Many companies are experimenting with ChatGPT and other large language or image models. They have generally found them to be astounding in terms of their ability to express complex ideas in articulate language. However, most users realize that these systems are primarily trained on internet-based information and can’t respond to prompts or questions regarding proprietary content or knowledge.

Leveraging a company’s propriety knowledge is critical to its ability to compete and innovate, especially in today’s volatile environment. Organizational Innovation is fueled through effective and agile creation, management, application, recombination, and deployment of knowledge assets and know-how. However, knowledge within organizations is typically generated and captured across various sources and forms, including individual minds, processes, policies, reports, operational transactions, discussion boards, and online chats and meetings. As such, a company’s comprehensive knowledge is often unaccounted for and difficult to organize and deploy where needed in an effective or efficient way.

Emerging technologies in the form of large language and image generative AI models offer new opportunities for knowledge management, thereby enhancing company performance, learning, and innovation capabilities. For example, in a study conducted in a Fortune 500 provider of business process software, a generative AI-based system for customer support led to increased productivity of customer support agents and improved retention, while leading to higher positive feedback on the part of customers. The system also expedited the learning and skill development of novice agents.

Like that company, a growing number of organizations are attempting to leverage the language processing skills and general reasoning abilities of large language models (LLMs) to capture and provide broad internal (or customer) access to their own intellectual capital. They are using it for such purposes as informing their customer-facing employees on company policy and product/service recommendations, solving customer service problems, or capturing employees’ knowledge before they depart the organization.

These objectives were also present during the heyday of the “knowledge management” movement in the 1990s and early 2000s, but most companies found the technology of the time inadequate for the task. Today, however, generative AI is rekindling the possibility of capturing and disseminating important knowledge throughout an organization and beyond its walls. As one manager using generative AI for this purpose put it, “I feel like a jetpack just came into my life.” Despite current advances, some of the same factors that made knowledge management difficult in the past are still present.

The Technology for Generative AI-Based Knowledge Management

The technology to incorporate an organization’s specific domain knowledge into an LLM is evolving rapidly. At the moment there are three primary approaches to incorporating proprietary content into a generative model.

Training an LLM from Scratch

One approach is to create and train one’s own domain-specific model from scratch. That’s not a common approach, since it requires a massive amount of high-quality data to train a large language model, and most companies simply don’t have it. It also requires access to considerable computing power and well-trained data science talent.

One company that has employed this approach is Bloomberg, which recently announced that it had created BloombergGPT for finance-specific content and a natural-language interface with its data terminal. Bloomberg has over 40 years’ worth of financial data, news, and documents, which it combined with a large volume of text from financial filings and internet data. In total, Bloomberg’s data scientists employed 700 tokens, or about 350 billion words, 50 billion parameters, and 1.3 million hours of graphics processing unit time. Few companies have those resources available.

Fine-Tuning an Existing LLM

A second approach is to “fine-tune” train an existing LLM to add specific domain content to a system that is already trained on general knowledge and language-based interaction. This approach involves adjusting some parameters of a base model, and typically requires substantially less data — usually only hundreds or thousands of documents, rather than millions or billions — and less computing time than creating a new model from scratch.

Google, for example, used fine-tune training on its Med-PaLM2 (second version) model for medical knowledge. The research project started with Google’s general PaLM2 LLM and retrained it on carefully curated medical knowledge from a variety of public medical datasets. The model was able to answer 85% of U.S. medical licensing exam questions — almost 20% better than the first version of the system. Despite this rapid progress, when tested on such criteria as scientific factuality, precision, medical consensus, reasoning, bias and harm, and evaluated by human experts from multiple countries, the development team felt that the system still needed substantial improvement before being adopted for clinical practice.

The fine-tuning approach has some constraints, however. Although requiring much less computing power and time than training an LLM, it can still be expensive to train, which was not a problem for Google but would be for many other companies. It requires considerable data science expertise; the scientific paper for the Google project, for example, had 31 co-authors. Some data scientists argue that it is best suited not to adding new content, but rather to adding new content formats and styles (such as chat or writing like William Shakespeare). Additionally, some LLM vendors (for example, OpenAI) do not allow fine-tuning on their latest LLMs, such as GPT-4.

Prompt-tuning an Existing LLM

Perhaps the most common approach to customizing the content of an LLM for non-cloud vendor companies is to tune it through prompts. With this approach, the original model is kept frozen, and is modified through prompts in the context window that contain domain-specific knowledge. After prompt tuning, the model can answer questions related to that knowledge. This approach is the most computationally efficient of the three, and it does not require a vast amount of data to be trained on a new content domain.

Morgan Stanley, for example, used prompt tuning to train OpenAI’s GPT-4 model using a carefully curated set of 100,000 documents with important investing, general business, and investment process knowledge. The goal was to provide the company’s financial advisors with accurate and easily accessible knowledge on key issues they encounter in their roles advising clients. The prompt-trained system is operated in a private cloud that is only accessible to Morgan Stanley employees.

While this is perhaps the easiest of the three approaches for an organization to adopt, it is not without technical challenges. When using unstructured data like text as input to an LLM, the data is likely to be too large with too many important attributes to enter it directly in the context window for the LLM. The alternative is to create vector embeddings — arrays of numeric values produced from the text by another pre-trained machine learning model (Morgan Stanley uses one from OpenAI called Ada). The vector embeddings are a more compact representation of this data which preserves contextual relationships in the text. When a user enters a prompt into the system, a similarity algorithm determines which vectors should be submitted to the GPT-4 model. Although several vendors are offering tools to make this process of prompt tuning easier, it is still complex enough that most companies adopting the approach would need to have substantial data science talent.

However, this approach does not need to be very time-consuming or expensive if the needed content is already present. The investment research company Morningstar, for example, used prompt tuning and vector embeddings for its Mo research tool built on generative AI. It incorporates more than 10,000 pieces of Morningstar research. After only a month or so of work on its system, Morningstar opened Mo usage to their financial advisors and independent investor customers. It even attached Mo to a digital avatar that could speak out its answers. This technical approach is not expensive; in its first month in use, Mo answered 25,000 questions at an average cost of $.002 per question for a total cost of $3,000.

