Trending March 2024 # Five Ways Ai And Ml Technology Can Revolutionise The Banking Experience For Better # Suggested April 2024 # Top 11 Popular

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The cutting-edge technologies of the new age are changing the landscape of many industries. Specifically, artificial intelligence and machine learning are bringing about a lot of changes in the environment surrounding us. Both the technologies are transforming the banking sector as well for better experiences. Following the highly impressive implications these technologies, more and more banks are adopting AI/ML technologies. A survey report presented by Narrative Science and the National Research Institute revealed that 32 percent of financial services executives assured that they have already started using AI solutions including predictive analytics, recommendation engine, and voice recognition. But one aspect that is creating hindrance in complete adoption of AI is the legacy system. Leaders are resisting the upgradation of current technology processes because it is a more traditional industry. These legacy system place obstacles in seamless AI integration process. Since banking is a more traditional industry, leaders are reluctant to upgrade or change current technology processes. The problem is these legacy systems often prevent seamless integration of

Revamping Traditional Credit Scoring Process

Artificial intelligence-based credit scoring can work more efficiently than traditional processes by enabling fast, accurate assessment of the potential borrower. These technologies can further remove biases as well. With the help of AI, banks can determine which person has a higher default risk and who is more creditworthy without even checking the extensive credit history. Additionally, ML models can perform credit scoring over and over again to learn from mistakes and make improvements when it comes to handling a huge financial dataset. Henceforth, the consumer will receive an early response from banks and understand their finances better.  

Managing and Mitigating Risks

Bank can utilize automated credit risk testing to better manage and mitigate risks after getting accurate reports without human error. Also, by analyzing the history of risk cases, AI can aid banks to predict several issues and stay risk-ready. By analyzing a vast amount of data which humans cannot do in a short time, AI algorithms can reduce risk assessments to shorter time span say few minutes.  

Substantially Preventing Frauds

Almost every financial institution is infected by fraud cases. This is where AI/ML technologies come into the picture. These technologies by analyzing spending patterns, location and client behavior can recognize abnormalities and alert the cardholder which consequently helps in reducing frauds. AI/ML enabled systems can not only track suspicious behavior but also if provided with additional information can block the transaction within a few seconds. This boon of AI centric fraud detection approach can help banks catch frauds in real-time rather than wasting time in taking steps to rectify the issue.  

Offering More Personalized Experience

Banks can offer a better and more personalized customer experience with the virtue of AI and ML. Consumers and businesses want more in less and that when they acknowledge the value of unique experiences and better options. ML algorithms can analyze data of individual consumer and monitor abnormalities. It can also notify members if their card was charged more than one time for a single expense. ML models can also forecast which banking tools might be used by a consumer and further recommend them in order to make them take better decisions.  

Automating Tasks for Better Service

Freeing up human resources and capacities by automating repetitive and usual tasks can add to better customer service. Using RPA technology, financial institutions can remove human error and re-architect workforce tasks. ML algorithms can use image recognition to recognize patterns in the banking cum legal agreements which would take around 360,000 labor hours per year with complete human involvement. Usage of chatbots can avail quick and reliable information to customers.  

