ArchivesThe Focus

S01E05: Loyalty and engagement: the new web3 and blockchain paradigm

on Share
The Focus
The Focus
S01E05: Loyalty and engagement: the new web3 and blockchain paradigm
Loading
/ 
Excerpt from episode S01E05 of the "Disruptive Talks" podcast: to see the complete program of the episode, visit this page. 
Subscribe to the podcast so you don't miss any content!

This content is part of the special episode on web3. If the topic interests you, you can find other similar pills:

This week, on our social media LinkedIn And Twitter, and through this episode, we dedicated ourselves to examining the Web3 world and loyalty, analyzing how this paradigm is reformulating the interaction between companies and consumers. While Gabriele offered us an insight into Web3 custody and NFTs with Starbucks' Odyssey program, we will receive this focus next Miro Radenovic, creator of uCollect.me and true architect of the Italian Web3.

In this focus, to stay on topic, I will try to tell you the general context in a few minutes: I will be concise, and I am aware that I will go quickly on some technicalities or mechanisms, so we are always available in case some of you need further information. You can contact us directly on social media or through the page contacts on this site.

HISTORY AND LIMITS OF TRADITIONAL LOYALTY PROGRAMS

They have their roots in commercial history, dating back to the S&H Green Stamps of the late 19th century and continuing with airline mileage programs in the 1980s. Despite their success, traditional loyalty programs have primarily focused on repeat transactions from existing customers, with the goal of maintaining a steady flow of purchases.

In reality, such programs did not focus on loyalty in the strict sense; rather, they have created a system in which the benefits “earned” by clients are contingent on their remaining in the program. A customer's decision to end the relationship with the brand results in the loss of all accumulated benefits. Additionally, traditional loyalty programs often fail to recognize the overall value that customers can bring to a brand, simply incentivizing purchases and fostering a purely transactional relationship that does not reflect authentic loyalty.

As the options available to consumers increase and their attention spans decrease, the need for true loyalty is more pressing than ever. Without focused attention on transformative technologies, new mental models and strategies that drive social change, the lifespan and effectiveness of traditional loyalty programs will decline.

INTRODUCTION TO WEB3 AND ITS ADVANTAGES

In this context, Web3 emerges as an effective alternative. This new way of consuming and interacting on the internet, which replaces credentials with a decentralized wallet and a private key, which uses blockchain, digital assets and virtual environments to offer greater decentralization, transparency and security; it definitely represents a fundamental change in the way users interact with the web, moving from simple consumers of content to owners and managers of their data.

The advantages of Web3 on the user side are multiple and significant: at the center we find the points that you always hear when talking about blockchain: trust, guaranteed by the immutable nature of the blockchain, which allows you to trace and verify every transaction without the need for intermediaries. Transparency, which ensures that every action is visible and verifiable by anyone, thus establishing an environment of greater fairness and honesty. Ownership of personal data returns to the hands of users, who can exercise direct control over their information and how it is used.

THE EVOLUTION TOWARDS WEB3 LOYALTY PROGRAMS

The move to Web3 loyalty programs marks a revolution in the industry, integrating the benefits of Web3 into customer loyalty strategies.

These programs, exploiting tokenization and smart contracts, allow the creation of a completely new system of rewards and incentives, based on the creation of on-chain value and the active participation of consumers.

Customers are no longer tied to a single brand or platform; they can carry and use their tokens and rewards in a much broader ecosystem, promoting a richer and more personalized user experience. Furthermore, the interoperability between different platforms and services amplifies the possibilities of using loyalty points, transforming them into digital assets with a tangible and exchangeable value.
Gamification, which Gabriele had anticipated, becomes a key element, encouraging users to actively participate and interact with the brand in ever new and creative ways. This not only increases engagement and involvement, but also creates a feeling of belonging and co-creation between the brand and its community.

The evolution towards Web3 loyalty programs therefore represents a significant step forward, which promises to strengthen the bond between companies and customers, creating a virtuous circle of loyalty and shared value.

