Category: Chat

With Examples from Google Analytics and Adobe Analytics

This is part I of the never-ending story on how to deal with Bots in your Analytics data. I review common, yet usually insufficient or even completely failing approaches. Why did I give up on AI-driven solutions like ReCaptcha, Akamai Bot Manager or Ad Fraud Detection tools? How good are the built-in Bot Filters? Should you at least maintain Bot Filters/Segments on top of GA views/AA Virtual Report Suites? Why does Server-Side Tracking exacerbate the Bot issues? I will finally give a peek at a client who saw Bot Traffic surging to over 40%, a case which made me reconsider entirely how to approach Bot Filtering.

The topic is as old as the world of Web Analytics: Bots (e.g. “crawlers” or “spiders”). They come without warning, wasting your time and money, and often causing spam in your data that is hard or impossible to repair.

Common Approaches to Bot Filtering

A: Web Analytics Tools’ built-in Bot Filters

The “Bot problem” has intensified due to the growing traffic from JavaScript-executing and cookie-setting Bots out there. Nevertheless — or because of that growth—, no Web Analytics solution has gotten the Bots under control. Even though there are built-in Bot Filters in both Google Analytics and Adobe Analytics, they detect only a fraction of the actual Bots. If I e.g. look at the likely soon sunsetting “Bots Report” in the old Adobe Analytics interface, it tells me that a mere 95k of my Pageviews came from Bots, while in reality it was close to 4 million.

Let me be short for a change and note that the archaic “Filter by IP” or “Bot Rules” interfaces are a nuisance to maintain and not a sensible option anyway. Bot Rules can handle only User Agent and IP addresses as rules. IP addresses can’t be used for filtering if you obfuscate or truncate the IP, which everybody does these days in Europe. And there are only a few Bots that are recognizable through their User Agent.

Google Analytics has made matters worse by no longer giving you data on network providers. That data, like Adobe’s “Domain” dimension, used to be one of the best ways to identify and filter Bots (at least after the fact). That being said, GA’s “exclude bots and spiders” flag is not much better than Adobe’s built-in Bot Filter. If you compare a GA View with and without the Bots flag, the difference is usually tiny. The views I looked at showed a mere 1% of Sessions less with the Bot Filtering flag.

Google Analytics 4 has Bot Filtering applied by default, and you cannot remove it, nor is there any way to verify what it does (black box):

“At this time, you cannot disable known bot traffic exclusion or see how much known bot traffic was excluded.”

B: Pretend Bots are irrelevant

Life with Bots is a plague. Many try to ignore Bots altogether and pretend that they don’t have much of an impact on the data, because their traffic is too small to be relevant, or that it is constantly at about the same level, so there is always the same margin of distortion. In some cases, this is true. Bots don’t attack each site the same way. Yet more often, it is wrong. Bots can strongly affect your entire Conversion Rate (I remember my first GA client in 2018 having nearly 50% of their sessions generated by Bots). But more frequently, they mess up the data for specific reports.

See this example of a comparably small Bot that spammed the site search with just about a thousand Pageviews, “typing” queries that only a bot can type, thus messing up our zero-results search terms report. This is a nuisance for the Search Management Team, because they prefer optimizing zero-result searches of humans:

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

C: Apply and Maintain Custom Filters / VRS Segments

Others spend a lot of time creating and updating View Filters in GA which unfortunately still only filter out data when it’s already too late (not retroactively). A common practice in Adobe Analytics is to keep enhancing “Bot Segments” in AA and put those on top of your Virtual Report Suites (see chapter “why you should use Virtual Report Suites”). That filters out Bots everywhere and retroactively AND reduces complexity for your users because they don’t ever need to learn about Bots nor see any Bot Segments. However, those filters/segments grow and grow, slow down queries, are prone to errors and a pain in the ass to maintain. And they become impossible to maintain once you deal with a true Bot rush (see the client case further down). Still, you need this approach at least to some degree.

D: Specialized AI-driven Bot Detection Solutions

Others again try to piggieback on Bot detection solutions. There are the simpler ones that believe they can do it all just by analyzing mouse movements. And there are the known Bot eaters like ReCaptcha, Akamai’s Bot Manager or PerimeterX (who claimed to have an Adobe Analytics integration, but disappeared when asked about specifics). These solutions are usually based on a mix of behavioral algorithms and pattern matches: The algorithmic part checks for certain bot-like behaviors of a user (usually identified by an IP address), while the pattern matches check the UserAgent/IP address against long lists of known bots. If you integrate the solutions the right way, they can drop their verdict (usually a “Bottiness score” or a “Bot/no Bot” flag) back into the browser or via a request to your server-side Tag Manager, and thus make this information consumable for Analytics tracking logic.

In my experience, none of these solutions has shown to be reliable enough nor practical enough for the type of Bot filtering that is needed for Analytics (disclaimer: I have not tried ReCaptcha, but from reading about it, it will have the same issues). Why?

