Trending February 2024 # Integrate WordPress Using Ai With 1000+ Applications # Suggested March 2024 # Top 7 Popular

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Nearly 91% of Fortune 100 companies use Microsoft Teams. Now that’s saying something about how important Teams is for a business. Microsoft Teams is a collaboration software that allows team members to communicate, share, and collectively work on tasks. It is an evolution of Skype and can be purchased as a part of the Office 365 suite of applications.

While Microsoft Teams is appreciated for all it offers, it has also been criticized for its lack of support for popular software outside of the 365 suite. Teams is not even directly compatible with GSuite software. However, GSuite software can be integrated with Microsoft Teams using Appy Pie Connect. In a similar vein, today we are going to discuss how you can integrate Microsoft Teams with WordPress.

The Necessity of WordPress-Microsoft Teams Integration

WordPress is a very popular tool for businesses. It is a freemium content management system that allows you to create and manage websites. Nearly 30% of all online websites are made with WordPress. This means that most companies that use Teams also use WordPress.

While one may argue that both software serve a very different purpose, there is one key thing to be remembered. Teams is a collaboration software and a website requires constant collaboration between every team member responsible for running the website. Unlike no-code website builders, WordPress requires knowledge of PHP, HTML5, SQL, and CSS. This means that a WordPress website requires dedicated website developers on it all times.

Apart from them, websites are constantly being updated and edited by marketing team members and other teams of an organization such as SEO, UI Design, and Content. There needs to be constant communication between all the teams to ensure the smooth functioning of the website. Teams serve that purpose of communication. One problem with Teams however, is that it has its own interface independent of WordPress. Teams does not offer any kind of integration that would allow you to connect to WordPress from the Teams interface.

For a ‘collaboration’ software, not offering users the ability to use WordPress from within the Teams interface is a massive oversight from Microsoft. It reduces the usability of the software they term as ‘the collaborative work environment.’

To give you perspective, here are the benefits you get if you integrate WordPress and Microsoft Teams for your organization:

Better Collaboration

The main purpose of integrating WordPress and Microsoft Teams is easier collaboration. Once they are integrated, team members can create posts and simultaneously communicate at the same time through the same interface. The designer can post pictures right when a writer is creating their content and an editor is editing. This can save teams a lot of time that is wasted in communicating over different platforms.

Using WordPress and Teams on the Same Interface

Given how many software we use in our day-to-day work, the ability to integrate and use a software on the same interface is a welcome change. When you integrate Teams and WordPress you can use the same interface for all your tasks related to these two software.

The Ability to Automate Tasks

Before we go ahead, here’s an infographic with a few statistics –

How to Integrate WordPress and Microsoft Teams

Coming to the big question, it is extremely easy to integrate WordPress and Microsoft Teams. All you need to do is use a workflow automation tool. And guess what? We have one for you. Appy Pie Connect is a workflow automation tool that lets you create detailed visual workflows that can help integrate two or more software and can also be used to automate simple tasks.

Connecting WordPress and Microsoft Teams is a simple 5-step process:

Step 1: Choose Microsoft Teams as a Trigger app and authenticate your app on Appy Pie Connect.

Step 2: Select the trigger that will start a data exchange between the two software. You can have multiple triggers.

Step 3: Pick WordPress as the action app and authenticate your WordPress account.

Step 4: Select a resulting action.

Step 5: Select the data you want to exchange between the two software. You can change this as per your needs. That’s it! Your Connect is now ready.

Connect WordPress+Microsoft Teams

Once you create this workflow, all data that you need to send from one software to the other will be automatically transferred. You can even add your team members and they can access both WordPress and Teams from the same interface. The data, triggers, and actions can all be changed as required.

Conclusion

Appy Pie Connect also provides other integrations for Microsoft Teams increasing the overall usability of Microsoft Teams as a software. If not that, there are hundreds of potential connects that might match your needs. Check Appy Pie Connect out today!

