Рубрика: AI

An IBM X-Force research project led by Chief People Hacker Stephanie “Snow” Carruthers showed that phishing emails written by humans have a 3% better click rate than phishing emails written by ChatGPT.

The research project was performed at one global healthcare company based in Canada. Two other organizations were slated to participate, but they backed out when their CISOs felt the phishing emails sent out as part of the study might trick their team members too successfully.

Jump to:

• Social engineering techniques were customized to the target business
• How threat actors use generative AI for phishing attacks
• How to protect employees from phishing attempts at work

It was much faster to ask a large language model to write a phishing email than to research and compose one personally, Carruthers found. That research, which involves learning companies’ most pressing needs, specific names associated with departments, and other information used to customize the emails, can take her X-Force Red team of security researchers 16 hours. With a LLM, it took about five minutes to trick the generative AI chatbot into creating convincing and malicious content.

SEE: A phishing attack called EvilProxy takes advantage of an open redirector from the legitimate job search site Indeed.com. (TechRepublic)

In order to get ChatGPT to write an email that lured someone into clicking a malicious link, the IBM researchers had to prompt the LLM. They asked ChatGPT to draft a persuasive email (Figure A) taking into account the top areas of concern for employees in their industry, which in this case was healthcare. They instructed ChatGPT to use social engineering techniques (trust, authority and proof) and marketing techniques (personalization, mobile optimization and a call to action) to generate an email impersonating an internal human resources manager.

Figure A

Next, the IBM X-Force Red security researchers crafted their own phishing email based on their experience and research on the target company (Figure B). They emphasized urgency and invited employees to fill out a survey.

Figure B

The AI-generated phishing email had a 11% click rate, while the phishing email written by humans had a 14% click rate. The average phishing email click rate at the target company was 8%; the average phishing email click rate seen by X-Force Red is 18%. The AI-generated phishing email was reported as suspicious at a higher rate than the phishing email written by people. The average click rate at the target company was low likely because that company runs a monthly phishing platform that sends templated, not custom, emails.

The researchers attribute their emails’ success over the AI-generated emails to their ability to appeal to human emotional intelligence, as well as their selection of a real program within the organization instead of a broad topic.

How threat actors use generative AI for phishing attacks

Threat actors advertise tools such as WormGPT, a variant of ChatGPT that can answer prompts that would be otherwise blocked by ChatGPT’s ethical guardrails. IBM X-Force noted that “X-Force has not witnessed the wide-scale use of generative AI in current campaigns,” despite tools like WormGPT being present on the black hat market.

“While even restricted versions of generative AI models can be tricked to phish via simple prompts, these unrestricted versions may offer more efficient ways for attackers to scale sophisticated phishing emails in the future,” Carruthers wrote in her report on the research project.

SEE: Hiring kit: Prompt engineer (TechRepublic Premium)

On the other hand, there are easier ways to phish, and attackers aren’t using generative AI very often.

“Attackers are highly effective at phishing even without generative AI … Why invest more time and money in an area that already has a strong ROI?” Carruthers wrote to TechRepublic.

Phishing is the most common infection vector for cybersecurity incidents, IBM found in its 2023 Threat Intelligence Index.

“We didn’t test it out in this project, but as generative AI grows more sophisticated it may also help augment open-source intelligence analysis for attackers. The challenge here is ensuring that data is factual and timely,” Carruthers wrote in an email to TechRepublic. “There are similar benefits on the defender’s side. AI can help augment the work of social engineers who are running phishing simulations at large organizations, speeding both the writing of an email and also the open-source intelligence gathering.”

How to protect employees from phishing attempts at work

X-Force recommends taking the following steps to keep employees from clicking on phishing emails.

• If an email seems suspicious, call the sender and be sure the email is really from them.
• Don’t assume all spam emails will have incorrect grammar or spelling; instead, look for longer-than-usual emails, which may be a sign of AI having written them.
• Train employees on how to avoid phishing by email or phone.
• Use advanced identity and access management controls such as multifactor authentication.
• Regularly update internal tactics, techniques, procedures, threat detection systems and employee training materials to keep up with advancements in generative AI and other technologies malicious actors might use.

Guidance for stopping phishing attacks was released on October 18 by the U.S. Cybersecurity and Infrastructure Security Agency, NSA, FBI and Multi-State Information Sharing and Analysis Center.

Subscribe to the Cybersecurity Insider Newsletter

Strengthen your organization's IT security defenses by keeping abreast of the latest cybersecurity news, solutions, and best practices.

Delivered Tuesdays and Thursdays Sign up today

Image by Editor

If you’ve been using Python for a while, you’ll have likely stopped using the old-school format() method to format strings. And switched to the more concise and easy-to-maintain f-Strings introduced in Python 3.6. But there is more.