Web3 Customer, created with Adobe Firefly

The power of community and co-creation

I previously spoke about communities: they too are experiencing a renaissance, these technologies allowing individuals with common interests and aspirations to be connected with an ease never seen before, a true tangible value for brands.
The focus shifts from a profit-oriented digital marketing logic, typical of Web2, towards an authentic and genuine exchange between people, recalling the pioneering spirit of the early days of the web.

In this context, communities are not simply recipients of advertising messages, but become active partners in the co-creation of value.

In Web3, and we will focus on this shortly, well-structured tokenomics (or Token Economics) lays the foundations for ambitious projects, allowing brands to collaborate directly with their community without the need for advertising intermediaries. This approach not only reduces customer acquisition costs, but also strengthens control over consumer relationships. Blockchain provides the platform for trust, transparency and ownership, essential elements for unlocking authentic loyalty that cannot be achieved through traditional frameworks alone.

Practical applications and case studies

  1. NFT program by Nike (.SWOOSH)
    With a strategy that aims to involve the community in the co-creation and trade of digital collectibles, with royalties redistributed to the creators
  2. NFT program by Starbucks (Odyssey Points)
    Starbucks Odyssey generated 172.5k in royalties from its NFTs, with secondary trading volume of 2.3M.
  3. AirBaltic, the Latvian airline, introduced Planies, a collection of NFTs that serves as a loyalty program.
  4. Warner Bros is experimenting with NFT programs tied to popular movies and TV shows. It generated significant buzz and interest with the release of NFT bundles for the movie “The Batman,” with 2,000 NFTs selling for between $30 and $100 in just a short time.
  5. Bleacher Report Rewards (B/R W2E)
    Bleacher Report launched B/R W2E (Watch to Earn), a tokenized community activation program that elevates the sports spectator experience. Participants can earn tokens by interacting with real-time trivia, which can then be used to access premium content and exclusive events or traded in the Bleacher Report marketplace. This rewards model, powered by Web3 technology, uses smart contracts to create and distribute native tokens that can be earned and redeemed, demonstrating the potential for gamification and interaction in loyalty.
    Bleacher Report launched B/R W2E, a tokenized community activation program, which saw high participation from users. With the aim of elevating the sports spectator experience, the program demonstrated the potential of gamification and interaction in strengthening customer engagement and loyalty.

These projects illustrate how Web3 loyalty programs can take different forms and how they can improve the brand experience, encouraging customer loyalty and engagement through the use of innovative technologies.

The future of Web3 loyalty programs

All this represents not only a technological transition, but also a cultural and economic paradigm shift.
The adoption of models based on blockchain and tokenization opens up new scenarios for the creation of value and the active participation of consumers. It is expected that these Web3 loyalty programs can offer a democratization of engagement, where loyalty is no longer one-way, but a two-way street.

In the future, we may see an evolution where loyalty is no longer solely about transactions, but also about digital identity, shared governance and greater user control over their data.

This could translate into greater bargaining power for consumers and a more balanced relationship with brands. Furthermore, the ability to transfer and exchange tokens between different platforms could generate a more fluid and interconnected loyalty ecosystem, with significant impacts on retention and new customer acquisition.

To conclude, I would say that the evolution of loyalty programs in the Web3 context leads us to reflect not only on the economic implications, but also on the philosophical and social ones. The transition towards Web3 invites us to reconsider our relationship with the consumer, in the right continuation of the growing digital transformation that is taking place in all sectors.

This reflection opens up new questions: how will the concept of value change in the context of loyalty based on community and co-creation?
How can decentralization influence the power dynamic between consumers and brands?
And again, how will marketing strategies evolve in response to these new forms of loyalty?

As we explore these questions, I invite you to listen this interesting discussion on today's issues with Miro Radenovic, an expert who is shaping the future of Italian Web3. His vision and experiences will provide us with further insights into how loyalty programs are evolving and how we can be an active part of this revolution.