Conclusion

The cutting-edge technologies of the new age are changing the landscape of many industries. Specifically, artificial intelligence and machine learning are bringing about a lot of changes in the environment surrounding us. Both the technologies are transforming the banking sector as well for better experiences. Following the highly impressive implications these technologies, more and more banks are adopting AI/ML technologies. A survey report presented by Narrative Science and the National Research Institute revealed that 32 percent of financial services executives assured that they have already started using AI solutions including predictive analytics, recommendation engine, and voice recognition. But one aspect that is creating hindrance in complete adoption of AI is the legacy system. Leaders are resisting the upgradation of current technology processes because it is a more traditional industry. These legacy system place obstacles in seamless AI integration process. Since banking is a more traditional industry, leaders are reluctant to upgrade or change current technology processes. The problem is these legacy systems often prevent seamless integration of AI . However, to stay relevant to the competitive time, banks need to deploy financial technology solutions to their processes. The needs of consumers and their demand from the bank are increasing and AI can promisingly deliver to that. Also, machine learning technology enables banks to stay less dependent on human expertise which implies that human staff can focus more on improving customer experience. Here are the five areas where the AI/ML technology can improve the banking experience substantially.Artificial intelligence-based credit scoring can work more efficiently than traditional processes by enabling fast, accurate assessment of the potential borrower. These technologies can further remove biases as well. With the help of AI, banks can determine which person has a higher default risk and who is more creditworthy without even checking the extensive credit history. Additionally, ML models can perform credit scoring over and over again to learn from mistakes and make improvements when it comes to handling a huge financial dataset. Henceforth, the consumer will receive an early response from banks and understand their finances chúng tôi can utilize automated credit risk testing to better manage and mitigate risks after getting accurate reports without human error. Also, by analyzing the history of risk cases, AI can aid banks to predict several issues and stay risk-ready. By analyzing a vast amount of data which humans cannot do in a short time, AI algorithms can reduce risk assessments to shorter time span say few minutes.Almost every financial institution is infected by fraud cases. This is where AI/ML technologies come into the picture. These technologies by analyzing spending patterns, location and client behavior can recognize abnormalities and alert the cardholder which consequently helps in reducing frauds. AI/ML enabled systems can not only track suspicious behavior but also if provided with additional information can block the transaction within a few seconds. This boon of AI centric fraud detection approach can help banks catch frauds in real-time rather than wasting time in taking steps to rectify the issue.Banks can offer a better and more personalized customer experience with the virtue of AI and ML. Consumers and businesses want more in less and that when they acknowledge the value of unique experiences and better options. ML algorithms can analyze data of individual consumer and monitor abnormalities. It can also notify members if their card was charged more than one time for a single expense. ML models can also forecast which banking tools might be used by a consumer and further recommend them in order to make them take better decisions.Freeing up human resources and capacities by automating repetitive and usual tasks can add to better customer service. Using RPA technology, financial institutions can remove human error and re-architect workforce tasks. ML algorithms can use image recognition to recognize patterns in the banking cum legal agreements which would take around 360,000 labor hours per year with complete human involvement. Usage of chatbots can avail quick and reliable information to customers.Undoubtedly, AI and ML adoption by financial institutions are driven by consumer demand. For bank customers, having a secure and personalized experience has become their basic need. Since banks must depend on customer loyalty, they need to embrace AI/ML technologies to deliver better solution and services.

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5 Ways Mobile Tech Can Enhance Employee Experience For Insurance Companies

Digitalization is introducing a new era in insurance claims management. Workers have evolved to digital-first, and connected customers expect their insurance firms to provide personalization and quick and proactive responses, giving consistent answers and ultimately solving their problems.

Still, today, the insurance claims process can be cumbersome to the claims adjuster. Their workflow overlaps and alternates between using a mobile device in the field and a laptop in the car or office. Their hardware is not optimized for use on the road or as a field-ready solution, and tools and equipment carry high operating costs. Claims adjusters grapple with these imperfect technology solutions while working with people who have experienced a disruption in their lives and are seeking a timely resolution to their claim.

With the right technology partner and solutions, claims adjuster teams can streamline the workflow to help employees increase efficiency and productivity; empower associates with a single multi-functional secure enterprise-grade device at a lower cost of ownership; and have the right tools to manage and analyze associates’ devices and the network they rely on.

Samsung’s hardware and software solutions can help improve and streamline today’s often-unwieldy claims process. Here’s how Samsung’s mobile technology can optimize the claims process for your insurance adjusters.

1. Leverage the power of Samsung and Android through Galaxy devices

The full product line of Samsung Galaxy mobile devices — including the rugged phones able to withstand extreme temperatures— offers a diverse array of smartphones, tablets, foldables, and wearables that help unlock your employees’ productivity in the office and at the claim site.

