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8 Factors to Consider Before Buying or Building your AI Solution

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Artificial intelligence (AI) – it’s here, it’s there, it’s everywhere. Businesses in every vertical – retail, finance, healthcare, manufacturing, transport, supply chain, entertainment, and technology – use AI.

More than a quarter of companies using AI attribute at least 5% of their earnings before interest and taxes to AI. Almost two-thirds of early AI adopters note AI gives them a competitive advantage. Given the benefits, it’s hardly surprising that some businesses have FOMO on AI.

But when the company is ready to adopt AI, they face a dilemma – should they buy or build? Read on to learn about factors to consider when deciding on AI solutions for your business.

AI for business

Startups and big enterprises ready to adopt AI have two ways to go about it: Build their own AI model or buy commercially available AI software. Both options involve the use of open-source AI or off-the-shelf product such as free live chat apps

Before dissecting the build vs buy dilemma, it’s important to understand open-source and proprietary AI software.

Open-source AI

Open-source AI includes freely available AI tools. These could be algorithms, datasets, ready-to-use application programming interfaces (APIs), libraries of codes, or a combination of all. Some of the free AI and machine learning (ML) platforms are Tensorflow, Python, PyTorch, KNIME, Apache Spark, and H2O.ai.

Usually, each AI software has specific use cases like speech recognition, computer vision, ML, natural language processing (NLP), or big data analytics. For instance, TensorFlow and PyTorch are suited for building ML and deep learning models. Libraries like Keras and OpenNN, on the other hand, are artificial neural network (ANN) frameworks. Businesses leverage these libraries and platforms to build their own AI system based on their requirements.  

Commercial AI software

From chatbots and conversational AI to automation and advanced data science, there are many ready-to-use AI tools.  Though it’s costly, these tools can save a lot of resources and time for the company. The most common commercial model of AI software is the annual subscription model that includes necessary licenses, support, and service from vendors. Another type is “pay-as-you-go”  where one can access the AI software as an API.

Major tech companies like Google, Microsoft, Amazon, IBM, and Salesforce also offer AI and ML as a platform service. These AI services provide end-to-end cloud-hosted platforms for developing and deploying AI models.

Breaking down the build vs buy dilemma

Imagine you need a new house – would you buy or build one? Choosing between the two depends on a lot of factors. What kind of house do you want? What resources do you have on hand? How much time and money do you have? And how much of each are you willing to spend?

Deciding on whether you want to build or buy your AI for your business is similar. Let’s look at the factors you should consider before taking the plunge.

The need: Why do you need AI?

Diving into algorithms and neural networks before figuring out what you want from AI is a bit like diving into the ocean to find a lost treasure that might or might not exist. What is your business looking to achieve by using AI tools? Is AI a core component of future business growth? Or is it to automate a task or improve business processes?

If AI is the core of your business, build it. If you want to use AI for just specific business operations, then buy. A chatbot provider needs to build its own NLP model. But a small online startup doesn’t have to build a huge NLP model to have a chatbot on its website. It can simply buy the tool from conversational AI providers like Dialpad or Drift and improve customer experience.

The difficulty: How complex is the AI solution?

The complexity of the AI solution the business is looking to adopt is also an important element to think about. There are many tried and tested products in the market that you can buy for common AI applications, like sales and marketing automation processes, predictive forecasting, chatbots, speech-to-text, or machine translation.

But sometimes, the data involved is sensitive or the business is looking for a novel solution.  Commercial tools are simply not suitable or sufficient in these cases. Netflix, for instance, built its own proprietary AI model that offers personalized movies and shows recommendations based on user data. It was worth it to build because Netflix considers this feature critical to its business.

On the other hand, Woodside Energy, an oil and gas company, leveraged IBM Watson’s ML and NLP tech to make 30 years of data on oil platform operations accessible throughout the company.  Woodside owns the data, but shared it with IBM to tap their expertise instead of building it in-house.

The human effort: Do you have the right people?