Since Python 3.8 there are some nifty f-string features you can use for debugging, formatting datetime objects and floating point numbers, and much more. We’ll explore these use cases in this tutorial.

Note: To run the code examples you need to have Python 3.8 or a later version installed.

1. Easier Debugging

When you’re coding, you might use print() statements to print out variables to verify if their values are what you expect them to be. With f-strings, you can include variable names and their values for easier debugging.

Consider the following example:

length = 4.5  breadth = 7.5  height = 9.0    print(f'{length=}, {breadth=}, {height=}')

This outputs:

Output >>> length=4.5, breadth=7.5, height=9.0

This feature is especially helpful when you want to understand the state of your variables during debugging. For production code, however, you should set up logging with the required log levels.

2. Pretty Formatting Floats and Dates

When printing out floating point numbers and datetime objects in Python, you’ll have to:

• Format floating point numbers to include a fixed number of digits after the decimal point
• Format dates in a particular consistent format

F-strings provide a straightforward way to format floats and dates according to your requirements.

In this example, you format the price variable to display two places after the decimal point by specifying {price:.2f} like so:

price = 1299.500  print(f'Price: ${price:.2f}')

Output >>> Price: $1299.50

You’d have used the strftime() method to format datetime objects in Python. But you can also do it with f-strings. Here’s an example:

from datetime import datetime  current_time = datetime.now()  print(f'Current date and time: {current_time:%Y-%m-%d %H:%M:%S}')

Output >>> Current date and time: 2023-10-12 15:25:08

Let's code a simple example that include both date and float formatting:

price = 1299.500  purchase_date = datetime(2023, 10, 12, 15, 30)  print(f'Product purchased for ${price:.2f} on {purchase_date:%B %d, %Y at %H:%M}')

Output >>>  Product purchased for $1299.50 on October 12, 2023 at 15:30

3. Base Conversions in Numeric Values

F-strings support base conversion for numeric data types, allowing you to convert numbers from one base to another. So you don’t have to write separate base conversion functions or lambdas to view the number in a different base.

To print out the binary and hexadecimal equivalents of the decimal number 42 you can use f-string as shown:

num = 42  print(f'Decimal {num}, in binary: {num:b}, in hexadecimal: {num:x}')

Output >>>  Decimal 42, in binary: 101010, in hexadecimal: 2a

As seen, this is helpful when you need to print out numbers in different bases, like binary or hexadecimal. Let’s take another example for decimal to octal conversion:

num = 25  print(f'Decimal {num}, in octal: {num:o}')

Output >>> Decimal 25, in octal: 31

Remember Oct 31 = Dec 25? Yes, this example is inspired by “Why do developers confuse Halloween with Christmas?” memes.

4. Useful ASCII and repr Conversions

You can use the !a and !r conversion flags within f-strings to format strings as ASCII and repr strings, respectively.

Sometimes you may need to convert a string to its ASCII notation . Here is how you can do it using the !a flag:

emoji = "🙂"   print(f'ASCII representation of Emoji: {emoji!a}')

Output >>>  ASCII representation of Emoji: 'U0001f642'

To access the repr of any object you can use f-strings with the !r flag:

from dataclasses import dataclass    @dataclass  class Point3D:  	x: float = 0.0  	y: float = 0.0  	z: float = 0.0    point = Point3D(0.5, 2.5, 1.5)  print(f'Repr of 3D Point: {point!r}')

Python dataclasses come with the default implementation of __repr__ so we don't have to explicitly write one.

Output >>>  Repr of 3D Point: Point3D(x=0.5, y=2.5, z=1.5)

5. Formatting LLM Prompt Templates

When working with large language models like Llama and GPT-4, f-strings are helpful for creating prompt templates.

Instead of hardcoding prompt strings, you can create reusable and composable prompt templates using f-strings. You can then insert variables, questions, or context as needed.

If you’re using a framework like LangChain you can use the PromptTemplate features of the framework. But even if not, you can always use f-string-based prompt templates.

Prompts can be as simple as:

prompt_1 = "Give me the top 5 best selling books of Stephen King."

Or a slightly more flexible (but super simple still):

num = 5  author = 'Stephen King'  prompt_2 = f"Give me the top {num} best selling books of {author}."

In some cases it's helpful to provide context and a few examples in your prompt.

