbooks from a curated set of blog posts – all summarized by AI. AI Actions Actions are approachable (for example, a button, a widget, or a link) and aren’t very different from the kinds of controls that users typically expect on a website and app. They are humanistic by design and seek to add more context, give more support, or round the edges of the task a user is trying to complete. Using an AI Action starts the AI-powered conversation or generates relevant, contextually valuable content. : AI Assists are event and context-driven prompts that allow you to start an AI-powered conversation with your consumers. For example, starting a conversation with a customer if their search query is vague by asking follow-up questions in order to refine the query to garner the best, most relevant result. AI Assists AI Assists enter a conversation thoughtfully and are opt-in. This means you are not forced to interact with a blank chatbot without context – assists usually come with an understanding of what the user is trying to do. These controls are only possible because Algolia is in a unique position to understand an end-users history with an app, while also understanding their intent in real-time. Together, these can lead to some powerful, magical AI-powered experiences. Some of the ways customers are using our AI framework : A user who is unable to find the perfect gift for their partner at a popular apparel site may be greeted by an AI Assist that guides them to the perfect gift. The AI Assist recognizes the context ( ) and generates a recap message for the user with prompts and suggestions for what to buy. Rescuing consumers from a failing “search and discovery” session x+ browse sessions, y+ search queries, 0 clicks : When a consumer is looking to make a set of purchases (say for example, a new home theater that has a number of individual items that make the theater come together) and adds the first item to their cart, AI Action can provide a contextually intelligent input to them. In this case, an item such as “ ” can be shown on the UI. These actions can show related items that complete the purchase, generate AI-powered content, share links to curated blogs and sources, etc. Building dynamic, contextual shopping lists Build out the ideal home theater Helping end-users understand why a search result or recommendation is important to build trust in the ability to surface the best suggestions for them. It also gives them an understanding of how to make better search queries. An AI Action popover next to a recommendation carousel gives an AI-generated summary of the contextual reasons that were responsible for this recommendation. For example, “ ’”. Why was this result recommended to me? We chose this result of a Kale Salad because of your query ‘lunch foods’ and your historical preference of ‘organic only We’re excited to share more! While these explorations are incredibly promising, they are just the tip of the iceberg for the AI revolution. We’re excited to expose more use cases with this Conversational AI framework, either as programmable primitives or as ready-to-use patterns that customers can simply adopt. We expect this framework to interact with every part of Algolia’s portfolio. Just like InstantSearch and Autocomplete, this will be a valuable addition to your user experience toolkit. To help shape development and get early access, join us by signing up for our waitlist. . Sign up for our waitlist to join our first cohort of customers Also published here.