Evaluate the AI capabilities of your in-house experts. Can your pool of technical experts develop AI frameworks?  Or does your firm have the resources to hire such experts? If yes, then the firm can think about building an AI model in-house.

Tech companies, AI vendors, and large enterprises in the banking, retail, and healthcare sectors have a huge pool of AI experts working exclusively on AI and have the resources to develop their solutions in-house.

Bank of America, for example, built its own virtual banking assistant, Erica. By the time the bank’s AI assistant was in the beta phase in 2017, the bank had a dedicated team of more than 100 people working on the project.

However, if the firm doesn’t have technical expertise or resources to hire, it’s best to purchase from a vendor. For example, a small marketing firm can buy an AI-powered content creation tool rather than investing millions to hire a team and develop it in-house.

The time: What’s the deadline?

Do you need the AI software immediately? Or can you wait to get a custom tool?

If your business needs the tool at once, and there are already multiple products available to purchase, then buying is the ideal solution. But, if the company needs a custom tool that needs time to build, the firm can develop on its own, given it has the resources to do it.

The cost: What’s the budget?

Many enterprises fail to understand the true cost of building or buying an AI model. Both options require heavy investment to get the right people, hardware, and software.

A company can cut some costs by building AI using free, open-source software. But there are other prices to pay. From getting training data to buying necessary software and hardware like cloud storage, expensive computing power, and AI operationalization software, there are significant hidden costs involved. Companies have to cough up more to hire (expensive and rare) AI experts. The average base pay for an AI job is easily above $100,000 in the USA.

Buying a third-party AI solution solves the problem of having to spend on hiring an AI team. But commercial tools can still be expensive.  A custom AI solution can cost from $6000 to over $300,000 per solution while a third-party software can cost up to $40,000 per year. Firms will have to weigh the costs of both options and decide.

The integration:  Can your current tech support AI?

It’s critical to gauge how well the existing IT infrastructure can integrate with AI, whether building or buying. The team should look at whether the AI platform can sync with their current tech stack. More often than not, open-source platforms have integration and scaling issues.

Commercial vendors take care of the dreaded task of AI integration, scaling, and compatibility issues. But it’s always better to check the compatibility of the vendor tool with the existing ecosystem. If you’re integrating an AI solution to optimize your lead generation process, it should seamlessly integrate with your existing CRM. Ignoring this can become costly to fix.

The service: What happens when something goes wrong??

With commercial products, there’s always vendor service and support. The AI vendor has responsibility and accountability for the smooth functioning of the tool. Your team can rely on them  to fix bugs and other issues that crop up in the AI model. While building AI, the company has to deal with any issues that crop up without much support.

The security: Is your AI safe?  

Both open-source and commercial AI platforms have potential security risks. Unlike popular opinion that open source has more vulnerabilities, nearly 90% of IT leaders think it’s as secure or more secure than proprietary software. So the question is not about which option is more secure but which needs more support. The answer is building with open source. AI vendors take care of security issues and fix bugs and patches themselves. But building AI in-house means having a team dedicated to looking at potential security issues. This adds significant costs to the budget.

Building vs buying AI: Weighing the pros and cons

Let‘s make your decision-making on AI solutions a little easier. Here are some common advantages and disadvantages of building vs buying AI.

Pros and cons of building AI

Building an in-house AI using open-source frameworks suits firms that have unique data and the capability to invest time and resources. Its advantages include:

  • no or low-cost investment in the tech
  • better customization based on needs
  • control over both data and the model
  • flexibility to change the model when required
  • community support

However, open-source AI platforms have several gaps like:

  • high turnaround time
  • difficulty in hiring AI experts and data scientists
  • lack of service and support to solve issues
  • added security cost  
  • compatibility issues with other software

Pros and cons of  buying proprietary AI

Buying an off-the-shelf commercial tool might be best for companies that don’t have the human resources to build their own solution or lack unique datasets. Its advantages include:

  • specialized knowledge for a particular AI use case
  • availability of large, well-organized training datasets
  • seamless integration of AI tools into existing infrastructure
  • ability to scale AI models
  • continuous vendor service and support to solve issues

But commercial products have their own drawbacks like:

  • high investment cost
  • fewer customization options
  • risk of losing access to critical data
  • vendor lock-in periods

Still confused? Go for the lean strategy

If you’re still confused after assessing all factors, think about the lean AI strategy. The strategy comes from the Japanese manufacturing industries’ philosophy called the lean manufacturing process. Adopted by carmakers like Toyota, the strategy aims to reduce production waste without sacrificing efficiency and quality.