1. If the solutions say someone is likely a bot, this is often true, but too often also not.
2. Moreover, they miss out on way too many real bots.
3. Most importantly, the AI part in them usually means they can’t make their behavior-based Bot verdict until after the first couple of Pageviews — because they first need to see some behaviour before getting a reliable score. So in the moment when our Tag Management System has to decide whether to track that first Pageview, the Bot detectors can’t tell yet whether this is a Bot or a human (or a zombie).

It’s not the fault of the solutions, they just don’t mesh with the way Analytics tracking works

I am not saying these solutions suck. It’s not their fault. But first, they are built to be Bot “Managers”, not Bot “Filters”. So they are built to prevent excessive load on your servers and fraud attempts, but often don’t mind if a slow-moving crawler checks out thousands of product pages. And second, Analytics solutions unfortunately don’t give you an option to say:

“Please delete everything we already tracked from this Bot, and also tell our Analytics vendor that they shall not bill the server calls incurred by this dude.”

So at the client we will take a closer look at in a bit, we could never use the signals from these Bot Detection solutions.

AI-driven Bot Detection is not as good as it sounds

After defending these Bot Detection solutions somewhat, I have to lash out at them a bit. First, I was shocked how many really obvious and really traffic-heavy Bots (easily identifiable by their network domains) one particular expensive solution missed completely, even after days of the same IP addresses spamming the site. That was the nail in the coffin for my attempt to piggieback on them for Analytics-oriented Bot Filtering. Multiple improvement rounds did not change much. See some examples:

Special Case: Ad Fraud Detection Tools, or how to lose a a lot of money due to overzealous AI

The other case where I need to lash out at AI-driven Bot detection was a particularly costly one (for the client): If you are in “Performance Marketing for Bots” aka Social & Display Advertising (and Paid Search to some extent as well), you have probably run into Bot-induced ad fraud problems of massive scale at some point. Thanks to Augustine Fou (who owns an ad fraud Analytics tool himself) for an insightful presentation on the topic for Adam Greco’s SDEC (some parts of it are in this article, but better become an SDEC member for free). And of course, there is Tim Hwang’s book “Subprime Attention Crisis” on which there was a fabulous episode of the Analytics Power Hour in January.

Some of these Ad Fraud Detection tools block ads for users whom they believe to be Bots or any other kind of user that likely won’t buy (“window shoppers” etc…). For example, the solution the client tested (I won’t name it here) stopped showing Google ads to supposed “Bots/non-converters/enemies/etc.”. The goal was to have less clicks that end up not converting anyway — because you pay for each click after all. So they expected a decrease in traffic in exchange for an increase in Return on Ad Spend. And the tool vendors bragged about the millions they would save.

The solution did lower the traffic drastically, but also dragged down Revenue. The Conversion Rate and the Return on Ad Spend increased only a bit. After switching off the solution again, Revenue and Traffic both skyrocketed. It was clear now: the ad fraud detection blocked ads for way too many humans.

If you want to avoid such costly test drives, demand that the Bot Filter vendor does a “dry run“ where their tool runs, but does not really filter anything and simply marks users it would filter out. Then compare that sample to your Analytics, e.g. via a common user ID key or an IP address (Bot Filtering is one of the “legitimate uses” for tracking PII like the IP) to see what their tool would have filtered out. I demanded this, but got only about 5 IP addresses they would have filtered (of which 2 were from humans), and then I was shut out from the discussion. The test run started, and the client lost a lot of money.

So anytime “AI” is mentioned as a cure-all method, always be over-cautious, because usually the people offering this AI haven’t understood the complexity of the problem sufficiently yet.

But back to our main topic: How can we get those Bots out of the Analytics data reliably, and before they get into Analytics in the first place? Let’s introduce the client example that changed the way I approached Bot Filtering entirely.

The Client Case: An uncontrollable Bot Rush

Approach C (maintaining segments on top of Virtual Report Suites) sums up my life with Bots as well. At least every month, someone reported that some on-site search report looked weird. Then we found out that some new crawler had taken to crawl all potential search result pages for products associated with brand “Mickey Mouse” and competitors. Another frequent case were freakingly low Product Conversion Rates for certain brands or products going down to near zero because of Bots.

The “solution” was usually to find a clearly identifying but not too complex (ideally one condition) criterion for that crawler/pingbot/whatever (usually the Network Domain was the best indicator), add that criterion to the Bot Segments, then tell people to reload the report, and then we could go back to actually useful work.

This worked decently well while the Bot traffic (Visits) was below 10–15% of the traffic measured by Analytics. Occasionally people asked why data from the past had changed, but it was not grave usually. I am saying “the traffic measured by Analytics” because many Bots are nice enough and do not execute Analytics scripts. Or they understand that this will make it easier for the website to eventually catch and block them. Or they are too stupid.