Content Curation and Governance

As with traditional knowledge management in which documents were loaded into discussion databases like Microsoft Sharepoint, with generative AI, content needs to be high-quality before customizing LLMs in any fashion. In some cases, as with the Google Med-PaLM2 system, there are widely available databases of medical knowledge that have already been curated. Otherwise, a company needs to rely on human curation to ensure that knowledge content is accurate, timely, and not duplicated. Morgan Stanley, for example, has a group of 20 or so knowledge managers in the Philippines who are constantly scoring documents along multiple criteria; these determine the suitability for incorporation into the GPT-4 system. Most companies that do not have well-curated content will find it challenging to do so for just this purpose.

Morgan Stanley has also found that it is much easier to maintain high quality knowledge if content authors are aware of how to create effective documents. They are required to take two courses, one on the document management tool, and a second on how to write and tag these documents. This is a component of the company’s approach to content governance approach — a systematic method for capturing and managing important digital content.

At Morningstar, content creators are being taught what type of content works well with the Mo system and what does not. They submit their content into a content management system and it goes directly into the vector database that supplies the OpenAI model.

Quality Assurance and Evaluation

An important aspect of managing generative AI content is ensuring quality. Generative AI is widely known to “hallucinate” on occasion, confidently stating facts that are incorrect or nonexistent. Errors of this type can be problematic for businesses but could be deadly in healthcare applications. The good news is that companies who have tuned their LLMs on domain-specific information have found that hallucinations are less of a problem than out-of-the-box LLMs, at least if there are no extended dialogues or non-business prompts.

Companies adopting these approaches to generative AI knowledge management should develop an evaluation strategy. For example, for BloombergGPT, which is intended for answering financial and investing questions, the system was evaluated on public dataset financial tasks, named entity recognition, sentiment analysis ability, and a set of reasoning and general natural language processing tasks. The Google Med-PaLM2 system, eventually oriented to answering patient and physician medical questions, had a much more extensive evaluation strategy, reflecting the criticality of accuracy and safety in the medical domain.

Life or death isn’t an issue at Morgan Stanley, but producing highly accurate responses to financial and investing questions is important to the firm, its clients, and its regulators. The answers provided by the system were carefully evaluated by human reviewers before it was released to any users. Then it was piloted for several months by 300 financial advisors. As its primary approach to ongoing evaluation, Morgan Stanley has a set of 400 “golden questions” to which the correct answers are known. Every time any change is made to the system, employees test it with the golden questions to see if there has been any “regression,” or less accurate answers.

Legal and Governance Issues

Legal and governance issues associated with LLM deployments are complex and evolving, leading to risk factors involving intellectual property, data privacy and security, bias and ethics, and false/inaccurate output. Currently, the legal status of LLM outputs is still unclear. Since LLMs don’t produce exact replicas of any of the text used to train the model, many legal observers feel that “fair use” provisions of copyright law will apply to them, although this hasn’t been tested in the courts (and not all countries have such provisions in their copyright laws). In any case, it is a good idea for any company making extensive use of generative AI for managing knowledge (or most other purposes for that matter) to have legal representatives involved in the creation and governance process for tuned LLMs. At Morningstar, for example, the company’s attorneys helped create a series of “pre-prompts” that tell the generative AI system what types of questions it should answer and those it should politely avoid.

User prompts into publicly-available LLMs are used to train future versions of the system, so some companies (Samsung, for example) have feared propagation of confidential and private information and banned LLM use by employees. However, most companies’ efforts to tune LLMs with domain-specific content are performed on private instances of the models that are not accessible to public users, so this should not be a problem. In addition, some generative AI systems such as ChatGPT allow users to turn off the collection of chat histories, which can address confidentiality issues even on public systems.

In order to address confidentiality and privacy concerns, some vendors are providing advanced and improved safety and security features for LLMs including erasing user prompts, restricting certain topics, and preventing source code and propriety data inputs into publicly accessible LLMs. Furthermore, vendors of enterprise software systems are incorporating a “Trust Layer” in their products and services. Salesforce, for example, incorporated its Einstein GPT feature into its AI Cloud suite to address the “AI Trust Gap” between companies who desire to quickly deploy LLM capabilities and the aforementioned risks that these systems pose in business environments.

Shaping User Behavior

Ease of use, broad public availability, and useful answers that span various knowledge domains have led to rapid and somewhat unguided and organic adoption of generative AI-based knowledge management by employees. For example, a recent survey indicated that more than a third of surveyed employees used generative AI in their jobs, but 68% of respondents didn’t inform their supervisors that they were using the tool. To realize opportunities and manage potential risks of generative AI applications to knowledge management, companies need to develop a culture of transparency and accountability that would make generative AI-based knowledge management systems successful.

In addition to implementation of policies and guidelines, users need to understand how to safely and effectively incorporate generative AI capabilities into their tasks to enhance performance and productivity. Generative AI capabilities, including awareness of context and history, generating new content by aggregating or combining knowledge from various sources, and data-driven predictions, can provide powerful support for knowledge work. Generative AI-based knowledge management systems can automate information-intensive search processes (legal case research, for example) as well as high-volume and low-complexity cognitive tasks such as answering routine customer emails. This approach increases efficiency of employees, freeing them to put more effort into the complex decision-making and problem-solving aspects of their jobs.

Some specific behaviors that might be desirable to inculcate — either though training or policies — include:

• Knowledge of what types of content are available through the system;
• How to create effective prompts;
• What types of prompts and dialogues are allowed, and which ones are not;
• How to request additional knowledge content to be added to the system;
• How to use the system’s responses in dealing with customers and partners;
• How to create new content in a useful and effective manner.

Both Morgan Stanley and Morningstar trained content creators in particular on how best to create and tag content, and what types of content are well-suited to generative AI usage.

