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Retail management software helps businesses streamline operations and achieve efficient management of their retail activities. From inventory control to point-of-sale systems, this comprehensive software solution offers a range of features designed to enhance productivity and profitability. In this article, we will explore the benefits and functionalities of the retail management system and discuss how it can positively impact businesses of all sizes. 

Whether you’re a small boutique or a large chain, implementing the right retail management solution can revolutionize your operations and drive success in the competitive retail landscape. Consider the features and prices of these solutions and find the best fit for your business.

Key Features of Retail Management Software

Let’s explore the key aspects that top retail management software should encompass and how each feature contributes to enhancing efficiency and streamlining processes.

Point of Sale (POS) Systems

A reliable and efficient POS system is essential for seamless transactions and a smooth customer experience. The best retail management software includes advanced POS capabilities, such as barcode scanning, sales tracking, loyalty program, and integrated payment processing.

Inventory management

Managing inventory is one of the best factors that every retailer needs to focus on. By maintaining accurate inventory data, businesses can minimize stock outs, reduce excess inventory costs, and ensure optimal product availability. Now advanced inventory software will include features such as managing inventory level, syncing real-time inventory data, forecasting inventory demands, and creating purchase orders to send to suppliers.

Customer Relationship Management (CRM)

Retail management software with CRM functionality allows businesses to track customer information, purchase history, and preferences. With a centralized CRM system, retailers can provide personalized experiences, targeted marketing campaigns, and effective customer support, ultimately fostering customer loyalty and retention.

Reporting and Analytics

Data-driven insights are essential for making informed business decisions. These insights help retailers identify trends, optimize pricing strategies, and identify areas for improvement, leading to data-backed decision-making and improved profitability.

Integration Capabilities

The best retail management software offers integration capabilities with e-commerce platforms, accounting software, and third-party applications. For example, Magestore retail management software can seamlessly integrate with Magento platform, ERP systems such as Netsuite, SAP, Microsoft Dynamics, and accounting systems such as QuickBooks, Xero, and Sage.

Top 5 Best Store Management Software

Magestore Retail Software PWA

Magestore is a leading retail management software provider known for its comprehensive and customizable solutions. With a focus on enhancing the efficiency of retail operations, Magestore retail software offers a range of features designed to optimize inventory management, streamline point-of-sale systems, and provide personalized customer experiences.

Key Features:

• Inventory Management: Real-time stock tracking, stock transfers, low stock notifications, automated replenishment, and advanced demand forecasting, ensuring optimal stock levels and minimizing stockouts.
• Point of Sale (POS): Integrated POS system, barcode scanning, multiple payment methods.
• Customer Management: Customer data, loyalty programs, personalized promotions, self-checkout system.
• Reporting and Analytics: Sales reports, real-time inventory reports, and performance analytics.
• Integration: Seamless integration with Magento eCommerce, accounting tools, and ERP systems.
• Multi-Store Management: Multi-store retail management software, centralized management of multiple store locations, including inventory and sales.
• Supplier management: Create and automate purchase orders based on the available stocks and demand forecast. 

Pricing: 

The price is flexible and depends on many factors such as the number of stores, complexity, and your support demand.

Lightspeed Retail

With a user-friendly interface and robust features, Lightspeed Retail offers comprehensive solutions to manage inventory, streamline point-of-sale transactions, and drive customer engagement.

Key Features:

• Inventory Management: Centralized inventory control, automated purchasing, and stock tracking.
• Point of Sale (POS): Customizable POS system, integrated payments, offline mode.
• Customer Management: Customer profiles, loyalty programs, targeted marketing campaigns.
• Reporting and Analytics: Sales reports, inventory analysis, employee performance tracking.
• Integration: Integrations with e-commerce platforms, accounting software, and more.

Pricing: 

Starting from $99/month. Lightspeed Retail offers different pricing plans based on business size and specific requirements. 

Square for Retail

Square for Retail is a comprehensive store management software solution offered by Square, a well-established and trusted brand in the payment processing industry. Designed specifically for retailers, Square for Retail provides user-friendly features and integrated payment capabilities to streamline operations and enhance customer experiences.

Key Features:

• Inventory Management: Automated stock tracking, purchase orders, and inventory alerts.
• Point of Sale (POS): Easy-to-use POS system, contactless payments, offline mode.
• Customer Management: Customer profiles, email marketing, customer feedback collection.
• Reporting and Analytics: Sales reports, inventory analytics, employee sales tracking.
• Integration: Integrates with Square’s ecosystem, including Square payments and other tools.

Pricing: 

Starting from $60/month. Square for Retail offers transparent and competitive pricing, with additional transaction fees based on payment processing. 

Epicor Retail Cloud

Epicor Retail is a comprehensive retail management software solution designed to meet the complex needs of retailers across various industries.

Key Features

• Point of Sale (POS) System: Epicor Retail offers a flexible and scalable POS system, enabling retailers to efficiently process transactions, accept various payment methods, and provide personalized customer service.
• Inventory Management: The software provides robust inventory management capabilities, including real-time inventory tracking, automated replenishment, and advanced demand forecasting, ensuring optimal stock levels and minimizing stockouts.
• Customer Engagement: Epicor Retail offers customer relationship management tools to help retailers build customer loyalty through personalized promotions, targeted marketing campaigns, and enhanced customer service.