A New Era for Shopping: The Rise of AI-powered Conversational Commerce

How to Personalize Your Ecommerce Search Engine

Cómo personalizar su motor de búsqueda de comercio electrónico

Comment personnaliser votre moteur de recherche de commerce électronique

अपने ईकॉमर्स खोज इंजन को वैयक्तिकृत कैसे करें

eコマース検索エンジンをパーソナライズする方法

Como personalizar seu mecanismo de pesquisa de comércio eletrônico

Cách cá nhân hóa công cụ tìm kiếm thương mại điện tử của bạn

如何个性化您的电子商务搜索引擎

Streamlining Data Debugging for Game Engine Development with Algolia and Rust

I teamed up with Starschema Full Stack Engineer Soma Osvay to write about a project near and dear to my heart: developer documentation. Soma and I both hope you enjoy this article where we cover the use-case and challenges and the implementation Soma came up with. Here at Starschema, we have a lot of projects we’ve done as part of a consultation with . When we’re supporting these existing solutions or wanting to develop a new project, we often want to search through all of the documentation. We currently don’t have a solution, costing us man-hours since we have to do this work manually. markdown documentation Use Case We need to be able to search for projects related to specific topics to check implementation specifics, get ideas from the code, and much more. The Sales team also needs a solution to determine what kind of projects our team has completed for certain topics and be able to quickly communicate it back to potential clients. It would also be great to have something like an internal Stack Overflow for our Developers since we are doing a lot of deep technical work in Tableau. Project Managers also need to be able to determine which employees have worked with certain technologies so they can get questions answered quickly and easily. In short, we need to be able to search for our own projects based on the following attributes: Technologies used (coding languages, databases, etc) Project keywords (tags) Project documentation itself such as install instructions, technical documentation, etc. Timeframe of when the project occurred All of these attributes exist within the internal documentation markdown files – we just need a way to search them. Implementation Plan The plan is to use a as a proof of concept which will: NodeJS CLI Scrape the top GitHub public repositories and grabs the README markdown file (which will ultimately represent our internal projects) Stores the documentation files into Algolia alongside the project’s basic information (title, programming language(s), tags, etc) The CLI will contain advanced logging and command line arguments for ease of use. We also want to host it on the web so people can try it out. Challenge The biggest challenge at hand is the record size — Algolia only allows our records to be 100KB at the most. However, most markdown documentation files are much larger. The solution is that we need to split up the markdown files into multiple pieces within the Index. We also then need to make sure that when we search for something, a single project only appears once—even though it is split into multiple records. Fortunately, Algolia has a distinct feature so we can de-duplicate these results very easily. Indexer Implementation To make using the Indexer as easy as possible, I opted to create a CLI for it as mentioned above. After supplying the arguments required, the tool will automatically initialize the repository, remove any existing records, and configure the relevancy settings. Powering the tool is a straightforward GitHub API that grabs the requested number of top repositories and extracts all of their metadata and downloads the README file. It’ll also filter out repositories that are missing an owner or missing a README file, giving us the best results. Finally, it’ll also convert the markdown content to HTML for easier rendering in the frontend. To keep the record size in check, the tool will automatically split up READMEs over 50k characters into additional records. This way the records won’t get too large but one record should still serve almost all repositories. It’ll then sync this information over to Algolia 100 records at a time so we can meet their batching recommendations as described in the documentation. here Frontend Implementation To serve as my starting point, I took advantage of the library released by Algolia and launched an InstantSearch.js boilerplate. From here, I was able to add additional widgets provided by InstantSearch.js such as the pagination and the page size selector which worked great. create-instantsearch-app As we also collected repository metadata with the markdown, we also needed to customize the hits component to include this additional information. Often times the metadata is as important as the library description so developers can at a glance see if it’s a popular library, who released it, the tags, and much more. I also added facets so Users could filter by the programming language, tags, or how many times it has been forked. The final piece to the puzzle was adding the ‘Open documentation’ button allowing you to quickly and easily read the markdown content for the repository in a pop-up without leaving the application. If the record we are clicking on has multiple rows, it’ll automatically load the additional records and concatenate them in the display—awesome! Conclusion This project was a fun test and really showed me how flexible Algolia is for different use cases such as ours. The ready-to-go widgets saved me a lot of time during prototyping and having relevant results from the first few keystrokes is super impressive. I also think it would be super interesting if we were able to harness the power of Algolia Recommend if we were able to generate enough events from Users clicking on projects internally. You can view a live demo of the GitHub test project , with a button that will set you up with the default demo credentials to view our index. Interested in the backend indexing code? You can find that on GitHub! here here Also published here.