“Everything Is Moving Very Fast”

One of the executives we interviewed said, “I can tell you what things are like today. But everything is moving very fast in this area.” New LLMs and new approaches to tuning their content are announced daily, as are new products from vendors with specific content or task foci. Any company that commits to embedding its own knowledge into a generative AI system should be prepared to revise its approach to the issue frequently over the next several years.

While there are many challenging issues involved in building and using generative AI systems trained on a company’s own knowledge content, we’re confident that the overall benefit to the company is worth the effort to address these challenges. The long-term vision of enabling any employee — and customers as well — to easily access important knowledge within and outside of a company to enhance productivity and innovation is a powerful draw. Generative AI appears to be the technology that is finally making it possible.

In this post, we will look at how technology contributes to the growth and success of small enterprises.

1. Streamlining Operations

Implementing small business technology solutions can automate and streamline various aspects of small business operations. This includes using project management software, customer relationship management (CRM) systems, inventory management tools, and accounting software. Streamlining operations not only saves time and reduces manual errors but also allows small businesses to allocate resources more efficiently.

Tip: Regularly assess your business processes and identify areas that can be automated or improved with technology. This continuous evaluation ensures that your technology solutions remain aligned with your evolving business needs.

2. Enhancing Customer Engagement

Technology enables small businesses to engage and connect with their customers more effectively. Social media platforms, email marketing software, and customer service tools allow businesses to communicate and build relationships with their target audience. Customer relationship management systems help businesses track customer interactions and preferences, providing insights to deliver personalized experiences and improve customer satisfaction.

Tip: Leverage data from customer interactions to create targeted marketing campaigns and personalized offers. Use automation tools to send timely and relevant messages to your customers, enhancing their engagement and loyalty.

3. Expanding Market Reach

The internet and digital marketing platforms provide small businesses with the opportunity to reach a broader audience beyond their local market. Creating a professional website, utilizing search engine optimization (SEO), and leveraging online advertising channels allow small businesses to attract and engage customers from different regions or even globally. E-commerce platforms enable businesses to sell products or services online, further expanding their market reach.

Tip: Continuously monitor and optimize your online presence to ensure your website is discoverable and user-friendly. Leverage analytics tools to track website traffic, visitor behavior, and conversion rates to make data-driven improvements.

4. Improving Decision-Making with Data

Technology provides small businesses with access to valuable data and analytics, enabling informed decision-making. Through data analysis, businesses can gain insights into customer behavior, market trends, and operational performance. This data-driven approach allows small businesses to make strategic decisions, optimize processes, and identify growth opportunities more effectively.

Tip: Invest in data analytics tools and dashboards that can consolidate and visualize your business data. Regularly review and analyze the data to uncover patterns, identify bottlenecks, and make data-backed decisions to drive growth.

5. Facilitating Remote Work and Collaboration

Advancements in technology have made remote work and collaboration more feasible for small businesses. Cloud-based tools, project management software, and communication platforms enable teams to work together efficiently, regardless of geographical location. This flexibility opens up opportunities to access talent from anywhere, increase productivity, and reduce overhead costs.

Tip: Establish clear communication protocols and project management workflows to ensure effective collaboration among remote teams. Use video conferencing tools for virtual meetings and foster a culture of transparency and accountability to maintain productivity and engagement.

6. Embracing Emerging Technologies

Small businesses should stay informed about emerging technologies that have the potential to transform their industries. Technologies such as artificial intelligence, machine learning, blockchain, and the Internet of Things can offer new opportunities for growth and innovation. Being open to adopting and integrating these technologies into your business strategy can give you a competitive advantage.

7. Data Security and Privacy

Data security and privacy are critical considerations when using technology in small businesses. Implement robust cybersecurity measures, such as firewalls, encryption, and secure data storage, to protect sensitive customer information and intellectual property. Regularly update software and educate employees on best practices for data security to minimize the risk of data breaches.

8. Customer Relationship Management (CRM) Systems

A dedicated CRM system can help small businesses manage customer relationships more efficiently. It allows businesses to track customer interactions, store contact information, and monitor sales pipelines. Utilize CRM software to streamline sales and marketing processes, personalize customer interactions, and nurture long-term customer loyalty.

9. Continuous Learning and Skill Development

Encourage continuous learning and skill development among employees to keep up with technological advancements. Provide access to online courses, training resources, and workshops to enhance digital literacy and proficiency. Embrace a culture of learning and innovation to ensure your small business remains adaptable and competitive in the digital age.

10. Scalable and Flexible Technology Solutions

Choose technology solutions that are scalable and flexible to accommodate your growing business needs. Consider cloud-based software and platforms that allow you to easily scale up or down as your business evolves. This scalability enables small businesses to adapt to changing demands and seize new opportunities without significant disruptions.

11. Regular Technology Assessments

Regularly assess your technology infrastructure to ensure it aligns with your business goals and remains up to date. Conduct technology audits to identify areas for improvement, eliminate outdated systems, and explore new technologies that can drive growth. Stay proactive in evaluating and optimizing your technology stack to maximize its impact on your small business.

Conclusion

Technology serves as a catalyst for small business growth. By leveraging technology effectively and staying agile in an ever-evolving digital landscape, small businesses can unlock their full potential, adapt to changing customer expectations, and drive sustainable growth.

While funding options may vary depending on the business’s stage and needs, it’s essential for small business owners to explore the available options and develop effective funding strategies. Read on to explore various funding options and strategies that can help small businesses obtain the necessary capital for success.

1. Self-Funding and Bootstrapping

Self-funding, also known as bootstrapping, involves using personal savings or assets to finance your small business. This option allows you to retain full control over your business and avoid debt. However, it may limit the initial capital available and may require personal financial sacrifices to invest in your business’s growth.

2. Friends and Family

Seeking financial support from friends and family members is a common option for small business owners. It involves borrowing money or receiving investments from people you have personal relationships with. While this option may offer flexibility and lenient terms, it’s essential to approach such arrangements professionally and have clear agreements in place to avoid potential conflicts.