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Pebblely Ai: Creating Stunning Product Photos With Ai Technology

In the digital era, having high-quality images is essential for businesses to attract customers and showcase their products effectively. However, creating captivating product photos can be time-consuming and expensive. That’s where Pebblely AI comes in. Pebblely is an AI-based image curation platform that leverages machine learning technology to help users discover and create the ideal images for their projects. Whether you’re an e-commerce business owner, a marketer, or a social media influencer, Pebblely can assist you in generating stunning product photos with ease. Let’s explore how this innovative platform works and the benefits it offers.

See More: Janitor AI Tutorial: A step-by-step-guide

Pebblely is a website that utilizes AI technology to empower users in creating beautiful product photos. With its AI-powered background generator and product image generator, Pebblely simplifies the process of producing high-quality images for various purposes, such as websites, emails, and social media. By leveraging machine learning algorithms, Pebblely ensures that users can easily generate professional-looking images that make their products stand out in the digital landscape.

Pebblely’s AI product image generator is another game-changer in the world of image curation. This powerful tool can take any ordinary photo and transform it into a lively and eye-catching image. Whether you want to enhance the lighting, adjust the colors, or add artistic effects, Pebblely’s AI product image generator can do it all. It allows users to breathe life into their product photos and create visuals that resonate with their target audience.

For e-commerce businesses, having high-quality product images is crucial to drive sales and engage potential customers. Pebblely understands the importance of showcasing products in the best light, and that’s why it is highly recommended for e-commerce businesses. By utilizing Pebblely’s AI technology, businesses can create visually stunning images that effectively highlight their products’ features and benefits. With Pebblely, businesses can present their offerings in a compelling and professional manner, ultimately increasing customer trust and conversion rates.

Pebblely goes beyond the traditional product photo editing capabilities. By incorporating state-of-the-art technologies like GPT and DALL-E, Pebblely can generate diverse image variations of the same product effortlessly. This means that users can experiment with different styles, backgrounds, and compositions to find the perfect image that aligns with their brand and target audience. With the ability to generate unlimited variations, Pebblely empowers businesses to create captivating visuals that resonate with their customers’ preferences.

In today’s social media-driven world, having Instagram-worthy product photos is essential for businesses to establish an online presence and attract a larger audience. Pebblely understands this need and allows users to create stunning snapshots of their products that are ready to be shared on Instagram and other social media platforms. With its AI technology, Pebblely can enhance the visual appeal of product photos, ensuring they stand out in the crowded social media feeds. From adjusting the lighting and colors to adding filters and effects, Pebblely equips users with the tools to create visually captivating images that are sure to catch the attention of potential customers.

Check Also: How to Use Janitor AI on Mobile

Maintaining visual consistency across product photos is crucial for building a strong brand identity. Pebblely AI ensures that users can create consistent and cohesive product images by offering a range of customization options. Users can select specific styles, backgrounds, and compositions that align with their brand aesthetics. This consistency helps businesses establish a recognizable and memorable visual presence, enhancing brand recall and trust among customers.

High-quality and visually appealing product photos have a significant impact on customer engagement and conversion rates. When customers are presented with captivating images that showcase products in the best light, they are more likely to be intrigued and interested in making a purchase. Pebblely AI enables businesses to create compelling visuals that grab the attention of their target audience, leading to increased engagement and ultimately higher conversion rates.

Pebblely AI offers a wide range of customization options, allowing users to experiment with different styles, backgrounds, and effects. This versatility enables businesses to unleash their creativity and create unique product photos that resonate with their specific target audience. With the ability to generate unlimited variations, businesses can continuously refine and optimize their visuals to achieve the desired impact.

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Ai With Python – Data Preparation

AI with Python – Data Preparation

We have already studied supervised as well as unsupervised machine learning algorithms. These algorithms require formatted data to start the training process. We must prepare or format data in a certain way so that it can be supplied as an input to ML algorithms.

This chapter focuses on data preparation for machine learning algorithms.

Preprocessing the Data

In our daily life, we deal with lots of data but this data is in raw form. To provide the data as the input of machine learning algorithms, we need to convert it into a meaningful data. That is where data preprocessing comes into picture. In other simple words, we can say that before providing the data to the machine learning algorithms we need to preprocess the data.