For example, the available S Pen with Samsung DeX and Microsoft integration turns any Galaxy device such as Galaxy Z Fold4 into a mobile productivity powerhouse, with the ability to capture signatures and mark up documents and images. For capturing information on-site, Galaxy devices offer some of the highest-quality HD cameras and displays on the market.

2. Provide unprecedented mobility and value through DeX in vehicle

Through Samsung DeX, adjusters can connect Galaxy devices such as Galaxy Z Fold4 and the latest Galaxy S23 series to any screen for a mobile-powered desktop computing experience.

This is increasingly helpful for many claims adjusters who work off a rugged laptop in the vehicle. They pay for OS updates and wait through a five- to eight-year average timeline for recycling hardware.

However, using Samsung DeX can enable full mobility. In a vehicle, a rugged touchscreen monitor is paired with a keyboard and touchpad and then connected to a Samsung tablet or smartphone. There are regular OS updates through Android, shorter hardware cycles of one to two years, and lower average initial costs than most laptops. The DeX offering includes a desktop UX for Android apps, including the Microsoft and Google Workspace, virtual desktop environments, and videoconferencing.

3. Empower claims adjusters with one Samsung mobile device

A Samsung tablet or mobile phone, partnered with DeX, can help your claims adjusters in several ways:

Improve EX and CX by seamlessly transitioning from mobile to desktop on critical applications. The mobile device can also provide push notifications, paperless documentation, route optimization, and a way to manage tasks.

Reduce costs by providing a way to connect to networks, make video and audio recordings, capture photos, and connect to a printer, all through one device.

Enable Know Your Customer by helping claims adjusters scan IDs and documents on the go, and use the S Pen to accept signatures and mark up documents.

4. Partner with insurance ecosystem partners

Claims adjusters use several software and hardware solutions specific to the industry. In addition, Samsung’s mobile devices and software solutions can seamlessly partner with them to improve your adjuster’s productivity.

Claims adjusters can secure their Samsung mobile device in a vehicle with Ram Mounts rugged keyboard, pad, mount, and strap. In addition, virtual desktop infrastructure apps and key claims adjuster apps are optimized for DeX, including Citrix, VMware, Guidewire, and Verisk.

The home office can securely manage and deploy all these apps with the Samsung Knox security platform built into all Galaxy smartphones, tablets, and wearables. Future-proof connectivity via Wi-Fi 6 and 5G and enjoy a simple deployment model with lower TCO in a solution optimized for enterprise applications.

What’s next for the future of finance?

Samsung surveyed 1,000 finance professionals about the future of mobile tech. Here’s what they said. Download Now

Other partners in the Samsung ecosystem include IBM, Targus, Salesforce, Otter Box, and ServiceNow.

5. Optimize the insurance claims process

Providing your insurance adjusters with a Galaxy mobile device can optimize the broader claims process across analytics, information security, and agent support in the following ways.

Data analytics

Samsung’s Knox Asset Intelligence provides insight into application-based usage and network utilization, hardware-based analytics and real-time location monitoring.

Information security

This includes hardware-backed security in conjunction with your MDM, secured shared associate access, and Zero Trust security enablement.

Agent support

This includes remote log capture for IT admins, access to a mobile IT helpdesk, and Push to Talk with Microsoft Teams.

For a full overview of all Samsung technology solutions for the Finance industry, please visit this page. And sign up for a Samsung Business Account to get exclusive offers, including volume pricing discounts, on our Rugged devices, as well as our newest devices like Galaxy S23 series, Galaxy Z Fold4 and Galaxy Z Flip4.

Top 7 Ways Blockchain Can Enhance App Development Experience

Just like every other emerging technology, there is a lot of hype surrounding blockchain technology but does all that hype really justified or is it just a tech bubble that is about to burst very soon? Let’s look at some numbers.