3. Small Business Loans

Small business loans are a traditional funding option offered by banks, credit unions, and other financial institutions. These loans provide capital with a defined repayment schedule and interest rate. Small business owners need to present a solid business plan, financial records, and collateral to qualify for a loan.

It’s crucial to carefully review terms and interest rates to ensure the loan is manageable for your business.

4. Crowdfunding

Crowdfunding platforms allow businesses to raise funds from a large number of individuals who contribute varying amounts. This option leverages the power of the crowd and can provide not only financial support but also help validate your business idea and build a community around your brand.

Effective crowdfunding campaigns require compelling pitches, engaging rewards, and strong marketing efforts to attract backers.

5. Grants and Government Programs

Various grants and government programs are available to support small businesses in specific industries or locations. These funding options often have specific eligibility criteria and application processes. Research local, regional, and national grant programs relevant to your business’s industry or specific needs.

Applying for grants may require significant effort, but it can provide non-repayable funds to support your business’s growth.

6. Angel Investors and Venture Capital

Angel investors and venture capital firms are sources of funding for small businesses with high growth potential. Angel investors are individuals who provide capital in exchange for equity or ownership in the company. Venture capital firms, on the other hand, invest larger amounts of capital in exchange for equity stakes.

These funding options often come with expertise and mentorship from experienced investors, but they also involve giving up partial ownership and decision-making control.

7. Business Incubators and Accelerators

Business incubators and accelerators are programs designed to support early-stage startups by providing funding, mentorship, and resources. These programs often require entrepreneurs to go through a competitive application process. In addition to financial support, incubators and accelerators offer guidance, networking opportunities, and access to a supportive community of fellow entrepreneurs.

8. Alternative Financing Options

In addition to traditional funding methods, small businesses can explore alternative financing options. These may include invoice financing, where you sell your outstanding invoices to a third party for immediate cash, or merchant cash advances, where you receive a lump sum in exchange for a portion of future sales.

While these options can provide quick access to capital, it’s important to carefully assess the terms and potential impact on your cash flow.

9. Business Credit Cards

Business credit cards can be a convenient and flexible funding option for small businesses. They allow you to access a revolving line of credit that you can use for various expenses.

It’s important to choose a credit card with favorable terms, such as low interest rates and rewards programs that align with your business needs. However, it’s crucial to use business credit cards responsibly and avoid accumulating excessive debt.

Takeaway

Exploring funding options and developing effective strategies is essential for small businesses to secure the necessary capital for success. Whether through self-funding, seeking support from friends and family, obtaining small business loans, utilizing crowdfunding, accessing grants and government programs, seeking angel investors or venture capital, participating in business incubators and accelerators, or exploring alternative financing options and business credit cards, small business owners have a range of options to consider.

You need to carefully evaluate each option, consider the associated terms and risks, and choose the funding approach that best supports your business’s growth and long-term financial stability.

Why is it so important? Because falling afoul of criminal law can lead to severe consequences, including hefty fines, damage to your reputation, and even imprisonment. It’s not just about knowing the law—it’s also about understanding how to navigate it to ensure the sustainability of your startup.

Isn’t it worth a bit of your time now to avoid potentially devastating legal issues later? Keep reading to uncover the potential pitfalls in criminal law for startups and learn how you can steer clear of them.

Understanding Criminal Law In The Context Of Business

Criminal law isn’t just about high-profile trials—it also intersects with the business world in many ways. For startups, navigating this landscape can be particularly challenging due to the unique environments they operate in and the high stakes involved.

The types of criminal offenses that could apply to businesses range from fraud and embezzlement to tax evasion and bribery. For a more comprehensive understanding of how criminal law applies to businesses, you can click here.

With a basic understanding of criminal law in a business context, let’s discuss some specific pitfalls that startups often encounter.

Potential Pitfalls In Criminal Law For Startups

Startups, with their unique environments and challenges, can be especially vulnerable to certain legal pitfalls. Here’s where they often run into trouble:

1. Corporate Fraud

This refers to dishonest activities that a company undertakes to give an advantage to itself or an individual. Startups, due to their often rapid growth and sometimes lax oversight, can be particularly vulnerable to instances of fraud, such as false financial reporting or insider trading.

2. Embezzlement

This occurs when someone with access to company funds or assets misappropriates them for personal gain. As startups often have smaller teams and more trust-based environments, they can be especially susceptible to such actions.

3. Tax Evasion

Startups are required to accurately report income and pay due taxes. However, in an attempt to maximize profits or due to simple oversight, some startups may end up underreporting income, overstating deductions, or hiding money offshore, leading to tax evasion charges.

4. Bribery

This involves attempting to influence someone in a public or legal duty by offering, giving, or receiving something of value. Startups looking for quick wins might be tempted to resort to such measures, but the repercussions can be severe.

5. Intellectual Property Violations

Intellectual property often forms the core of a startup, whether it’s software code, a business model, or a product design. Infringing on someone else’s Internet Protocol (IP) rights, even unknowingly, can lead to criminal charges.

6. Employment Law Issues

Employment law covers a range of issues, from wage and hour violations to discrimination and harassment claims. Mishandling these matters can result in criminal liability for startups.

7. Regulatory Compliance

Startups operating in heavily regulated industries, like healthcare or finance, are required to be particularly diligent about compliance. Failing to follow industry regulations can lead to criminal charges.

Now that we’ve identified the common legal pitfalls startups face, let’s explore some proactive steps you can take to avoid falling into these traps.

How To Avoid These Pitfalls

Awareness of potential legal issues is just the first step. It’s equally important to have strategies in place to avoid these pitfalls. Here are some precautionary measures you can take:

1. Hire A Competent Legal Advisor

It’s worth investing in good legal counsel who specializes in business law. They can help you navigate complex legal landscapes, ensure compliance, and advise on potential legal risks. For instance, they can guide you on the legal nuances of protecting your intellectual property or structuring employee contracts to comply with employment law.