Consider this example:

# context  user_context = "I'm planning to travel to Paris; I need some information."    # examples  few_shot_examples = [  	{      	"query": "What are some popular tourist attractions in Paris?",      	"answer": "The Eiffel Tower, Louvre Museum, and Notre-Dame Cathedral are some popular attractions.",  	},  	{      	"query": "What's the weather like in Paris in November?",      	"answer": "In November, Paris experiences cool and damp weather with temperatures around 10-15°C.",  	},  ]    # question  user_question = "Can you recommend some good restaurants in Paris?"

Here’s a reusable prompt template using f-strings. Which you can use for any context, example, query use case:

# Constructing the Prompt using a multiline string  prompt = f'''  Context: {user_context}    Examples:  '''  for example in few_shot_examples:  	prompt += f'''  Question:  {example['query']}nAnswer: {example['answer']}n'''  prompt += f'''    Query: {user_question}  '''    print(prompt)

Here’s our prompt for this example:

Output >>>  Context: I'm planning to travel to Paris; I need some information.    Examples:    Question:  What are some popular tourist attractions in Paris?  Answer: The Eiffel Tower, Louvre Museum, and Notre-Dame Cathedral are some popular attractions.    Question:  What's the weather like in Paris in November?  Answer: In November, Paris experiences cool and damp weather with temperatures around 10-15°C.      Query: Can you recommend some good restaurants in Paris?

Wrapping Up

And that's a wrap. I hope you found a few Python f-string features to add to your programmer’s toolbox. If you’re interested in learning Python, check out our compilation of 5 Free Books to Help You Master Python. Happy learning!

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she's working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more.

More On This Topic

• Charticulator: Microsoft Research open-sourced a game-changing Data…
• Step up your Python game with Fast Python for Data Science!
• 25 Github Repositories Every Python Developer Should Know
• The 6 Python Machine Learning Tools Every Data Scientist Should Know About
• Three R Libraries Every Data Scientist Should Know (Even if You Use Python)
• KDnuggets News, May 25: The 6 Python Machine Learning Tools Every Data…

A recent interview by Medical Device Network with GlobalData medical analyst Alexandra Murdoch shares interesting insights into cybersecurity for medical devices. Murdoch said that one of the reasons why most organizations have a hard time securing their devices is the rapid adoption of new technologies, which has not been accompanied by the aggressive establishment of cyber defenses.

Over the past couple of years, healthcare facilities have not only digitized but also integrated into their regular operations a host of advanced medical devices including IoT appliances and connected wearables/implants. However, the security of these devices has been on the back burner for some time since healthcare providers and patients have focused on addressing more urgent concerns.

Now, there is hardly any excuse not to deal with cyber threats and risks forthrightly. The accelerated digitalization and use of connected devices in the healthcare field is and will always come with the possibility of encountering cyber attacks. There is a need to take advantage of all available solutions and leverages to keep threats at bay.

The rise of new regulations

Even governments, which usually tend to be untimely when it comes to cybersecurity concerns, are showing proactivity in countering threats to connected digital medical devices. The United States Food and Drug Administration (FDA) has been ramping up the production of guidelines for the medical technology field. The FDA also launched a program for voluntary cybersecurity labeling for IoT and medical devices.

Additionally, several regulations now require device makers to undertake post-market surveillance for medical devices (PMS). It is not enough for product manufacturers and sellers to ensure that the medical devices they are selling are safe, effective, and secure out of the box. They also need to monitor their products as they are being used by healthcare providers and patients. There are three main regulations prescribing PMS, as described below:

• US FDA 21 CFR Part 822 – Part 822 of Title 21 of the US FDA Code of Federal Regulations requires medical device manufacturers to conduct PMS on Class II and Class III devices that are implanted in the human body for at least a year, are deemed to be life-sustaining, or to those which have malfunctioned and caused adverse effects on users.
• MedWatch – Another regulation that calls for PMS is the FDA’s medical product safety reporting program, which covers patients, healthcare providers, and buyers of medical devices. This program requires the reporting of issues and serious problems encountered during the use of such devices. This does not necessarily shift the burden of PMS to consumers, but it empowers them to ensure that device manufacturers have no excuse for failing to monitor the faults of their products.
• EU 2017/745 – More popularly known as the European Union Medical Device Regulation (MDR), this regulation compels medical device producers to submit a PMS plan alongside the technical documentation of their products. They may be required to submit a post-market surveillance report or a periodic safety update report depending on the class under which their products belong.

Data science in medical device cybersecurity

Data science plays an important role in different aspects of securing medical devices from cyber threats. In particular, it is useful in complying with new cybersecurity regulations aimed at connected devices used in healthcare.

Data science is applicable to post-market surveillance. In the modern context, data science entails a combination of statistics, mathematics, advanced analytics, specialized programming, as well as AI to produce insights and other useful knowledge from various data from different sources, including structured, unstructured, and noise-laden data.

