Tag: ✍️ Opinions

The Potential of AI Facial Recognition, From Applications to Ethical Considerations
This article has been contributed by Vivek Gujar, Director, IndoAI Technologies P Ltd.

One of few innovations today which gathered attention and interest is AI facial recognition. From empowering marginalized communities to revolutionizing essential services, the applications of this breakthrough technology are as diverse as its impact. This technology involves artificial intelligence and biometrics, and holds promise for a wide range of applications, from improving security measures to revolutionizing the customer experience. However, alongside its transformative potential, AI facial recognition raises significant ethical considerations that require careful consideration and proactive safeguards.

The Technology: It uses advanced algorithms to analyze and interpret facial features from digital images or video streams. This technology identifies unique facial attributes such as contour, proportion: extract and interpret distinctive facial features such as the distance between the eyes, the shape of the nose, blinking & smile ( liveness factor) and the contour of the jaw and texture by using deep learning models, specifically convolutional neural networks (CNN). Through iterative training on massive datasets, the accuracy of these algorithms continues to improve, enabling accurate facial recognition and verification capabilities.

Applications in Various Fields
Applications in Key Sectors
Access to Healthcare and Telemedicine
Education
Agriculture and Rural Development
Social Distribution
Employment and Skills
Women’s Safety and Empowerment
Access to Government Services
Ethical considerations are paramount

Applications in Various Fields
1. Security and Surveillance:
- Law Enforcement: AI facial recognition can help identify suspects, locate missing persons, mob moods and prevent criminal activity.
- Border Control: Airports and border crossings use this technology to enhance security measures and streamline immigration procedures.
- Critical Infrastructure: Facial recognition process can streamline visitor management for Government building, defence establishment, etc.
1. Personalization and Customer Experience:
- Retail: Retailers are using AI facial recognition to analyze customer demographics and preferences, provide customized product recommendations and improve the shopping experience.
- Hospitality: Hotels and entertainment venues are using facial recognition for faster check-in, personalized service and increased guest satisfaction.
1. Public Safety and Health Care:
- Contact Tracing: During pandemics and outbreaks, facial recognition helps with contact tracing and identifies people who may have been exposed to an infectious disease.
- Access Control: Hospitals, healthcare facilities, pharma, etc use facial recognition for secure access control to ensure restricted areas are protected.
Applications in Key Sectors

Financial Inclusion at the Bottom of the Pyramid (BoP):
- Access to Banking Services for the BoP: AI facial recognition simplifies the bank account opening process for BoP individuals and facilitates extensive documentation.
- Microfinance: Optimizing loan applications through facial recognition helps people access loans faster, pursue entrepreneurial ventures, and improve their livelihoods.
Access to Healthcare and Telemedicine
- Access to Healthcare in Rural Areas: Doctors with the help of Facial recognition technology remotely monitor patients in rural health centers to ensure timely intervention and identify needed Improve access to health services.
- Telemedicine: Facial recognition improves security during virtual visits and makes telemedicine platforms more accessible and efficient for remote communities.
Education
- Access to online learning: Facial recognition technology ensures secure access to digital learning platforms, closing educational gaps and providing equal learning opportunities.
- Attendance Tracking: Facial recognition reduces administrative burden and increases efficiency by simplifying attendance tracking in schools with limited infrastructure. A group photo of a class in a second can save several hours of roll call.
Agriculture and Rural Development
- Fair Trade Practices: Facial recognition verifies farmers’ identities at collection points, ensuring fair prices for agricultural products and increasing transparency in trade practices.
- Crop Seed Distribution: AI facial recognition assists in crop seed distribution and provides valuable insights into weather conditions to sustainably optimize crop yields.
Social Distribution

Direct Benefit Transfer (DBT): Facial recognition can verify the identity of beneficiaries during the distribution of social assistance benefits, reducing losses and ensuring effective use of government initiatives.

Public Distribution System: Facial data may be key to getting access to essential items such as rice under the public distribution system (PDS).

Facial Recognition Market Size Worldwide from 2019 to 2032

Employment and Skills
- Job Matching: Facial recognition accelerates the employment process by matching BoP job seekers with suitable employment opportunities based on their skills and qualifications, thereby increasing economic empowerment and self-employment.
Women’s Safety and Empowerment
- Safety App: Smartphone app with facial recognition technology makes women safer by issuing emergency alerts in stressful situations, providing immediate assistance, and promoting empowerment.
Access to Government Services
- Facilitate Government Services: Facial recognition simplifies the authentication process at local e-government centers, allowing seamless access to government programs, land records, and important certificates. eKYC through Facial Recognition helps Senior citizens in pension, health benefits, etc. Government can keep accurate tab of orphans, special people, senior citizens of old age homes.
- Disaster Relief: Identification and Aid Distribution: During natural disasters, facial recognition allows authorities to quickly identify victims and efficiently distribute aid, ensuring rapid aid to those in need.
Ethical considerations are paramount

AI facial recognition has great potential, but its widespread use raises significant ethical concerns that require careful consideration and improvement strategies:
1. Privacy and Data Security:
- Consent: In the wake of DPDP act, users must be fully informed about what is collected. The storage and use of facial data is subject to explicit consent for each application. The ongoing deepfakes worldwide is worth mention here.
- Data Protection: Robust encryption protocols and strict access controls are essential to protect facial data from unauthorized access and potential misuse.
1. Bias and Discrimination:
- Algorithmic Bias: Facial recognition algorithms can exhibit biases that lead to inaccurate identification or disproportionate targeting of certain demographic groups.
- Fairness and Justice: Developers must prioritize fairness and justice in data collection and algorithm design to reduce the risk of perpetuating social bias.
1. Surveillance and Civil Liberties:
- Mass Surveillance: The widespread use of facial recognition for surveillance purposes raises concerns about the impact on individual privacy and civil liberties.
- Regulatory Oversight: Clear regulatory and oversight mechanisms are essential to ensure responsible deployment and use of AI facial recognition systems.
Navigating the complex landscape of AI facial recognition requires a fine balance between upholding ethical principles and protecting fundamental rights while harnessing the transformative potential of technology is need today. Technologists, policymakers, ethicists, and citizens need to help AI facial recognition become a force for positive change, shaping a future that promotes safety, efficiency and inclusion while respecting individual autonomy and dignity. Through thoughtful debate with society’s cooperation, transparent governance and ethical responsibility, this technology is for progress and prosperity based on principles of justice, equity and respect for human rights.

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How AI is Revolutionizing Supply Chain Management: A Deep Dive
This article has been contributed by Atul Kumthekar, Co-Founder, 3-F Software – a Design Thinking Company.

The use of artificial intelligence in various fields is increasing day by day. In this context, the supply chain does not fall behind. The supply chain is simply a distribution network that connects brands and consumers through distributors. In this article, let’s learn more about what a supply chain is, areas where AI can be used in the supply chain, how AI can be useful in enhancing the supply chain, and more.

What Is A Supply Chain
Problem And Approach So Far
Areas Where Ai Be Used In Supply Chain?
What Is Ai From Technological Standpoint?
How Can Ai Be Useful In Supply Chain?