The lean AI strategy also aims for the same: implement AI incrementally while reducing resource costs and risks. The business can start with a small AI project that delivers a minimum viable AI product (MVAP).

The build-vs-buy decision tree

With MVAP in mind, consider the following decision tree. Scout for an available solution. If there are multiple commercial products, buying would be the best solution. If there are not many vendors offering the required tool, look for a partner to develop a custom AI solution. This saves the company time, efforts, and resources of building from scratch.

However, if both buying and partnering don’t work, the company can build its MVAP. Aiming at MVAP helps the company kickstart the AI project sooner. With established deadlines and goals, companies can assess the performance and business impact of MVAP faster. As the project matures, the business gets more opportunities to evaluate and undertake more ambitious AI projects.

Winning with AI

Like all deliberations about implementing new technologies, it’s important to make an informed decision on AI in your company. Whether you choose to build or buy your AI, just keep in mind that AI is not going anywhere anytime soon – so start early to reap the rewards early.

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The marketing lifecycle: An overview

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The marketing lifecycle: An overview

Remember when digital marketing was simple? Create content, throw it over the wall, hope for the best.

Note that we said “simple,” not effective.

To be effective is more complicated, and this keeps accelerating. There are so many options, so many channels, and so many audiences, that effective digital marketing requires a term to which people often react strongly—

Process.

Very few people inherently like the idea of “process.” It brings forth visions of rigidity and inertia.

But there simply has to be a framework in which to produce and publish effective marketing assets. Without this, you have nothing but chaos from which productive work gets done accidentally, at best.

How did it get this way for the enterprise? How did things become so interconnected?

  • Marketing isn’t a point in time, it’s an activity stream. It’s a line of dominoes you need to knock over, roughly in order. Lots of organizations do well at some, but fail on others, and thus break the chain of what could be an effective process.
  • Marketing activities overlap. It’d be great if we could do one thing at a time, but the marketing pipeline is never empty. Campaigns target different audiences at the same time, and new campaigns are being prepared as existing campaigns are closing.
  • Marketing involves a lot of actors at vastly different levels. There’s your content team, of course, reviewers, external agencies and contractors, designers, developers, and—of course—stakeholders and executives. Each group has different needs for collaboration, input, and reporting.

Some of the best business advice boils down to this: “Always understand the big picture.” You might be asked to do one specific thing in a process, but make sure you understand the context of that specific thing—where does it fit in the larger framework? Where does it get input from? How are its outputs used?

In this article, we’re going to zoom out for an overhead view of how Optimizely One helps you juggle the complete marketing lifecycle, from start to finish, without letting anything drop.

1. Intake 

Ideas are born everywhere—maybe with you, maybe with your staff, maybe with someone who has no connection with marketing at all, and maybe from an external source, like an ad agency or PR firm. Leading organizations have found a way to widen the top end of their pipeline—the start of their content marketing funnel—and take in more ideas from more sources.

Good ideas combine. Someone has one half of an idea, and someone else has the other half. The goal of effective collaboration is to get those two pieces together. One plus one can sometimes equal three, and more ideas mean better ideas overall. Creativity is about getting more puzzle pieces on the table so you can figure out which ones fit your strategy.

How do you manage the flow of ideas? How do you make sure good ideas don’t get dropped, but rather become great content? The only way to publish great content is to get ideas into the top end of the pipe. 