In conducting PMS, device makers do not only collect reports of malfunctions, defects, security vulnerabilities, and instances of attacks. The data they gather is not only used to file reports. They can also take advantage of the massive amounts of data they collect to analyze problems and determine the right responses, come up with an efficient system to address recurrent issues, and ensure compliance with all applicable regulations.

Additionally, data science enables the analysis of patterns and anomalies to generate predictive models that can facilitate the detection of vulnerabilities and attacks. This detection can even be undertaken in real-time by leveraging big data and artificial intelligence. With this, healthcare providers, device users, and device manufacturers can get alerts regarding potential threats and respond in promptly a promptly manager. For example, nuseveralorts of infusion pump issues from users and some healthcare providers may not trigger the issuance of product alerts or recalls, but AI-powered analytics may already be picking patterns that indicate serious anomalous activities. In this case, the problem is unlikely to be ignored and will be addressed promptly before it can result in serious consequences.

Moreover, data science helps identify cases that can trigger vulnerabilities or make it easy for threat actors to find attack surfaces and operate discreetly. Correspondingly, it can aid the formulation of solutions to plug security loopholes. In the case of applying security controls, for instance, data science can help find the best points to implement user verification mechanisms such as biometric recognition and multifactor authentication.

Protecting patients and enhancing healthcare

Data science is a highly suitable complement to medical cybersecurity systems given the growing aggressiveness and sophistication of cyber attacks as well as the emergence of new regulations. It can help develop systems that bolster medical device cybersecurity not only in terms of detection but also when it comes to mitigation and prevention.

This is not exactly a novel idea. Data science has already been integrated in cybersecurity, one way or another. However, it bears emphasizing how crucial data management, analysis, and presentation is in securing devices, particularly web-enabled devices that can directly affect people’s health or lives.

There is a synergy between data science and medical device cybersecurity, and not many recognize it. Some may have taken cognizance of this connection, but they are not taking advantage of it. It is important to harness the benefits at the intersection of data science and cybersecurity especially as the use of connected medical devices grows and threats on them grow exponentially.

Twelve Labs is building models that can understand videos at a deep level Kyle Wiggers 9 hours

Text-generating AI is one thing. But AI models that understand images as well as text can unlock powerful new applications.

Take, for example, Twelve Labs. The San Francisco-based startup trains AI models to — as co-founder and CEO Jae Lee puts it — “solve complex video-language alignment problems.”

“Twelve Labs was founded … to create an infrastructure for multimodal video understanding, with the first endeavor being semantic search — or ‘CTRL+F for videos,’” Lee told TechCrunch in an email interview. “The vision of Twelve Labs is to help developers build programs that can see, listen and understand the world as we do.”

Twelve Labs’ models attempt to map natural language to what’s happening inside a video, including actions, objects and background sounds, allowing developers to create apps that that can search through videos, classify scenes and extract topics from within those videos, automatically summarize and split video clips into chapters, and more.

Lee says that Twelve Labs’ technology can drive things like ad insertion and content moderation — for instance, figuring out which videos showing knives are violent versus instructional. It can also be used for media analytics, Lee added, and to automatically generate highlight reels — or blog post headlines and tags — from videos.

I asked Lee about the potential for bias in these models, given that it’s well-established science that models amplify the biases in the data on which they’re trained. For example, training a video understanding model on mostly clips of local news — which often spends a lot of time covering crime in a sensationalized, racialized way — could cause the model to learn racist as well as sexist patterns.

Lee says that Twelve Labs strives to meet internal bias and “fairness” metrics for its models before releasing them, and that the company plans to release model-ethics-related benchmarks and data sets in the future. But he had nothing to share beyond that.

Mockup of API for fine tuning the model to work better with salad-related content.

“In terms of how our product is different from large language models [like ChatGPT], ours is specifically trained and built to process and understand video, holistically integrating visual, audio and speech components within videos,” Lee said. “We have really pushed the technical limits of what is possible for video understanding.”

Google is developing a similar multimodal model for video understanding called MUM, which the company’s using to power video recommendations across Google Search and YouTube. Beyond MUM, Google — as well as Microsoft and Amazon — offer API-level, AI-powered services that recognize objects, places and actions in videos and extract rich metadata at the frame level.

But Lee argues that Twelve Labs is differentiated both by the quality of its models and the platform’s fine-tuning features, which allow customers to automet the platform’s models with their own data for “domain-specific” video analysis.

On the model front, Twelve Labs is today unveiling Pegasus-1, a new multimodal model that understands a range of prompts related to whole-video analysis. For example, Pegasus-1 can be prompted to generate a long, descriptive report about a video or just a few highlights with timestamps.