Pricing: 

Epicor doesn’t publish their prices on the website, so you can contact them directly for more information

Retail Pro

Retail Pro is a widely recognized and comprehensive retail management software solution trusted by retailers worldwide. It provides a wide range of features and capabilities tailored to meet the specific needs of retail businesses.

Key Features:

• Point of Sale (POS) System: Customizable POS interface, barcode scanning, and support for multiple payment methods.
• Inventory Management: Real-time inventory tracking, automatic reordering, and stock transfer capabilities.
• Reporting and Analytics: Sales reporting, inventory analysis, employee performance tracking, and comprehensive analytics.
• Integration: Integrates with various third-party systems, including e-commerce platforms and accounting software.

Pricing: 

For detailed pricing information and tailored solutions, it is recommended to visit the official Retail Pro website or contact their sales team.

Which Types of Businesses Need Retail Management Systems?

Retail management software is suitable for a wide range of businesses operating in the retail industry. Here are some types of businesses that can benefit from using software for retail stores:

Brick-and-Mortar Retail Stores

Whether it’s a small boutique, a department store, or a chain of retail outlets, retail management software helps businesses efficiently manage inventory, streamline point-of-sale transactions, track sales, and analyze customer data.

Omni-Channel Retailers

Retailers with both online and physical store presence can benefit from a retail management solution that enables seamless integration between various sales channels. This helps maintain consistent inventory, pricing, and customer information across different platforms. Besides, to help increase customer satisfaction with omnichannel support, retailers can use an AI chatbot to interact with their customers on eCommerce platforms.

Specialty Retailers

Businesses specializing in specific product categories, such as electronics, fashion, or home goods, can use retail management software to effectively manage and track their unique inventory requirements, including product variants, sizes, and colors.

Wholesalers and Distributors

Retail management software can be utilized by wholesalers and distributors to manage inventory levels, handle sales orders, and track shipments to retail customers efficiently.

Franchise Operations

Franchise businesses operating multiple retail locations can benefit from retail management software to ensure consistency in inventory management, pricing, and reporting across all franchise units.

Conclusion

It’s important for businesses to assess their specific requirements, scale, and budget when considering this software for retail shops. Pricing plans may vary based on factors such as business size, additional features, and implementation scope. Consulting with the respective software providers and exploring detailed pricing information will help businesses make informed decisions.

Featured image by Clark Street Mercantile on Unsplash

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You may wonder to know that Microsoft Outlook has more than 400 million active users globally, making it the most used platform for email communication. Most businesses, even tech giants prefer it using Outlook Add-ins development to streamline their business communication and generate leads faster. 

In this blog, we will learn about Custom Outlook Addins, and their step-by-step process of development, setup, and installation. Let’s get started. 

What exactly is an Outlook add-in?

An Outlook Add-In is a small piece of software that works in conjunction with Microsoft Outlook. An Outlook Add-In is often used when a user is viewing or composing an email to provide a limited but useful set of functionalities.

Add-Ins may be created by developers for all of Microsoft’s major Office programs. A Word Add-In may allow you to insert specific content (for example, from a text or image library) into a document.

What functions does an Outlook Add-in have?

•   Outlook add-ins may help in searching the content of an open email for a certain Customer or Case number, allowing you to show them relevant information from your main system.
•   It allows users to effortlessly copy and paste emails into your CRM system. Alternatively, it may scan the body of an open email for a specific Customer or Case number, allowing you to show them relevant information from your main system.

Is Outlook Add-In Development Difficult?

Outlook Add-Ins are, indeed, the most advanced members of the Microsoft Office 365 Add-In family. They frequently demand lengthy platform capability determination, the usage of the Graph API (or, in some cases, the EWS API), the exchange of an Outlook token for a Graph API token, and so on.

How Do I Set Up Outlook Add-Ins?

•  Users install your Outlook Add-In (either individually or for their entire association). Some Add-Ins are available through App-source (Microsoft’s business software store), while others can be installed directly by your clients using Outlook or their Azure Active Directory tenant.
•  When you open an email after installing your Add-on, the icon(s) for your Add-In should appear on the ribbon (the position varies by platform). When the user clicks on your Add-In’s icon, your Add-In appears, typically in a window on the right-hand side of the email.
•  The Add-In works in the same way that an App does within Outlook. The operations of the Add-In generally read some information from the email and make it available to your primary programme.
•  Access to the email server from which the email was served may be required for more complex Add-Ins. For example, these Add-Ins require access to attachments or threads. To obtain critical information, Microsoft exposes APIs such as the Graph API to these Add-Ins.

 Prerequisites

1. Install create-react-app

npm install -g create-react-app

2. Install Yeoman

npm install -g yo

3. Install Office Add-in Project Creator

npm install -g yo generator-office

How to create an Outlook Add-in using React?

Create the add-in

An Office Add-in may be made using Visual Studio or the Yeoman Office Add-in generator. In contrast to Yeoman, which creates a Node.js project that can be maintained using Visual Studio Code or any other editor, Visual Studio creates a Visual Studio solution. To create and test your add-in locally, make the appropriate selection and then follow the on-screen instructions.