What Is A Supply Chain

If you are a living being, you are part of some supply chain! There is no escaping! Essentially it is a distribution network. From Brand to Consumers via Distributors Products and goods move from Brand to Consumer and Money flows in the reverse order. This is the best simplistic definition of supply chain that I have ever come across.

This brings in a lot of complexities and the challenges thereby. Complexity is at multiple levels and scale because of various challenges:
- Scale and affordability at various layers mean plethora of different solutions used from start to end. From excel sheet to SAP like expensive ERP
- International nature of today’s world
- Prediction of demand to meet supply in time
- Management of unsold in case of retail chain
- Inventory management at various levels
Supply Chain Management

Problem And Approach So Far

Various integrations and APIs helped intercommunication between Tally like software to SAP and Excel both People have addressed various issues using the latest technologies of the day. Blockchain being a recent example especially for global trade. The blockchain technology proved that the documentation time saving is possible and also that assurance of quality and source can be guaranteed beyond doubt.

But these technologies and solutions did not address more complex problems like demand predicting, analyzing vast data.

Now Artificial Intelligence is the new kid in town to help ease out these complexities a bit!

Areas Where Ai Be Used In Supply Chain?

Now that we know what was possible so far and some clue to unsolved problems, let us look into the problem areas little more closely. I got following response when I enquired on ChatGPT which was pretty accurate! So, I am going to reproduce the same with my elaborations on interesting ones –
1. Demand Forecasting: AI algorithms can analyze historical data, market trends, weather patterns, and other variables to predict demand more accurately. This helps in optimizing inventory levels and reducing stockouts or overstock situations.
2. Inventory Management: AI-driven systems can optimize inventory levels by analyzing factors like lead times, demand variability, and supplier performance. This ensures that inventory is maintained at optimal levels, reducing carrying costs while ensuring products are available when needed.
3. Route Optimization: AI algorithms can optimize delivery routes considering factors like traffic patterns, weather conditions, and delivery windows. This reduces transportation costs, improves delivery times, and minimizes fuel consumption.
4. Supplier Relationship Management: AI can analyze supplier performance data to identify trends, assess risk, and predict supplier behaviour. This helps in making informed decisions about supplier selection, negotiation, and risk management.
5. Warehouse Management: AI-powered systems can optimize warehouse operations by automating tasks such as picking, packing, and sorting. This improves efficiency, reduces errors, and lowers operational costs.
6. Quality Control: AI can analyze sensor data and visual inspections to detect defects in products or packaging. This ensures that only high-quality products are shipped, reducing returns and improving customer satisfaction.
7. Supply Chain Visibility: AI can provide real-time insights into the entire supply chain, allowing businesses to track inventory levels, monitor supplier performance, and identify potential bottlenecks or disruptions.
8. Predictive Maintenance: AI can analyze equipment sensor data to predict when maintenance is needed, reducing downtime and preventing costly equipment failures.
9. Risk Management: AI can analyze various risk factors such as geopolitical events, natural disasters, and supplier financial stability to identify potential risks to the supply chain. This enables businesses to develop contingency plans and mitigate risks proactively.
10. Sustainability: AI can optimize supply chain processes to minimize environmental impact, such as reducing carbon emissions through optimized transportation routes or minimizing waste through better inventory management.
But before I add my elaborations let us peek a bit into what are typical AI techniques

What Is Ai From Technological Standpoint?

The word artificial intelligence typically deals with large data. I used to mock AI saying this is our Ajji’s (Grandmother’s) wisdom! What a person gathers over years or our ancient culture have already noted down. But that’s more or less about how humans behave, individually Vs in masses. But let’s not divert there!! May be in some other blog

It is impossible to make ANY sense out of the vast data that is flowing in today. The producing brands (Manufacturers) need to listen to those in a meaningful way, in order to save any costs and time. You can employ men (and women) power in all the countries you deal with. That is one possible solution. But then there is issue of miscreants and malicious feeds. And even any number of these will not match the scale and capability of technology today. So why not technology, if that is one way?

The AI basically started with machine learning which rests heavily on statistics. Let’s say you have an excel sheet of student’s marks. And you want to know the distribution. Most likely you will get a Gaussian curve. Meaning most students near about average and few below and few above, the curve tapering down both directions. This is understandable.

Gaussian Curves Example

But now consider the problem – can I find out if students have copied? I will leave this problem to you and get back to me on any thoughts!

Ah ha! Welcome to machine learning.

A typical approach in machine learning is give enough sample data, ask the algorithm to understand the pattern and apply this learning to test data and judge the output and then think about improving output. Yes, you probably have guessed it right, that the output is probabilistic. You need to get comfortable with this! (And we are not going to quantum mechanics as yet)

The above paragraph is the essence of Artificial Intelligence. The major differential between machine learning and AI started emerging with Large Language Models (LLM) which started putting chatGPT like tools into public hands. How do they work? Are they the advanced versions of Google search? Well, basically they can address much much much more parameters to analyze. From few hundreds to millions! That is amazing. Even our brains may not be doing so. Take a look at Hugging Face and you will get the picture. The company started with chatbot application and with multiple rounds of funding reached at LLM models. And if you care to read “Attention is all you need” mentioned in references at the end, you will need to look at Transformer architecture (see diagram below)

Transformer Architecture – Source: http://rpradeepmenon.medium.com/

With this, let’s get back to Supply chain problems.

How Can Ai Be Useful In Supply Chain?

Let us look at some of the problems mention in the earlier section (“Areas where AI be used in supply chain?”).

Demand Forecasting is definitely the one. This is not humanely possibly and achievable by employing any number of employees in any number of countries. Will LLM be useful here? Definitely. This is because it fits into the criteria of what LLM kind of models can work on. Basically, millions of parameters.

Inventory Management is related to Demand Forecasting. For a big brand, it is not just about warehouse but localized inventory to supply quickly to local market. You may have seen Duzo like B2C supplier having small storage locations nearby your area with many biker riders picking things from there and delivering in 10 minutes! What inventory to maintain at such local locations can be better judged only by AI models.

Route Optimization a well-known problem in industrial engineering and operations research (Traveling salesman problem) The solution to this was once modelled as Simplex algorithm (Narendra Karmarkar) The large parametrization that is possible with LLM models will throw and interesting solutioning scenarios of solving this problem.

Supplier Relationship Management also known as CRM (Customer Relationship Management) is an important area where sales and support are managed. Salesforce the world leader in this domain is funding good amounts for the growth in Artificial Intelligence and LLM (Large Language Models). Pretty soon we will see a day to day usable output from these efforts.

Quality Control is an important area. During Covid days, this gained importance and quality people could not reach work location. The usual technique was to deploy cameras, send images which could be checked using AI with possible false positives but reducing the overall load. Radiology is one good example where this is used today.