Based on Blockchain figures, the Blockchain marketplace is anticipated to achieve a $20 billion mark by 2024. International spending blockchain technology was $2.1 billion in 2023 which climbed to $2.7 billion in 2023. 69 percent of banks are already tinkering with blockchain technology and a number of them have reported savings of anywhere from $8 billion to $12 billion each year.

These figures clearly point towards a bright future for blockchain technology. You could have a common misconception which blockchain technology generally finds its application in the monetary industry. Could it affect different businesses in precisely the exact same manner as banks and financial institutions? What about mobile program development? Can blockchain impact app development? If so, then how?

If you’re interested in answers to all these questions, then you’re in the ideal location. Within the following guide, you will learn about how blockchain technology may reap mobile program developers and boost the cell program experience of consumers.

Top 7 Ways Blockchain Can Enhance App Development Experience 1. Distributed Management and Control

As a result of its distributed architecture, there’s not any single point of collapse. It’s essentially handled by a dispersed thing powered by a huge worldwide network of computers. The dispersed network functions as a server providing service to customers.

Also read: Top 7 Work Operating Systems of 2023

2. Security

Another benefit of decentralization is safety. By eliminating a centralized entity from the film, it gives control from the hands of numerous blockchain participants. Each transaction is listed in a dispersed ledger and confirmed by numerous nodes on the network.

The listed activity makes blockchain monetary trades much secure than conventional ones and provides a degree of confidence to the information. Because of additional level of confidence blockchain technology is more difficult to hack cybercriminals. The blockchain also has validation and encryption facilities to create your financial trade even more protected.

App programmers may also use personal blockchain technologies to restrict access to certain users. On top of that, you don’t need to include on peer-to-peer decentralization network capacity for this.

Personal Blockchain compels users to authenticate their identities prior to getting access to rights. App developers may also implement limitations on the amount and kinds of trades a user may create.

3. Transparency

Are you worried about your information getting into the wrong hands? If so, then blockchain is for you. With only authorized users allowed to access your info and make modifications to it, you do not need to think about your information becoming encrypted or stolen.

On top of that, each blockchain participant is able to observe the changes produced ensuring transparency and lessening the odds of fraudulent transactions. As it employs a computer system, which transmits information continuously, once the changes have been made to the ledger or block, everybody is informed about the modifications.

Also read: Top 5 Automation Tools to Streamline Workflows for Busy IT Teams

4. Scalability

Blockchain’s unique design makes it an perfect selection for mobile programs. It isn’t just more powerful but also provides about the scalability and versatility . Since the information transfer through numerous cubes related to the blockchain system, the possibility of information compromise is a lot lower.

The open-source character of blockchain makes the tools and technology more accessible. It doesn’t just develops along with your business needs but also offer you more flexibility as compared to other technology. Programmers are able to benefit from personalization choices and produce a solution that’s tailor designed to meet the company requirements.

5. Small Learning Curve 6. Debugging

Since blockchain information is protected from cryptographic encryption, hackers will find it hard to hack it. A blockchain network utilizes both private and public keys. Users have a personal key while the public key can be found on the network.

Also read: Top 10 IT Skills in Demand for 2023

7. Frequent Updates

The same as the cellular app business, the speed where blockchain technologies is evolving is mindboggling. The development means that we may observe regular upgrades coming out every couple of months.

The upgrades help app programmers to maintain their mobile programs current and also make certain that their programs will fulfill all current standards and satisfy the long term needs of companies.

Moreover, blockchain program developers share user information to enhance cellular programs. The sharing of information enables developers to continuously refine and enhance the program consumer experience.

Top 10 Machine Learning Frameworks For Ai & Ml Experts

Machine learning frameworks play a crucial role in developing and deploying artificial intelligence and machine learning models. They provide a comprehensive set of tools, libraries, and resources that enable AI and ML experts to build robust, efficient, and scalable solutions. This article will delve into the top 10 machine learning frameworks that every AI and ML expert should be familiar with. From industry giants to emerging contenders, these frameworks empower professionals to tackle complex challenges and drive innovation in AI and ML.