 

Optimizely One can streamline and accelerate your content intake using templated intake forms mapped to intelligent routing rules and shared queues. Everyone in your organization can know where content is developed and how to contribute to ideas, content, and campaigns currently in-process. Your content team can easily manage and collaborate on requests, meaning content development can become focused, rather than spread out across the organization. 

2. Plan

Campaigns don’t exist in a vacuum. They share the stage with other campaigns—both in terms of audience attention and employee workload. Leading organizations ensure that their campaigns are coordinated, for maximum audience effect and efficiency of workload.

Pick a time scale and plan it from overhead. What campaigns will you execute during this period? In what order? How do they overlap? Then, break each campaign down—what tasks are required to complete and launch? Who owns them? In what stage of completion are they in? What resources are required to complete them? 

Good marketing campaigns aren’t run in isolation. They’re a closely aligned part of an evolving body of work, carefully planned and executed.

 

Optimizely One provides comprehensive editorial calendaring and scheduling. Every marketing activity can have an easily accessible strategic brief and dedicated workspaces in which to collaborate. Your content team and your stakeholders can know, at a glance, what marketing activities are in-process, when they’re scheduled to launch, who is assigned to what, and what’s remaining on the calendar.  

3. Create 

Good content takes fingers on keyboards, but that’s not all. 

Content creators need frameworks in which to generate effective content. They need the tools to share, collaborate, structure, stage, and approve their work. Good content comes in part from tooling designed to empower content creators. 

Your content team needs a home base—the digital equivalent of an artist’s studio. They need a platform which is authoritative for all their marketing assets; a place that everyone on the team knows is going to have the latest schedules, the latest drafts, the official assets, and every task on the road to publication. 

Content creation isn’t magic—it doesn’t just appear out of the ether. It comes from intentional teams working in structured frameworks. 

 

Optimizely One gives your editors the tools they need for the content creation process, AI-enabled editing environments for fingers-on-keyboards, all the way through intelligent workflows for collaboration and approvals. Authors can write, designers can upload and organize, project managers can combine and coordinate, stakeholders can review, and external teams can collaborate. All within a framework centered around moving your campaigns forward. 

4. Store 

Leading organizations look at content beyond its immediate utility. Everything your content teams do becomes an incremental part of an evolving body of work. Content doesn’t appear and disappear; rather, it continually enlarges and refines a body of work that represents your organization over time. 

Good creative teams remix and transform old ideas into new ones. They can locate content assets quickly and easily to evolve them into new campaigns quickly. They don’t reinvent the wheel every time, because they lean on a deep reservoir of prior art and existing creative components. 

Digital asset and content management should store content in a structured, atomic format, allowing your organization to store, retrieve, organize, and re-use marketing assets quickly and easily. 

 

Optimizely One gives your content team a place to store their content assets, from text and rich media. Content can be archived and organized, either manually, or by using AI to automatically extract tags. Content can be stored as pure data, free from presentation, which makes it easy to re-use. Your content team will always know where to find work in progress, media to support emerging campaigns, or assets from past campaigns. Brand portals make it easy to share assets with external organizations.

5. Globalize 

Business happens all over the world in every language. To effectively compete around the world, your content needs to be globalized. 

Globalization of content is a holistic practice that affects every part of the content lifecycle. Words need to be translated, of course, but you also need to consider cultural globalization—images and symbols that might change—as well as globalization for numbers, currency, and time zones. Going even deeper, you might have to make design changes to accommodate things like differing word lengths and the flow of text. 

Beyond simply changing content, your work process is affected. When does translation happen? Who is authorized to order it? Who can perform it? How do you bring external translation companies into your internal processes, and how does this affect the flow of content through your organization?  

 

Optimizely One helps you manage the entire globalization process, whether it’s done in-house or automatically via one of our translation partners. Your customers can be served content in their language and culture, and you can carefully control the alternate, “fallback” experience for languages not yet available, or when you’re not translating all of your content.  

6. Layout 

Some experiences need to be visually composed from a palette of content and design components. Designers and marketers want to see exactly what their content looks like before they publish. 