Predictive Maintenance is also undergoing big impact due to AI. More the historical data, better is the prediction and preventive measures, which save bigtime in repairs cost. You can send your support technicians for preventive maintenance in time than to fix things! I had personally developed and delivered an IOT solution for X-ray manufacturer to send technician based on number of X-rays taken instead of earlier method of end of year. No AI but you can sense where the next step could be!

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Important!

Having seen all this, you must have realized that AI is not a magic wand. Also, it is not an overnight solution. One needs to select an existing model, if not develop a new one, then customize it, play with it and most importantly mature it to predict better and better. All this happens asymptotically. Meaning the gap between actual solution and reality will keep reducing slowly but surely. Keep in mind the dynamics of reality is also dynamic! So, something that worked well may not work equally well in future.

More importantly…

All AI solution providers stress importance of human touch. The solution needs to be looked at from human perspective. This means that the output is not harmful to certain section of community, or simply very negative etc. And even in this space there are tools and datasets evolving to improve solution from this perspective.

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Medical Advancements and Challenges in AI Yet How Is It Changing the Industry?

This article has been contributed by Dr. Sravani Reddy G, Founder & CEO, Soprav Health Consulting.

AI is fundamentally transforming the healthcare industry by revolutionizing various aspects of patient care, diagnosis, treatment, and administrative processes. Through the utilization of advanced algorithms and machine learning techniques, AI enables healthcare providers to analyze vast amounts of data quickly and accurately, leading to improved clinical decision-making. Additionally, AI-powered technologies enhance efficiency in tasks such as medical imaging interpretation, predictive analytics for disease prevention, personalized treatment planning, and drug discovery. Moreover, AI-driven solutions streamline administrative tasks, optimize workflows, and reduce healthcare costs. Overall, AI is catalyzing a paradigm shift in healthcare, driving innovation, improving patient outcomes, and enhancing the overall quality and accessibility of healthcare services.

The vast realm of artificial intelligence (AI) is rapidly evolving, pushing boundaries, and revolutionizing healthcare. Recent advancements, including improved access to extensive health data, faster computer processing, a growing pool of AI experts, and accessible AI tools, are seamlessly integrating AI into modern healthcare. Despite initial hesitancy, the healthcare sector is embracing AI to address workforce shortages, meet patient expectations, and leverage technological innovations.

Managing the Intricacies of Healthcare Data
Interpretability
Ensuring Ethical Considerations and Patient Privacy
Integration with Clinical Workflow
Cost and Resource Constraints

However, the healthcare sector has traditionally been slow to embrace such technology and it is essential to acknowledge that we are still in the early stages of this journey, with much ground left to cover. Some of the concerns surrounding involve:

Managing the Intricacies of Healthcare Data

While access to patient information has improved, AI integration faces a significant hurdle due to the intricacies and complexities of healthcare data. Unlike sectors with abundant and readily available data, healthcare data is diverse, complex, and fragmented across different systems. This presents unique challenges for AI implementation, requiring innovative strategies for integration, interoperability, and standardization.

Interpretability

Many AI models, such as deep learning algorithms, are often considered “black boxes” due to their complex nature, making it challenging for healthcare professionals to understand how they arrive at specific conclusions or recommendations. Improving the interpretability of AI systems is crucial for building trust and acceptance among clinicians.

Ensuring Ethical Considerations and Patient Privacy

In the healthcare sector, where decisions directly impact patient lives, ensuring ethical considerations and patient privacy is paramount. AI technologies must adhere to rigorous ethical standards and regulatory guidelines to safeguard patient data and maintain trust in healthcare systems. Transparency, accountability, and patient-centric approaches are essential to responsible AI integration.

Total Data Storage Capacity Compared To The Total Amount Of Healthcare Data

Integration with Clinical Workflow

While some healthcare sub-sectors have experts familiar with basic AI tools, but many medical practitioners still face workflow issues. Integrating AI seamlessly into existing clinical workflows and EHR systems remains a challenge. Healthcare professionals require user-friendly interfaces and interoperable AI solutions that enhance existing practices without disrupting patient care or workflow efficiency.

Cost and Resource Constraints

Developing and implementing AI technologies in healthcare can be costly and resource-intensive, particularly for smaller healthcare facilities or resource-limited settings. Addressing cost barriers and providing adequate training and support for healthcare professionals to use AI tools effectively is essential for ensuring equitable access and adoption across different healthcare settings.

Although challenges such as data complexity and regulatory obstacles persist, the adoption of AI in healthcare is steadily increasing. As the world becomes more interconnected, particularly through avenues like medical tourism, AI and ML present considerable opportunities to enhance the healthcare journey. It’s widely acknowledged that AI holds the potential to revolutionize patient care, emphasizing precision medicine and personalized services. Moving forward, it’s imperative that the advancement of AI in healthcare remains grounded in a commitment to innovation and empathy, ultimately striving to enhance outcomes and elevate the quality of life for individuals globally.

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Understanding Labor Laws and Compliance: A Comprehensive Guide
This article has been contributed by Anju Sethi, HR Head, Brightsun Travel.

India’s business landscape thrives on a robust legal framework, with labor laws playing a pivotal role in ensuring fair treatment for both employers and employees. Regardless of size or industry, every organization must comply with these central and state-specific regulations. This article serves as a comprehensive guide, unpacking the importance of labor law compliance and outlining key legislations that govern workplaces in India.

Why Compliance Matters
Building a Compliance Framework
Key Labor Laws in India
Additional Laws for Consideration

Why Compliance Matters

Compliance with labor laws isn’t just a legal obligation; it’s a strategic imperative. A well-designed compliance program fosters a positive work environment, improves employee morale, and reduces operational risks. Here’s how:
- Enhanced Operations: A structured approach to compliance streamlines HR processes, minimizes errors, and ensures timely execution of employee-related tasks.
- Improved Quality of Care: Adherence to labor laws guarantees employee well-being through fair wages, safe working conditions, and mandated leaves. This translates to a more engaged and productive workforce.
- Reduced Costs: Non-compliance can lead to hefty penalties, legal battles, and reputational damage. Following labor laws helps organizations avoid these costly repercussions.
Building a Compliance Framework

Implementing an effective compliance program requires a systematic approach. Here’s a breakdown of the key steps:
- Design and Development: Identify relevant labor laws based on factors like location, industry, and size of the organization. Develop policies, processes, and procedures aligned with these laws.
- Implementation and Monitoring: Train HR personnel on the new protocols. Establish a risk assessment matrix to pinpoint areas of potential non-compliance and institute regular monitoring mechanisms.
Key Labor Laws in India