1.TensorFlow

Developed by Google Brain, TensorFlow is one of the most popular and widely adopted machine learning frameworks. Known for its flexibility and scalability, TensorFlow provides a comprehensive ecosystem for building and deploying machine learning models across various platforms and devices. With support for high-level and low-level APIs, TensorFlow offers AI and ML experts a wealth of tools and resources.

2.PyTorch

PyTorch has gained immense popularity among researchers and practitioners due to its dynamic computational graph and intuitive Python interface. Developed by Facebook’s AI Research lab, PyTorch provides a flexible and expressive framework for building and training deep learning models. Its emphasis on simplicity and usability makes it a top choice for prototyping and experimenting with new AI and ML ideas.

3.Keras

Based on TensorFlow, Keras offers a user-friendly and intuitive interface for building and training deep learning models. Known for its simplicity and ease of use, Keras enables AI and ML experts to iterate and experiment with different architectures and hyperparameters quickly. With its focus on rapid prototyping, Keras has become a go-to framework for beginners and experienced practitioners alike.

4.Scikit-learn

Scikit-learn is a versatile and widely used machine learning library in Python. It provides a rich collection of algorithms and tools for data preprocessing, feature selection, model evaluation, and more. With its comprehensive documentation and straightforward API, scikit-learn is an excellent choice for implementing machine learning techniques and workflows.

5.Microsoft Cognitive Toolkit (CNTK)

The Microsoft Cognitive Toolkit, or CNTK, is a robust deep learning framework offering high performance and scalability. Developed by Microsoft Research, CNTK supports distributed training across multiple GPUs and machines, making it suitable for large-scale AI and ML projects. Its rich features and support for various programming languages make it a valuable tool for AI experts.

6.Theano

Theano is a popular Python library that allows for efficient computation and optimization of mathematical expressions, especially in deep learning. It provides a flexible and efficient framework for building and training neural networks, and its symbolic expression capabilities enable automatic differentiation and GPU acceleration.

7.MXNet

MXNet is a flexible and efficient deep learning framework that supports multiple programming languages, including Python, R, and Julia. Known for its scalability and performance, MXNet allows AI and ML experts to build and deploy models across various devices and platforms. Its modular design and support for dynamic and static computation graphs make it a powerful tool in the machine learning ecosystem.

8.Caffe

Caffe is a deep learning framework focusing on speed, modularity, and expressiveness. With its pre-trained models and easy-to-use interface, Caffe is well-suited for tasks such as image classification, object detection, and segmentation. It provides a comprehensive ecosystem for training and deploying deep neural networks, making it a popular choice in computer vision and other domains.

9.Torch

Torch is a scientific computing framework that strongly emphasizes deep learning and GPU acceleration. Built on Lua, Torch provides a flexible and efficient platform for training and deploying neural networks. Its ecosystem includes powerful libraries such as NN for building neural networks and image processing, making it a preferred choice for researchers and AI practitioners.

10.XGBoost

Chatgpt & Ppc: 7 Ways Sems Can Leverage Ai Tools

With the arrival of widely available generative AI like ChatGPT and Bard, there is a whole slew of additional things PPC account managers can now automate.

How To Access Generative AI

One of my favorite ones is a Chrome extension called GPT for Sheets and Docs.

After you install it, you can add GPT formulas to cells in Google Sheets by typing formulas in the form of ‘=GPT(A2,B2)’.

So rather than working with a single prompt at a time in a chat interface, you can run the same prompt at scale on many cells.

Now we’re ready to start using GPT to help us with PPC work.

[Discover:] Our best PPC resources for education and upskilling

1. How To Use ChatGPT For Keyword Discovery

While Google’s Keyword Planner is a great tool for finding new keywords, ChatGPT provides a good alternative. You can prompt it with the landing page you want keywords for and get a few suggestions.

To make the keyword suggestions a bit more relevant, you can ask ChatGPT to include a particular term in each one:

Once ad groups are up and running, you might want to find some additional related keywords to target.