In some cases, this is easy—everyone should be able to see what a web page looks like before it goes live. But what about your mobile app? What about display advertising? A social media update? 

And what happens when you’re modifying content based on behavior and demographics? If you want to see how your web page will look for someone from California who has visited your site before and already downloaded your whitepaper on their iPhone…can you? 

Content no longer leaves your organization on a single channel. Composition and preview is always contextual—there is no single, default experience. Leading organizations want full control over their visual presentation and they know that they need to see their content through the eyes of their customers.  

 

Optimizely One provides the tools to visually compose experiences across multiple channels and can preview that experience when viewed through the personalization lens of whatever demographic and behavioral data you can dream up. And this works regardless of channel: web, email, display advertising—everything can be previewed in real-time. 

7. Deliver 

Content can’t do any good unless it can reach your customers. You need to publish your content to them, wherever they are, which means having the flexibility to push content into multiple channels, in multiple formats. 

A consumable piece of media is an “artifact.” Your content is the idea and message that make up that artifact. Leading organizations develop their content separate from any concept of an artifact, then transform it into different formats to fit the channel that will spread their message most effectively. 

Sure, make a web page—but also push that content to your mobile app, and into your social networks. Broadcast a text message, and an email. While you’re at it, push the information into the display panel in the elevators. Let’s be bold and broadcast it on the TV screens that play while your customers fill up with gas. 

The key is delivery flexibility. The world of content delivery has changed remarkably in just the last few years. It will no-doubt change more in the future. No platform can anticipate what’s coming, so you just need the flexibility to be ready to adapt to what happens. 

 

Optimizely One provides complete delivery flexibility. Our systems store your content separate from presentation, and allow multiple ways to access it, from traditional websites to headless APIs to connect your content to mobile apps or other decoupled experiences. Your content can be combined with internally-stored content or third-party content to provide a seamless “content reservoir” to draw on from all of your channels. 

8. Personalize 

Throughout this lifecycle, we’ve moved from content, to artifacts, and now on to “experiences.” 

One person consuming an artifact—reading a web page, listening to a podcast, watching a video—is an experience. Just like one piece of content can generate more than one artifact, one artifact should enable thousands of experiences. 

Technology has advanced to the point where all of those experiences can be managed. Instead of every customer getting the same experience, it can be personalized to that specific customer in that specific moment. 

You can do this using simple demographic or technographic data—perhaps you cut down the information and make your content more task-oriented when you detect someone is on a mobile device. However, the real power comes when you begin tracking behavior, consolidating information about your customers, and giving them specific content based on what you’ve observed. 

Leading organizations have a single location to track customer behavior and data. For every experience, they know exactly what this customer has done, how they’ve interacted with the organization, and they can predict what they’ll do next. Content and artifacts will morph themselves to fit each individual experience. 

 

Optimizely One connects both customer behavior and demographics along with the tools to activate that data to affect your customers’ experiences. Our platform allows you to track customer behavior and match that with customer demographics—this includes behavior tracking for customers you can’t even identify yet. Based on that behavior and stored data, editors can modify experiences in real-time, changing content and design to match to what each individual customer is most likely to respond. Or let the machine do the work, with personalized content and product recommendations. 

9. Experiment 

No matter how much you know, customers will always surprise you. The right answer to persuading your customer to take an action might be something you’re not even thinking of. Or, you might have an idea, but you’re not confident enough to bank on it. And let’s face it—sometimes, you just love two different ideas. 

Wouldn’t it be great if you could publish more than one thing? 

You absolutely can. And you absolutely should.

Leading organizations let go of the idea that an experience is bound to one version of an artifact. Don’t just write one title for that blog post—write three. Publish them all and show them randomly. Let your customers tell you—by their next action—which one was the right one to use. 

Experimentation allows you to try new things without the inertia of re-considering and re-drafting all your content. Ideas can go from your mind to pixels on the screen quickly and easily, and you can see what works and what doesn’t. Try a new title, or next text on a button. Does it give you better results? If so, great, keep it. If not, throw it away and try something else.