“Enterprise organizations recognize the potential of leveraging their vast video data for new business opportunities … However, the limited and simplistic capabilities of conventional video AI models often fall short of catering to the intricate understanding required for most business use cases,” Lee said. “Leveraging powerful multimodal video understanding foundation models, enterprise organizations can attain human-level video comprehension without manual analysis.”

Since launching in private beta in early May, Twelve Labs’ user base has grown to 17,000 developers, Lee claims. And the company’s now working with a number of companies — it’s unclear how many; Lee wouldn’t say — across industries including sports, media and entertainment, e-learning and security, including the NFL.

Twelve Labs is also continuing to raise money — and important part of any startup business. Today, the company announced that it closed a $10 million strategic funding round from Nvidia, Intel and Samsung Next, bringing its total raised to $27 million.

“This new investment is all about strategic partners that can accelerate our company in research (compute), product and distribution,” Lee said. “It’s fuel for ongoing innovation, based on our lab’s research, in the field of video understanding so that we can continue to bring the most powerful models to customers, whatever their use cases may be … We’re moving the industry forward in ways that free companies up to do incredible things.”

Photo by Google DeepMind

In the age of rapid technological advancement, artificial intelligence (AI) has emerged as a transformative force with the potential to reshape industries and enhance our daily lives. At the heart of AI’s capabilities lies data—the lifeblood that fuels its learning and decision-making processes. The importance of having reliable data cannot be stressed enough, as it acts as the foundation for AI algorithms to perform effectively. Furthermore, ensuring data accessibility and upholding ethical privacy practices have also become critical factors that will shape the success of AI in the near future.

Reliable Data: The Backbone of Informed Decisions

Businesses today rely heavily on AI-generated insights to make informed decisions, ranging from inventory management and customer support to product development and advertising campaigns. However, the old saying “garbage in, garbage out” remains true. Bad data can lead to misleading conclusions and poor decisions, resulting in financial losses and missed opportunities.

The reliability of data becomes even more critical when considering the potential impact of false information or disinformation. In an era where misinformation spreads like wildfire, AI algorithms trained on unreliable data could inadvertently amplify and perpetuate falsehoods. This underscores the importance of establishing rigorous data quality standards and robust fact-checking procedures to ensure that AI’s outputs are accurate.

Democratizing Access to Valuable Data Insights

In the realm of AI, access to valuable insights derived from data is often concentrated within big tech companies. However, the potential applications of AI extend far beyond tech giants. From healthcare and agriculture to transportation and finance, AI-powered solutions can revolutionize industries and benefit companies of all sizes. This is why it’s important for everyone to have access to the insights from data that AI uses, not just a select few.

Unlocking the potential of AI for broader societal gain requires democratizing data access. Small and medium-sized enterprises, researchers, startups, and even individuals should have the opportunity to harness the power of AI-driven insights. Imagine a future where a local farmer can utilize AI to predict optimal harvest times, or a small retailer can use AI to make informed decisions about new store locations. This vision demands a shift from data hoarding to data sharing.

Consumer Privacy and the Competitive Edge of Big Tech

Striking a balance between consumer privacy and business interests is essential to ensure that AI-driven insights benefit society at large without compromising individual rights or disadvantaging businesses. However, delving into the relationship between big tech companies and consumer data privacy reveals a more complicated story. While these companies present themselves as champions of protecting personal data, a deeper look suggests that their motives might be driven by gaining a competitive advantage, rather than just ethical concerns.

Hidden behind the push for data privacy may actually be a strategic business play. Some big tech companies hold onto user data not only to safeguard it, but to also use it to refine their own products and business models, all while preventing competitors from accessing the same data. This gives them a unique edge in the market, enabling personalized experiences and targeted advertising that competitors can’t match. In this way, data privacy becomes a means to secure and consolidate their market dominance.

However, this strategy blurs the line between ethical responsibility and business advantage. It raises the question of whether these efforts genuinely prioritize user well-being or if they are calculated moves to maintain a strong market position. Whether their intentions are rooted in genuine concern or strategic gains, the outcomes will determine how technology like AI will affect our lives in the years ahead.

Paving the Way for Ethical and Inclusive AI

The journey to harnessing the full potential of AI begins with recognizing the pivotal role of data. Good data is the bedrock upon which AI innovation thrives, and ensuring its accessibility and reliability is paramount. To fully unleash the capabilities of AI, we must democratize insights for the benefit of all types of businesses, organizations, and individuals.

By fostering a culture of collaboration, adhering to rigorous data quality standards, and championing data privacy, we can pave the way for AI applications that serve both business interests and the greater good. The future of AI centers on our collective commitment to shaping a world where data is a force for progress, accessibility is a core principle, and reliability is the hallmark of AI-driven insights.