So rather than starting with a landing page URL, we’ll prompt ChatGPT to find keywords similar to existing ones.

In this example, I’ll show how that was done in Google Sheets.

Prompt Inputs GPT Response find 5 keywords similar to these: 1. Amazon PPC Optimization

2. Amazon Advertising Strategies

3. Amazon Ads Optimization

4. Amazon PPC Strategies

5. Amazon Sponsored Ads Automation

The first column contains the prompt, the second column the list of existing keywords, and the third column the formula that gets the response from GPT: =GPT(A2,B2).

The beauty of using GPT in a sheet is that it’s quite easy to just change the keywords in the second column while using the same prompt and formula to generate the ChatGPT response.

2. How To Use GPT For Keyword Classification

But what happens when a list of suggested keywords, whether from ChatGPT or another tool, gets too long?

We all know that Google rewards relevance through higher Quality Score. So we should split the list of keywords into smaller related groups.

Turns out ChatGPT is quite good at grouping words by relevance.

In my first attempt, I tried to help ChatGPT understand what might be a good categorization, so I added a category name after the first few keywords in my prompt. But I found there was no need to explain categorization, and issuing the same prompt without examples yielded equally good results.

In this prompt for ChatGPT, we provided examples of how to categorize keywords:

In this prompt, we didn’t provide examples for classification, but the quality of the response remained the same:

This output could be more useful if presented in a table, so read on to the tips and tricks section of this post to learn how to ask ChatGPT for that.

3. How To Use GPT To Create Ads ChatGPT ad headline suggestions can far exceed the requested character limits.

The response to this prompt illustrates a known limitation of ChatGPT: it’s not good at math, and the headlines it suggested tended to be too long.

ChatGPT isn’t good at math because it works by predicting what text would logically appear next in a sequence. It might know that “1+1=” is usually followed by “2,” but it doesn’t do the math to know this. It looks for common sequences.

This is a known issue and seems to be getting addressed. In my most recent experiments this week, ChatGPT is now writing strings that are shorter and more likely to fit into the limited ad space provided by Google for headlines:

When looking for additional ad text variations, providing the current assets helps ChatGPT do better.

[Recommended Read] → PPC Expert Tips: Budgets, Testing & Optimization

4. How To Use GPT For Search Terms Optimization

Now we can use this data for optimization. The problem with search terms is that there can be a lot of them, and manually working your way through them can be tedious and time-consuming.

So I asked ChatGPT if it could take all my search terms for an ad group and rank them by relevance.

Since it already understands the concept of relevance, I didn’t need to explain, and got the following result:

Some search terms ChatGPT considers more relevant to a company selling PPC management software:

Some search terms ChatGPT considers less relevant to a company selling PPC management software:

I found this extremely helpful when researching negative keyword ideas. I could focus my attention near the bottom of the relevance list, where the terms were indeed more likely to be less relevant to what the landing page offered. This is by no means a perfect solution, but it helps prioritize things for busy marketers. 5. How To Use GPT For Shopping Feed Optimization

GPT understands semantics and relationships and knows that “Nespresso” is a brand of kitchen appliances.

With no need for you to define brands and product categories, you can give GPT a product detail page URL in a Google Sheet and ask it to fill in a few blanks as I did below:

what’s the brand of the product on the page Nespresso what’s the product on the page Nespresso Vertuo Next Premium Coffee & Espresso Maker with Frother what’s the category of the product on the page Kitchen Appliances

As you can see, with just a landing page as input and some simple prompts, GPT is very capable of explaining what the product on the page is.

6. How To Use GPT For Building PPC Audiences

Another important PPC targeting lever is audiences. For example, Performance Max campaigns and search campaigns can be optimized by attaching audiences to them.

So next, I tried using GPT for developing audiences. Advertisers should focus on their own first-party audience data, but there are ways GPT can be used when first-party data is not available.

Here I used ChatGPT as I might use a research assistant.