Our Journey to Building a Searchable Index with Algolia and NodeJS

Nuestro viaje para crear un índice de búsqueda con Algolia y NodeJS

Notre parcours vers la création d'un index consultable avec Algolia et NodeJS

Algolia और NodeJS के साथ एक खोज योग्य इंडेक्स बनाने की हमारी यात्रा

Algolia と NodeJS を使用して検索可能なインデックスを構築するまでの道のり

Nossa jornada para construir um índice pesquisável com Algolia e NodeJS

Hành trình xây dựng chỉ mục có thể tìm kiếm của chúng tôi với Algolia và NodeJS

我们使用 Algolia 和 NodeJS 构建可搜索索引的旅程

For most B2B marketers, it can be hard to keep up with the rapid pace of changes. Not staying up-to-date with key trends and best-practices can cost marketers and companies in lost market share, future opportunities and bottom-line profits. Today we’ll cover costly B2B marketing mistakes that many of the B2B companies I’ve been assessing so far in 2023 are making, and some B2B marketing best-practices you can apply right away. Now although these marketing mistakes are in the context of B2B SaaS and original equipment manufacturers (OEMs) that I’ve been reviewing, this will be valuable for any business owners and marketers looking to optimize their marketing, cut waste and greatly improve business profitability from marketing efforts in 2023. With that said, let’s jump right in. Marketing automations, lead nurturing, effective sales calls and retargeting visitors with ads is much more costly and prone to human error when it’s done manually instead of automatically with a page or a form specific segmentation strategy. 1) Companies using the same lead capture form for “contact us” and sales enquiries. Asking your lead what profession or general company title they hold at their business helps equip your sales team automatically with key pain points and aspirational goals so they can speak the language of your potential customer. Here's how we did it at LJ Welding Automation: 2) Not incorporating a self-identifying sales persona question on lead capture forms. Offer a free 15 minute consultation, a free trial, a discount offer, useful templates or something else that is a byproduct of the business that your customer may want. It should be a no-brainer and add value to the potential lead and help them along their buyers journey. 3) Not using any offers that entice visitors into becoming a lead. Landing pages are designed for the person to do 1 thing: become your lead. It's done by removing interactive element on the page except the 'submit' button. Normal web pages have menu and footer navigation that ruin conversion rates for getting leads. 4) Not using landing pages or thank you pages and relying on in-line messages instead. every Similarly with Thank you pages, after a visitor converts to a lead, you can immediately show them product demo videos and testimonials to keep the sales conversation going while your sales team prepares to make contact. Finally, Landing and thank you pages can help segment traffic for advertising based on if they became a lead yet or not. In-line thank you messages are a poor substitute for proper landing page structure and miss an unacceptable amount of strategic marketing value. On landing pages, you need to be pushy to get people to take action and . I’ve seen pop-up on exit intents capture lead info from hundreds of leads who would otherwise not have completed a form. 5) Not using pop-up on exit intent for landing pages. nudge them RELATED: Nudge Theory Best-Practices For Business 6) Online chat bot asking the wrong questions to visitors. This chatbot example is better off asking a question to learn more about the visitor’s pain point they’re trying to fix, or if they can find what they’re looking for, or see if they’re eligible for a special promo offer like a demo or discount. Chat bots are meant to help convert traffic into leads for your sales team or assist with customer service support. By using these microblogging sites, you're able to get your content ranking far higher in search results for organic traffic rather than relying on your website’s domain authority. 99% of companies will not outrank these three micro-blogging sites, so use them ASAP. 7) Not using HackerNoon , Substack or LinkedIn Articles to drive traffic from larger audience pools, as well as earn backlink authority. How-to: Publish your article first on your website, but then repost them on these other sites. Make sure to link back to the original on your website to drive new free eyeballs from those platforms and improve your website's authority. (canonical tagging) You can learn a lot about the effectiveness of sales collateral by seeing how much traffic it drives back to your website and becomes leads. You can't effectively improve what you don't measure. 8) Not using traceable and trackable links in their sales brochures and spec sheets. LinkedIn is a major B2B opportunity for new business where Twitter can be more superficial but provide social-proof opportunities on socials. 