Over 40% Bots, the slippery Type

But in late 2020, some crazy Bot wave started, and the 10–15% went up to over 40% until March ’21. The Bots became like slippery worms, changing their network domain names and IP addresses all the time, so after finding and adding 100 new Bot domains on Monday to our Bot segment, we could add another 50 on Tuesday, Wednesday and Thursday. It was insane. The expensive IT-held Bot Management tool detected … wait for it … nothing!

Server-Side Tracking exacerbates the Bot Issues

Curiously, this problem only affected the server-side tracking technologies (actually Client-to-Server-to-Vendor). Why is that? To make a long story way too short, in server-side tracking, your browser never sends a request to “google-analytics.com/collect” or “…omtrdc.net/b/ss” etc… Thus, even if the Bot is one of those who do not want to get tracked by Analytics, it can’t evade tracking! So if you switch to Server-Side, get ready to deal with that additional Bot traffic.

But I digress… So we had this massive increase in Bot traffic, and it felt like shoveling water out of a house without a roof during massive rainfalls. Sysiphus live. We asked around whether IT or Marketing or agencies or anybody could help explain what was going on. Nothing relevant surfaced.

And … that’s it for part I. Read Part II to find out how we solved the problem by turning the concept of Bot Filtering upside down. Coming soon!

Don’t forget to give us your 👏 !

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Bots & Analytics: Common Failing Approaches to Bot Filtering, including AI was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Is it possible, or even worth trying?

To answer this question I’ve spent a lot of time thinking and investigating neural science, algorithmic trading, crypto trading and found something really interesting.

Disclaimer: if you are reading this article, I assume that you’re acknowledged about stock/crypto markets and have basic understanding why you need a bot or you’re just interested in their theory. This article won’t contain any code parts and it’s for educational purposes.

About Trading Bots

Let’s start with facts. Trading bots are not new and there are plenty of publications about it, they are used by financial structures, but are they really helpful? I think so, but if you are a programmer or you acknowledged basic concepts of scripting, you are able to write your own bot in few hours. But will it earn enough money for you? Will it have stable earnings? Will it ever hit 100,000$ annual return? I’m going to answer these questions in this post and give you some shots to move forward.

1.What is a trading bot?
A trading bot is an algorithm that turns specific market conditions into order decisions (usually buy, sell or hold). That’s it, nothing special.

2. What types of trading bots exist?
All types that any trader could take, as the bot is an algorithm written by programmers, they could put any logic in it.
In general, we have different types of traders:
• Long-term traders — investors;
• Swing traders — those who place orders on a week, month, or year basis;
• Day traders — those who place few orders through the day without moving them overnight;
• Scalping traders — those who place many orders per day, an hour, or even a minute.
All these trader types could be implemented in a bot.

3. Where trading bot could place orders?
Anywhere you want and could get to broker’s API or through reverse engineering. But you may even simulate your own broker conditions, commissions, taxes, spread, price just by making simple random movements, this simulation won’t contain the psychological part of a market, but it will be a good start for testing a bot. More about designing a great bot I’ll publish separate writing.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

4. What about bot implementation types?
It’s better to say, types of algorithmic trading. As the bot is a complete program that implements the trading behavior of a trader in an automated way.
• Neural Network or Artificial Intelligence bots — could be simple single-perceptron bots based on few neurons, complex LSTM networks, or even artificial news analysis based on keywords heuristics and ranking.
• Quantitive trading — based on a strategy combining any criteria for making a decision, it could be comparing indicators, price action, finding patterns, etc.
• Semi-automatic bot — will use some algorithms to suggest to traders what to do. Indicators based on some strategy are part of this type.
• Genetic algorithms — it could be put into the machine learning/neural networks part, but it’s not really investigated enough to decide this type of algorithm as a machine learning approach. Genetic algorithm implementations differ from person to person and are investigated by universities throughout the world and are part of a bigger topic than this one (but I’ll discuss it later, stay tuned)

Alright, I’ve answered the above four basic questions you may think about before starting writing any bot. But what’s next? After understanding what is a bot, we may think about measuring their KPI.

Measuring Bots Quality

To measure the bot’s quality we may use the back-testing technique, which assumes any time range on a specific timeframe to execute orders based on previously available data. Let’s take some heuristics to work with, such as available cash, a timeframe it works with, stop loss and take profit ranges. We may use any other based on any trading strategy we want (for example neural network bots could determine their heuristics based on market conditions).

The best way to determine strategy quality is to write your own using TradingView. Using their documentation you’re able to write a strategy on Pine Script (which has really simple syntax).

As you may see in the picture above, I’ve implemented a simple strategy based on entering a long position after each green bar.