India’s labor law landscape is vast, encompassing numerous central and state-specific legislations. Here’s a glimpse into some of the most crucial ones:
- The Punjab Shops & Commercial Establishments Act, 1958: This law regulates work conditions in shops and commercial establishments, including IT, non-IT, and BPO sectors. It mandates fair wages, working hours, leaves, and safety standards, promoting a healthy work-life balance.
- The Punjab Labor Welfare Fund Act, 1965: Established to improve employee living standards, this act mandates contributions from both employers and employees into a welfare fund. The collected funds are used for initiatives like healthcare and educational support for workers and their families.
- The Equal Remuneration Act, 1976: This act guarantees equal pay for men and women performing similar work or work of a similar nature. It prohibits gender-based discrimination in recruitment and ensures fair compensation practices.
- The Maternity Benefit Act, 1961: This act safeguards the employment rights of women during pregnancy and childbirth. It mandates paid maternity leave for a specified period, enabling women to focus on their well-being during this critical time.
- The Minimum Wages Act, 1948: This act establishes the minimum wage payable to employees for their work, preventing exploitation and guaranteeing a decent standard of living. The government periodically reviews and revises minimum wages based on cost-of-living factors.
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Additional Laws for Consideration
- The Payment of Wages Act, 1936: Guarantees timely wage payment without unauthorized deductions.
- The Payment of Bonus Act, 1965: Entitles eligible employees to a bonus payment based on company performance.
- The National & Festival Holidays Act, 1963: Mandates three national holidays (Republic Day, Independence Day, and Gandhi Jayanti) and empowers states to declare additional festival holidays.
- The Payment of Gratuity Act, 1972: Provides gratuity payments to long-serving employees as a token of appreciation for their dedication.
- Employees’ Provident Funds and Miscellaneous Provisions Act, 1952: Creates a social security scheme for employees, encouraging long-term savings for retirement.
- Employee State Insurance Act, 1948: Offers medical benefits to employees in case of sickness, maternity, employment injury, and permanent or partial disability.
- The Sexual Harassment of Women at Workplace (Prevention, Prohibition and Redressal) Act, 2013: Prohibits sexual harassment of women in workplaces and mandates the formation of internal committees to address complaints.
India – Composition of Labor Force

In conclusion, navigating India’s labor laws can be complex, but with a proactive approach and a commitment to compliance, organizations can foster a positive work environment, protect employee rights, and achieve long-term success. Regularly seeking legal counsel and staying updated on evolving regulations is key to maintaining a compliant and ethical workplace. This collaborative effort extends beyond legal adherence.

The government can strengthen enforcement, employers can prioritize compliance training and communication, and employees can actively participate in upholding their rights. Importantly, compliance isn’t just about following regulations; it’s an investment in a thriving work ecosystem. Fair treatment and a healthy work environment benefit everyone – employees experience greater satisfaction and productivity, companies attract and retain top talent, and the nation enjoys a skilled workforce that fuels economic growth. By viewing labor laws as tools for mutual benefit, India can create a future where compliance fosters prosperity for all.

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How to Excel in Internships: Dos and Don’ts for Students
This article has been contributed by Aarul Malaviya, Founder, AI enabled – Zamit.

Internships serve as invaluable opportunities for students to gain practical experience, explore career paths, and develop essential skills for the professional world. Excelling in internships requires a strategic approach and a commitment to learning and growth. To make an informed decision thorough research is required which would eventually lead to paving the way to one’s desired career.

Dos
Don’ts

Dos

Let’s dive right into the key considerations that students should keep in mind to make the most out of their internships:
- Set Clear Goals: While internships come with guidelines, it’s essential to have a clear plan for what you want to achieve. Identify specific skills you’d like to acquire, aim for stellar reviews, or focus on anything that will further your career.
- Be Proactive and Take Initiative: Take initiative and seek out opportunities to contribute to projects or tasks. Don’t wait for tasks to be assigned to you; instead, volunteer for assignments and demonstrate your enthusiasm and willingness to learn. If you identify areas where you can add value or improve processes, don’t hesitate to propose ideas or solutions.
- Build a Robust Network: Build relationships with your colleagues, supervisors, and other professionals in your field. Networking can open doors to future opportunities and provide valuable insights into the industry.
- Seek Feedback and Treat Mistakes as Learning Opportunities: Request feedback on your work regularly to gauge your progress and identify areas for improvement. Use constructive criticism as an opportunity to grow and refine your skills. Don’t be afraid to make mistakes; instead, view them as learning opportunities.
- Stay Curious: Stay curious and open-minded throughout your internship. Be willing to explore new ideas, technologies, or methodologies that can enhance your skills and knowledge. Don’t hesitate to ask questions if you’re unsure about something. Asking for clarification or guidance shows your interest in learning and your commitment to doing a thorough job.
Don’ts

While embracing the aforementioned one should take great care to avoid the following pitfalls while navigating the internship landscape:
- Don’t Forget to Follow Up: After completing a task or project, it’s essential to follow up with your supervisor to ensure that you’ve met expectations and to seek feedback for improvement. This demonstrates your commitment to excellence and your willingness to learn and grow.
- Don’t Neglect Your Professional Development: Your internship presents a valuable opportunity to invest in your professional growth. Take advantage of this time to develop new skills, expand your knowledge, and hone existing competencies. Seek out learning opportunities, whether through formal training sessions, shadowing experienced professionals, or taking on challenging projects.
- Don’t Burn Bridges: Even if your internship doesn’t lead to a job offer later, maintaining positive relationships with your colleagues and supervisors is crucial. Building and nurturing these connections can open doors to future opportunities, whether through referrals, recommendations, or networking.
- Don’t Underestimate the Power of Soft Skills: Focus on developing soft skills like communication, teamwork, and problem-solving during your internship. Practice effective communication, collaborate with colleagues, and approach challenges with creativity. These skills are crucial for success in any professional setting and will make you a valuable asset to future employers.
- Don’t Forget to Express Gratitude: Expressing gratitude is a simple yet powerful way to leave a positive impression and build goodwill with your colleagues and supervisors. Take the time to thank them for the opportunities, guidance, and support they’ve provided during your internship.
Important Aspects Internship Students have Learned

Takeaway

Excelling in internships necessitates a proactive attitude, a dedication to continuous learning and development, and maintaining professionalism. Students can optimise their internship experience and establish a strong foundation for a successful career by following the aforementioned guidelines. By embracing these principles, interns can make meaningful contributions, gain valuable experience, and position themselves for future opportunities in their chosen fields.

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The Impact of AI on Digital Marketing Campaigns and the Latest AI Technologies Shaping ROI and Campaign Optimisation in the Industry
This article has been contributed by Prateek Sethi, Founder, Trip Creative Agency.

The dawn of the 21st century significantly changed people’s personal and professional lives. Among other shifts, new-age technologies have gained considerable traction by altering the dynamics for the better. As a result, artificial intelligence (AI), formerly regarded as a futuristic concept, has established itself as a reality. This technology has revolutionised various industries, with digital marketing being no exception. Thus, this cutting-edge technology has become a necessity rather than a luxury over the years.

According to a joint analysis by NASSCOM and BCG, India’s AI market is expected to reach $17 billion at a CAGR of 25–35% between 2024 and 2027. This expansion is attributed to various factors, including changing customer behaviour and increased enterprise technology spending, among others. In fact, everyone is familiar with the term artificial intelligence. It refers to the development of computer-generated programs and software capable of performing tasks that would require a human mind. These activities include learning, problem-solving, natural language comprehension, pattern recognition, visual information perception, depth perception and decision-making.