Create the add-in project

1. Go to the Visual Studio menu bar, and choose File  >  New  > Project.
2. In the list of project types under Visual C# or Visual Basic, expand Office/SharePoint, choose Add-ins, and then choose Outlook Web Add-in as the project type.
3. Name the project, and then choose OK.
4. After Visual Studio generates a solution, Solution Explorer shows the two projects in the solution. The Message-read.HTML file opens in Visual Studio.

<body class="ms-font">
    <div class=”content-main">
        <h1>prop</h1>
        <table class="ms-Table">
            <thead>
                <tr>
                    <th>Prop</th>
                    <th>Value</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td><strong>Id</strong></td>
                    <td class="val"><code><label id="id"></label></code></td>
                </tr>
                <tr>
                    <td><strong>class</strong></td>
                    <td class="val"><code><label id="class"></label></code></td>
                </tr>
                <tr>
                    <td><strong>Message</strong></td>
                    <td class="val"><code><label id="Msg_Id"></label></code></td>
                </tr>
                <tr>
                    <td><strong>From</strong></td>
                    <td class="val"><code><label id="from"></label></code></td>
                </tr>
            </tbody>
        </table>
    </div>
</body>
'use strict';


(function () {


    Office.on Ready(function () {
        $(document).ready(function () {
            loadItemProps(Office.context.mailbox.item);
        });
    });


    function loadItemProps(item) {
        $('#id').text(itemId);
        $('#class').text(class);
        $('#Msg_Id').text(Msg_Id);
        $('#from').html(from.displayName + " &lt;" + from.emailAddress + "&gt;");
    }
})();
HTML,
body {
    width: 100%;
    height: 100%; 
    margin: 0;
    padding: 0;
}


td. val {
    word-break: break-all;
}


.content-main {
    margin: 10px;
}

    });

Can I use the generator for Office Add-in development?

• Yes, you can. You can use pure HTML, Angular, React anything you like!

Is it good to use TypeScript for Office Add-in?

• Yes, VS Code has great support for TypeScript

How do I install an Outlook add-in?

1. Open Visual Studio, click the menu bar and select File > New > Project.
2. In the list of project types under Visual C# or Visual Basic, expand Office/SharePoint, choose Add-ins, and then choose Outlook Web Add-in as the project type.
3. Name the project, and then choose OK.
4. Here you will see two projects appear in Solution Explorer. The MessageRead .html file opens in Visual Studio.
5. Navigate to the Settings > Integrated apps > Add-ins page in the admin center.
6. Choose an option and then follow the steps.
7. Make your add-in selection if you chose to add an add-in from the Office Store.
8. You can sort the available add-ins into three categories: Suggested for you, Rating, and Name. The Office Store only provides free add-ins. Paid add-ins are not currently supported. Accept the terms and conditions after selecting an add-in to proceed.
9. To designate whom the add-in is deployed to, select Everyone, Specific users/groups, or Just me on the next screen. To find certain users or groups, utilize the Search box.

•    Choose Deploy.
•    When the add-in is activated, a green tick displays. To test the add-in, follow the instructions on the website.
•   When you’re finished, click Next. If you’ve only deployed to yourself, you can change who has access to the add-in to add more users.

Step-by-step process of Outlook Add-in development using React.js

Given below is the step-by-step process available for Outlook Add-in development using React.js. 

Step 1: Create a Development Environment

• To begin, ensure that Node.js and NPM (Node Package Manager) are installed on your machine. These tools are required for installing and managing dependencies. Make a new directory for your project and launch a terminal or command prompt from within it.

Step 2: Develop a new React.js application.

• To build a new React.js application, enter the following command in the terminal:

npx create-react-app outlook-addin

This command will generate a new directory named “Outlook-addin” that contains a basic React.js project structure.

Step 3: Install Outlook Add-in Requirements

• Enter the project directory by typing:

cd outlook-addin

Install the following dependencies for developing Outlook Add-ins with React.js:

npm install @microsoft/office-js-helpers office-ui-fabric-react

Step 4: Set up the Office.js Helper and the Office UI Fabric.

• Open your project’s src/index.js file and add the following import statements at the top:

import { initializeIcons } from '@uifabric/icons';

import { initialize } from '@microsoft/office-js-helpers';

• Then, before rendering the React app, execute the initialize Icons() and initialize() routines. This guarantees that the required Office.js Helper and Office UI Fabric configurations are correctly set up:

initializeIcons();
initialize();


ReactDOM.render(
  <React.StrictMode>
    <App />
  </React.StrictMode>,
  document.getElementById('root')
);

Step 5: Create an Outlook Add-in Component 

• Make a new file called OutlookAddin.js in the src directory. The core component for your Outlook Add-in will be contained in this file. Add the following code to the file:

import React from 'react';


function OutlookAddin() {
  return (
    <div>
      <h1>Welcome to Outlook Add-in Development!</h1>
      {/* Add your Outlook Add-in UI components here */}
    </div>
  );
}


export default OutlookAddin;

Step 6: Integrate the Add-in Component 

• Remove the old code from the src/App.js file and import the OutlookAddin component. Substitute the OutlookAddin component for the App component:

import React from 'react';
import OutlookAddin from './OutlookAddin';


function App() {
  return (
    <div>
      <OutlookAddin />
    </div>
  );
}


export default App;

Step 7: Create and test the add-in

• To build the React app, enter the following command in the terminal:

npm run build

• When the build process is finished, a build directory is generated in your project directory. Modify the SourceLocation value in the manifest.xml file in the public directory to point to the generated app:

<bt:Urls>
  <bt:Url id="messageReadTaskPaneUrl" DefaultValue="https://localhost:3000" />
</bt:Urls>

• To start a local server, save the file and then run the following command:

npm start

• This will open a new browser window with your Outlook Add-in.