2. Create Robust Internal Policies And Procedures

Implementing clear, robust policies and procedures can help ensure everyone in your startup understands the rules and adheres to them. For example, establishing a strict policy against any form of bribery and educating your team about it can prevent legal issues down the line.

3. Perform Regular Compliance Checks And Audits

Regular internal audits can help identify potential legal issues before they become serious problems. In the case of startups in regulated industries, these checks ensure that you’re always in line with the latest regulations.

4. Conduct Staff Training And Education

Regularly training your team about your company’s legal obligations and their role in maintaining compliance can prevent many legal issues. A well-educated team member is less likely to unknowingly infringe on someone else’s IP or commit other offenses that can lead to criminal charges.

Proactively taking these steps can go a long way in safeguarding your startup from potential criminal law pitfalls.

Final Thoughts

Navigating the legal landscape can be daunting, but it’s a critical part of the journey for every startup. The potential pitfalls of criminal law are not insurmountable obstacles, but rather signposts guiding you towards safer paths.

By taking the right steps, you can mitigate risks and focus on what really matters—building and growing your business. Remember, the spirit of entrepreneurship is not just about taking risks—it’s also about managing them.

Understanding the potential legal pitfalls and knowing how to avoid them is a sign of a savvy entrepreneur. After all, a successful startup is not just built on great ideas, but also on a solid legal foundation. So, here’s to building a startup that’s not just innovative, but also legally sound!

In this article, we will explore the concept of geo-targeting, its benefits, and how to set up geo-targeted ads in Google Ad Words.

Why Geo-Targeted Ads?

In the vast expanse of the internet, not all website visitors are created equal. While a large volume of traffic may seem appealing, it’s the quality of that traffic that truly matters. Geo-targeted ads enable businesses to narrow down their audience to a specific geographic location, ensuring that the website visitors they attract are more likely to be interested in their products or services. By reaching the right people in the right place, businesses can improve their conversion rates and generate higher revenue.

What Is Geo-Targeting?

Geo-targeting refers to the practice of delivering content, advertisements, or promotions to individuals based on their geographic location. It involves tailoring marketing efforts to specific regions, countries, states, cities, or even neighborhoods, depending on the targeting requirements of the business.

What Is Geo-Targeted Marketing?

Geo-targeted marketing is a strategic approach that focuses on tailoring marketing messages and campaigns to specific geographic areas. It allows businesses to create personalized experiences for customers based on their location, cultural preferences, language, and other factors relevant to the target audience in a particular region.

How Does Geo-Targeting Work?

Geo-targeting leverages various technologies to determine a user’s location. Some common methods include IP address tracking, GPS data, Wi-Fi signals, and user-provided information such as postal codes. Once the user’s location is identified, marketers can serve targeted advertisements or content based on the predefined criteria associated with that location.

Why Does Geo-Targeting Matter?

Geo-targeting matters because it allows businesses to optimize their marketing efforts and resources. By focusing on specific regions or locations, businesses can avoid wasting resources on audiences that are unlikely to convert. Instead, they can direct their marketing budget towards attracting visitors who are more likely to become customers, thereby increasing their chances of success.

Geo-Targeting vs. Geofencing

While geo-targeting and geofencing are related concepts, they differ in their approach. Geo-targeting aims to reach specific individuals based on their location, while geofencing involves creating a virtual boundary around a physical location and delivering content or ads to users within that boundary. Geo-targeting is broader in scope and can cover larger areas, whereas geofencing is more precise and focused on a specific physical area.

What Are The Three Major Types of Geo-Targeting?

1. Location Targeting

This type of geo-targeting focuses on reaching users in specific geographic locations, such as countries, states, cities, or zip codes. It allows businesses to target regions where their products or services are available or to tailor their marketing messages to local customs and preferences.

2. Audience Targeting

Audience targeting involves reaching users based on demographic factors such as age, gender, language, or interests. By combining demographic information with geographic data, businesses can create highly targeted campaigns that resonate with specific audience segments in different locations.

3. Weather Targeting

Weather targeting takes into account the local weather conditions of a particular location to deliver relevant ads or promotions. This type of targeting is especially useful for businesses that offer weather-dependent products or services, such as outdoor equipment, travel agencies, or seasonal clothing retailers.

How to Set Up Geo-Targeting Ads in Google Ad Words

Google Ad Words, now known as Google Ads, offers powerful tools and features to implement geo-targeted ads effectively. Here’s a step-by-step guide to setting up geo-targeting ads in Google Ads:

1. Define your target locations

Determine the specific regions or areas you want to target with your ads. This could be a country, state, city, or even a radius around a specific location.

2. Set up location targeting in Google Ads

Sign in to your Google Ads account and navigate to the campaign you want to edit. Under the campaign settings, go to the “Locations” tab and click on the “+ Location” button. Enter the desired locations and select the targeting options that best suit your campaign objectives.

3. Refine your targeting

Google Ads offers additional options to further refine your geo-targeting. You can exclude certain locations, target specific languages spoken in a region, or adjust your targeting based on factors like income level or user interest.

4. Create ad copy and extensions

Craft compelling ad copy that resonates with your target audience in each specific location. Consider tailoring the messaging to address local preferences, cultural nuances, or regional events. Additionally, utilize ad extensions like location extensions to provide more information and encourage visits to your physical store.

5. Monitor and optimize

Regularly review the performance of your geo-targeted campaigns. Analyze key metrics such as click-through rates, conversion rates, and return on investment (ROI) for different locations. Use this data to make informed decisions and optimize your ads for better results.

Geo-Targeting Tips and Best Practices

Here are some tips and best practices to maximize the effectiveness of your geo-targeting efforts:

1. Prioritize Certain Locations

Identify regions that align with your business objectives and allocate a higher portion of your budget to those areas. By focusing your resources on high-potential regions, you can generate more targeted traffic and improve conversion rates.