Refine, refine, refine. The idea that you publish content in one form and just hope it’s the right one is a set of handcuffs that can be tough to shake. But the results can be impressive.

 

Optimizely One allows you to quickly create and publish multiple variations of content and content elements to any channel. You can separate your content into elements and try different combinations to see which one drives your customers to move forward in their journey, then automatically route more traffic through winning combinations. You can manage feature rollouts and soft-launches, enabling specific functionality for specific audiences in any channel. 

10. Analyze 

The key to a learning and evolving content team is a transparent and unflinching look into what happens to your content after it’s published.

Analytics need to be considered in the context of the entire content domain. What content performs well but has low traffic? What content is consumed often but never moves customers down their buying journey? Customer behavior needs to be tracked carefully, then used to segment customers into audiences, based on both your content team’s observations and insights provided by AI. 

 

Optimizely One offers complete behavior tracking and content analysis, showing you what content works, what content doesn’t, and what your customers are doing during every step of their relationship with your entire digital estate. 

Juggle the entire lifecycle 

“Publishing myopia” prevents most organizations from truly benefiting from the power of their content and marketing technology. Too many ideas are undercut by an obsession with the publish button. We rush content out the door and just throw it over the wall and hope it lands. 

Within that mode of thinking, great ideas get trapped under the surface. Great content is delivered to only one channel in one language. Great experiences never see the light of day because content exists in only one form. And every customer sees the same thing, no matter how their own experience might benefit from something else. 

Remember: the marketing lifecycle is a series of stages

Each stage builds on the last and allows content to grow from a random idea your team takes in from the field and turns it into a spectacular multi-channel experience which rearranges and modifies itself to fit each customer. 

Juggling all of the steps in the marketing lifecycle can be done, but it’s easy to lose the forest for the trees and get too myopic about individual steps in this process. Leading organizations step back, consider the entire cycle from start to finish, and make sure their ideas, their products, and their messages are enhanced and strengthened in every step. 

 

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Comparing Credibility of Custom Chatbots & Live Chat

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Building Customer Trust: Comparing Credibility of Custom Chatbots & Live Chat

Addressing customer issues quickly is not merely a strategy to distinguish your brand; it’s an imperative for survival in today’s fiercely competitive marketplace.

Customer frustration can lead to customer churn. That’s precisely why organizations employ various support methods to ensure clients receive timely and adequate assistance whenever they require it.

Nevertheless, selecting the most suitable support channel isn’t always straightforward. Support teams often grapple with the choice between live chat and chatbots.

The automation landscape has transformed how businesses engage with customers, elevating chatbots as a widely embraced support solution. As more companies embrace technology to enhance their customer service, the debate over the credibility of chatbots versus live chat support has gained prominence.

However, customizable chatbot continue to offer a broader scope for personalization and creating their own chatbots.

In this article, we will delve into the world of customer support, exploring the advantages and disadvantages of both chatbots and live chat and how they can influence customer trust. By the end, you’ll have a comprehensive understanding of which option may be the best fit for your business.

The Rise of Chatbots

Chatbots have become increasingly prevalent in customer support due to their ability to provide instant responses and cost-effective solutions. These automated systems use artificial intelligence (AI) and natural language processing (NLP) to engage with customers in real-time, making them a valuable resource for businesses looking to streamline their customer service operations.

Advantages of Chatbots

24/7 Availability

One of the most significant advantages of custom chatbots is their round-the-clock availability. They can respond to customer inquiries at any time, ensuring that customers receive support even outside regular business hours.

Consistency

Custom Chatbots provide consistent responses to frequently asked questions, eliminating the risk of human error or inconsistency in service quality.

Cost-Efficiency

Implementing chatbots can reduce operational costs by automating routine inquiries and allowing human agents to focus on more complex issues.

Scalability

Chatbots can handle multiple customer interactions simultaneously, making them highly scalable as your business grows.