I asked it what things a certain type of consumer might care about, and then I asked it to suggest some keywords those consumers might use if they were searching for something.

The keywords suggested by ChatGPT can then be used to create a custom audience segment in Google Ads.

Use ChatGPT as a research assistant to generate ideas about qualities of your business a prospect might care about: Using ChatGPT, follow up those qualities with a request for related keywords a prospect might search for. Use the suggested keywords to create a custom audience segment in Google Ads. 7. How To Use ChatGPT To Optimize Landing Page Relevance

So I asked ChatGPT to tell me what it thought the pages on my site were about. If some of the answers seemed too loosely related to what I’m selling, this could give me ideas for how to optimize those pages with more text related to our core business.

Use the Google Ads landing page report to grab landing page to analyze with GPT:

GPT shows the topic it believes each of the landing pages is about:

Here’s an example where ChatGPT said my landing page was about a topic that seems quite broad:

Prompt: what is the topic of this page, in 5 words or less?

Response: Integrations Solutions

With that information, I can tweak my landing page to be more PPC focused, or I could exclude that page from automated campaigns, for example, by using the URL exclusion feature of Performance Max campaigns.

ChatGPT Tips And Tricks

As you noticed, GPT’s responses take a variety of forms, from paragraphs to bullet point text, code, and tabular data. You can request the type of response you want rather than leave it to chance.

For example, after I asked ChatGPT for headline suggestions and got a list of headlines, I followed up with this prompt:

And it responded with this:

GPT responses can be requested in a table format, making it easier to work with the output.

Note, again, that it’s still bad at math, so the numbers can’t be trusted (and this was also a problem in tests of GPT-4) – but it is convenient that you can so easily work with data in tables.

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Conclusion

Try some of these examples on your own accounts, but be vigilant and monitor what the machines are doing for correctness and applicability to your business goals.

Human oversight through techniques like automation layering is a form of PPC insurance that is becoming more relevant in PPC every day, as AI like GPT shifts what it means to be a PPC marketer.

More resources:

Featured Image: Cast Of Thousands/Shutterstock

Can You Optimize Landing Pages For Better Lead Conversions?

blog / Digital Marketing How to Optimize Landing Pages: The Best Practices for More Conversions

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This blog highlights the concept of landing page optimization and its importance for lead generation and overall business success.

What Does it Mean to Optimize a Landing Page?

Simply put, optimizing a landing page means testing and improving the key aspects of a website and making sure that it will get the highest conversions from visitors. It is a subset of Conversion Rate Optimization (CTO) that involves factors like A/B testing and experimentation to achieve the desired outcomes. Thus, the purpose of landing page optimization is to optimize the conversion rate, which is the percentage of visitors who complete the intended action (filling out a form or signing up for a newsletter). Additionally, there are several elements of a landing page that can be optimized, including the page headline, Call to Action (CTA), layout and design, content, and more.

Actions Needed to Optimize the Landing Page

Landing Page Optimization (LPO) can be done using multiple actions, but the two most important ones are mentioned below.

1. Assessing the Data

Analyzing the data is the most important action a digital marketer should take while optimizing the landing page. In essence, this entails analyzing the landing page’s performance and identifying opportunities for improvement. By evaluating data, you can find the pages with the largest bounce rates, the lowest conversion rates, and the most dramatic drop-offs in the conversion process.

2. Making Iterative Adjustments

Analyzing the data is one thing, but it is of no use if there is no action taken to make incremental modifications to the landing page. This entails making minor modifications and testing them to determine how they affect the page’s performance. For instance, changes can be made to the title/headline of the page, CTA, layout, content, or any other element of the page. Now, one can optimize the page’s performance over time by making iterative and testing modifications.

Businesses can consistently enhance their landing pages, increase conversion rates, and ultimately achieve their marketing goals by implementing these two strategies.