It won’t work on Binance or similar crypto brokers as using 0.01% stop loss and 0.5% profit. It’s possible to automate trading on this percentage and even place a stop-limit order in its range, but it won’t be possible to survive with commissions that a broker proposes.

For example, Binance proposes a 0.04% commission for a maker order (any market order) on a basic VIP level. This will execute any stop-loss order in 0.01 + 0.04 * 2 = 0.09% loss for 0.5% profit. This strategy won’t survive with a 1:5 profit ratio, as we have only 2.79% profitable percent (that means only 2% of all placed orders are winning, others are executed with stop loss).

Using full statistics taken from TradingView we are able to design our bot or back-test strategies faster and easier before we will use real market or paper-trading for writing a program.
After you see something like this:

Where you have a good net profit in combination with a profitable percent higher than 60 (at least), you may think about implementing a real trading bot.

But wait, does it mean that bots are effective? Can we answer now on this question? Sure.

Do Trading Bots Help Earning Money?

Of course yes. That’s why many hedge funds, banking structures, and big financial companies hire machine learning and algorithmic specialists. Those people are responsible for implementing automated trading bots to play in the real markets with big money.

So how much money do they make? Can we calculate and answer?

It depends on many factors. At least heuristics programmers put in it. Risk management, market conditions, available cash, etc.
You may find that a good prediction inside a trading day with low volatility could give about 0.6-1% of a stock move. If your bot won’t lose any trade in a trading month, you would earn about 20% each month, for the “all-in” strategy, a 10k$ account will earn 2000$.
We may use margin accounts with leveraged orders, risk managements, or pyramiding, using short stop loss or long take profits or different trading bot types, all of them will work differently.
A bad algorithm will just blow up whole your account if any mistake was written. So make sure you make all unit tests and use your bot on paper trading before starting using it with real money.

So could it make 100k$ in a year? Sure, proper risk management, strategy, and a proportional amount of money could give you this return.

Comparing Machine Learning and Quantitive Algorithms

It’s an important topic to discuss the difference between neural networks and quantitive analysis. What are the reasons to use machine learning or neural network algorithms? They could see patterns based on historical data better than people.

How do they determine patterns? It depends on a neural network type. You may implement an LSTM network or a single-layer perceptron. There are plenty of different investigations for other types of networks, so it depends solely on your choice.

Do they effective? Yes. They really find patterns with a properly written algorithm by using learning with a teacher or without a teacher. I was implementing a single-perceptron neural network and put few indicators in this bot (some of them my own indicators, some of them based on RSI, Bollinger Bands, and Stochastic RSI), I’ve created a function that takes a time range and runs neural network through candles finding a bar after which there’s a constant bullish or bearish movement. The second function was teaching neural networks using bar indicators and put a 1 or 0 as an output neuron (1 is buy prediction and 0 is hold).

Then I took neural weights and wrote a strategy using Pine Script to visually represent the bot’s predictions. And it worked better than I was thinking.

So this approach makes a big sense, but it requires more heuristics to learn, like where to put stop and take limit orders, what risk to use, etc. But this is a good example of using neural networks in practice and it works.

The quantitive approach is similar to neural networks, but instead of using machine learning to determine patterns, the programmer should determine them by himself. This makes it easier to determine the exact strategy, when to leave a position and how to manage risks. More than that, in a quantitive approach it’s possible to make not an automated bot, but a great indicator with a collection of compared conditions, and the trader will make his own decision using this analysis and overall market situation.

Is It Stable?

It depends. It depends on stock and market conditions, chosen strategy, and algorithm type. A bot could make a 2% return for a day and then the market will change its direction and it will blow up day’s profit and make a 3% loss.
Programmers should care about risk management.

For example, simply stop bot for the day if day’s loss or profit reached some mark (like 1% loss for the day and 2% profit is enough). Market conditions are changing between years, months, days, or even minutes. Fundamental news, panic on market, or market movers making big buys or sells (like Tesla on Bitcoin). These moments could break any algorithm including complex neural networks if they don’t implement risk management and proper stops.

Conclusion

Trading bots are an effective way of increasing your income with automated trading, but it should be made in a planned way, well tested before starting it on real money. Make sure you’re well acknowledged about technologies you’re using for making and algorithmic trading, aware of pre-made bots that are offered for some price (many of them are working on patterns for only 1–2 weeks they’re tested for promotion).

Later I’ll write some publications about practical usage of algorithmic bots, how to write proper strategies on Pine Script, also with examples of genetic trading and my investigations, so stay tuned!

Don’t forget to give us your 👏 !

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Get Rich With Trading Bots was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Chatbot technology has been around for a while now, and it seems to be gaining in popularity and global market value every year, and for a number of reasons. For one, modern chatbots leverage artificial intelligence and machine learning to create truly engaging experiences and handle more complex challenges, which is something that their predecessors couldn’t achieve. In the grand scheme of things, modern, AI-driven chatbot technology can help grow your business in the online world by elevating your marketing, sales, and support processes.