Insights

Useful links:

Go directly to others Focus:

S01E04: Hashrate record for Bitcoin, what it means and what implications for the network

on Share
cover BTC s01e04 Hashrate
cover BTC s01e04 Hashrate
The Focus
S01E04: Hashrate record for Bitcoin, what it means and what implications for the network
Loading
/ 
Pill extracted from episode S01E04 of the "Disruptive Talks" podcast: see the complete program of the episode on this page.

Great news has come to us these days from the world of Bitcoin mining: its hashrate has reached a new all-time high!

This new record was recorded precisely on October 12, 2022, when the hashrate reached the record figure of 255 exahash per second according to data from BTC.com.

This is a significant increase compared to levels at the beginning of the year, when the hashrate hovered around 190 exahash per second.

What is hashrate and why is it important

But what exactly is hashrate and why is it so relevant to Bitcoin? Let's try to understand it together.

Simply put, hashrate measures the overall computing power of the Bitcoin network. That is, how many billions of billions (or exa) of hash operations can all miners perform every second to validate transactions and generate new blocks.

The higher the hashrate, the more calculations are performed every second, and the more secure the network is from potential attacks.

How mining works

Mining is the process by which blocks of transactions are validated and new bitcoins are generated. Let's try to understand how it works in simple words:

  • Miners around the world compete with each other to solve complex mathematical problems called “proof-of-work”.
  • Whoever manages to solve the problem first can validate and add a new block of transactions to the blockchain.
  • As a reward, he receives a certain number of bitcoins newly generated by the protocol.

Simplified explanation for beginners

Imagine the blockchain as a large digital ledger where all bitcoin transactions are recorded.

To add each page of this book (a block), miners must solve complex mathematical puzzles, using very powerful computers.

Whoever succeeds first can write the new page and receive bitcoin as a reward. In this way transactions are validated in a secure and decentralized way.

Progressive increase in difficulty

As miners and the overall computing power of the network increase, the difficulty of the mathematical problems to be solved increases accordingly.

This serves to keep the average time between blocks constant, around 10 minutes.

It is very important that the overall hashrate is high, because more computing power equals greater network security.

Hashrate as a key indicator

The hashrate is therefore a key indicator of the health and value of the Bitcoin network.

When it drops sharply, such as due to a mass shutdown of miners in a region, the price of BTC usually suffers as investors fear for the security of the network.

On the contrary, a growing hashrate like in this period could mean that more and more miners believe in the project and invest in new specialized hardware (ASIC), making the network stronger and more secure over time.

Other technical metrics to monitor

In addition to hashrate, there are other technical metrics to keep an eye on to assess the health of the Bitcoin network:

  • The number of active nodes, which indicates the level of decentralization and diffusion of the Bitcoin protocol. The more nodes there are, the more resilient and difficult the network is to attack or censor.
  • The Lightning Network payment nodes and channels, indicating the scalability and adoption of Bitcoin as a fast payment system.
  • The average transaction fees, which indicate network congestion.
  • The number of active wallets, showing real user adoption.

By monitoring these indicators we can understand the true health and prospects of Bitcoin as a digital monetary network.

High hashrate can also have negative effects

Increased mining difficulty

However, a hashrate that is too high can lead to an excessive increase in mining difficulty. This could make mining less profitable for smaller miners, heightening the risk of centralization.

Network congestion

Furthermore, if the hashrate grows too quickly, the network may not be able to adequately handle the increase in transactions that need to be validated. This would lead to network congestion with longer waiting times and higher fees for users.

High energy consumption

Finally, prohibitive hashrate levels require enormous energy expenditure to power the thousands of ASICs needed. This raises concerns of environmental sustainability and fuel efficiency.

For these reasons, hashrate growth must be monitored carefully, balancing security and decentralization of the network with other factors.

We will continue to monitor the evolution of this and other technical metrics to assess the health of Bitcoin as an innovative digital monetary system.