Disadvantages of Chatbots

Limited Understanding

Chatbots may struggle to understand complex or nuanced inquiries, leading to frustration for customers seeking detailed information or support.

Lack of Empathy

Chatbots lack the emotional intelligence and empathy that human agents can provide, making them less suitable for handling sensitive or emotionally charged issues.

Initial Setup Costs

Developing and implementing chatbot technology can be costly, especially for small businesses.

The Role of Live Chat Support

Live chat support, on the other hand, involves real human agents who engage with customers in real-time through text-based conversations. While it may not offer the same level of automation as custom chatbots, live chat support excels in areas where human interaction and empathy are crucial.

Advantages of Live Chat

Human Touch

Live chat support provides a personal touch that chatbots cannot replicate. Human agents can empathize with customers, building a stronger emotional connection.

Complex Issues

For inquiries that require a nuanced understanding or involve complex problem-solving, human agents are better equipped to provide in-depth assistance.

Trust Building

Customers often trust human agents more readily, especially when dealing with sensitive matters or making important decisions.

Adaptability

Human agents can adapt to various customer personalities and communication styles, ensuring a positive experience for diverse customers.

Disadvantages of Live Chat

Limited Availability

Live chat support operates within specified business hours, which may not align with all customer needs, potentially leading to frustration.

Response Time

The speed of response in live chat support can vary depending on agent availability and workload, leading to potential delays in customer assistance.

Costly

Maintaining a live chat support team with trained agents can be expensive, especially for smaller businesses strategically.

Building Customer Trust: The Credibility Factor

When it comes to building customer trust, credibility is paramount. Customers want to feel that they are dealing with a reliable and knowledgeable source. Both customziable chatbots and live chat support can contribute to credibility, but their effectiveness varies in different contexts.

Building Trust with Chatbots

Chatbots can build trust in various ways:

Consistency

Chatbots provide consistent responses, ensuring that customers receive accurate information every time they interact with them.

Quick Responses

Chatbots offer instant responses, which can convey a sense of efficiency and attentiveness.

Data Security

Chatbots can assure customers of their data security through automated privacy policies and compliance statements.

However, custom chatbots may face credibility challenges when dealing with complex issues or highly emotional situations. In such cases, the lack of human empathy and understanding can hinder trust-building efforts.

Building Trust with Live Chat Support

Live chat support, with its human touch, excels at building trust in several ways:

Empathy

Human agents can show empathy by actively listening to customers’ concerns and providing emotional support.

Tailored Solutions

Live chat agents can tailor solutions to individual customer needs, demonstrating a commitment to solving their problems.

Flexibility

Human agents can adapt to changing customer requirements, ensuring a personalized and satisfying experience.

However, live chat support’s limitations, such as availability and potential response times, can sometimes hinder trust-building efforts, especially when customers require immediate assistance.

Finding the Right Balance

The choice between custom chatbots and live chat support is not always binary. Many businesses find success by integrating both options strategically:

Initial Interaction

Use chatbots for initial inquiries, providing quick responses, and gathering essential information. This frees up human agents to handle more complex cases.

Escalation to Live Chat

Implement a seamless escalation process from custom chatbots to live chat support when customer inquiries require a higher level of expertise or personal interaction.

Continuous Improvement

Regularly analyze customer interactions and feedback to refine your custom chatbot’s responses and improve the overall support experience.

Conclusion

In the quest to build customer trust, both chatbots and live chat support have their roles to play. Customizable Chatbots offer efficiency, consistency, and round-the-clock availability, while live chat support provides the human touch, empathy, and adaptability. The key is to strike the right balance, leveraging the strengths of each to create a credible and trustworthy customer support experience. By understanding the unique advantages and disadvantages of both options, businesses can make informed decisions to enhance customer trust and satisfaction in the digital era.

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The Rise in Retail Media Networks

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A shopping cart holding the Amazon logo to represent the rise in retail media network advertising.