9) Putting equal effort on all social media platforms instead of focusing on the 1 or two that matter most (LinkedIn and Twitter). The best way to know your top two is by looking at your to see where traffic is coming from that converts into leads. referral Google analytics sources I’ve written about this plenty of times, but it’s so simple: all-lower-case-no-spaces-use-dashes-avoid-special-characters.jpeg 10) Not naming product image files in a way that's friendly to search engine indexers. Naming correctly matters not just for when a file goes on your own website, but also when you're trying to rank your product images quickly on Google search using free microblogging sites mentioned earlier and others, like and . Behance Dribbble If you’re in the business of improving reliability and efficiency for others, , this is about walking the talk for your own business. 11) Using patchwork softwares for CRM, web and marketing automations instead of a unified solution like HubSpot or comparable equivalents. working smarter and not harder A unifying all-in-one solution will help avoid unplanned downtime, hidden costs from popping up and making sales and marketing's job easier so they get more done. This can be as simple as an automated email a month after a sale to offer follow-up support, customized tips, suggest YouTube videos customers may now find useful or offer a customer satisfaction survey... whatever the priority is at the time for your business, incorporate that. 12) Not surveying customers post-sale to fine-tune the persona research or earn new product reviews, testimonials or case study material. Trade magazines and journals love publishing original guest posts and thought leadership content if it’s not overly promotional. They will only publish fresh content that hasn't been posted elsewhere first. Ask the Editor about their editorial calendar to find where you can add the most relevant value. You can find these publications easily at your trade shows as media sponsors, or type in your competitor's brand into Google and hit the “news tab” like below: 13) Not seeking out and creating guest posting opportunities when so much effort goes into content creation. It’s remarkable how much effort I’ve seen go into great quality content, only for it to be posted with no call-to-actions, no links sprinkled into other related-content on the B2B’s website just to sit there without either promotion or strategic purpose. For example, did you notice what I did under point #5? 14) Not interlinking content on the company blogs with more of their articles or a lead generation special offer. If you don’t do this, your competitor is stealing your traffic cheaply at the most valuable stage of the buyer’s journey. If you bid on it as well, it won't cost you much, but it dramatically increases your competitors' cost to compete for it due to your higher . 15) Not bidding on the company's own brand keywords for Google or Bing. Quality Score A lot of people don't know this, but Bing’s grown to account for . Windows PCs use Bing by default, and most companies provide workers with PCs over Macs, and that’s why you shouldn't neglect it for your B2B marketing. 16) Neglecting Bing and only using Google Ads. 8.85% of all online searches in 2023 You can create video content funnels that drive leads based on your buyers journey by using playlists of videos in an order and eventually running ads to get steady of new leads. You can also make ‘how-to’ style troubleshooting videos or common FAQs to take pressure off your customer support team. Create playlists based on products and short videos answering regular frequently asked questions. 17) Not using Youtube as an automated lead generation tool or customer self-serve troubleshooting solution. This is a massive waste of advertising budget because it always performs badly and doesn’t drive measurable results. Ads should drive traffic only to landing pages or other interactive element call-to-actions like “Call Now” unless it's very low cost-per-click and sending the right kind of traffic to content that is at the top of your funnel. 18) Running paid ads to a home page or other web page instead of to a specific landing page and offer. Be explicit about what you want the reader or viewer to do and don’t make them have to think. Showing your logo at the end of a video but not including a website address, a phone number, an interactive element to click on for more information or claim a special offer is a major, common missed opportunity that super-charges your marketing content. Make sure your call-to-actions are contextual to the content, and they will work for you 24/7. 19) Not including relevant call-to-actions to written content and video content. Content should be produced with the intention of helping your buyer personas through the awareness, consideration, decision and delight stages of their buyer's journey. Understand whose pain point you're fixing before you start creating content and use language that is familiar to them when describing your solution. 20) Creating the content before deciding who it’s meant for. Segmenting leads automatically from forms as mentioned in #2 helps to drip feed content that’s relevant to leads over a length of time that fits within your typical sales cycle. 21) Not incorporating marketing automation into the lead nurturing processes. The in B2B marketing is growing every day. Check out the recommended link for more inspiration. 