So, beyond being a buzzword, AI has certainly become a critical tool, reshaping the way companies approach marketing campaigns and bringing in new avenues for creative, personalised, and impactful marketing campaigns that resonate deeply with audiences.

Impact of AI on Digital Marketing Campaigns
AI Technologies: Transforming the Future of the Marketing Industry

Impact of AI on Digital Marketing Campaigns

From campaign creation to optimisation, AI’s influence extends across the entire digital marketing spectrum.
- Advanced profiling: Every business understands the need for profiling today. Without a thorough understanding of the target audience, marketers will not be able to develop tailored marketing strategies. Knowing the perfect client profile enables businesses to tailor messages and other information to their specific requirements and expectations. According to Deloitte research, simply putting the customer’s name in their emails can increase open rates by 5.2%, while more tailored communications show a rise of 11%–55%. However, maintaining and calculating every piece of data is time-consuming. Thus, with the ability to analyze vast amounts of customer data, including demographics, online behaviour, and past interactions, AI emerged as a helping hand, executing complicated consumer segmentation activities in real time.
- Personalise the campaign: A personalised campaign has become a thing of today. Because nobody wants to receive a dull and generic message; the difference between ‘Hi there’ and ‘Hi XYZ’ is tremendous; when anything talks personally, people automatically feel more significant and interested. This is where AI applies customer insights to adapt messaging, content, and offers to specific tastes. Consider receiving product recommendations that are exactly what you need or social media ads that are relevant to your interests. This level of personalisation increases customer engagement and brand loyalty, providing desired outcomes for everyone.
- Predictive Marketing: What if firms could predict the future and anticipate client requirements before they arise? While AI cannot tell businesses which lottery number to choose, it assists in determining the best marketing plan for a given situation. It accomplishes this by analysing data and evaluating habits to predict what the customer wants and when they want to buy. Thus, AI systems use historical data and user behaviour to forecast future actions. This allows marketers to anticipate client demands and proactively provide relevant information and offers, resulting in higher conversions and revenues.
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AI Technologies: Transforming the Future of the Marketing Industry
- AI-powered chatbots: AI chatbots have become critical for modern customer service. With their round-the-clock availability, they serve as virtual representatives, providing prompt and efficient assistance to customers. They can handle a wide range of basic queries, from product information to order status. By taking care of routine tasks, chatbots free up human agents to tackle complex issues, thereby improving the overall efficiency of the team. Furthermore, this not only helps businesses save time and money but also enhances the customer experience, leading to increased customer satisfaction and commitment.
- AI-powered fraud recognition: AI enables marketers to detect fraudulent actions in real-time. This not only prevents organisations from losing ad revenue due to theft, but it also ensures that campaign measurements are right. AI systems can examine vast amounts of data and spot any suspicious patterns, allowing marketers to respond swiftly and prevent any damage. This new technology has changed the way marketers promote and made the Internet advertising environment more secure.
- AI-driven A/B testing: When it comes to testing marketing efforts, typical A/B testing can be time-consuming and tiresome. However, with the help of AI, this procedure can be automated and expedited. AI algorithms may analyse various campaign components, such as headlines, pictures, and landing pages, to determine which ones work better than others. Using this iterative technique, AI can continuously recommend campaign modifications and optimise ad spending for optimum impact. This allows firms to get superior results from their marketing efforts more efficiently and effectively.
Market Value of Artificial Intelligence (AI) in Marketing Worldwide from 2020 to 2028

It’s a Win-Win for All!

There is no denying the fact that AI is revolutionising digital marketing by providing unrivalled data-driven insights and automation capabilities. Understanding the impact of AI and embracing strategic AI deployment enables businesses to design intelligent campaigns, forge stronger consumer connections, and achieve optimal results in the ever-changing marketing landscape. Thus, to stay ahead in the competitive world of digital marketing, businesses must embrace AI and use it to their advantage. Although nothing is going to replace the creative human controlling all this information and knowledge, for now.

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Predictive Analytics in Healthcare: Utilizing AI to Anticipate and Minimize Adverse Health Events
This article has been contributed by Dr Narendra Vankar, Co-Founder, Quantum CorpHealth Pvt. Ltd.

India’s health landscape is rapidly evolving. We are moving towards the best possible medical care with experienced, trained doctors; new advancements in medical technology; and the introduction of personalized, AI-based healthcare, perfectly tailored to fit a patient’s symptoms and health history. Medical professionals can now use AI tech to anticipate and mitigate health issues, from the minor to the adverse. AI is able to make the jobs of medical professionals easier by analyzing vast amounts of data, while predictive AI can identify patterns and predict patient outcomes. Predictive AI helps in intervening in a timely, proactive manner to anticipate and minimize adverse health events. Here we explore the pros, cons, and future prospects of predictive AI in healthcare.

Applications
Benefits
Challenges

Applications
- Disease Prevention and Early Intervention: The first and foremost way that Predictive Analytics can help in healthcare is for disease prevention and early intervention. The earlier the diagnosis, the better the chances for recovery and survival. By analyzing patient data like medical records, genetic data, and lifestyle factors, predictive AI can determine the onset or progression of a disease, enabling medical professionals to provide their patients with information on changes in lifestyle, preventative medication, and early screenings to prevent the onset or progression of a disease.
- Personalized Treatment Planning: Genetic information and biomarkers are key to being able to make personalized treatments for patients. Predictive AI makes this much easier to do by predicting patient outcomes based on this information, and their reactions to potential treatment paths. Predictive AI can optimize outcomes and minimize adverse side effects and complications.
- Hospital Resource Management: A major concern in hospital management is perfect record-keeping and effortless, patient-friendly operations. AI can help by forecasting patient demand, optimizing bed occupancy, and identifying bottlenecks in the workflow like availability of ORs and other medical processes like MRI and CT scans. Predictive AI allows hospitals to effectively allocate resources, like staffing and equipment, to ensure a smooth and easy patient experience. This is especially helpful for patients and loved ones when they are already worried about their health and wellness.
- Population Health Management: Not only can AI predict and analyze data on an individual level, but they can also analyze the data of specific population pockets, identifying high-risk communities, predicting health outcomes, and target interventions. This can reduce the overall cost of healthcare for individuals and improve accessibility, especially for communities that are below the poverty line. This can also help medical providers organize disease management programs, reducing the risk of hospital readmissions, illness complications and exacerbations.
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Benefits

As we have seen through the previous points, Predictive AI can have many benefits for medical professionals and patients alike. Here, we explore them in more detail.
- Early disease detection and prevention can help to identify individuals who are at high-risk of diseases like diabetes and heart disease. Not only does genetic predisposition play a part in this, but also lifestyle factors and medical history. Early measures to mitigate and reduce the likelihood or adverse effects of the disease can be taken by the medical provider to give their patients a long and healthy life.
- Nowadays, individuals are looking to healthcare providers to give them an individualized treatment plan, tailor made to their exact specifications. No longer does the one-size-fits-all method of treatment work. Predictive AI can determine the most effective treatments per the patient’s medical history, lifestyle, and genetics. Medical professionals do not need to try and test methods of cure any longer.
- As a result of these individualized treatment plans, predictive AI can be indirectly responsible for improved patient outcomes. Medication adjustments, lifestyle modifications, and behavioral interventions can improve patient outcomes and satisfactions, and also improve quality of life for at-risk patients.
- Last but not the least, predictive AI can benefit both patients and medical providers at an operational level. For medical institutions, predictive AI can help effectively allocate resources that save time and money, giving patients a smooth and stress-free experience. Patients can also save their costs due to personalized and proven treatment plans, and not spend additionally on unnecessary scans and medications.
Artificial Intelligence in Health Care Market Size Worldwide