Data preprocessing steps

Follow these steps to preprocess the data in Python −

Step 1 − Importing the useful packages − If we are using Python then this would be the first step for converting the data into a certain format, i.e., preprocessing. It can be done as follows −

import numpy as np import sklearn.preprocessing

Here we have used the following two packages −

NumPy − Basically NumPy is a general purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays.

Sklearn.preprocessing − This package provides many common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for machine learning algorithms.

Step 2 − Defining sample data − After importing the packages, we need to define some sample data so that we can apply preprocessing techniques on that data. We will now define the following sample data −

input_data = np.array([2.1, -1.9, 5.5], [-1.5, 2.4, 3.5], [0.5, -7.9, 5.6], [5.9, 2.3, -5.8])

Step3 − Applying preprocessing technique − In this step, we need to apply any of the preprocessing techniques.

The following section describes the data preprocessing techniques.

Techniques for Data Preprocessing

The techniques for data preprocessing are described below −

Binarization

This is the preprocessing technique which is used when we need to convert our numerical values into Boolean values. We can use an inbuilt method to binarize the input data say by using 0.5 as the threshold value in the following way −

data_binarized = preprocessing.Binarizer(threshold = 0.5).transform(input_data) print("nBinarized data:n", data_binarized)

Now, after running the above code we will get the following output, all the values above 0.5(threshold value) would be converted to 1 and all the values below 0.5 would be converted to 0.

Binarized data

[[ 1. 0. 1.] [ 0. 1. 1.] [ 0. 0. 1.] [ 1. 1. 0.]] Mean Removal

It is another very common preprocessing technique that is used in machine learning. Basically it is used to eliminate the mean from feature vector so that every feature is centered on zero. We can also remove the bias from the features in the feature vector. For applying mean removal preprocessing technique on the sample data, we can write the Python code shown below. The code will display the Mean and Standard deviation of the input data −

print("Mean = ", input_data.mean(axis = 0)) print("Std deviation = ", input_data.std(axis = 0))

We will get the following output after running the above lines of code −

Mean = [ 1.75 -1.275 2.2] Std deviation = [ 2.71431391 4.20022321 4.69414529]

Now, the code below will remove the Mean and Standard deviation of the input data −

data_scaled = preprocessing.scale(input_data) print("Mean =", data_scaled.mean(axis=0)) print("Std deviation =", data_scaled.std(axis = 0))

We will get the following output after running the above lines of code −

Mean = [ 1.11022302e-16 0.00000000e+00 0.00000000e+00] Std deviation = [ 1. 1. 1.] Scaling

It is another data preprocessing technique that is used to scale the feature vectors. Scaling of feature vectors is needed because the values of every feature can vary between many random values. In other words we can say that scaling is important because we do not want any feature to be synthetically large or small. With the help of the following Python code, we can do the scaling of our input data, i.e., feature vector −

# Min max scaling

data_scaler_minmax = preprocessing.MinMaxScaler(feature_range=(0,1)) data_scaled_minmax = data_scaler_minmax.fit_transform(input_data) print ("nMin max scaled data:n", data_scaled_minmax)

We will get the following output after running the above lines of code −

Min max scaled data

[ [ 0.48648649 0.58252427 0.99122807] [ 0. 1. 0.81578947] [ 0.27027027 0. 1. ] [ 1. 0. 99029126 0. ]] Normalization

It is another data preprocessing technique that is used to modify the feature vectors. Such kind of modification is necessary to measure the feature vectors on a common scale. Followings are two types of normalization which can be used in machine learning −

L1 Normalization

It is also referred to as Least Absolute Deviations. This kind of normalization modifies the values so that the sum of the absolute values is always up to 1 in each row. It can be implemented on the input data with the help of the following Python code −

# Normalize data data_normalized_l1 = preprocessing.normalize(input_data, norm = 'l1') print("nL1 normalized data:n", data_normalized_l1)

The above line of code generates the following output &miuns;