Further information:

Go directly to others Bitcoin Point:

Sorry, episodes not found

S01E04: Cybersecurity – present and future of AI to detect cyber threats and attacks

on Share
Punto AI s01e04 Security Cover
Punto AI s01e04 Security Cover
The Focus
S01E04: Cybersecurity - present and future of AI to detect cyber threats and attacks
Loading
/ 
Pill extracted from episode S01E04 of the "Disruptive Talks" podcast: see the complete program of the episode on this page.

Today we live constantly connected online, for work, study, entertainment. Our lives are immersed in digital. But this unfortunately also attracts hackers. In Italy alone, serious cyber attacks grew by 169% in 2022 (Clusit Report 2023)!

To protect ourselves, we must use the most advanced tools. And this is where artificial intelligence makes a difference, with its ability to quickly process large amounts of data and spot anomalies.

How AI can help us

Thousands of pieces of information are generated in our systems every second. It is impossible for a human to monitor everything and understand if there is an attack. AI, however, succeeds.

With machine learning algorithms trained on normal network models, AI can quickly identify anomalous traffic, suspicious access, infected files. And thus block an attack in the bud, before it causes damage!

But not only. Today, AI is also used to thoroughly analyze software and applications, looking for flaws in codes that could be exploited by hackers. By finding these vulnerabilities, systems can be hardened.

Threat detection and vulnerability assessment

Let's look in more detail at two main ways in which AI is used in cybersecurity:

Threat detection

Threat detection, or the identification of cyber threats, is one of the most widespread uses of AI in the cybersecurity field.

Artificial intelligence algorithms are trained on large quantities of data to learn to recognize the typical "signatures" of different types of cyber attacks and malware. This allows them to analyze network traffic in real time and identify anomalous patterns that could indicate an attack in progress.

For example, if they detect an anomalous spike in traffic coming from a certain IP address, or they notice repeated attempts to access a server with different passwords, they can classify these activities as suspicious and send a real-time alert to security analysts.

AI-based threat detection systems integrate supervised, unsupervised and deep learning machine learning techniques to process terabytes of network data, detecting increasingly faster and more sophisticated threats.

Compared to traditional signature-based systems, AI can also identify zero-day attacks and unknown malware, thanks to its ability to recognize anomalies in traffic patterns.

The vulnerability assessment

Vulnerability assessment, on the other hand, aims to identify latent flaws and weaknesses in systems before they are exploited in an attack.

Again, AI is used to analyze large amounts of data relating to network configurations, application source code, installed software versions, etc., in search of known or unknown vulnerabilities.

For example, machine learning algorithms can scan millions of lines of code to identify bugs, logical flaws, backdoors, or other flaws that could be exploited by hackers. While deep learning techniques help analyze system and network configurations to find weaknesses in terms of permissions, authentication and encryption.

By proactively identifying these vulnerabilities, organizations can patch them before they are exploited, dramatically reducing the attack surface. Automating vulnerability assessment using AI greatly accelerates and enhances this crucial cybersecurity process.

The future of AI in cybersecurity

AI will be increasingly integrated into IT infrastructures as a central component of security. Machine learning algorithms will allow us to process and cross-reference enormous quantities of data in real time, identifying increasingly faster and more sophisticated threats.

Response automation will become more advanced, with AI agents capable of applying patches, isolating compromised components, modifying configurations to stop attacks in real time.

Hackers will also use AI, so security experts will need to constantly update themselves on new techniques. Ethical, transparent and inclusive AI systems will be needed to avoid prejudice and abuse.

In conclusion, the future of AI in cybersecurity is full of opportunities but also complex challenges. With forward-thinking minds and open hearts, we can build a safer and more prosperous digital tomorrow for all.

Insights

Go directly to others AI point:

Sorry, episodes not found

S01E04: CRISPR and the new frontiers of Genome Editing – Potential and ethical implications

on Share
FOCUS S01E04 - CRISPR
FOCUS S01E04 - CRISPR
The Focus
S01E04: CRISPR and the new frontiers of Genome Editing - Potential and ethical implications
Loading
/ 
Pill extracted from episode S01E04 of the "Disruptive Talks" podcast: see the complete program of the episode on this page.