Step 8: Install the Add-in in Outlook.

• You must side-load your Add-in to test it in Outlook. Navigate to the “File” tab in Outlook. Choose “Options” and then click!

Conclusion

Creating Outlook add-ins using React.js has a number of advantages, such as giving front-end web developers a comfortable environment to work in and enabling the development of dynamic, reusable UI components using the React component model. We looked at the step-by-step approach to creating an Outlook add-in with React.js.

In this blog, we explored the architecture of Outlook add-ins, the structure, and requirements of Outlook add-ins as well as how add-ins interact with Outlook.

Featured image by Kevin Ku on Pexels

The post A Step-By-Step Guide on Outlook Add-in Development appeared first on noupe.

Words — both good and bad — travel far.

So, what your customers experience and then talk about with their peers (online/offline), sets the tone for your sales graph.

Plus, customer feedback is important for businesses to better understand their needs, wants, and desires. This key information can then be used to improve the product or service you’re offering and make it more attractive to future customers.

For example:

You may have seen ads for a new car model with a statement like — The features that set this vehicle apart include: X, Y, and Z. This means that the manufacturer has taken customer feedback into account when designing the car. In fact, they want to know what features are most important to potential customers so they can make sure to include them in their product lineup.

Interestingly, customer satisfaction plays a much bigger role in influencing consumer behavior than price and other factors. That’s why most companies look for repeat business — they know that happy customers lend credibility to their brand and are less likely to defect to competitors.

In this article, we explore the key differences between reviews and testimonials (the two forms of customer feedback) and a few ways you can land more poppy words of customer appreciation.

Reviews vs. Testimonials — What’s The Difference?

Length And Detail

Reviews

Reviews are to-the-point assessments that allow customers to share their opinions and experiences regarding a product or service. They provide a quick summary of the customer’s viewpoint — enabling potential buyers to make informed purchase decisions. Plus, this conciseness allows quick scanning, efficient information gathering, and comparison of multiple products with ease.

[Source]

They often focus on specific aspects — like quality, durability, functionality, and customer support efficiency. For example, reviews are critical in eCommerce as they offer insights from previous customers, helping you weigh pros and cons. They can be in the form of star ratings accompanied by comments that highlight strengths or weaknesses.

Testimonials

On the other hand, testimonials are more detailed and personalized accounts of a customer’s experience. They delve deeper into the customer’s narrative — providing a comprehensive and in-depth perspective. 

[Source]

They often highlight specific challenges faced and the outcomes or benefits experienced from using a product or service. Service-based businesses utilize testimonials to showcase their expertise and build trust.

Plus, they include additional details like the customer’s name, photo, and sometimes their profession or location — making them more credible. Testimonials can be featured on websites, social media, marketing materials, or shared in video format to effectively communicate positive impact.

Source And Platform

Reviews

When customers want to share their experiences and opinions about products or services, they often turn to third-party review platforms — like Yelp, TripAdvisor, Amazon, Google Reviews. And dedicated review sections on ecommerce websites act as central hubs for customer feedback. If you run an eCommerce or subscription business on Shopify, there are plenty of third-party apps that can help you capture reviews from your customers. 

These third-party review platforms are the goto resources for potential buyers and users who are seeking information and insights before making a purchase. 

[Source]

Unlike testimonials, which are carefully selected and displayed by businesses, reviews on these platforms are typically un-curated. They provide a more authentic and balanced representation of the customer’s experience, as they include both positive and negative feedback.

Also, while testimonials are a direct testament to the brand and their offering, reviews can often relate to elements that are external. For example, if your business uses a billing system that is unreliable, or if your physical store is located in a mall that does not offer free parking, these criticisms can often show up against your own product or service. 

The transparency offered by these platforms allows future buyers to assess the reputation and quality of a product or service. They can gain access to a range of perspectives and consider the various positive and negative aspects highlighted by different customers.

Testimonials

Testimonials are feedback directly provided to a company by satisfied customers. They’re usually obtained through specific requests or surveys. And are displayed on the company’s own platforms — such as their website, social media profiles, or marketing materials — to establish trust and credibility with potential customers. A sales-driven organization may also showcase testimonials in their PowerPoint slides to prospective clients. 

[Source]

Unlike reviews, testimonials are carefully handpicked by the company, and usually undergo some level of editing to correctly reflect their strengths. Companies seek out testimonials to gather positive feedback that clearly demos the value they provide.

While testimonials may be edited for clarity or grammar, the core message conveyed by the customer remains intact. The editing process aims to present the testimonials in the best possible light without compromising the brand authenticity. Given that testimonials are pivotal in conversion optimization, marketers typically run a lot of A/B tests with respect to the specific words used, presentation, and associated call to action. 