L1 normalized data: [[ 0.22105263 -0.2 0.57894737] [ -0.2027027 0.32432432 0.47297297] [ 0.03571429 -0.56428571 0.4 ] [ 0.42142857 0.16428571 -0.41428571]]

L2 Normalization

It is also referred to as least squares. This kind of normalization modifies the values so that the sum of the squares is always up to 1 in each row. It can be implemented on the input data with the help of the following Python code −

# Normalize data data_normalized_l2 = preprocessing.normalize(input_data, norm = 'l2') print("nL2 normalized data:n", data_normalized_l2)

The above line of code will generate the following output −

L2 normalized data: [[ 0.33946114 -0.30713151 0.88906489] [ -0.33325106 0.53320249 0.7775858 ] [ 0.05156558 -0.81473612 0.57753446] [ 0.68706914 0.26784051 -0.6754239 ]] Labeling the Data

We already know that data in a certain format is necessary for machine learning algorithms. Another important requirement is that the data must be labelled properly before sending it as the input of machine learning algorithms. For example, if we talk about classification, there are lot of labels on the data. Those labels are in the form of words, numbers, etc. Functions related to machine learning in sklearn expect that the data must have number labels. Hence, if the data is in other form then it must be converted to numbers. This process of transforming the word labels into numerical form is called label encoding.

Label encoding steps

Follow these steps for encoding the data labels in Python −

Step1 − Importing the useful packages

If we are using Python then this would be first step for converting the data into certain format, i.e., preprocessing. It can be done as follows −

import numpy as np from sklearn import preprocessing

Step 2 − Defining sample labels

After importing the packages, we need to define some sample labels so that we can create and train the label encoder. We will now define the following sample labels −

# Sample input labels input_labels = ['red','black','red','green','black','yellow','white']

Step 3 − Creating & training of label encoder object

In this step, we need to create the label encoder and train it. The following Python code will help in doing this −

# Creating the label encoder encoder = preprocessing.LabelEncoder() encoder.fit(input_labels)

Following would be the output after running the above Python code −

LabelEncoder()

Step4 − Checking the performance by encoding random ordered list

This step can be used to check the performance by encoding the random ordered list. Following Python code can be written to do the same −

# encoding a set of labels test_labels = ['green','red','black'] encoded_values = encoder.transform(test_labels) print("nLabels =", test_labels)

The labels would get printed as follows −

Labels = ['green', 'red', 'black']

Now, we can get the list of encoded values i.e. word labels converted to numbers as follows −

print("Encoded values =", list(encoded_values))

The encoded values would get printed as follows −

Encoded values = [1, 2, 0]

Step 5 − Checking the performance by decoding a random set of numbers −

This step can be used to check the performance by decoding the random set of numbers. Following Python code can be written to do the same −

# decoding a set of values encoded_values = [3,0,4,1] decoded_list = encoder.inverse_transform(encoded_values) print("nEncoded values =", encoded_values)

Now, Encoded values would get printed as follows −

Encoded values = [3, 0, 4, 1] print("nDecoded labels =", list(decoded_list))

Now, decoded values would get printed as follows −

Decoded labels = ['white', 'black', 'yellow', 'green'] Labeled v/s Unlabeled Data

Unlabeled data mainly consists of the samples of natural or human-created object that can easily be obtained from the world. They include, audio, video, photos, news articles, etc.

On the other hand, labeled data takes a set of unlabeled data and augments each piece of that unlabeled data with some tag or label or class that is meaningful. For example, if we have a photo then the label can be put based on the content of the photo, i.e., it is photo of a boy or girl or animal or anything else. Labeling the data needs human expertise or judgment about a given piece of unlabeled data.

There are many scenarios where unlabeled data is plentiful and easily obtained but labeled data often requires a human/expert to annotate. Semi-supervised learning attempts to combine labeled and unlabeled data to build better models.

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Pros And Cons Of Using Ai In Web Development

Can the boundless potential of AI propel web development to new heights, or does it come at a cost? The pros and cons of integrating AI in web development intertwine in a complex dance of innovation and challenges. 