The Best Way to Optimize a Landing Page for Lead Generation

While optimizing a landing page for lead generation, the first thing is to make changes in the design and content of the page. The objective behind such adjustments is to encourage visitors to the page to share their contact details. Some tips to optimize the landing page for lead generation include:

1. Keep the Page Simple 2. An Engaging Heading is Everything

The page headline is the first thing a visitor sees. So make sure it is attention-grabbing and clearly expresses the value proposition.

3. Visible Call to Action (CTA)

The CTA should be prominently displayed on the website and employ action-oriented language. Make the CTA stand out by using contrasting colors.

4. Use Forms

The form on the landing page should be simple to fill out and should only request the necessary information.

5. Add Social Proof

Social proof, such as customer testimonials or ratings and reviews, can foster trust in those who visit the page and encourage them to share their contact information.

6. Test and Modify

Constantly test and modify several variations of the landing page to evaluate which version has the best performance. Now, make small modifications and track the impact on conversion rates.

ALSO READ: Digital Marketing Analytics: Everything You Should Know

Landing Page Optimization Tools

Businesses can use various landing page optimization tools and technologies to improve the performance of their landing pages. Here are some popular choices:

It is a free A/B testing and personalization software that allows businesses to test alternative versions of their landing pages and track the impact on conversion rates.

Benefits:

Easy to use

Targets specific user segments, such as geographic location or device type

Offers a range of customization options; it is easy to make changes in images, headlines, CTAs, etc

Unbounce is a landing page builder and optimization platform that offers a variety of capabilities to businesses, including A/B testing, lead generation tools, and analytics. Moreover, its drag-and-drop visual builder makes it simple to create conversion-ready landing pages and customize them.

Benefits:

Availability of numerous templates

No additional requirement for design experience

Supports smart traffic tools and A/B testing

Optimizely is an A/B testing and personalization technology that assists businesses in optimizing their landing pages and increasing conversion rates. It includes capabilities like A/B testing, multivariate testing, and audience targeting.

Benefits:

Easy-to-understand statistics

Outstanding knowledge base

The visual editor is simple to use

It is a website optimization tool that provides businesses with heat maps, scroll maps, and other user behavior data to help them optimize landing pages and increase conversion rates. This landing page optimization software is ideal for marketing and product teams, small enterprises, e-commerce organizations, and digital agencies.

Benefits:

Enables you to learn how users expect to interact with your site

Simple to install and use

It is an inbound marketing platform with a landing page builder and optimization capabilities. Additionally, it offers tools like A/B testing, lead collection forms, and analytics to help businesses enhance the performance of their landing pages.

Benefits:

Availability of a drag-and-drop editor that is simple to use

Comprehensive analytics and reporting

Integration with additional HubSpot tools such as forms, lead capture, and Customer Relationship Management tool (CRM)

These are just a handful of the landing page optimization tools accessible to organizations. In addition, it is critical to investigate and compare several possibilities to find the best fit for a company’s demands and budget.

Landing Page Optimization Checklist

The following points need to be addressed while optimizing the landing page of a website.

The headline should be constructed in a way that expresses the value proposition and captures the visitor’s attention

The images on the landing page should be visually appealing and relevant to the product or service being offered

The CTA should be prominent and use action-oriented wording to motivate visitors to take the required action

If there is a form present on the landing page, it should require only the necessary information and be simple to fill out

The landing page should be optimized for mobile devices to deliver a pleasant user experience on all platforms

The landing page should load quickly to avoid creating a negative impression on visitors and reducing conversion rates

A/B testing can assist in identifying the most effective aspects of a landing page and continuously improving its effectiveness

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Landing page optimization is an important cog in the digital marketing wheel, a field that has been growing steadily over the last few years and continues to do so. According to the CMO Survey, marketers are expected to devote 57% of their overall budget to digital marketing activities in 2023, with spending expected to rise by 16%.

If you want to address different aspects of the digital marketing stream, enrolling in Emeritus’  insightful and important digital marketing courses can help. They will assist you in learning various digital marketing techniques, such as strategizing, evaluating, curating, and developing a sustainable digital presence.

By Mikhil Pathak

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