There are numerous chatbots that you can try depending on your needs and long-term goals, but before you start integrating and optimizing this technology, you need to gain a deeper understanding of its potential. Integrating a chatbot into a WordPress site is a relatively straightforward process nowadays, however, it’s important to understand its uses and potential applications in order to leverage it for growth and success in 2021 and beyond.

Let’s talk about the key benefits of chatbot technology for your WP site that you need to know in 2021.

Cut waiting times to zero

A slow loading time can ruin a website’s chances of ranking high in the relevant search results, simply because modern customers won’t wait more than three seconds for your website to load fully and properly. So why would they wait around for you to answer their questions or address their concerns? Your ability to address the needs of your customers immediately can significantly boost your conversions, customer retention, and the lifetime value of the individual — but if your reaction time is slow, you risk losing them for good.

That’s why it’s so important to have a live chat software on your website so that your customer service agents can engage with them quickly. But what happens when people try to reach you when your team is out of the office? Keep in mind that people want answers to their questions now, not later or the next day, which is why it’s so important to have a chatbot that will engage with your customers and cut the wait times to zero.

Nowadays, chatbot technology is advanced enough to provide conversational experiences, and is able to handle more complex customer queries, ensuring that the customer will most likely find a solution to their problem by engaging with your chatbot right there on the spot. If their problem is more complex and requires a human customer success agent, then the chatbot can easily forward the request to your team.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Create an engaging experience

In the competitive online world, engaging your customers in meaningful conversation across a variety of relevant touchpoints and channels should be one of your top priorities. Fortunately, there are many ways you can elevate customer engagement through proper branding, personalized communication, gamification, social proof and content marketing, and more. But what you might not have known is that you can also leverage your website’s chatbot to deliver personalized, engaging experiences to your users and returning customers.

One of the biggest benefits of conversational AI is its ability to personalize the user experience on your website for the individual according to their browsing behavior, purchase history, and the way they interact with your brand. Most importantly, though, a conversational chatbot will also make the interaction feel natural and organic, moving the conversation towards a solution and a desirable outcome for both you and the user.

Complete your omni-channel support structure

Building omni-channel experiences should be one of your key long-term goals if you want to boost brand awareness and trust, but at the same time reach more potential customers and generate qualified leads. Keep in mind that the modern customer uses numerous digital channels to find relevant information, products, and the solutions to their problems, and it’s important for your brand to have a presence on all those channels in order to provide seamless support and guidance.

Having an omni-channel support structure is one of the key digital marketing trends and sales tactics nowadays, and a website chatbot would help you complete your omni-channel strategy. Installing a chatbot on your WordPress site will allow you to deliver a seamless brand experience when a customer ventures to your site from a social media platform, for example, by offering a consistent brand narrative and a quick fix to their problems.

Use a chatbot to boost your marketing strategy

In recent years, chatbot marketing has been on the rise across platforms and industries because of its ability to guide customers towards different solutions seamlessly and without them having to engage in conversation with a support agent. Not every customer wants or needs to talk to a human representative, as many people simply want a quick answer to their question in order to decide on a product. Whether they have a simple question or want to learn more about your products or services, you can easily guide them down the right path with a conversational chatbot on your site.

This is one of the best ways to use a chatbot in your marketing strategy, as the chatbot can offer different conversation paths based on the customer’s needs. As you might have guessed, this allows you to perfectly align the interaction with the user intent.

For a user who is looking for quality information but is not ready to buy anything, your chatbot can select some high-quality articles or direct them to your learning center. But for the customer who is ready to buy, the chatbot can help improve sales by allowing them to place an order immediately, or provide them with additional information that will prompt them to buy.

Leverage chatbots to drive business intelligence forward

Data science is quickly taking over as a key driver of business success in a world where technology is affecting the future of customer service and marketing. Now that millions of people are using online services and platforms to buy products, educate themselves, and build meaningful connections with their favorite brands, you need to leverage the right tech to collect and organize that valuable data.

The more information you have about your customers and your market, and the prevailing trends in your industry, the more equipped you are to make better strategic decisions and investments.

That said, you can’t capitalize on such vast amounts of data without the help of artificial intelligence, which is why AI-driven big data analysis is the key to the success of data science in your company. As you might have guessed, a conversational chatbot can act as another smart data collection tool that will generate valuable insights from customer interactions on your website.

You can use this data to drive numerous key processes forward, and optimize your approach to sales, marketing, and support based on the data that your chatbot collects automatically. You even take things a step further by using your chatbot to deliver engaging feedback options to your website visitors and inspire them to leave more detailed feedback to help you drive innovation and your entire business forward.