Challenges

While there are clearly many benefits of Predictive AI, we also have to keep in mind the challenges of integrating technology into our medical processes. As with any improvements, it is time-consuming and can be confusing. To make informed choices we must be aware of both the pros and cons of any new advancements.
- Firstly, the healthcare system in India is in disarray, and this has been especially proven by the Covid-19 pandemic. Analysis of data is easy when all the data is in one place, but healthcare records in India are fragmented at best and unfinished at worst. Inaccurate and incomplete data can lead to biased and inaccurate predictions, compromising the effectiveness of the predictive model.
- Second, there is always the question of data privacy and security when we talk about data and AI models. Protecting patient data and privacy is paramount, and medical institutions must take utmost care to safeguard this data and adhere to strict regulations when using predictive AI.
- Due to the novelty and familiarity of this technology, predictive AI is still not fully trained to cover the entire spectrum of human data. Therefore, some of their predictions can be biased, leading to unfair or discriminatory outcomes. It is the responsibility of the medical professional to determine whether the AI driven outcome will work for their patient.
- Lastly, as humans we may become over reliant on this technology, leading to depersonalization when dealing with patients. It is easy to forget in the amount of patients that doctors see that each patient is dealing with an unfamiliar and scary situation, and it is important to retain the human touch and sensitivity that AI cannot recreate.
In conclusion, predictive AI holds great promise in the med-tech and healthcare field. While keeping in mind the numerous benefits that predictive AI has, we must also keep in mind the challenges, and strive to achieve a balance between the two, making the patient the center of attention. Patient-centric treatment and care is the responsibility and first priority of every doctor, and should not be affected regardless of any new technologies being introduced.

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How to Craft an Impressive Internship Resume: Step-by-Step Guide

This article has been contributed by Mr. Rajat Vashishta, Career Coach & Founder, Resumod.

The job market has become quite competitive and an internship is the stepping stone for a successful career. As you are applying for your dream internship, your resume should create a good impression on the recruiter. Although, internships look great on resumes securing a great internship starts with a killer resume.

To impress potential employers, a professional resume is a get-go and this is why we’ve puttogether a guide that’ll be helpful to create an internship resume that stands out. Not only does it cover your educational background but also highlights your unique qualifications and experiences.

Whether you are a college student or a recent graduate, an impressive internship resume will turn out to be a game-changer. This comprehensive guide shares a step-by-step guide to creating an impressive resume to capture the attention of hiring managers and help you with the internship of your dreams.

Why an Internship Resume is Essential?
How To Write An Internship Resume In 7 Steps?

Why an Internship Resume is Essential?

Before you delve into the nitty-gritty of resume crafting, it’s important to know how can you make your internship resume outstanding. Although a traditional resume focuses on past experiences, an internship resume talks about your academic success, extra-curricular activities, and relevant skills which primarily focuses on your potential as a future intern.

If you are applying for an internship role, then it is recommended to tailor it specifically to highlight your qualifications that resonate with the employers as it maximizes your chance of getting interviews. If all goes well, then you are hired for the internship opportunity.

How To Write An Internship Resume In 7 Steps?

When it comes to internships, employers know that you might not have years of experience but they look for candidates that have the potential to work with them. The key is to highlight your potential, genuine enthusiasm, and eagerness to learn for the internship. Use each resume section to portray how your qualifications and skills can make you a valuable asset to the organization.

Step 1: Preparing Your Content

The first step in the process of crafting an impressive internship resume is to gather and organize your content. Start by compiling the relevant experiences, skills, and achievements, then align them according to the requirements set for the internship position you want to target. Once you are clear on that, finalize the structure and format of the resume and ensure that it effectively highlights your accomplishments and strengths.

Internship Trends

Step 2: Craft a Compelling Summary

Your resume’s summary or objective serves as your introduction to potential employers, providing them with a brief overview of your career goals, interests, and relevant experiences.

Craft a compelling summary or objective statement that succinctly highlights your strengths and aligns with the internship opportunities you’re pursuing. Keep it concise yet impactful, allowing hiring managers to quickly grasp your qualifications and motivations.

Step 3: Showcase Your Academic Achievements

A strong educational background and good academic achievements are crucial when it comes to your resume and they can demonstrate your persistence in front of potential employers. Ensure you organize your educational information clearly and highlight the relevant projects, and academic records to showcase your excellence and commitment to learning and growing.

Step 4: Voluntary and Extracurricular Work Experience

Good academic records are essential but voluntary and extracurricular experience is an added benefit on your resume. Employers today believe that it’s essential that a candidate can’t be judged based on just good academics but a practical experience through volunteer work or extracurricular activities is a great to understand more about the candidate. List relevant volunteer work or extracurricular activities that emphasize the transferable skills you’ve developed and how they align with the internship roles you’re interested in following.

Step 5: Skills Demonstration

Candidates today must possess both hard and soft skills that comply with the internship and employers choose such candidates that have both the skills. Identify and list the skills you possess for the internship role comprising of technical skills, languages, and certifications, which are essential to the role you’re applying for. Mention achievements that demonstrate your skills in the resume.

Step 6: Add Additional Sections for Enhancement

To make your resume even better, add sections that showcase your unique strengths, qualifications, and experiences as it’ll enhance your chances to get the role. Highlight the experiences, projects, and leadership roles you have taken while in college which further provides the managers a comprehensive understanding of your capabilities and background. If you have links to a personal website or social media page that demonstrate your talent, then be sure to mention it in your resume giving additional information to the employers.

Step 7: Polish Your Resume

Every important detail has been on your resume and it’s time to submit it. But before you submit it, take some time to review and polish it giving it a professional touch. Ensure that there are no grammatical, spelling, or formatting errors; also make it Applicant Tracking System (ATS) friendly as it maximizes your chance of passing through the initial screenings. Make sure you maintain consistency in font, formatting, and style throughout it so it appears polished and cohesive to potential employers.

In summary, crafting an impressive internship resume is an essential step toward securing internship opportunities and starting your professional journey. By following this step-by-step guide, you’ll be well-equipped to create an outstanding resume that effectively highlights your qualifications and positions you as the top contender for internship positions. Try out these tips into action and take the first step towards achieving your career goals.

Follow these tips to get your first internship!

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How Mobicule Utilise AI to Improve Debt Collection Process

This article has been contributed by Siddharth Agarwal, Founder and Managing Director, Mobicule Technologies Pvt Ltd.