Content And Tone

Reviews

Reviews provide a diverse range of feedback — from positive and negative to neutral perspectives. This offers a well-rounded view of a product, service, or experience. 

Customers share specific details about their experience in reviews, providing an in-depth assessment. They discuss the pros and cons, highlight liked or disliked features, and address any encountered issues. By including such specific information, reviews offer key purchase worthiness insights for other customers or users.

[Source]

The tone of reviews varies based on individual experiences and writing styles. They also reflect the level of customer engagement and satisfaction they experience. Some convey enthusiasm and satisfaction, praising the positive attributes, while others express frustration or disappointment, pointing out the lacks.

Reviews offer customers a free-speech platform to express their genuine opinions and feelings about a product. This emotional element adds depth and authenticity to the feedback which directly translates into true buying advice — which they may not get any other way (well, because company’s sales reps only shine a spotlight on the pros and almost never the cons).

Testimonials

Testimonials at their core exist for promotional purposes, focusing on highlighting the positive aspects of a product or service. They aim to showcase benefits, value, and success stories associated with the customer’s experience, creating a positive perception.

To do so, they emphasize transformation, improvement, or satisfaction, demo-ing how the customer’s situation or life improved due to the product or service in focus.

[Source]

Plus, testimonials highlight measurable success metrics, reinforcing the effectiveness and value of the endorsed product or service.

Testimonials have a highly positive tone — often expressing gratitude and admiration for the company’s offerings. This is precisely why the best and most compelling feedback are carefully curated and edited, to build trust and credibility.

How To Capture More Reviews And Testimonials For Your Business?

Before you start collecting more testimonials and reviews from your happy (or unfortunately, dissatisfied) customers, please make sure you’re respectful of their time and preferences. Make it clear that their feedback is valuable and appreciated. 

Let’s take a quick look at a few ways you can increase the likelihood of capturing more reviews and testimonials to promote your business and build trust with potential customers:

• Reach out to your happy and satisfied customers directly and request their feedback. You can send personalized emails or SMSes asking them to share their thoughts about their experience with your product or service.

• Implement survey tools (like Jotform) to capture customer feedback and reviews. This can be done through online surveys, email surveys, or even feedback forms on your website. Make sure to ask specific questions that encourage customers to quickly share their experiences and opinions.

• Offer incentives or rewards to customers who leave reviews or provide testimonials. This can be in the form of discounts, exclusive offers, or entry into a giveaway. Incentives can motivate customers to take the time to drop a review.

[Source]

• Reach out to the satisfied customers who have provided positive feedback and request their collaboration for creating video or textual testimonials. Offer assistance and guidance in the process — such as providing interview questions or helping with video recording/editing, if needed.

• Simplify the process of leaving reviews or testimonials. Provide clear instructions and direct links to review platforms or testimonial submission forms. The probability of your customers dropping a review is directly proportional to the ease of doing so.

• Get social with your customers on social media platforms. Encourage them to share their experiences publicly by mentioning or tagging your business in their posts. Monitor social media mentions and reviews to respond promptly and encourage further engagement.

• Focus on delivering exceptional customer service and a positive overall experience. Ultra-happy customers are more likely to voluntarily leave reviews or provide testimonials without being prompted. An omnichannel customer service that maximizes positive experiences make customers happy.

• After a purchase or interaction with your business, send follow-up emails or messages to customers. Thank them for their support and encourage them to share their feedback or leave a review. Timing is crucial, so consider sending these requests while the positive experience is still fresh in their minds.

Final Words

The game is all about happy customers. If you keep your customers satisfied, they’re likely to refer you, and it won’t really matter which format you use to land chest-puffing reviews or testimonials.

However, it is worth considering the differences here, because each form of feedback has a unique place in your overall business strategy. Review these formats side by side and see what makes sense for your business and for your customers.

Image by Mohamed Hassan from Pixabay

The post Reviews vs. Testimonials [Differences + What’s Best for Your Business] appeared first on noupe.

Society is growing to rely on Artificial Intelligence more and more. Over the years AI has become embedded in everyday life, such as our smartphones and devices that control smart technology in our homes. One of the major components of AI is its ability to continuously learn so that it is constantly getting better and becoming more efficient. There are so many capabilities and it’s hard to imagine the limits of this technology. One such capability is to help businesses better understand their customers.

With customer expectations higher than they have ever been, it is important for companies to adapt and continue to be aware of those expectations. One report shows that about 80% of customers would prefer not to do business with a brand after just one bad experience. This shows that the bar is extremely high for companies to get it right from the beginning. That’s where AI can help.

There are plenty of people who have already come to embrace the idea of AI-enhanced CX (AI/CX). It has been found that 70% of executives believe their industry is ready to adopt AI/CX and three out of four predict AI will have a vital role in the future of their organizations.

When used in a contact center, artificial Intelligence can also save a business time and money, which is always a plus. However, being able to tap into the knowledge behind what drives customer engagement is key to success. The great thing is that AI will do the work for you and you get all the benefits. So, this is what AI can do for you.

Improving Customer Service

Having a responsive and helpful customer service department is essential to any business that wants to be successful.  90% of consumers say “immediate” response time is very important when they have a question. Customers need to be able to have direct access to businesses to file complaints or resolve any issues they may have. So having different channels where people can express those things is necessary for companies to retain customers. Multiple kinds of AI-powered software have been developed to enhance the customer experience.