Over to you

Integrating a chatbot into your WordPress site, whether you’re using it as a SaaS site, an Ecommerce store, or a blog, would be a wise business move in 2021 and going forward. There are many benefits to having a conversational chatbot as a key part of your brand experience, and in the long run, you can expect this technology to help you acquire and retain customers with ease.

Don’t forget to give us your 👏 !

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5 Benefits of Chatbot Technology for Your WordPress Site in 2021 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

“Speech Recognition” August 2021 — summary from Arxiv, Europe PMC and Springer Nature

Arxiv — summary generated by Brevi Assistant

It’s challenging to personalize transducer-based automated speech recognition system with context information which is inaccessible and vibrant during version training. Experiments reveal that the design improves standard ASR model performance with about 50% relative word mistake rate decrease, which also substantially outperforms the baseline approach such as contextual LM biasing. In this paper, we provide AISHELL-4, a sizable real-recorded Mandarin speech dataset gathered by 8-channel round microphone selection for speech processing in conference scenario. Provided most open resource dataset for multi-speaker tasks are in English, AISHELL-4 is the only Mandarin dataset for conversation speech, supplying added worth for information diversity in speech neighborhood.

Subword units are commonly utilized for end-to-end automated speech recognition, while a completely acoustic-oriented subword modeling approach is somewhat missing out on. Experiments on the LibriSpeech corpus show that ADSM plainly surpasses both byte pair encoding and pronunciation-assisted subword modeling in all cases. The task of speech recognition in far-field settings is adversely influenced by the resonant artefacts that evoke as the temporal den….tion of the sub-band envelopes. Further, the series of actions associated with envelope dereverberation, attribute removal and acoustic modeling for ASR can be applied as a solitary neural processing pipeline which enables the joint learning of the dereverberation network and the acoustic design.

As speech-enabled gadgets such as smartphones and smart speakers become increasingly common, there is expanding rate of interest in building automatic speech recognition systems that can run straight on-device; end-to-end speech recognition versions such as frequent neural network transducers and their variants have lately emerged as prime prospects for this task. Automatic speech feeling recognition is a challenging task that plays an important function in all-natural human-computer communication. Among the main challenges in SER is information scarcity, i. e., insufficient quantities of thoroughly labeled information to construct and fully discover intricate deep learning models for emotion classification.

Please keep in mind that the text is machine-generated by the Brevi Technologies’ Natural language Generation model, and we do not bear any responsibility. The text above has not been edited and/or modified in any way.

Source texts:

• https://arxiv.org/abs/2108.07493v1
• https://arxiv.org/abs/2104.03603v4
• https://arxiv.org/abs/2104.09106v3
• https://arxiv.org/abs/2108.05520v2
• https://arxiv.org/abs/2104.02207v3
• https://arxiv.org/abs/2108.02510v4

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Europe PMC — summary generated by Brevi Assistant

This work concentrates on durable speech recognition in air traffic control service deliberately a novel processing paradigm to integrate multilingual speech recognition into a solitary structure using three cascaded components: an acoustic model, a pronunciation model, and a language version. We confirm the proposed method utilizing huge quantities of real Chinese and English ATC recordings and attain a 3.95% label mistake rate on English words and chinese characters, surpassing various other prominent strategies. History Clinicians routinely make use of impacts of speech as an element of mental condition examination. Individuals with predominantly positive v. adverse signs and symptoms could be identified with an accuracy of 74.2%. Goal To explore the impact of optimal power output of bone conduction hearing tools on speech recognition in silent and in sound in skilled users of bone transmission hearing gadgets. Outcomes Both speech recognition in quiet and speech recognition in sound improved substantially when using the gadget with high vs. lower maximum power output. Goal To contrast differences in audiologic results between slim modiolar electrode CI532 and slim side wall electrode CI522 cochlear dental implant receivers. Approaches Comparison of postoperative AzBio sentence scores in silent in adult cochlear implant recipients with SME or SLW matched for preoperative AzBio sentence scores in peaceful and helped and alone pure tone standard. Objective Congenital acoustic atresia triggers severe conductive hearing loss disturbing acoustic development. Individuals with aural atresia had fairly high proper response rates for monosyllables with low right response rates by patients with SNHL. Function Knowing target location can enhance grownups’ speech-in-speech recognition in complicated auditory atmospheres, yet it is unidentified whether children listen uniquely in space. This research study reviewed covered up word recognition with and without a pretrial cue to location to characterize the impact of listener age and masker type on the advantage of spatial cues.

Please keep in mind that the text is machine-generated by the Brevi Technologies’ Natural language Generation model, and we do not bear any responsibility. The text above has not been edited and/or modified in any way.