Jeff White is the Founder and Chief Executive Officer of Gravy Analytics. He is passionate about building disruptive technologies with the potential to change entire industries. Prior to Gravy Analytics, he founded several technology companies and led them to successful exits.

More On This Topic

• Inflection-1: The Next Frontier of Personal AI
• The Evolution of Tokenization — Byte Pair Encoding in NLP
• From Oracle to Databases for AI: The Evolution of Data Storage
• Analyzing the Probability of Future Success with Intelligence Node’s…
• The Evolution of Apache Druid
• The Evolution From Artificial Intelligence to Machine Learning to Data…

Image by Editor

Predictive analytics will play a key role in business decision-making in 2023, with AI, machine learning, and data science being utilized by businesses, large and small, to boost revenue and achieve maximum growth. Capable of processing huge amounts of data to find hidden and valuable insights, predictive analytics is key to unlocking potential.

In this article, we will focus on how predictive analytics work in a business environment, using data to make informed decisions that can make a big difference.

What is Predictive Analytics?

Predictive analytics processes large amounts of data, analyzes it to find useful and relevant information, and then develops predictive models to provide valuable insights relating to different scenarios, both past and present. Thanks to these scenario-based insights, predictions can be made about future events, enabling businesses to make better decisions in terms of identifying new trends, changing product offerings, and more.

Image from Qualtrics

Predictive analytics is an advanced tool but still requires expert human knowledge to be used effectively. Analytics provides the facts and relevant data, while it is up to the user to identify how it can be applied to real-world, future scenarios. Combined, predictive analytics and decision-making processes can help businesses achieve tangible results.

Predictive Analytics In Practice

A common use case for predictive analytics is in marketing, with a common example being behavioral targeting. This involves leveraging consumer data to create better marketing strategies, whether that is web content, social media campaigns, or direct advertising, allowing businesses to reach new customers.

This works by evaluating historical behavioral data and using it to predict how customers may behave in the future. This can help to provide accurate forecasts in terms of sales trends at various times in the year, such as the holiday period, assisting marketers in creating better, more targeted campaigns.

As well as looking into sales trends, predictive analytics can also assess the sales funnel, checking the effectiveness of each stage, from initial awareness to a completed purchase. For example, algorithms could determine how many content/ ad offerings a lead typically interacts with, and when, before ending a purchase or critical action. This can help to improve targeted ad campaigns in the future, giving insight into when a customer is more likely to interact during the customer lifecycle.

This method can also identify what types of content are regularly interacted with, whether that is a social media post or a PDF download within an app. With PDF SDKs, customers can quickly download PDF content such as vouchers or product information to their phone, a clear sign of buying intent.

Combining Predictive Analytics and Decision Making

In business, it is becoming common practice to combine predictive analytics and decision-making, relying on advanced algorithms, past behavioral data, and statistics to accurately predict future behaviors. Not only does this help businesses make more accurate decisions, but it also allows them to be made quickly, providing a competitive advantage.

Image from Analytica

Decisions can be regarding market trends, customer interactions, marketing campaigns, investment-related risks, and anything else that can have a significant impact on a business.

Combining Predictive Analytics and Decision Making: The Benefits

For some people, the benefits of predictive analytics may not be clear, preferring to rely on existing processes to shape the future of their business. However, there are several benefits that cannot be overlooked, especially if a company is aiming to grow quickly in this modern and competitive digital environment.

The benefits of predictive analytics combined with decision-making include:

• The use of machine learning and artificial intelligence makes it possible to predict future outcomes and trends, allowing the decision-making process to determine the best action.
• With accurate predictions, businesses can stay ahead of the competition and take action quickly to launch marketing campaigns or new products.
• Accurately anticipate customer needs and changes in the market to make the necessary adjustments.
• Extremely large data sets can be analyzed which would not be possible using manual techniques. These data sets can include customer demographics or purchasing trends, helping to identify new, previously untapped opportunities.
• Identify potential threats before they can become an issue, helping to safeguard operations and allowing businesses to take a more proactive approach.
• Helps to better allocate marketing resources, targeting only relevant customers so time or money is not wasted on leads that do not meet the criteria in terms of the targeted demographic or are unlikely to result in a conversion.

Making Informed Decisions with Data: Best Practices

When creating a predictive model that is based on predictive analytics with the aim of making more informed business decisions, there are certain dos and don’ts that can have a significant impact on its effectiveness.

Below are five best practices that should be followed when creating a predictive model.