What is CRISPR

CRISPR is the acronym for Clustered Regularly Interspaced Short Palindromic Repeats, a family of bacterial genetic elements discovered in 1987 but whose function was only recently understood. These are short DNA sequences that contain the "instructions" to recognize and cut specific sequences of viral DNA during infection, as a defense mechanism of the bacteria.

The discovery

CRISPR was first observed in 1987 by Yoshizumi Ishino and colleagues in E. coli. However, its function remained unknown until the 2000s, when several research groups realized the role of CRISPR-Cas in bacterial immunity.

In 2005, three independent teams published their findings on CRISPR as an adaptive immune system. Pioneering researchers included Alexander Bolotin, Eugene Koonin, John van der Oost and Francisco Mojica.

The turning point came in 2012, when Emmanuelle Charpentier and Jennifer Doudna understood how to exploit CRISPR-Cas9 to modify DNA in a targeted way, laying the foundations for an innovative genome editing technique.

How CRISPR works

Simply put, the CRISPR system works a bit like the “genetic scissors” we use to cut DNA. Let's imagine we have a pair of special scissors, equipped with a mini-computer that acts as a GPS and tells the scissors the exact point where to cut.

This mini-computer is made up of a short sequence of RNA, called the “guide sequence”. The enzyme that acts as scissors is the Cas9 protein. By modifying the guide RNA sequence, we can precisely direct the Cas9 scissors towards the gene we want to cut, a bit like setting a destination on the GPS.

Once Cas9 has cut the DNA at the programmed point, the cell activates its repair mechanisms, which mend the cut. However, we can exploit this process by simultaneously providing the correct piece of DNA, which will be incorporated in place of the defective sequence. It is as if, while the cell mends the tear, we replace a torn piece of fabric with a new one. In this way we are able to correct the mutations underlying various diseases.

Impact of CRISPR

CRISPR has revolutionized biomedical research because it allows the genome to be edited with unprecedented precision, versatility and simplicity. The applications are countless and are leading to concrete progress in various fields.

For example, CRISPR is used to create animal models, such as mice or monkeys, with genetic mutations identical to those that cause human diseases such as Huntington's disease or cystic fibrosis. These "model organisms" allow us to study the mechanisms of diseases in more depth and test new drugs or gene therapies.

In the field of somatic gene therapy1, CRISPR has been successfully used to correct mutations underlying serious diseases such as beta-thalassemia, sickle cell anemia and Duchenne muscular dystrophy by modifying the DNA of patients' somatic cells.

CRISPR also makes germline editing possible2 and embryos. For example, harmful genetic mutations could be corrected before a child is born, ensuring a life free from serious diseases. However, the long-term impacts of such interventions are unpredictable and raise delicate ethical questions.

Ethical implications

While correcting genetic defects in embryos could eradicate terrible diseases, modifying the germ line can transmit unexpected alterations to descendants, with implications that go far beyond the individual. Additionally, cosmetic or human enhancement applications raise concerns about eugenics.

In the future, if properly regulated, CRISPR could truly defeat debilitating genetic diseases before birth. But given the unpredictability of long-term effects, caution is in order.

This is why in 2015 an international group of scientists called for a moratorium on the use of CRISPR in human embryos intended for implantation, due to the risk of reckless approaches and unwanted effects. Despite this, in 2018 the Chinese researcher He Jiankui announced the birth of the first children with DNA modified via CRISPR, causing controversy and his sentence to 3 years in prison.

The debate has also been heated in Italy. In 2017, the National Committee for Bioethics expressed its opinion recommending a ban on any experimentation on embryos, while it considered basic research on germ cells not intended for pregnancy to be admissible.

Possible topics for debate

It will be important, at the right time, to open a public debate to decide where to draw insurmountable boundaries on the use of CRISPR technology.

For example, is it right to modify the human embryo? And if one day it were possible to create genetically modified humans, would we allow it?