22) Not learning and using ChatGPT or equivalents to write social media post drafts, find hashtags for a post, or drill down on their sales persona data for further insights. list of AI applications RECOMMENDED: How-to Use AI Marketing for Your B2B Marketing in 2023 A B2B SaaS wanted to increase SQLs by 8% a month. I looked at their sales process and saw a big problem: they were trying to drive leads toward a product demo sales call that could have been a 3 minute product sales video for that persona, but weren’t making the primary call-to-action try for free for X days which was an option found buried in the pricing page FAQs. In terms of micro-commitments, speaking with a sales person is a bigger ask than watching a product video demo and then trialing a software for free. Instead of sales people offering scripted product demo presentations, they would be most effective by doing customized onboarding for new SQLs who signed up to try the businesses SaaS for free. 23) Making the sales process unnecessarily more difficult. Structured data is what shows up at the top of a google search before website results. In order to rank your website needs a Moz domain authority above 30. You can find out your website's authority . Here’s a to write up all of mine. 24) Not using structured data mark-ups on key web pages like products, events, about page etc. here free resource I use Offers, recommendations, products, services, and posts made in your Google Business profile are favoured in search results over regular searches. That's because it's Google's digital properties and they promote their own stuff first. can cover more than 1/3 of the first page of local search results and is free to use (for now). 25) Not using the Google Business listing to its potential. Google's snack pack This comes back to knowing your audience before getting started. A business owner will have different motivations and pain points than a process engineer, a manager or an end-user. A good tip is whoever the customer is you're quoting for the case study, their business title is probably the sales persona audience to focus on. 26) Not writing case studies from the buyer persona perspective and being too general in the appeal. I’ve mentioned some in previous articles but good free ones I like are and . For SaaS companies especially, submitting published posts from  HackerNoon and LinkedIn Articles and guest posting sites can also be added to a company's profile as “News Mentions”. 27) Not backlinking articles after publishing, or having any plan to make backlinks happen. Instapaper Folkd Crunchbase The most cost-effective use of an ad budget is retargeting people you know are interested in your company already, versus prospecting for new business with people who are unfamiliar with you and your product. . If your ad budget is small, stick to retargeting only at first. 28) Not retargeting web traffic at all. Retargeting is the low-hanging fruit Web traffic that’s top of the funnel isn’t as valuable to the business as traffic that is in the decision stage of your sales cycle. 29) Not bucketing web traffic retargeting based on where the visitor is in the buyers journey. I’ve seen OEMs that don’t use their "90 day full money back" guarantees on sales pages and marketing collateral, and it really hurts their conversion rates. By including this information, the company reverses the risk away from the customer, so they have nothing to lose by trying the product. 30) Not promoting key selling information like free trials or money back guarantees. Sales videos are by far more effective than hero images but leaving the space blank isn’t helping convert more traffic into leads. 31) Not using hero images or sales videos on their landing pages. It’s ok to link off the website if it’s an important reference, but make sure it’s opening a new tab in the browser because your web traffic isn’t likely to return to your website once they've left. 32) Linking out to other websites without opening a new tab. Cost breakdowns of a marketing agency are typically as follows: ⅓ wage, ⅓ business overhead, ⅓ profit. The more time they spend over what they’ve allotted to you, the less profit a marketing agency makes. In essence, they are incentivized to do as little as possible without getting fired. That mark-up from marketing agencies is better spent on your business by paying and incentivizing high-quality marketing talent of your own, expanding advertising budgets and improving business long-term competitiveness and profitability. 33) Spending money with ad or marketing agencies instead of running  in-house. Now although lost opportunities from having these common but costly marketing mistakes is in the past, you now know what you can do to get ahead of your competition, just by making sure these costly and preventable marketing mistakes don't happen on your watch. If you know of a potential company, SaaS or OEM, tell them to . I’m open to all serious enquiries. Thanks for reading! PS: I’m looking to transition from consulting back to working for a B2B in need of a marketing director or VP. connect with me on LinkedIn