Chatbots are one way that artificial intelligence can improve customer service. They are very relevant in today’s world, as 67% of people expect to use messaging apps to talk to businesses. They can easily answer frequently asked questions or simpler questions that do not require intervention from a customer service representative. They’re also available 24/7 so people have access at all times, without the need for shift workers. 

Receiving feedback from customers is one of the best ways for companies to know how they are doing and what they need to improve. AI is a tool that can exceed what the standard survey provides. For example, AI can facilitate online focus groups in real-time and opens up a channel for communication, where you can have a conversation with people and participants don’t have to give one-sided responses. 

Predicting Behavior

Being able to predict customer behavior has a major impact on the way companies conduct their business. In predicting behavior, brands can offer a more personalized experience and drive sales. Artificial intelligence can capture and analyze customer data to a much higher extent than ever before.

Programs, like Google Analytics, Facebook Ad Insights, Adwords, and CRM data are designed to track customers across platforms and generate reports with information about people’s online habits. They will track things such as which websites people visit and specifically what products they are looking at. They can get very detailed to even include how long someone stays on a page and how often they return.

By knowing ahead of time how people shop and what products/services they are looking for, brands can plan accordingly. They are able to design their websites in a way that draws people’s attention to exactly what they want and makes them more user-friendly. They can also put their time and effort into funding the design of products that they know people want. 

Targeted Marketing

Marketing has drastically changed over the years. Traditional marketing no longer makes the cut and now people listen more to ‘influencers’ and spend so much of their time on social media, where they are seeing ads bombarding them in a whole new way.

It’s no coincidence when someone logs onto Instagram and sees ads for products they specifically like. That is because these ads are tailored for and targeted at each individual. AI is to thank for that. Algorithms are built into these platforms that run based on AI technologies and machine learning to make recommendations on content for users. It can almost be eerie in the ads that users see, especially if someone has just been searching for that very product being advertised.

Having AI keep up with customer data allows brands to know exactly how to customize their marketing tactics for each person. This keeps the customer drawn in and clicking that add to cart button. It also helps prevent churn and retains the best customers by detecting when they are starting to lose interest and can launch new campaigns to keep their attention.

Artificial intelligence is only going to continue improving and has so much potential in businesses. Brands are in a unique position, compared to those in the past, in the way they can use technology to tap directly into customer insights. By having a better understanding of customers’ expectations and habits, a company can completely transform itself to cater to its customers. The customer experience is everything. And AI is a powerful tool that can revolutionize the way customers interact with a brand. So, to get started with integrating AI to create a better customer experience, check out your options with LiveVox.

Images by LiveVox Team

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When you want to buy something, you seldom directly opt for an unknown brand or a new shopping portal you’ve only heard of. This is because you are driven to trust the brands or e-commerce portals, or a particular offline store based on your previous experience. You feel connected because somehow you find items that align with your interests and preferences. 

In offline stores, one can say it largely depends on the relationship building of the owners and the shoppers. When it comes to online shopping, there is no face-to-face meeting. Yet, your favorite brands know what you like and recommend products you will likely purchase sooner or later. 

This is no magic. Say hello to recommendation systems, one of the most frequently used machine learning techniques. 

What are recommendation systems?

Recommendation systems are advanced data filtering systems powered by AI, recommending the most relevant item to a particular customer or user. It uses behavioral data and machine learning algorithms to predict the most relevant content or services for customers.

For instance, when you watch a web series or a movie on Netflix, you will see recommendations for shows and movies of similar genres. Netflix uses recommendation systems to analyze your watching preferences and browsing activities to suggest relevant content. 

Doing this helps reduce users’ time to browse through thousands of contents to find a new show or movie to watch.

Recommendation Systems: The Science Behind It

A recommendation system is an algorithm that uses big data to suggest relevant products and services to users. Since big data fuels recommendations, the inputs required for model training are crucial. 

It can work on various types of data depending on your business goals. For example, it can be based on user demographic data, past behavior, purchase habits, interactions with your product or website, and even search history. Recommendation systems analyze the data to identify patterns and then make suggestions for products most likely to appeal to the users.

For instance, on Amazon, you will discover related products on the homepage, upon adding items to your cart, reviewing a particular product, or completing a purchase. There is always a recommended list of items that align with your past purchase and overall browsing patterns. 

Recommendation systems can offer personalized experiences to customers by providing relevant recommendations on various topics. They also help businesses increase customer engagement and loyalty by recommending related items and services.

Why use Recommendation Systems?

An essential component of recommendation engine algorithms is filtering based on different aspects. The recommender function gathers information about the user and predicts the most relevant product. For example, they predict user ratings and preferences based on recurring actions.

Recommendation system models are crucial in helping us have a hassle-free and seamless user experience. It also helps expose more inventory or content that we might not discover otherwise amidst the enormous volumes of data. There are many uses for recommendation engines:

• Personalized content: Improves the on-site experience by creating dynamic recommendations for a diverse audience.
• Better search experience: Categorizes products based on their features.
• Increased sales: Recommends products to customers based on their purchase history, allowing them to make more informed decisions. 
• Re-engagement strategies: Helps bring users back by suggesting relevant items or services they may be interested in.
• Cross-selling and up-selling opportunities: Identifies complementary items that customers may have yet to consider or be aware of. 
• Collecting valuable customer data: Generates useful insights into user behavior and preferences. 
• Optimizing marketing campaigns: Analyzes customer data to refine targeted promotions and campaigns to drive higher conversion rates.