2. Target Local Keywords

Incorporate location-specific keywords into your ad copy and website content. This helps search engines understand the relevance of your offerings to users in a particular location and increases the chances of your ads appearing in local search results.

3. Avoid Creating Competing Campaigns

If you target overlapping locations with multiple campaigns, there is a risk of competing against yourself and wasting resources. Instead, consolidate your efforts into a single campaign with well-defined targeting parameters to ensure efficiency and avoid unnecessary competition.

Conclusion

In conclusion, the decision to buy targeted website traffic and implement targeted traffic strategies is a strategic move that can significantly impact the success of your online business. By harnessing the power of geo-targeted ads, businesses can precisely reach their desired audience in specific locations, leveraging the benefits of location targeting, audience targeting, and even weather targeting. This focused approach allows businesses to optimize their marketing efforts, prioritize certain locations, and tailor their messaging to resonate with local preferences.

Through platforms like Google Ads, setting up geo-targeted ads has become more accessible and effective, enabling businesses to reach the right people in the right place at the right time. By following best practices and continuously refining their campaigns based on data-driven insights, businesses can maximize their return on investment, drive targeted traffic to websites, and increase the chances of conversions and revenue generation.

Embracing geo-targeting and leveraging targeted traffic strategies is an essential component of a successful digital marketing strategy in today’s competitive landscape.

In this article, we will explore key strategies and considerations for small business owners to develop a compelling brand identity that resonates with their customers.

1. Define Your Brand Values

Start by defining your brand values, which are the guiding principles that shape your business’s identity. Identify what your business stands for, what you believe in, and how you want to be perceived by your customers. Your brand values should align with your target audience’s values and reflect the unique aspects of your business.

2. Craft Your Brand Story

A compelling brand story helps customers connect with your business on an emotional level. Share the journey of your business, including its origins, mission, and values. Highlight what sets your business apart and the problems you aim to solve for your customers. Your brand story should be authentic, engaging, and consistent across all communication channels.

3. Design a Memorable Logo and Visual Identity

Your logo and visual identity play a significant role in representing your brand. Design a logo that captures the essence of your business and resonates with your target audience. Choose colors, fonts, and visual elements that align with your brand values and create a cohesive visual identity across your website, packaging, marketing materials, and social media platforms.

4. Consistent Brand Messaging

Consistency in brand messaging is vital for reinforcing your brand identity. Develop a tone of voice that reflects your brand’s personality and consistently use it in all communication channels, including your website, social media posts, emails, and customer interactions. Ensure your messaging is aligned with your brand values and resonates with your target audience.

5. Engage with Your Customers

Building a strong brand identity involves actively engaging with your customers. Encourage feedback, respond to customer inquiries and reviews, and maintain a strong presence on social media. Foster a sense of community around your brand, and seek opportunities to create memorable experiences that leave a positive impression on your customers.

6. Deliver Consistent Brand Experience

Consistency extends beyond messaging and visual elements. It’s essential to provide a consistent brand experience across all touchpoints. Train your employees to embody your brand values and deliver excellent customer service that aligns with your brand identity. Whether it’s in-person interactions, phone calls, or online experiences, ensure that every customer touchpoint reflects your brand’s essence.

7. Collaborate with Influencers

Partnering with influencers in your industry can significantly boost your brand identity. Identify influencers whose values align with your brand and reach out to them for collaborations, sponsored content, or product reviews. Influencers can help increase brand awareness, credibility, and reach among your target audience.

8. Leverage User-Generated Content

User-generated content (UGC) is a powerful tool to strengthen your brand identity. Encourage your customers to share their experiences, testimonials, and creative content related to your brand. Repost and share UGC on your website and social media platforms to showcase real-life examples of how your brand adds value to customers’ lives.

9. Monitor and Adapt to Customer Feedback

Listen to your customers and actively seek feedback to refine your brand identity. Regularly monitor online reviews, social media comments, and customer surveys to understand how your brand is perceived and identify areas for improvement. Use this feedback to make necessary adjustments and ensure your brand remains relevant and resonates with your audience.

10. Stay Consistent and Evolve

While consistency is crucial, it’s also essential to evolve and adapt to changes in your industry and market. Continuously evaluate your brand identity and assess its effectiveness. Stay updated with industry trends, consumer preferences, and emerging technologies to ensure your brand remains fresh, relevant, and capable of meeting the evolving needs of your customers.

Conclusion

Building a strong brand identity is a vital component of small business success. By defining your brand values, crafting a compelling brand story, designing a memorable logo and visual identity, maintaining consistent brand messaging, engaging with your customers, delivering a consistent brand experience, collaborating with influencers, leveraging user-generated content, monitoring customer feedback, and staying consistent while evolving, you can establish a powerful brand that resonates with your target audience.

Remember, building a brand identity is an ongoing process that requires consistency, authenticity, and a deep understanding of your customers’ needs and desires.

The frenzy surrounding the launch of Large Language Models (LLMs) and other types of Generative AI (GenAI) isn’t going to fade anytime soon. Users of GenAI are discovering and recommending new and interesting use cases for their business and personal lives. Many recommendations start with the assumption that GenAI requires a human prompt. Indeed, Time magazine recently proclaimed “prompt engineering” to be the next hot job, with salaries reaching $335,000. Tech forums and educational websites are focusing on prompt engineering, with Udemy already offering a course on the topic, and several organizations we work with are now beginning to invest considerable resources in training employees on how best to use ChatGPT.

However, it may be worth pausing to consider other ways of interacting with GPT technologies, which are likely to emerge soon. We present an intuitive way to think about this issue, which is based on our own survey of GenAI developments, combined with conversations with companies that are seeking to develop some versions of these.

A Framework of GPT Interactions

A good starting point is to distinguish between who is involved in the interaction — individuals, groups of people, or another machine — and who starts the interaction, human or machine. This leads to six different types of GenAI uses, shown below. ChatGPT, where one human initiates interaction with the machine is already well-known. We now describe each of the other GPTs and outline their potential.