33 Common & Costly B2B Marketing Mistakes to Avoid

Many crypto tokens remain viable despite their lack of popularity compared to first-movers Bitcoin and Ethereum, and have very solid technical foundations that are attractive to investors and essential for the development of blockchain-based applications. A notable example is DASH, which was forked from Litecoin in 2014 to offer near-zero fees, developer-friendliness, safety through good anonymity, and a financial infrastructure for real-world transactions. In December 2017, DASH saw its all-time high at $1,493.59, and it has since plummeted to trade around $40 by the end of 2022. Following a lackluster performance by the crypto market last year, this year has brought a resurgence of activity and growth, resulting in many coins, among them DASH, coming to life. It posted a sharp 43.23% gain over the course of January, showing signs of sustained upward movement. However, is it able to reclaim its all-time high and return to its former glory? \ Taking a closer look at the features and development progress of the DASH network, we can determine what drives its growth, and whether it can regain prominence. How does Dash work? DASH, a of 'digital cash', was launched as a fork of Litecoin in 2014, initially named Darkcoin or Xcoin before it was renamed DASH to avoid the darknet association. In its initial incarnation, it aimed to target confidentiality and online privacy, but since its rebranding has focused primarily on payments. portmanteau Because of their similarity, Dash and Bitcoin are often compared, although Dash handles its blockchain in a completely different manner. The major difference is that it has a tier-based network architecture that boosts transaction speed and privacy. Moreover, Dash is the first and oldest decentralized autonomous organization (DAO), with masternodes having the ability to vote for proposals. After a proposal passes, it can be implemented by Dash's developers. Among the examples is Dash's Core Team's proposal to increase block size to two megabytes in 2016, which was approved within 24 hours. The Dash team has developed a wide range of innovative features that make it stand out from the crowd. The following are just a few: Masternodes. Masternodes are powerful servers backed by DASH collateral for delivering advanced blockchain services and governance. A masternode owner with at least 1,000 DASH holds autonomous governance rights on the network and can vote on proposals. Some critics claim Dash is not truly decentralized since Masternodes perform many crucial functions, so anyone who controls most Masternodes has power over the entire network as well. InstantSend. Dash users can use this feature to send funds instantly to each other without waiting for transactions on the Dash blockchain to be confirmed. There are a number of wallets that integrate it. CoinJoin. A decentralized process of combining transactions, making it difficult for anyone to trace the transaction history. The Dash Core wallet implements CoinJoin, enabling the processing of any amount of Dash. Additionally, the Dash Electrum wallet also offers this feature. It is important to note that the number of rounds in CoinJoin can impact the level of privacy provided. To enhance privacy, the recommended number of rounds is four by default. However, some transactions may undergo additional rounds to further increase privacy. ChainLocks. Using LLMQ Signature Requests/Sessions, this feature eliminates 51% mining attacks, which most Proof-of-Work blockchains are susceptible to, and increases immutability. ChainLocks performs a network-wide measurement/vote of the "first-seen" rule. Each block is signed by a few hundred masternodes who form an LLMQ and extend the active chain by signing the first block they see. In the event that at least 60% of network nodes see the same block as the first block, they will be able to create a P2P message (CLSIG) and distribute it to the rest of the network. As a result, regular users can consider transactions fully confirmed after the first on-chain confirmation inside a ChainLocks-protected block. Reorganization of signed/locked blocks does not allow transactions to disappear from the chain, thereby enabling a transaction to be accepted without waiting for several confirmations. In addition, it affects the economics of mining by taking away the incentive for miners to reorganize chains, making attacks based on secrecy and selfish mining impossible. The Dash Platform. Using this revolutionary Web3 technology stack, developers can build decentralized applications combining architectural components such as Drive, which provides consensus-based data validation, as well as DAPI, which converts Dash's P2P network into a cloud-based system using a decentralized HTTP API. Using one of the easy-to-use SDKs available in Javascript, Objective-C, or Java, developers can access the platform, which will soon be released on the testnet, so they can develop in their native programming language without having to learn a new one. upcoming On-chain analysis of Dash As of January 31, 2023, there were 80.48K newly created addresses receiving their first DASH deposit and 69.25K active addresses transacting in DASH, a weekly increase of 70.66% and 66.1% respectively, according to the data from IntoTheBlock. Also interestingly, DASH whale holders have also been more active over the past month, with transactions in the range of $100K - $1 million jumping by a whopping 330%.\ Aside from that, the UTXO Age indicator can be used for understanding the volume of transaction activity relative to historical transactions on the Dash blockchain. Analyzing UTXOs (unspent transaction outputs) by age groups we can understand how DASH is distributed among different user cohorts, roughly divided into long- and short-term holders. We are currently seeing an increase in short-term unspent transaction outputs, which indicates a large amount of transaction volume is taking place. The reason for this could be either many addresses fearing to miss out or high volumes coming from large investors. In light of the above data showing a significant increase in large-sized transactions, the second explanation seems more plausible. UTXOs generally rise during strong bull markets with more capital flowing in than previously. Conclusion Dash's adoption is accelerating, and this could be due to it becoming increasingly popular among institutional investors, which complements their extensive work in the Digital cash realm. Moreover, its value increases due to the development of its blockchain and new web3 innovations. Multiple on-chain indicators confirm the uptrend, suggesting the rally may continue to grow. While it is unrealistic to expect a sky-high ATH anytime soon, DASH has a good chance of gaining more ground moving forward.

Is Dash Soaring Back Due to a New Wave of Interest?