Product recommendations on online shopping apps, social media news feeds, and Google ads are examples of recommendation systems in our day-to-day life. 

Engineering Behind Recommendation Systems

Recommender Systems (RSs) are advanced software tools and techniques that recommend products to a user that they find useful. The recommendations involve different decision-making processes, like things to buy, music to listen to, or online news to read.

The four clearly defined, logical steps of a recommendation system are:

1. Data Collection

The first and most crucial stage in developing a recommendation engine is obtaining the appropriate data for each user. There are two kinds of data:

• Explicit data is information gathered from user inputs like product ratings, reviews, likes, and dislikes.
• Implicit data comprises details obtained from user behaviors such as web searches, clicks, cart actions, search logs, and order histories

Since each user’s data profile will change over time to become more distinctive, it is also essential to gather consumer characteristic information like

Demographics (age, gender)	User demographics refer to the various characteristics of individuals, such as age, gender, income, education level, location, and cultural background. In recommendation systems, user demographics can be used to personalize recommendations and improve user experience. In addition, it encourages engagement and loyalty to drive more business.
Psychographics (interests, value)	Psychometrics is the scientific study of human mental processes and behavior and can be used to develop more accurate user recommendations. By tailoring recommendations based on personality, interests, and behavior, companies can increase users’ likelihood of engaging with products or services.
Feature Information (genre, object type)	Feature information such as genre, object type, and keywords can also be used to personalize recommendations. By leveraging these characteristics, companies can create more targeted and relevant content for their users. For example, an online clothing retailer could use feature information to suggest items based on a user’s style preferences or the season.
Contextual Information (time of day, location)	Contextual information such as time of day, weather conditions, and location can be used to determine what content is most appropriate for each individual at any given moment. For instance, if a user is located in a city that experiences hot summers and cold winters may be prompted with seasonal items or activities suitable for the local climate.

2. Data Storage

Data storage is essential for any business as it allows them to store and access customer information. By monitoring user data, a business can gain insights into its target audience, including purchasing habits and preferences.

 There needs to be enough scalable storage as you gather more data. Depending on the data you gather, you have various storage options, including NoSQL, a regular SQL database, MongoDB, and AWS.

Considerations for selecting the best storage alternatives for Big Data include portability, integration, data storage space, and ease of implementation.

3. Data Analysis

Data analysis is required to provide prompt recommendations. Therefore, the data must be probed and examined. Data analysis techniques that are most frequently used include:

• Real-time analysis involves the system using tools to assess and analyze events as they are happening. Then, when we wish to make prompt recommendations, this strategy is typically used.
• Batch analysis is a method for routinely processing and evaluating data. When we wish to send emails with recommendations, this strategy is typically used. 
• A near-real-time analysis is practical when you do not immediately require the data. It allows you to process and analyze the data in minutes rather than seconds. The main application of the strategy is when we offer suggestions to users while they are still on the website.

4. Data Filtering

The data must be appropriately filtered after analysis to produce insightful recommendations. During filtration, Big Data is subjected to various matrices, mathematical algorithms, and rules for the best suggestion. The recommendations that result from this filtering depend on the algorithm you choose.

Recommendation engines rely on understanding relationships. Relationships give recommender systems a wealth of information and a thorough knowledge of their clients. Three different kinds typically exist: User to product: When specific users have a preference or affinity for particular goods they need, there is a user-product relationship User to user: Users with comparable preferences for a given good or service form user-user relationships Product to user: Product-product relationships develop when two products are similar, whether through description or appearance. These are then shared with an interested user

How do Recommendation Engines Gather Data?

Recommendation engines gather data based on the following:

• User Behavior: Information regarding user interaction with a product can be gleaned from user behavior data. Rating clicks and purchase history can all be used to gather it.
• User Demographics: Users’ data, such as their age, education, income, and location, are tied to their demographic data.
• Product Attributes: Information about a product’s attributes includes information on the product itself, such as the genre of a book, the movie actors, or the cuisine of a dish.

Challenges and Advantages of Using Recommendation Systems

Challenges: 

Implementing a recommendation system is rather complicated. The key challenges are: 

• Data Collection and Integration: The first challenge is collecting data from various sources, such as customer profiles, product catalogs, purchase histories, social media accounts, etc., and then integrating the data into a unified format for the recommendation engine. The process can be time-consuming and labor-intensive.
• Algorithms: Choosing the right algorithms for your recommendation engine is also challenging since you need to build an algorithm that understands user preferences and makes accurate predictions based on those preferences. Additionally, new algorithms must be implemented as customer preferences change over time.
• Accuracy: Once you have chosen the best algorithms for your purposes, it can still be difficult to ensure accuracy in the system’s output. This becomes even more difficult as the amount of data increases.
• Scalability: As your business grows, so does the volume of data, and it can be difficult to ensure that your recommendation engine can handle these large volumes of data without a performance decrease.
• Data Storage: Storing all of the necessary data requires considerable space and resources. Additionally, the data must be kept up-to-date to ensure the system’s output accuracy.
• Privacy and Security: Customers may be concerned about privacy when they share their personal information with a recommendation engine. It is important to have a strong security measure that protects customer data and prevents unauthorized access or use for malicious purposes.