The recovery of loans is frequently the top priority for lenders in the field of lending because the risk of defaulting is constant. Lenders are increasingly using technology-driven solutions to help in the recovery process as delinquency rates rise, as long as they stay within the bounds of legal regulations.

Today’s digital age is characterized by a discernible trend away from traditional methods and toward digital services and client interactions. This change is especially noticed in India, where a new frontier in debt collection techniques is being shaped by the adoption of Artificial Intelligence (AI) and machine learning (ML). AI has become a crucial component of debt collection operations, providing opportunities for process optimization and streamlining. Integrating AI and ML technology has become imperative due to the increasing demand for quick and effective debt resolution solutions. These developments are radically changing the dynamics of interactions between lenders and borrowers, which will eventually result in better debt recovery outcomes.

Mobicule is leading the way in this technological revolution with its all-inclusive debt resolution system that integrates AI and ML technologies. Through the use of these cutting-edge technologies, Mobicule can classify consumers into different risk groups according to a wide range of factors, such as past banking habits and borrower’s past payment patterns. This division makes it easier to create customized plans meant to reduce the risks involved with debt recovery. AI-powered solutions are excellent at focusing on particular customer groups via the best possible channels of communication, which reduces the workload for both the bank and the borrowers. Our platform can also detect early warning signs of possible defaults by using predictive analytics, which lowers bounce rates and prevents possible financial losses.

Lenders are provided with actionable insights through real-time analytics and customizable dashboards, which facilitate the development and execution of efficient debt resolution plans. Automation features reduce errors and delays that come with manual operations by streamlining communication procedures. Examples include sending payment reminders by SMS, email, WhatsApp, voice bot, and IVR.

Lenders must make sure that all correspondence complies with legal compliance requirements. AI enables omnichannel interaction by using data to start conversations through favoured channels and at the best moments, therefore maximizing communication tactics. Mobicule’s AI-powered technology customizes resolution techniques to each borrower’s unique scenario, maximizing success, in contrast to traditional one-size-fits-all approaches. The system also effectively handles massive amounts of data, classifying them into relevant legal avenue and expediting the debt resolution procedure. The platform is built intelligently and can be used across delinquency cycle which are pre, early, mid and late delinquency. To sight an example the platform can automate the entire settlement process without any manual intervention if it falls within mandates removing any bias in the processing.

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Voice bots powered by AI examines communication between borrowers and agents to provide a more comprehensive grasp of the attitudes and preferences of borrowers. In a similar vein, chatbots driven by AI enable direct communication between borrowers and lenders, guaranteeing prompt responses and improved client experiences.

Artificial intelligence (AI) technologies also provide enhanced security measures by spotting trends that point to previous fraudulent activity and identifying possible threats ahead of time. The re-engagement algorithms powered by AI are essential for promoting borrower compliance since they give priority to contacting borrowers who have interacted with payment links but have not yet fulfilled their obligations. Artificial intelligence (AI) is transforming debt collection procedures by increasing productivity of the team, improving borrower’s experience, working on the engagement rate and eventually deriving better results for lenders.

Mobicule is a pioneer in products focused on mobile field force and has successfully deployed and managed some of the largest mobile field force implementations across Sales & Distribution, Telecom, and BFSI. With its experience and domain knowledge now spanning 2 decades it has established its niche in Debt Collection, KPI & Gamification, and Digital KYC integrated end to end with internal (core banking, EPR, CRM) and external systems (credit bureaus, govt portals). Backed by a strong R&D and adopting the latest and upcoming technologies it has managed to establish a niche market position. Mobicule’s steadfast vision and commitment to add value to its customers, employees, and all its stakeholders have put it on a rapid growth path to attain a leadership position in the business domains it caters to.

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How to Start B2B ABM Under a Scrappy Budget?
This article has been contributed by Samyak Tripathi, Account Based Marketing Expert at Grux.digital.

ABM has become a category on its own in the last couple of years, a vast pool of tech, services, and communities have come into the picture,

It has evolved into a priority for anyone selling enterprise and mid-market tech.

Many marketers believe it to be a fluff narrative taken by software vendors while some see it as a new ‘channel’ to pump up their pipeline numbers.

Let’s dissect B2B ABM in 2024, one step at a time.

Why ABM? The Real Reason
The B2B Tech Marketing Landscape in 2024
Complex Enterprise Digital Footprints
Privacy Laws & Marketing Restrictions (Put on your Glasses for this one)
How to Get Started With ABM on a Low Budget?
Where Can Things Get Messy?
Why ABM? The Real Reason

You may have seen a lot of case studies on your social media feeds flashing ABM as the new way that fixes everything wrong with B2B marketing in the last decade.

Far away from narratives, a mix of market shifts and marketing constraints have led to the ABM wave…

Account Based Marketing

The B2B Tech Marketing Landscape in 2024

ABM and SaaS go hand-in-hand and pretty much everything has been said about the SaaS funding winter already, just recapping the story here:
- 2020- Global SaaS Market secures $58.3 Billion in funds
- 2021- Investments rapidly shoot to $136 Billion (Woahh)
- 2022- The winter starts, SaaS raises $109 Billion globally
- 2023- Funding crunch, a drop of 33.3%, $72 Billion raised
On a similar trajectory,
- 2020- The Indian SaaS Market secures around $2.6 Billion in funds
- 2021- SaaS Investments climb to 170% with $4.5 Billion
- 2022- Registers all-time high bagging $6 Billion
- 2023- Investments take a steep drop of 68% to $1.3 Billion
Global SaaS vs Indian SaaS Revenue

Note, that SaaS revenues and valuations have still been predicted to grow steadily from 2025.

Why did I waste a minute to lay down the obvious?

So that we can clear this out right away:
- There are exponentially more alternatives in any software category today
- Marketers have less cash and time but need to ensure more bottom-line predictability
Complex Enterprise Digital Footprints

Enterprise Digital Footprints are becoming crazy (Yes, it’s more complicated than an eBook, Boss!)

While funds were skyrocketing during the pandemic, it is not alien to assume that people became increasingly active on different digital channels.

From Facebook and Twitter in 2019, most professionals acutely participate in professional communities, networks, and digital events and it is a core component of their decision-making.

A series of dozens of touchpoints, reviews, research, and interactions lead to an enterprise deal opening as opposed to being completely sales and brand-led like before.

Additionally, a big chunk of buyers are taking months longer to convert post the first sales touchpoint if we leave a minority share of ideal buyers aside.

Further on performance marketing, we have observed that scaling profitably is harder to achieve in 2024, thanks to increasing competition.

B2B Sales Cycle

Inferring from this:
- Buying journeys are more complex, longer, and more spread out in 2024
- Complex digital footprints have enabled buyers to go through a lot more touchpoints before they even open a sales conversation
This means the B2B Lead Generation practices that worked great in 2020 like:
- Email Blasts and Nurtures
- Content syndication
- Blindly burning 6 to 7 figures on Google ads…do not cut it anymore!
We can also infer that since marketers need more predictability on bottom-line metrics…Content Marketing, SEO, and PR cannot be the sole contributors as they don’t provide the necessary proactivity on the “Who & How” of Enterprise B2B marketing.