While AI offers improved efficiency, enhanced user experiences, and personalized interactions, concerns arise regarding job displacement and ethical considerations. 

As the lines between human ingenuity and artificial intelligence blur, we delve into the enigmatic realm of AI-powered web development, unraveling its promises and perils.

The Use of AI in Web Development

The use of artificial intelligence (AI) in web development has revolutionized the way websites are designed and optimized. Web development continues to evolve, offering personalized, efficient, and dynamic experiences for users across various industries. 

While AI’s integration into web development began several years ago, it gained significant momentum with the introduction of tools like Wix ADI and Grid in 2024. These tools made history by leveraging machine learning algorithms to autonomously generate custom websites based on user preferences and content.

Here are six ways in which AI is utilized in web development today.

#1 Chatbots and Virtual Assistants

AI-powered chatbots and virtual assistants enhance the user experience by providing real-time support, answering inquiries, and guiding visitors through websites.

#2 Personalized Content Recommendations

AI algorithms analyze user behavior, preferences, and historical data to deliver tailored content recommendations, improving user engagement and conversion rates.

#3 Automated Testing and Bug Detection

AI-based testing frameworks automate the detection of software bugs, vulnerabilities, and performance issues, ensuring smoother functionality and enhanced security.

#4 Natural language processing (NLP)

NLP algorithms enable sentiment analysis, language translation, and voice recognition, enabling interactive features like voice search and speech-to-text capabilities.

#5 Content Generation and Curation

AI algorithms can generate and curate content, such as blog posts, news articles, and product descriptions, based on specific keywords, target audiences, and desired styles.

#6 Data Analytics and Insights

AI-powered analytics tools analyze website data, user patterns, and trends to provide valuable insights for optimizing user experience, marketing strategies, and conversion rates.

The Pros of Using AI for Website Development

Improved personalization:

AI algorithms can analyze user behavior, preferences, and historical data to deliver personalized experiences. By leveraging this data, websites can tailor content, recommendations, and interfaces to individual users, creating a highly relevant and engaging experience.

Time efficiency:

AI-powered tools automate various tasks involved in website development, significantly reducing the time required to design, build, and optimize websites. For instance, AI can generate layouts, design interfaces, and optimize code, streamlining the development process and enabling websites to be built and deployed more quickly. 

Improved accuracy and efficiency:

AI-based testing and bug detection tools can automatically identify and fix coding errors, ensuring smoother functionality and improved performance. This automation eliminates the need for manual debugging, reducing human error and saving valuable time and resources. The use of AI also promotes efficient coding practices, leading to cleaner codebases and easier maintenance.

Enhanced user experience:

AI algorithms can analyze user interactions, preferences, and feedback to optimize user experiences. By understanding user needs and behavior, websites can provide intuitive navigation, personalized recommendations, and responsive interfaces.

Scalability and adaptability:

Websites integrated with AI can dynamically adjust content, layouts, and features to meet changing user demands. This flexibility allows websites to handle increased traffic, adapt to different devices and screen sizes, and accommodate future growth.

Data-driven insights:

AI analytics tools can process and analyze vast amounts of data, providing valuable insights into user patterns, preferences, and behaviors. By harnessing these data-driven insights, businesses can make informed decisions to optimize website performance, marketing strategies, and conversion rates.

To sum this all up, by leveraging AI, websites become scalable, adaptable, and capable of delivering a highly engaging and optimized user experience. 

If you want to leverage AI in web development, partnering with a superior Miami web design company can be instrumental. Their experts can navigate the complexities of AI implementation, ensuring a responsible and effective integration that aligns with the specific needs and values of your business. 

One thing you shouldn’t forget, though, is that the integration of AI in web development also comes with certain challenges. Read on to find out what these are.

The Cons of AI Usage in Web Development

If you want to ensure a responsible and ethical implementation of AI in web development, it is important to consider the following challenges and drawbacks:

Lack of human creativity:

AI algorithms can automate various tasks, but they often lack the human touch and creativity that comes with human design and artistic skills. Websites created solely by AI may lack originality and fail to evoke a unique emotional connection with users.