CoachGPT is a personal assistant that provides you with a set of suggestions on managing your daily life. It would base these suggestions not on explicit prompts from you, but on the basis of observing what you do and your environment. For example, it could observe you as an executive and note that you find it hard to build trust in your team; it could then recommend precise actions to overcome this blind spot. It could also come up with personalized advice on development options or even salary negotiations.

CoachGPT would subsequently see which recommendations you adopted or didn’t adopt, and which benefited you and which ones didn’t to improve its advice over time. With time, you would get a highly personalized AI advisor, coach, or consultant.

Organizations could adopt CoachGPT to advise customers on how to use a product, whether a construction company offering CoachGPT to advise end users on how best to use its equipment, or an accounting firm proffering real-time advice on how best to account for a set of transactions.

To make CoachGPT effective, individuals and organizations would have to allow it to work in the background, monitoring online and offline activities. Clearly, serious privacy considerations need to be addressed before we entrust our innermost thoughts to the system. However, the potential for positive outcomes in both private and professional lives is immense.

GroupGPT would be a bona fide group member that can observe interactions between group members and contribute to the discussion. For example, it could conduct fact checking, supply a summary of the conversation, suggest what to discuss next, play the role of devil’s advocate, provide a competitor perspective, stress-test the ideas, or even propose a creative solution to the problem at hand.

The requests could come from individual group members or from the team’s boss, who need not participate in team interactions, but merely seeks to manage, motivate, and evaluate group members. The contribution could be delivered to the whole group or to specific individuals, with adjustments for that person’s role, skill, or personality.

The privacy concerns mentioned above also apply to GroupGPT, but, if addressed, organizations could take advantage of GroupGPT by using it for project management, especially on long and complicated projects involving relatively large teams across different departments or regions. Since GroupGPT would overcome human limitations on information storage and processing capacity, it would be ideal for supporting complex and dispersed teams.

BossGPT takes an active role in advising a group of people on what they could or should do, without being prompted. It could provide individual recommendations to group members, but its real value emerges when it begins to coordinate the work of group members, telling them as a group who should do what to maximize team output. BossGPT could also step in to offer individual coaching and further recommendations as the project and team dynamics evolve.

The algorithms necessary for BossGPT to work would be much more complicated as they would have to consider somewhat unpredictable individual and group reactions to instructions from a machine, but it could have a wide range of uses. For example: an executive changing job could request a copy of her reactions to her first organization’s BossGPT instructions, which could then be used to assess how she would fit into the new organization — and the new organization-specific BossGPT.

At the organizational level companies could deploy BossGPT to manage people, thereby augmenting — or potentially even replacing — existing managers. Similarly, BossGPT has tremendous applications in coordinating work across organizations and managing complex supply chains or multiple suppliers.

Companies could turn BossGPT into a product, offering their customers AI solutions to help them manage their business. These solutions could be natural extensions of the CoachGPT examples described earlier. For example, a company selling construction equipment could offer BossGPT to coordinate many end users on a construction site, and an accounting firm could provide it to coordinate the work of many employees of its customers to run the accounting function in the most efficient way.

AutoGPT entails a human giving a request or prompt to one machine, which in turn engages other machines to complete the task. In its simplest form, a human might instruct a machine to complete a task, but the machine realizes that it lacks a specific software to execute it, so it would search for the missing software on Google before downloading and installing it, and then using it to finish the request.

In a more complicated version, humans could give AutoGPT a goal (such as creating the best viral YouTube video) and instruct it to interact with another GenAI to iteratively come up with the best ChatGPT prompt to achieve the goal. The machine would then launch the process by proposing a prompt to another machine, then evaluate the outcome, and adjust the prompt to get closer and closer to the final goal.

In the most complicated version, AutoGPT could draw on functionalities of the other GPTs described above. For example, a team leader could task a machine with maximizing both the effectiveness and job satisfaction of her team members. AutoGPT could then switch between coaching individuals through CoachGPT, providing them with suggestions for smoother team interactions through GroupGPT, while at the same time issuing specific instructions on what needs to be done through BossGPT. AutoGPT could subsequently collect feedback from each activity and adjust all the other activities to reach the given goal.

Unlike the above versions, which are still to be created, a version of AutoGPT has been developed and was rolled out in April 2023, and it’s quickly gaining broad acceptance. The technology is still not perfect and requires improvements, but it is already evident that AutoGPT is able to complete a set of jobs that requires the completion of several tasks one after the other.

We see its biggest applications in complex tasks, such as supply chain coordination, but also in fields such as cybersecurity. For example, organizations could prompt AutoGPT to continually address any cybersecurity vulnerabilities, which would entail looking for them — which already happens — but then instead of simply flagging them, AutoGPT would search for solutions to the threats or write its own patches to counter them. A human might still be in the loop, but since the system is self-generative within these limits, we believe that AutoGPT’s response is likely to be faster and more efficient.

ImperialGPT is the most abstract GenAI — and perhaps the most transformational — in which two or more machines would interact with each other, direct each other, and ultimately direct humans to engage in a course of action. This type of GPT worries most AI analysts, who fear losing control of AI and AI “going rogue.” We concur with these concerns, particularly if — as now — there are no strict guardrails on what AI is allowed to do.

At the same time, if ImperialGPT is allowed to come up with ideas and share them with humans, but its ability to act on the ideas is restricted, we believe that this could generate extremely interesting creative solutions especially for “unknown unknowns,” where human knowledge and creativity fall short. They could then easily envision and game out multiple black swan events and worst-case scenarios, complete with potential costs and outcomes, to provide possible solutions.

Given the potential dangers of ImperialGPT, and the need for tight regulation, we believe that ImperialGPT will be slow to take off, at least commercially. We do anticipate, however, that governments, intelligence services, and the military will be interested in deploying ImperialGPT under strictly controlled conditions.