Privacy Laws & Marketing Restrictions (Put on your Glasses for this one)

Among everything else, we are seeing:
- Gmail and Yahoo imposed strict restrictions on mass emailing this February.
- The consistent updates made by the iOS for user privacy.
- Google’s announcement to phase off 3rd party cookies completely this year.
Making it hard for marketers to target, track, and test things at scale.

Hence, there is less room to perform hundreds of tests for months on end to crack a workable message without knowing the intent, fit, and priority of a prospective buyer.

All in all, the spray-and-pray approach is harder to execute in 2024 because of less data, increasing buying complexity, constrained budgets, and general unpredictability in the market.

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How to Get Started With ABM on a Low Budget?

Since we have covered the levers that have given rise to the ABM wave, let’s cover how you can get started with it under a scrappy budget.

You will need:
1. A Target Account List
2. An ABM Strategy
3. A Relevant Measurement Plan
4. A Workable Tech Stack
Getting Started with Target Account Lists

Any ABM program starts with a list. Here is how you create a list of high-intent and, ideal buyers.
- Go to CRM, and look at the Closed Won deals in the last year.
- Look at the Biggest converting deals last year.
- Analyze the fastest converting deals in the last year.
Next, filter these deals/customers by,
- Best Industries/Business Verticals
- Deal Size
- Tech Use Cases/Solution/Modules
- Stakeholders Involved along with their role
One could also go out of their way to do qualitative interviews with best performing Account Executives in Sales to uncover:
- If specific firmographics have been performing better or worse…why?
- Finding common objections of the deals on Hold or Lost at different deal stages vs Closed Won Deals.
- Are they observing any trends in the sales cycles of Closed Won vs Closed Lost Deals?
- Are there any other recent trends to look out for?
- When does a specific Stakeholder need to get involved in the buying process, and how to get them involved?
Now, after this exercise, you should know:
- [ ] Tier-1 Accounts, priority for the next quarter
- [ ] Tier-2 and Tier-3 Accounts, lower in priority…probably the next 2-4 quarters
Planning an ABM Strategy

There are a lot of complicated ABM methods out there, while I like to believe in simplicity.

Here is how I look at it:
1. Warmup
2. Accelerate
3. Close
Ideally, every account in your target list goes through these three steps before closing or expanding to your product/service.

Target List Steps

Ideally, an account would move from Tier-3 to Tier-1 as they engage with your marketing creatives.

Steps an account follows to move from Tier 3 to Tier 1

Let’s take a look at some examples of these campaigns.

Account Warmup Programs

Now for Step 1 of your marketing programs, we need to activate/reactivate accounts by increasing brand awareness and triggering intent to buy among champion stakeholders.
- Personalized Value Propositions
- Event and webinar invitations
- Community Meetups
- Resources to promote downloads
Account Warmup Programs

Pipeline Acceleration Programs

Let’s say you have some accounts warmed up. Now, let’s make sure we enhance buying intent among champion buyers, bring in other influencers-budget holders of the buying committee and accelerate deal velocity.
- Personalized Social Proof for target accounts.
- Objection handling campaigns.
- Multi-threading campaigns to involve influencers early in the buying journey.
- Competitor Comparisons and Positioning.
- Campaigns to highlight ease of onboarding.
Pipeline Acceleration Programs

Relationship Building Programs

Marketing Programs to build further trust and relevance among decision-makers and key influencers.

This usually happens by inviting key stakeholders to participate in marketing activities like:
- Podcast-Webinar Guest Invitations
- LinkedIn Guest Invitations
- Dedicated Product Walkthroughs and Social Proof
Relationship Building Programs

Creating a Relevant Measurement Plan

Creating an account-based marketing measurement plan enables you to manage and communicate short and long-term expectations.
- North Star Metric- The core indicator of your ABM success, this could be…Closed Won Accounts, Qualified Pipeline Coverage, Average Deal Value.
- L1 Metrics- Metrics that lead your progress towards the north star. These could be deal velocity, account penetration, and activation.
- L2 Metrics- Metrics that you might have to start measuring week-by-week. These could be form submissions, event participants, or even target account impressions.
Account-Based Marketing Measurement Plan

A Workable Tech Stack

There exists a $100K ABM Tech Stack, we’ll not go into that…

Let’s find a scrappy tech stack from a first principal basis.

To execute account-based marketing successfully, you need tech for:
- Account Research (To qualify accounts)
- Data Enrichment (To enrich accounts with excessive data on an account and prospect level)
- Personalization (To deliver account-based experiences)
- Data and Operations (To integrate multiple marketing channels seamlessly)
I have curated a list of tech that I love for all of these 4 purposes.

Tech List for Account Research, Data Enrichment, Personalization, Data and Operations

Where Can Things Get Messy?

We have talked a lot about being successful with ABM, let’s talk about what to look out for as Red Flags🚩

a) An Account Wishlist is not enough

The most common failure with ABM is that people stop targeting.

Let me explain, let’s say:
1. You did research on CRM to prepare target account lists, or,
2. You synced up with sales to get a ‘Wishlist of enterprise accounts’, and
3. You start pushing a bunch of campaigns to activate those accounts.
The distinction between targeted marketing and account-based marketing should be clear…they are not the same.

While preparing an account list does allow you to be more proactive, it does not guarantee better results.

An ABM team needs to ensure that target accounts are segmented into both Tiers & Segments to ensure personalized account-based experiences and relevant messaging.

The team also needs to ensure that only accounts with great buying intent and fit get included in ABM programs.

Buying Intent

b) Frequency in ABM is Critical

Marketing frequency is an essential element to define whether you have a solid ABM engine or not.

Frequency tells us if we are knocking on the door enough, it also gives benchmarks on the ideal time and budget for prospects to take a given action in the buying journey.

Most marketing teams have no idea of their frequency for a prospect over multiple channels, and how it impacts deal conversions.

Being aware of the winning ABM frequency is a superpower for any team!

ABM Frequency

c) Stress test ABM by Exploring New Marketing Channels

An ABM team should always be searching for personalized ways to reach potential buyers,

Limiting your ABM strategy to typical marketing channels like digital paid ads and emails could result in missed opportunities.

Laying down some phy-digital channels you could explore for ABM here:
1. Paid Search (Google, Bing)
2. Paid Social (LinkedIn, Meta)
3. Emails
4. Social Selling(Only for engaged accounts)
5. Gifting (Digital+Physical)
6. Direct Mail
7. Events and Roundups (Virtual+Physical)
8. Sponsorships and Promotional Partnerships(Podcasts, Newsletters, Communities)
9. PR Articles(Super targeted)
10. Programmatic and Display Ads
ABM Stress Test

Final Words

BM can be a lot of things, but it’s not a hoax and it’s not a bedtime fairy tale to tell your boss. It doesn’t need to cost $100K and could be executed under a scrappy budget if you know how to stitch the pieces together.

Thanks for reading, cheers!✌

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