The issues that could lead to debate following the possible commercial development of this science are numerous:

  • Eugenics derives, with "tailor-made children" selected by parents
  • Social inequalities, with high cost access and more advanced countries potentially excluding developing countries
  • Discrimination against non-genetically modified people
  • Gene doping in sport, with genetically enhanced athletes
  • Militarization and use to create biological weapons

These are ethically complex questions, which have no easy answers.

Conclusion

In conclusion, CRISPR is a technology with enormous potential but which raises delicate ethical questions.

If addressed with an open spirit, empathy and rationality, these questions can lead us to reconcile scientific progress and the protection of human dignity. The future of CRISPR is in our hands. It is up to us to shape it wisely for the common good.

Insights

1 Somatic gene therapy: consists of modifying the genome of somatic cells, i.e. cells that do not contribute to the germ line. The changes made are not passed on to offspring. It is used to treat hereditary diseases by correcting defective DNA in the patient's tissues and organs.

2 Germline: refers to primary germ cells (eggs and sperm) and their precursors. They contribute to the formation of embryos and the transmission of hereditary characteristics to offspring. Germline modification, unlike the somatic one, can transmit the genetic alterations introduced to descendants. This raises ethical concerns as the consequences could extend to future generations.

Useful links:

Go directly to others Focus:

S01E03: Prompting Tips #01 / Swapping Roles with ChatGPT

on Share
The Focus
The Focus
S01E03: Prompting Tips #01 / Swapping Roles with ChatGPT
Loading
/ 
Pill extracted from episode S01E03 of the "Disruptive Talks" podcast: see the complete program of the episode on this page.

This is the first article in the new “Prompting Tips” series, in which we will share useful prompt engineering techniques to get the most out of ChatGPT. Let's start with a very effective strategy: the “expert asking questions” technique.

But let's see what prompt engineering is in order to start this series with a basic minimum.

What is prompt engineering

Prompt engineering is the practice of carefully designing the textual inputs (called prompts) provided to generative AI models, such as ChatGPT, to guide them towards the desired output.

This is a highly sought-after emerging skill, requiring creativity and in-depth knowledge of the capabilities and limitations of AI models. The aim is to "instruct" the AI through specific prompts to make it produce useful, coherent and quality content.

Some common prompt engineering techniques include:

  • Break the request into logical sub-questions that guide the AI's reasoning (chain-of-thought prompting)
  • Provide the AI with a fictional context in which it must operate, for example by pretending to be an expert with many years of experience
  • Explicitly instruct the AI to ask clarifying questions before responding, to ensure it understands the request
  • Provide examples of ideal responses to train the AI on the desired level of detail and quality
  • Repeatedly correct the AI when it produces incorrect or inconsistent output, to refine its capabilities

Effective prompt engineering therefore requires fully understanding the capabilities of the AI model used and leading it step by step towards the desired goal. It is a very rapidly evolving field, crucial to making the most of the potential of generative AI.

The “expert asking questions” technique

This technique involves transforming ChatGPT into an "expert" in a specific sector, then telling him to ask all the questions necessary to better understand our request, before responding.

Through a specific prompt, we "transform" ChatGPT into an expert in a specific field, and above all we tell him to ask us all the questions necessary to fully understand our request before responding. This way we will get a much more accurate answer.

Step 1: Make ChatGPT an expert

We open a new chat window. If we use an already open chat, we start the prompt with “IGNORE ALL INSTRUCTIONS BEFORE THIS” to reset the previous instructions given to the chatbot.

After that, we tell ChatGPT his new job, specifying that he will have to act as a true expert in that field, as if he had worked in it for over 20 years. Adding a time window helps improve the quality of the output.

Finally, we give the precise task that he will have to carry out.

The prompt will be like this (the part to be modified is in red):

IGNORE ALL INSTRUCTIONS PRIOR TO THIS ONE. YOU ARE A [craft] . YOU HAVE BEEN AN EXPERT IN THE TRADE FOR OVER 30 YEARS. YOUR TASK IS NOW [instructions] .