Despite the steep implementation curve, companies continue to rely heavily on recommendation systems. This is because user recommendations have a very strong influence on user purchase decisions. The TWO major advantages of using a recommendation system will always outweigh all the challenges. 

Advantages: 

• Deliver Personalized and Relevant Content

Recommendation systems enable brands to customize the customer experience by identifying and suggesting items that may pique the customer’s interest. Additionally, the recommendation engine allows you to analyze the customer’s previous browsing history and current website usage. With the help of these real-time data, brands can deliver the most relevant product recommendation. 

For instance, Instagram often suggests pages or brands you might want to start following based on your preferences. Similarly, based on the kind of reels you watch, Instagram keeps suggesting new ones. This keeps the addiction on as you continue scrolling through new recommendations you can’t deny liking. It boosts engagement via personalized content recommendations. 

• Increase Sales and Average Order Value

You can significantly enhance your revenue and average order value (AOV) by bringing in more website visitors by adding recommended products.

A recommendation system enables users to drive more conversions and deliver a high level of relevance that will boost sales. It exposes the customers to more products they are more likely to purchase.

You can add multiple data sets to your recommendation algorithm using a recommendation engine. The data sets will help provide recommendations in real time.

For instance, when looking at a product on Amazon, you will always find two sections back-to-back – customers who viewed this have also viewed it, and customers who bought this also bought. These two sections will push you to consider different products that go well with the product you are trying to buy. 

Let’s say you are looking for a channel file on Amazon and have zeroed in on one of the options. As you scroll down to read more about its features and look at a comparative table (which Amazon always adds to propel better decision-making, ideally for the more priced product), you will come across two sections like this: 

Now, offline stores will call this push selling. This is exactly what Amazon is doing here. Recommending similar or related products often triggers human brains to explore those products and probably buy or add them to the cart. Towards the end of the purchase cycle, the order value automatically goes up from what it was when the cycle started. 

Filtering Algorithms Marketers Must Understand

To put a recommendation engine into proper functioning, marketers must also understand the algorithms that make up this system. Three major types of filtering algorithms are at play: Collaborative filtering content-based filtering Hybrid System

Collaborative Filtering

Collaborative filtering gathers information regarding customers’ activities or preferences to predict user interest. It is usually acknowledged based on their similarities with other users who might have the same preference. Similar customers are found using customer characteristics like demographics and psychographics. 

E-commerce platforms like Amazon and Myntra are pioneers in effectively implementing collaborative filtering. 

It works by gathering preferences from each user to determine a Customer X Product Matrix.

It is also known as the matrix factorization method and can be used to determine how similar user evaluations or interactions are. 

For example, according to the straightforward user-item matrix, Ted and Carol bought products B and C. Bob also searched for product B. Matrix factorization determines that users who enjoyed B also like C, making C a potential recommendation for Bob.

Collaborative filtering is divided as follows: 

User-item collaborative filtering	Like-minded customers are spotted based on similar rating patterns. It is a method for recommending products based on ratings from other users.
Item-item collaborative filtering	Similarities between multiple items are calculated. A recommendation approach that looks for comparable products based on the things consumers have already shown interest in or interact with.

Content-based Filtering

Predictions made by content-based engines are based on end-user interest in a particular product. The engine uses metadata to find and suggest related content items once a user interacts with a piece of content. 

You can spot this recommender system on news websites with prompts like “You may also be interested in reading” or “You may like.”

For instance, on Medium, you will always find recommendations for articles similar to the ones you are reading or have read. 

The recommendation engine algorithms filter content based on the following:

• User ratings: User ratings are of two types: Explicit and Implicit.  User profiles and star ratings are common sources of explicit data for recommendation systems (difficult to achieve). Implicit data is based on users’ engagement with the item. These are simple to obtain, including purchases, clicks, and views.
  
• Product similarity: The most effective method for recommending products based on how much a customer would like a certain item is product similarity. Users may be shown comparable products if they browse or look for a specific item. Here’s how Amazon does it.
  

• User similarity: It is a particular kind of algorithm for recommendation systems that suggest products based on product resemblances. Using data from previous user-product interactions, the algorithm generates suggestions. It assumes that individuals with similar interests will favor related goods. This is how Myntra does it: 

In Conclusion:

Note: The debate on privacy versus personalization has been hot since the recommendation systems rise. While such technologies have enabled organizations to provide tailor-made customer experiences through big data analytics, there is an inherent tension between these two concepts.

Companies must collect and store large amounts of data to generate personalized customer recommendations. Reports show that Big Data analytics has helped them increase revenue by 30%. 

On the other hand, companies must ensure that all collected data is securely stored and not misused. Survey results show 97% of consumers are concerned about their data being misused by companies and the government.

Ultimately, striking a balance between privacy and personalization should be the primary goal to ensure that customer data remains secure while providing an enjoyable customer experience. 

With this in mind, businesses can leverage big data-powered recommendation engines to offer a customized experience that will keep their customers returning for more.

Images from analytixlabs.co.in

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