1. Ensure data sets are fully understood before applying them to a predictive model. This includes knowing where the data is sourced from, how it was gathered, and its structure. Establishing that the data used is completely reliable is vital to guarantee the model makes accurate and relevant predictions.
2. You must also adopt a model that is appropriate to your business so that it is suited to the data being processed. Choose a single model that can be used across the business so that it can be easily optimized, instead of using a range of different models that could become complex and inefficient.
3. Before launching the model it must be thoroughly evaluated and validated to make sure it is trained correctly and generates the intended results. To do this, test the model on a range of data sets and refine it as needed, while ensuring the model also uses the most up-to-date techniques and methods.
4. Once up and running, the model will need ongoing monitoring to determine how it is performing. Always thoroughly test any new data sets and take the time to measure the results against the latest trends and market changes based on your research.
5. Schedule regular testing to judge the accuracy of the model, applying a range of cross-validation techniques to determine if the patterns shown within the training data are applicable to real-world scenarios.

Conclusion

Predictive analysis is an invaluable tool in the modern business world, helping companies to make informed decisions that can have a significant impact on the future of their operations.

Assisting with marketing campaigns, sales funnels, and product management, the predictive analysis uses advanced machine learning algorithms to provide insights into future customer behaviors and market events. Without these insights, businesses risk being left behind by their competitors, potentially missing out on lucrative opportunities and failing to understand their customer base.

Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed—among other intriguing things—to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.

More On This Topic

• Revolutionizing Data Analysis with PandasGUI
• How a Polytechnic Helps You Make the Tech-Business Connection
• DATAcated Expo, Oct 5, Live-streamed,Explore new AI / Data Science Tech
• Data science SQL interview questions from top tech firms
• Building Tech Skills in 2021
• Baidu Research Unveils Top 10 Tech Trends Forecast for 2022

Software company Atlassian’s recent acquisition of video messaging firm Loom, for $975 million, which is 36% less than Loom’s valuation of $1.5 billion in 2021, throws the spotlight on the fate of video messaging platforms. What witnessed a boom during pandemic, with app usage even hitting 21 times pre-covid levels, video conferencing platforms are losing their lustre post pandemic. This resulted in big tech companies swooping in to seemingly make profitable deals.

Catch When The Hype Drops

Recalling Zoho chief Sridhar Vembu’s personal philosophy of investing in companies when the ‘hype dies,’ seems to be a guiding principle for the latest M&A that has been happening in the video conferencing space.

Atlassian’s biggest deal of buying Loom when the hype around video calls have declined, may be construed as a favourable deal for the company. However, the deal is advantageous for Loom too considering its plunging market value.

In another case of plunging valuation, Hopin, a video teleconferencing and hosting platform, sold its business for $15 million to RingCentral, a cloud-based company in August. Believed to be worth $7.75 billion in 2021, Hopin lost close to 99% of its value.

Technology and communications company Verizon acquired BlueJeans, a video application designed for businesses, for $400 million in May 2020. However, a few months ago, Verizon announced that they will be shutting down BlueJeans in a phased manner. Interestingly, BlueJeans was launched two years before Zoom was released into the market.

The Growth and Tumble

Owing to a pandemic that forced the world to work out of homes, video conferencing platforms saw a huge surge in user base. However, with companies calling their employees back to the office, the decline was inevitable. Companies faced drops in market capitalisation, valuation, layoffs and sometimes acquisition too.

Video conferencing platform Zoom saw a huge spike in market cap during the pandemic, reaching as high as $158.99 billion. Today it stands at $18.57 billion. Not surprising, considering how even Zoom has called their employees to return back to work.

Source: CompaniesMarketCap

The recently acquired work communication tool Loom had been witnessing a steady rise. In 2020, the company hit a valuation of $350 million with 4 million users. The company raised fundings from companies such as Atlassian, Figma, Slack and others. By 2021, the company hit a $1.5 billion valuation. However, the valuation dropped at the time of acquisition by Atlassian.

Hubilo, a virtual and hybrid event platform, also faced the brunt post pandemic. The company that raised $150 million in 2021, laid off 35% of its staff early this year. Similarly, other virtual event platform hubs also faced a similar fate.

During the pandemic, companies also invested in video conferencing applications to boost their existing product. In August 2021, Microsoft acquired Israeli video streaming company Peer5 for an undisclosed amount in a bid to boost Microsoft Teams’ performance.

The Big Tech Players

Big tech companies such as Microsoft and Google that already provide video conferencing products through Teams and Meet respectively, also witnessed a spike in users during the pandemic with companies finding ways to meet the demand. However, by not being an exclusive video platform, the rise and fall in its user base owing to pandemic may not be a complete business damper. It’s the exclusive players that have witnessed a major change in their business.