Source texts:

• https://europepmc.org/article/MED/32833649
• https://europepmc.org/article/MED/34344490
• https://europepmc.org/article/MED/34369239
• https://europepmc.org/article/MED/34399646
• https://europepmc.org/article/MED/34370598
• https://europepmc.org/article/MED/34403280

Springer Nature — summary generated by Brevi Assistant

Speech recognition in loud environments is just one of the long-lasting research styles however remains a very essential challenge nowadays. We use the general public Arabic Speech Corpus for Isolated Words, three noise degrees, and 3 noise types. This work proposes an unique stochastic deep resilient network for speech recognition. The novelty of the SDRN network is in making use of NOWOA to acknowledge huge vocabulary separated and constant speech signals. The internal schedule of silent speech offers as a translator for people with aphasia and maintains human — machine/human communications functioning under various disturbances. In the approach, the tattoo-like electronic devices imperceptibly connected on facial skin record high-quality bio-data of numerous quiet speech, and the machine-learning algorithm released on the cloud acknowledges properly the quiet speech and lowers the weight of the cordless procurement module. Automatic speech recognition might potentially improve communication by giving transcriptions of speech in real time. We tested the performance of three cutting edge ASR systems on two groups of people with neurodegenerative condition and healthy and balanced controls. In the field of speech recognition systems, existing work concentrates only on the classification of speech right into a stammering speech or a regular speech. Significant renovations consisted of in this research study contrasted to previous implementations is developing a new deep-learning algorithm, which improves speech recognition for people dealing with stammering. Determining people’s sensations when they talk is relatively simple as a result of the tone and language with which they express themselves. With view analysis formulas in combination with voice recognition and the basic usage of NLP, it is feasible to produce intelligent systems that enable the analysis of people’s sensations based on the audible message that they discharge.

Please keep in mind that the text is machine-generated by the Brevi Technologies’ Natural language Generation model, and we do not bear any responsibility. The text above has not been edited and/or modified in any way.

Source texts:

• https://doi.org/10.1007/s10772-021-09847-7
• https://doi.org/10.1007/s10772-021-09851-x
• https://doi.org/10.1038/s41528-021-00119-7
• https://doi.org/10.1007/s10772-021-09836-w
• https://doi.org/10.1007/s10772-021-09828-w
• https://doi.org/10.1007/978-3-030-68663-5_10

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“Speech Recognition” August 2021 — summary from Arxiv, Europe PMC and Springer Nature was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Where is the next great marketing innovation coming from? The advertising industry has changed drastically over the last decade, especially outdoor advertising. New technologies are pushing the industry forward with artificial intelligence, machine learning, voice search, and digital out of home (DOOH). Also, we need to consider the outdoor advertising advantages when used in collaboration with voice technology.

More and more advertising space is now available online, and digital out-of-home (DOOH) solutions have expanded significantly.

Most recently, COVID-19 has become a major disruptor in the world of outdoor advertising. It has disrupted the ways in which humans interact with the world, but, in non-pandemic years, people spend 70% of their time outside at work or school, commuting to shopping, or doing other activities. Outdoor is where OOH shines as a medium-allowing advertisers to reach those who spend most of their time outdoors.

However, outdoor advertising is again on the rise. There’s a lot to keep an eye on in the space, but marketers need to be ready for what comes next.

The current state of outdoor advertising advantages

Outdoor, or Out-of-Home, advertising is making a comeback. With users becoming more and more tired of being bombarded with advertisements online and on their mobile devices, outdoor advertising is ideal for businesses seeking authentic exposure.

Out-of-home advertising allows consumers to see a brand in their natural environment and contributes heavily to an attractive brand image. It’s effective in creating brand loyalty and remaining consistent through multiple brands, products, and services. It’s also a great way to drive traffic to your business, which can lead to an increase in sales.

Basic outdoor advertising advantages

Creativity

This translates to better methods of attracting the attention of your audience and getting them to see your banner.

People can never avoid seeing one

There is no AdBlock service for outdoor advertising, and people will always have their attention attracted towards banners or billboards.

It is open to new technologies

OOH and DOOH have always been welcoming towards new technologies that can help them perform better and retain more engagement data.

It grants exposure to millions of people

When choosing the right location, a single banner, billboard, or bus-side ads, it can generate millions of views. Also, if it does not reach the desired number of sales, it surely generates a lot of brand exposure.

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4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Budget trends in OOH & DOOH advertising

The total DOOH market in Europe is worth nearly €9.4 billion in 2021. While this is small compared to the €28.5 billion achieved in the traditional OOH market, we expect it to grow to €13 billion by 2024, while conventional OOH will remain relatively flat. DOOH is now the second-fastest-growing advertising medium (trailing only the mobile internet) and is predicted to account for over 30% of OOH revenue in some mature markets.

Outdoor advertising problems and how to solve them

Out-of-home advertising, TV advertising, and other forms of advertising that do not provide a saving option focus on connecting with people while they are on the go or busy-with-hands. It can be while they are outside their homes or home doing “after-work” chores or routines.