As LL Cool J might say, “Don’t call it a comeback. It’s been here for years.”

Paid advertising is alive and growing faster in different forms than any other marketing method.

Magna, a media research firm, and GroupM, a media agency, wrapped the year with their ad industry predictions – expect big growth for digital advertising in 2024, especially with the pending US presidential political season.

But the bigger, more unexpected news comes from the rise in retail media networks – a relative newcomer in the industry.

Watch CMI’s chief strategy advisor Robert Rose explain how these trends could affect marketers or keep reading for his thoughts:

GroupM expects digital advertising revenue in 2023 to conclude with a 5.8% or $889 billion increase – excluding political advertising. Magna believes ad revenue will tick up 5.5% this year and jump 7.2% in 2024. GroupM and Zenith say 2024 will see a more modest 4.8% growth.

Robert says that the feeling of an ad slump and other predictions of advertising’s demise in the modern economy don’t seem to be coming to pass, as paid advertising not only survived 2023 but will thrive in 2024.

What’s a retail media network?

On to the bigger news – the rise of retail media networks. Retail media networks, the smallest segment in these agencies’ and research firms’ evaluation, will be one of the fastest-growing and truly important digital advertising formats in 2024.

GroupM suggests the $119 billion expected to be spent in the networks this year and should grow by a whopping 8.3% in the coming year.  Magna estimates $124 billion in ad revenue from retail media networks this year.

“Think about this for a moment. Retail media is now almost a quarter of the total spent on search advertising outside of China,” Robert points out.

You’re not alone if you aren’t familiar with retail media networks. A familiar vernacular in the B2C world, especially the consumer-packaged goods industry, retail media networks are an advertising segment you should now pay attention to.

Retail media networks are advertising platforms within the retailer’s network. It’s search advertising on retailers’ online stores. So, for example, if you spend money to advertise against product keywords on Amazon, Walmart, or Instacart, you use a retail media network.

But these ad-buying networks also exist on other digital media properties, from mini-sites to videos to content marketing hubs. They also exist on location through interactive kiosks and in-store screens. New formats are rising every day.

Retail media networks make sense. Retailers take advantage of their knowledge of customers, where and why they shop, and present offers and content relevant to their interests. The retailer uses their content as a media company would, knowing their customers trust them to provide valuable information.

Think about these 2 things in 2024

That brings Robert to two things he wants you to consider for 2024 and beyond. The first is a question: Why should you consider retail media networks for your products or services?   

Advertising works because it connects to the idea of a brand. Retail media networks work deep into the buyer’s journey. They use the consumer’s presence in a store (online or brick-and-mortar) to cross-sell merchandise or become the chosen provider.

For example, Robert might advertise his Content Marketing Strategy book on Amazon’s retail network because he knows his customers seek business books. When they search for “content marketing,” his book would appear first.

However, retail media networks also work well because they create a brand halo effect. Robert might buy an ad for his book in The New York Times and The Wall Street Journal because he knows their readers view those media outlets as reputable sources of information. He gains some trust by connecting his book to their media properties.

Smart marketing teams will recognize the power of the halo effect and create brand-level experiences on retail media networks. They will do so not because they seek an immediate customer but because they can connect their brand content experience to a trusted media network like Amazon, Nordstrom, eBay, etc.

The second thing Robert wants you to think about relates to the B2B opportunity. More retail media network opportunities for B2B brands are coming.

You can already buy into content syndication networks such as Netline, Business2Community, and others. But given the astronomical growth, for example, of Amazon’s B2B marketplace ($35 billion in 2023), Robert expects a similar trend of retail media networks to emerge on these types of platforms.   

“If I were Adobe, Microsoft, Salesforce, HubSpot, or any brand with big content platforms, I’d look to monetize them by selling paid sponsorship of content (as advertising or sponsored content) on them,” Robert says.

As you think about creative ways to use your paid advertising spend, consider the retail media networks in 2024.

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Cover image by Joseph Kalinowski/Content Marketing Institute

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