Potential bias and discrimination:

AI systems learn from existing data, which can be biased or discriminatory. If AI is used to make decisions about user experiences, content recommendations, or filtering, there is a risk of perpetuating existing biases, leading to unfair or discriminatory outcomes.

Complexity and learning curve:

Implementing AI in web development requires technical expertise and a learning curve. Developing and integrating AI models and algorithms can be complex and time-consuming, requiring skilled professionals to understand and manage the technology effectively.

Reliance on data availability:

AI algorithms heavily depend on the availability of quality data. Insufficient or biased data can lead to inaccurate or flawed AI-driven decisions and recommendations. Obtaining and maintaining reliable and diverse datasets can be a challenge, especially for smaller businesses or niche industries.

It is essential to carefully consider these cons and find ways to mitigate their impact to ensure a balanced approach to AI usage in web development.

Wrapping Up

However, it also presents drawbacks such as the lack of human creativity, potential bias and discrimination, complexity and learning curve, reliance on data availability, and concerns about job displacement. 

To ensure a responsible and ethical implementation of AI in web development, it is crucial to consider these challenges and drawbacks carefully. Mitigating the cons requires a balanced approach that combines human expertise with AI capabilities.

How To Build An Ai Model Using Python Programming Language?

How To Build an Advanced Artificial Intelligence Model Using Python Programming Language? Why Python Is Best For AI

We have seen a lot of people asking which programming language is best for building AI. Python being a general-purpose language made its way to the most complex technologies such as machine learning, deep learning, artificial intelligence and so on. Due to its easy learning, Prebuilt Libraries, less code, platform independence and massive community support it has gained popularity.

Here Is How You Can Build Your First AI Using Python

Step 1: Create A Python Program

To make a chatbot follow these steps.

Step 2:

Now Create a greeting and goodbye to your AI chatbot for use

In Python, we can make a list with multiple items. Make two lists: one for greetings and one for goodbyes. Give your AI chatbot some greetings and goodbyes inside square brackets ([]).

Step 3:

Create keywords and responses for your AI chatbot.

Let’s make a few more lists of keywords and responses for your AI chatbot. Identify the keywords and responses in these lists. Check that the keywords and responses are in the correct order. For example, if the user types in “book,” the AI chatbot will respond, “I know about a lot of books.” Both lists have the keyword “book” and the response “I know about a lot of books” in the third position.

Step 4:

Bring in the random module.

Each time a user interacts with the AI chatbot, we can select a random greeting and goodbye. To include this capability in our programme, we must first import the random module. Add the following to the first line of your programme: import random.

Step 5:

Greet the user

After creating the lists, choose a random greeting from the greeting list:

Print (random. Choice(greetings))

This will print a random greeting from the greetings list.

Allow the user to enter something as well. We can use the input function to show the user a prompt and get their response. We must also save their response in a variable so that we can use it in our programme.

User= input (“say something (or type bye to quit):”)

User= user.lower()

The user is the same as the user. lower() converts the user’s response to lowercase and saves the lowercase version in the user variable.

Step 6:

Continue interacting with the user until they say “bye”.

We can use while loop to keep interacting with the user as long as did not say “bye”. As long as the user response is not “bye,” this while loop will repeat its block of code.

While (user ! = “bye”):

Step 7

Check to see if the user’s response contains a keyword that the AI chatbot is already familiar with.

Within the while loop, we must determine whether the user’s response contains a keyword that the AI chatbot already recognises. To loop from the beginning to the end of the keywords list, we’ll use for loop. We’ll print the corresponding response from the responses list if the keyword at the current position in the list is in the user’s response.

Step 8:

Create a new keyword and response for the AI chatbot.

If the user’s response does not contain a keyword that the AI chatbot is already familiar with, we must teach it how to respond. Begin by adding a keyword found variable to our while and for loops. We’ll set it to false at the start of the while loop to indicate that it hasn’t been found. The keyword found variable will be set to true in the if statement within the for a loop.