According to IAB, Out of Home (OOH) advertising channels lose parts of the budget dedicated to marketing by companies compared to online advertising channels. It happens because outdoor advertising channels have a lower interaction time than online platforms, and there isn’t any precise tracking. Let’s find out some problems marketing teams face when using these channels:

Reduced time of exposure

The OOH & DOOH ads and other similar media need to be assimilated in a short amount of time. In some cases, the audience might not have that short amount of time to evaluate and come to a decision regarding the ad. Targeting media that has a short exposure time is difficult.

Not anyone driving past a billboard is part of the target market. Exposure time is short, so messages are limited to a few words and/or illustrations. What if users want more information?

Difficulty in measuring the audience

It is challenging to measure the audience regarding these forms of publicity. For example, to provide reliable metrics on a (D)OOH system’s audience, you need to use heterogeneous research or technological tools. But, more than challenging, it is not possible to have valuable information for these audiences that you can share with the brand the ad is about.

No Lead generation possibility

One of the strong points of outdoor advertising is the vast audience they target. There is 100% Human traffic, ads are unskippable, no ad blocking option, and people can view the ad every day if they have the same walking routine. Does your company have this data? Who are these people? Is there a way to get their phone, email, or name if they are interested in what they see? The current technology does not provide that.

The outdoor advertising advantages of using Voice Technology as a solution

The solution is hidden in Artificial Intelligence, specifically in Voice Technologies. Voice is the most natural way for people to communicate with each other, and lately with brands. It is natural to ask with voice questions like where to find this or that service.

How is this possible: thanks to voice applications.

Users face every day a call for a link to click. Outdoor advertisements like ads in buses, buildings, and more need to generate awareness and increase numbers on marketing reports. The same happens with an ad displayed on a billboard or a bus station display. However, there is a lot of friction to access these links while in the real world.

Through voice applications, you can enhance the user’s experience from OOH advertisements by sending it to a voice assistant. It can be easily activated via voice on a mobile phone and then let the conversation begin. The experience with the brand is naturally extended.

What is a Voice Application?
A Voice Application is a voice-enabled software application that can respond to spoken commands or queries. Such applications can be used in the home, workplace, in the car, or on mobile devices. In our case, we are referring to voice applications like Alexa Skills and Google Actions.

How can Voice Applications work for Outdoor Advertising

When a brand requests a D/OOH marketing campaign, all your company needs to do is to create and integrate a voice application dedicated to that campaign. Ipervox will help you create the voice application, be it an Alexa Skill or a Google Action. This voice app will be designed to enhance the user experience with the ad and extend the ad’s time of exposure to the user.

Next, your company will take care that the creatives include the wake word of the voice application inside of them. The wake word will serve the customer to easily access the voice app, using a single voice command.

When the user sees the advertising, the only CTA for them is to activate the voice application for further information. Also, they will be able to save the link, access it later, and much more than that.

What are the pros of this methodology (there are no cons)

Voice Applications are the bridge between Outdoor Advertisement and target audiences that, for the moment, can give power to these marketing channels. They can generate a higher number of leads, users & potential clients data, deeper analytics, and more.

An extended time of exposure

Thanks to the voice application dedicated to that campaign for different advertising channels, the user can ask their smartphone with the wake word and get all the information needed without stopping what they are doing. The communication happens in less than a minute; thus, there is no problem if the user is moving or with busy hands. The voice app can inform the user directly or send an SMS/email on their phones with detailed information regarding the offer.

Easy and frictionless way to measure the audience

You can bring back OOH advertising power thanks to the voice applications. The company can easily, through Ipervox, have the data of all interested users that want to know more about the publicity they were exposed to. Once the user activates the voice app, we can get the user’s data, like email or even phone number. This also gives the possibility of distinguishing the users from specific ads, giving valuable data for A/B testing, or even ad location and channel distribution.

Lead generation for each advertisement

Each advertising done through these channels can have the possibility of displaying a call to action with voice. The marketing company can also quickly gather data for each ad. The users that will activate the voice app to know more about the product can now be converted into identifiable leads. This is compared to previously, where there was no tracking of the users that gained interest from the ads.

You have to consider that, without the use of Voice Applications, the user needs to act immediately, either because the ad moves too fast or because they have to keep going. They need to memorize something from the ad, be it the company’s name, some link URL that invites them to go and visit it, or price, product logo, and more. A lot of information, little time.

Using Voice Applications instead, can really enhance the existing Outdoor Advertising Advantages and much more. In this case, the user does not need to stop what they are doing. Since the ad will lead the user to the voice app’s activation, they can ask their smartphone by voice to activate the app that gives more information regarding the advertisement. They can also ask for more information to be sent to their emails or phones as a text message and learn more about it later. There is no need to take notes, remember anything, or leave it for later (and probably forget about it).

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Outdoor Advertising Advantages When Combined With Voice Technology was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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