Webex by tech conglomerate Cisco is also pushing ahead with its web and video conferencing application, ready to take on Zoom as well.

All is Not Dead

The global market of video conferencing which stood at $10.6 billion in 2022, is expected to hit $19.1 billion in 2027. Established SaaS players are also working on enhancing their existing products by offering video features. For instance, Zoho recently unveiled its smart conference rooms solution on their existing AI-enabled platform Cliq. Here, the company allows customising room devices such as TV screens for supporting video meetings, and pushing for a work from anywhere culture post pandemic as well.

Source: Zoho

While we have witnessed a decline in the number of video conferencing platforms post-pandemic, which enabled software companies to swoop in and make the best of it via acquisitions, it is likely that more consolidation of video SaaS companies will occur in the near future.

The post Video SaaS Companies Face A Loom-ing Threat appeared first on Analytics India Magazine.

Software company Atlassian’s recent acquisition of video messaging firm Loom, for $975 million, which is 36% less than Loom’s valuation of $1.5 billion in 2021, throws the spotlight on the fate of video messaging platforms. What witnessed a boom during pandemic, with app usage even hitting 21 times pre-covid levels, video conferencing platforms are losing their lustre post pandemic. This resulted in big tech companies swooping in to seemingly make profitable deals.

Catch When The Hype Drops

Recalling Zoho chief Sridhar Vembu’s personal philosophy of investing in companies when the ‘hype dies,’ seems to be a guiding principle for the latest M&A that has been happening in the video conferencing space.

Atlassian’s biggest deal of buying Loom when the hype around video calls have declined, may be construed as a favourable deal for the company. However, the deal is advantageous for Loom too considering its plunging market value.

In another case of plunging valuation, Hopin, a video teleconferencing and hosting platform, sold its business for $15 million to RingCentral, a cloud-based company in August. Believed to be worth $7.75 billion in 2021, Hopin lost close to 99% of its value.

Technology and communications company Verizon acquired BlueJeans, a video application designed for businesses, for $400 million in May 2020. However, a few months ago, Verizon announced that they will be shutting down BlueJeans in a phased manner. Interestingly, BlueJeans was launched two years before Zoom was released into the market.

The Growth and Tumble

Owing to a pandemic that forced the world to work out of homes, video conferencing platforms saw a huge surge in user base. However, with companies calling their employees back to the office, the decline was inevitable. Companies faced drops in market capitalisation, valuation, layoffs and sometimes acquisition too.

Video conferencing platform Zoom saw a huge spike in market cap during the pandemic, reaching as high as $158.99 billion. Today it stands at $18.57 billion. Not surprising, considering how even Zoom has called their employees to return back to work.

Source: CompaniesMarketCap

The recently acquired work communication tool Loom had been witnessing a steady rise. In 2020, the company hit a valuation of $350 million with 4 million users. The company raised fundings from companies such as Atlassian, Figma, Slack and others. By 2021, the company hit a $1.5 billion valuation. However, the valuation dropped at the time of acquisition by Atlassian.

Hubilo, a virtual and hybrid event platform, also faced the brunt post pandemic. The company that raised $150 million in 2021, laid off 35% of its staff early this year. Similarly, other virtual event platform hubs also faced a similar fate.

During the pandemic, companies also invested in video conferencing applications to boost their existing product. In August 2021, Microsoft acquired Israeli video streaming company Peer5 for an undisclosed amount in a bid to boost Microsoft Teams’ performance.

The Big Tech Players

Big tech companies such as Microsoft and Google that already provide video conferencing products through Teams and Meet respectively, also witnessed a spike in users during the pandemic with companies finding ways to meet the demand. However, by not being an exclusive video platform, the rise and fall in its user base owing to pandemic may not be a complete business damper. It’s the exclusive players that have witnessed a major change in their business.

Webex by tech conglomerate Cisco is also pushing ahead with its web and video conferencing application, ready to take on Zoom as well.

All is Not Dead

The global market of video conferencing which stood at $10.6 billion in 2022, is expected to hit $19.1 billion in 2027. Established SaaS players are also working on enhancing their existing products by offering video features. For instance, Zoho recently unveiled its smart conference rooms solution on their existing AI-enabled platform Cliq. Here, the company allows customising room devices such as TV screens for supporting video meetings, and pushing for a work from anywhere culture post pandemic as well.

Source: Zoho

While we have witnessed a decline in the number of video conferencing platforms post-pandemic, which enabled software companies to swoop in and make the best of it via acquisitions, it is likely that more consolidation of video SaaS companies will occur in the near future.

The post Video SaaS Companies Faces A Loom-ing Threat appeared first on Analytics India Magazine.