To check if the keyword found is false, we’ll add an if statement inside the while loop but outside the for loop. If the user’s response did not include a keyword that our AI chatbot already recognised, we will ask the user which keyword we should learn and how we should respond. The new keyword and response will then be appended to the keywords and response lists using the append () function.

Step 9:

Request another response from the user.

Let’s ask the user for another response at the end of the while loop.

Step 10:

When the user says “bye,” choose a random goodbye.

How To Integrate Social Commerce Into Bigcommerce Store?

The global social commerce industry is set to grow 3x faster than traditional commerce, from $492B in 2023 to $1.2T by 2025. This showcases a major shift in consumers’ buying behavior. They are actively scrolling through their social channels, consuming content, and making impulse buying decisions.

Largely, making purchases through social media is not a planned action. Shoppers, casually come across any shoppable post featuring a ‘Buy Now’ CTA and end up actually buying the product, and then get through the course of their day. This behavior can also be credited to the seamless path for purchase and sales provided by social commerce.

So, if you have been looking to enhance the impact generated by your social commerce efforts, connecting social commerce to BigCommerce is your answer. To know what exactly it is and how to derive maximum benefit from it. Keep reading.

What is Social Commerce Integration with BigCommerce?

Also read:

Best Top 10 Paid Online Survey Website in the World

1. Connect Your Brand’s Catalog

Connecting your BigCommerce product catalog with social platforms like Facebook, Instagram, and TikTok is quick and seamless. It allows shoppers to easily discover your product, share them with peers and check out directly from the social platform.

Orders are managed from the BigCommerce platform just like storefront orders. By connecting your BigCommerce store to social commerce via different integration methods, you can start tagging your products from the catalog directly in your social content.

Shoppers can learn more about your products and can purchase directly within the platform without any hassle.

2. Tag Your Products

Product tags allow you to feature products from the BigCommerce catalog and help your customers have a brief knowledge of your deliverables. Businesses doing well use product tags in social commerce across different formats(feed, stories, reels, IGTV, life, etc). This becomes all the more important as people spend their time scrolling through different formats on social platforms.

Product tags not only provide the essential product information to the shoppers, but also boosts product discovery, and provide them with a seamless path to purchases.

3. Design Your Shop Following Brand’s Guidelines

After enabling shops on social commerce platforms, you get access to merchandising features provided by BigCommerce that allow you to moderate and customize the shopping experience for your shoppers.

Products can be curated based on collections, promotions, and launches under the pipeline. In fact, you can leverage users’ content within your product categories and make them shoppable to build shoppers’ trust, ignite product imagination and boost sales.

Also read:

Top 7 Work Operating Systems of 2023

4. Add CTAs To Your Content

The role of a powerful call to action button cannot be overlooked. It only subtly sweeps into the buying impulse of the shopper, but also critically enhances sales.

How can You Integrate Social Commerce into BigCommerce?

To be able to successfully bring social commerce and BigCommerce together, you would need help from platforms like Taggshop.

Taggshop is a shoppable content platform, leveraging which brands can turn their visual and social content into shoppable, curated galleries, and integrate them anywhere within the location of their BigCommerce store.

Now, let’s understand how you can achieve this through these steps.

 Begin by downloading the Taggshop app from the BigCommerce marketplace.

 Post-installation, add your preferred social channel and create a shoppable gallery by adding product tags.

 Next, moderate the content and customize the look of your gallery as it aligns with your brand.

Lastly, publish the shoppable gallery on the BigCommerce store. You can choose to do it either manually or automatically.

Let’s Call It A Wrap!

BigCommerce is a leading marketplace that is trusted by multiple successful eCommerce brands to conduct business. By bringing social commerce together with BigCommerce, brands, apart from leveraging the awe-inspiring features provided, can also boost sales significantly.

It is convenient for both buyers and sellers, dependable, and result-driven.

So, now if this blog gives you insights about the strategy, about time you try it out for your social commerce strategy.

Shubham Saxena

I am Shubham, work as a Digital Marketer at Taggshop by Taggbox. I would love to share my thoughts on the Social Media, Digital Marketing, SaaS Industry, Social Commerce, MarTech etc.

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