Applied to an example:

IGNORE ALL INSTRUCTIONS PRIOR TO THIS ONE. YOU ARE A BUSINESS DEVELOPPER IN THE IT SECTOR. YOU HAVE BEEN AN EXPERT IN THE TRADE FOR OVER 30 YEARS. YOUR TASK IS TIME TO FIND INNOVATIVE LEAD ACQUISITION TECHNIQUES FOR A SOFTWAREHOUSE

Step 2: Swap roles

We now arrive at the key point of this prompt engineering strategy: insert in the instructions for ChatGPT the explicit request to ask clarification questions before answering (we specify that, at the time of the preparation of this pill, only some models such as ChatGPT 4 were capable of swap roles).

This can be expressed for example like this:

YOU SHOULD ALWAYS ASK QUESTIONS BEFORE ANSWERING, SO YOU CAN FOCUS BETTER ON WHAT THE PERSON IS LOOKING FOR BEFORE PROVIDING MORE ACCURATE ANSWERS

This simple addition guides ChatGPT to actively delve into the initial request, instead of responding perfunctorily. The AI will behave as a human expert would: it will ask questions to investigate details, acquire context, understand objectives and constraints.

In this way the model can produce a much more centered, accurate and useful response to the user's real needs.

This is a powerful prompt engineering technique for two reasons:

  • It reverses roles, transforming AI from a simple responder to an active consultant who investigates the request.
  • Take advantage of ChatGPT's tendency to strictly adhere to the instructions given in the prompt.

Ultimately, this simple tips allows you to get the most out of AI responses in terms of quality, detail and precision. It is one of the most effective techniques that every prompt engineering expert should master.

To conclude, here is an example conversation with this prompt. The user prompt is in blue, the ChatGPT response is in red.

 YOU ARE A BUSINESS DEVELOPPER. YOU HAVE BEEN AN EXPERT IN THE TRADE FOR OVER 30 YEARS. YOUR TASK IS TIME TO FIND INNOVATIVE LEAD ACQUISITION TECHNIQUES FOR A COMMERCIAL BANK. YOU SHOULD ALWAYS ASK QUESTIONS BEFORE ANSWERING, SO YOU CAN FOCUS BETTER ON WHAT THE PERSON IS LOOKING FOR BEFORE PROVIDING MORE ACCURATE ANSWERS
I can suggest innovative lead acquisition techniques for a commercial bank. Before formulating a plan, I have a few questions: 1. What marketing channels are you currently using? 2. Do you have a specific budget for lead acquisition initiatives? 3. What methods have you used in the past and how did they work? 4. What differentiates you from your competitors? 5. What are your short- and long-term goals for lead acquisition? Once I have this information, I will be able to propose a more precise and relevant plan.
 - from 15 to 45 years, with preferences for young people - an account card with greater protection for minors - emailing, radio, digital campaign CPM - 10k - nothing - we are new/recent - establish a strong customer base
Thanks for the replies. Here are some innovative lead acquisition techniques based on your specific needs: 1. Social Media Advertising: Leverage the use of social media platforms such as Facebook, Instagram and LinkedIn. These platforms have powerful targeting tools that allow you to reach your target age group (15-45 years old). 2. Content Marketing: Create and share relevant and valuable content (blogs, infographics, videos) to attract the target audience. You can integrate a "call-to-action" at the end of your content to guide potential customers towards your offer. 3. SEO: Optimize your website for search engines by focusing on keywords related to banking for minors. This helps attract organic traffic to your website. 4. Digital Events: Organize webinars or live Q&As on the benefits of your card for minors. This is a great way to interact directly with your audience and collect leads. 5. Collaboration with influencers: Collaborate with influencers popular among young people. They can help promote your paper, reaching a wider audience of young people. 6. Strengthen email marketing: Segment your campaigns to reach the right people with the right message. Remember to always monitor metrics to understand which techniques work best for your company.

Go directly to others AI point:

Sorry, episodes not found