Implications for your Business

So, what does our framework mean for companies and organizations around the world? First and foremost, we encourage you to step back and see the recent advances in ChatGPT as merely the first application of new AI technologies. Second, we urge you to think about the various applications outlined here and use our framework to develop applications for your own company or organization. In the process, we are sure you will discover new types of GPTs that we have not mentioned. Third, we suggest you classify these different GPTs in terms of potential value to your business, and the cost of developing them.

We believe that applications that begin with a single human initiating or participating in the interaction (GroupGPT, CoachGPT) will probably be the easiest to build and should generate substantial business value, making them the perfect initial candidates. In contrast, applications with interactions involving multiple entities or those initiated by machines (AutoGPT, BossGPT, and ImperialGPT) may be harder to implement, with trickier ethical and legal implications.

You might also want to start thinking about the complex ethical, legal, and regulatory concerns that will arise with each GPT type. Failure to do so exposes you and your company to both legal liabilities and — perhaps more importantly — an unintended negative effect on humanity.

Our next set of recommendations depends on your company type. A tech company or startup, or one that has ample resources to invest in these technologies, should start working on developing one or more of the GPTs discussed above. This is clearly a high-risk, high-reward strategy.

In contrast, if your competitive strength is not in GenAI or if you lack resources, you might be better off adopting a “wait and see” approach. This means you will be slow to adopt the current technology, but you will not waste valuable resources on what may turn out to be only an interim version of a product. Instead, you can begin preparing your internal systems to better capture and store data as well as readying your organization to embrace these new GPTs, in terms of both work processes and culture.

The launch and rapid adoption of GenAIs is rightly being considered as the next level in the evolution of AI and a potentially epochal moment for humanity in general. Although GenAIs represent breakthroughs in solving fundamental engineering and computer science problems, they do not automatically guarantee value creation for all organizations. Rather, smart companies will need to invest in modifying and adapting the core technology before figuring out the best way to monetize the innovations. Firms that do this right may indeed strike it rich in the GenAI goldrush.

In this article, we will explore effective strategies to help you navigate through challenging times and revitalize your declining business.

1. Assess the Situation

The first step in addressing a sudden decline is to assess the situation objectively. Analyze the factors contributing to the decline, such as changes in market conditions, customer preferences, or the competitive landscape. Gather data, review financial statements, and identify key performance indicators to gain a clear understanding of the challenges at hand.

2. Revisit Your Business Plan

Take a fresh look at your business plan to determine whether it aligns with the current market conditions. Consider updating your mission, vision, and goals to reflect the changing landscape. Identify any gaps in your strategy and make necessary adjustments to adapt to the evolving needs of your target audience.

3. Reconnect with Existing Customers

In times of decline, it’s crucial to reconnect with your existing customers and understand their changing needs and expectations. Engage in open communication, conduct surveys or interviews, and seek feedback to identify areas where you can improve your products or services. Leverage the insights gained to tailor your offerings and reposition your brand effectively.

4. Innovate and Diversify

Explore innovative ideas and opportunities for diversification. Consider expanding your product or service line to cater to new customer segments or enter untapped markets. Embrace emerging technologies and trends that align with your business to stay relevant and gain a competitive edge. By fostering a culture of innovation, you can breathe new life into your declining business.

5. Focus on Marketing and Promotion

Ramp up your marketing and promotional efforts to regain visibility and attract new customers. Develop a comprehensive marketing plan that includes a mix of traditional and digital marketing strategies. Leverage social media platforms, content marketing, search engine optimization, and targeted advertising to reach your target audience effectively. Highlight your unique selling propositions and value proposition to differentiate yourself from competitors.

6. Optimize Operations and Cut Costs

Review your business operations and identify areas where you can streamline processes, reduce wastage, and cut unnecessary costs. Look for opportunities to improve efficiency, enhance productivity, and maximize resources. This might involve renegotiating contracts, leveraging technology to automate tasks, or restructuring your workforce. By optimizing operations, you can improve your bottom line and strengthen your business’s resilience.

7. Collaborate and Form Strategic Partnerships

Explore collaborative opportunities and strategic partnerships with other businesses in your industry. Identify complementary businesses that share your target audience and offer mutually beneficial opportunities. Collaborative marketing campaigns, co-branding initiatives, or joint product development can help expand your reach, tap into new markets, and leverage shared resources. Strategic partnerships can inject fresh energy into your declining business and open doors to new opportunities.

8. Foster a Culture of Kaizen

Embrace a mindset of kaizen – continuous learning and improvement. Encourage your team to acquire new skills, stay updated with industry trends, and explore innovative solutions. Invest in training programs, workshops, or online courses to enhance your knowledge and capabilities. By fostering a culture of learning, you can adapt to changing circumstances and proactively respond to challenges.

9. Enhance Customer Experience

Put a strong focus on enhancing the overall customer experience. Evaluate every touchpoint with your customers, from initial interaction to post-purchase support. Seek ways to provide personalized and exceptional service. Implement feedback mechanisms, such as surveys or customer reviews, to gather insights and address any pain points. By exceeding customer expectations, you can build loyalty and attract new customers through positive word-of-mouth.

10. Seek Expert Advice and Support

Last but not least, you shouldn’t do this alone. Don’t hesitate to seek guidance from industry experts, consultants, or mentors who can provide valuable insights and fresh perspectives. They can help you identify blind spots, offer strategic advice, and guide you through the recovery process. Additionally, consider joining business networks or associations to connect with fellow entrepreneurs facing similar challenges and learn from their experiences.

Conclusion

Experiencing a sudden decline in your business can be disheartening, but it’s important to approach it as an opportunity for growth and transformation. By assessing the situation, revisiting your business plan, reconnecting with customers, innovating, focusing on marketing, optimizing operations, seeking expert advice, fostering a culture of continuous learning, enhancing the customer experience, and forming strategic partnerships, you can navigate through the downturn and position your business for long-term success.

Remember, resilience and adaptability are key qualities of successful entrepreneurs, and with the right strategies, you can revive your declining business and thrive once again.