Most brands want to earn press mentions. They increase brand awareness, improve credibility, amplify reach, boost web traffic and help to increase sales. Earlier we discussed and to follow up, I’d like to share how I earned 4 press mentions in 2022 for free. These mentions came specifically from , , and live news channel. 10 popular ways on how to get press mentions for your brand Wall Street Journal (WSJ) Axios Crypto Washington Post Fox Business https://video.foxbusiness.com/v/6317659201112#sp=show-clips?embedable=true First a refresher from the . previous article The 10 most effective and conventional ways of earning press mentions are: Write a compelling press release. Build relationships with journalists. Utilize social media. Participate in industry events and conferences. Create a media kit. Utilize online press release distribution services. Collaborate with influencers. Create a company blog. Offer exclusive content or media. Utilize SEO. Now I’ll share the approach I used which takes a different spin on some of these methods. Axios Crypto Press Mention I found a call-to-action (CTA) at the end of an Axios Crypto article inviting Axios readers to talk about their crypto journey. Being a fan of Axios news, I mentioned that upfront and followed it up with my own crypto investing experience. responded to my email and after some back-and-forth, he invited me for an interview over Whatsapp. Brady Dale After the interview, Dale asked for a picture they could use to “Axios it up” and it turned out looking like this: Dale and I occasionally message each other on Whatsapp to stay in touch. Here’s a few suggestions for growing relationships with journalists. Quick Tips Building Relationships With Journalists Don’t be self-promotional about ideas for a story; they hate that. Add-value: Follow them on socials and share and interact with content you like and can add value to. It’ll help you stand out. Engage online: Find out what they need for their story or story ideas; be helpful and add-value. Listen more than you talk. Listen: The Wall Street Journal Press Mention Similar to Axios Crypto, WSJ invited readers to talk about their crypto experience. Crypto investors can still . fill out the form here About six weeks later, reached out inquiring about my experience with FTX and asked to speak that day. I made myself as available as possible and once our call was over and she spoke with her editor, she invited me to send over a picture of myself to be included in the article. I sent an edited picture (don’t do that) and didn’t get the original in before press time :-( Hannah Miao Regardless, Miao sent me an email as soon as it was published so I could see how it turned out. Quick Tips With Media Kits Have an unedited, professional picture of your self or a crisp, high quality brand image ready at all times. Screenshots of your proof are fine. Journalists can read meta data too. It’s a good practice to ask what they prefer when you can. Your title frames the context of your quote and press mention. Be deliberate. Get feedback on your media kit and improve it as you learn. The Washington Post Press Mention The day after the WSJ article was published, I got an email from with the subject line “Media inquiry -- Washington Post”. Mark read the WSJ article and was doing a story in the “context of everyday folks and wanted to illustrate what investors are going through right now.” Julian Mark Similarly to before with Miao, I made myself immediately available to speak with Mark and spoke candidly over the phone for about 40 minutes. Perhaps too candidly on my part, as WaPo is not known for crypto-friendly coverage (yet). Regardless, I enjoyed speaking with Mark and would happily do it again! Fox Business Press Mention Half an hour after Mark’s WaPo email inquiry came in, , Fox Business producer emailed me about the WSJ quote and asked if I’d be interested in doing a live interview on air. It was an exciting morning, to be honest. Ben Tillem In the email Tillem said what time the show goes live each day and asked what my availability would be. I responded quickly and made myself available for the following week. Tillem had a screening call with me; at the end let me know what questions to expect on air. That call was followed by a video and sound check 10 minute session with the technical team for angle, lighting sound etc. Utilize SEO Tips I put backlinks on all my press mention articles as this helps them rank quickly on search engines. I’d recommend , etc Backlinks: Instapaper Folkd I share across my social media channels and distribution networks to boost the content. It also helps rank the same content multiple times (Reddit especially). Social shares: I add press mentions on my Crunchbase profile as another kind of backlink. Crunchbase: on , , and wherever else you blog. (Canonical link at the bottom, linking to the original article to avoid website or page SEO penalization). Syndicate the news article Medium LinkedIn telegra.ph This is because embedded videos on high domain authority websites can rank on the first page of search results in a matter of minutes. Embed video in my content whenever possible. I was able to find the WSJ for using this technique that you can try too. Below are a couple of examples to give you some inspiration: Search your niche keywords using quotes on Google News. m for crypto investors I’ll add to the media outreach opportunities as I find them moving forward so bookmark this page in your browser and if you found this insightful. follow me on Twitter Have something to add? Leave it in the comments!

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