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“Natural language processing” (NLP) sounds complicated, but its applications are simple. Chances are, you already use NLP dozens or even hundreds of times per day.
- If you use predictive text on your phone, you’re using NLP.
- When you search for something on Google, you’re using NLP.
- If you ask a voice assistant like Alexa or Siri a question, you’re using NLP.
What exactly is natural language processing? What do you need to know about it? What impact does it have on digital marketing? Let’s find out.
What Is Natural Language Processing (NLP)?
Language is natural to humans, but the same can’t be said for computers. Understanding the context behind our words is a huge challenge for them. NLP is all about changing that.
Natural Language Processing is an area of artificial intelligence (AI) that leans on disciplines like computer science and computational linguistics to enable computers to interpret, comprehend, and manipulate the often arbitrary, ruleless world of human language. As such, its ultimate goal is to help computers make sense of the things we say in a way that adds value.
As I noted above, NLP has a ton of use cases, many completely embedded in our everyday life. For instance:
- Translation tools like Google Translate use it to produce translations between languages that make sense, rather than just a literal word-for-word translation
- Word processors (think Microsoft Word and Google Docs) use it to assess the grammatical accuracy of written text
- Call centers use interactive voice response applications to respond to certain customer requests
It’s also the driving force behind search engines like Google becoming “smarter.” While keywords are still highly valuable, search behavior is becoming more complex because we expect search engines to understand what we mean. Consider the following search:
As humans, we understand the searcher is Brazilian and wants to know if they need a visa to visit the US.
Previously, Google struggled to discern the true meaning, so it served an unhelpful result for US citizens visiting Brazil. However, advances in NLP now allow it to understand the importance of the common word “to” in this query, thereby enabling it to provide a more relevant result.
NLP vs. AI vs. Machine Learning
To a non-computer scientist, NLP sounds a lot like machine learning and AI. In reality, all three are closely intertwined, but subtly different. To understand their relationship, you need to understand a third term: deep learning.
- Artificial intelligence encompasses anything we do to make machines smarter, whether that’s a software application, a smart fridge, or a car.
- Machine learning is a subset of artificial intelligence covering anything to do with systems learning for themselves, free of human intervention.
- Deep learning is a subset of machine learning, applied specifically to large data sets.
Where does natural language processing fit in? Well, it’s a part of AI, but it also overlaps with both machine learning and deep learning.
The Evolution of Natural Language Processing
While it sounds hyper-modern, natural language processing has existed in one form or another for several decades, although it’s come a long way since the early days.
The History of Natural Language Processing
- Started in the 1950s as machine translation, when linguist Leon Dostert of Georgetown University used an IBM 701 computer to translate Russian to English.
- The Soviet Union soon launched its own competing machine translation project to translate English into Russian. By 1964, the USSR had become the world leader in machine translation.
- In 1966, Joseph Weizenbaum programmed the first chatbot, named Eliza. It was only capable of holding very limited conversations, mostly based on reordering the user’s input to form questions.
- Whereas these early examples of NLP were held back by the need to develop complex sets of handwritten rules and parameters, in the late 1980s the field was revolutionized by early forms of machine learning.
How it Is Now: The Effects of NLP on Digital Marketing
Marketing has always been about context; getting into the heads of our audience to understand what they are (and aren’t) telling us. It helps us answer questions like:
- What persuaded them to click our ad?
- What made them bounce off the landing page?
- What made them add to cart, then abandon?
NLP gives us more context by helping us understand not just the exact words being used, but what they mean. That makes it hugely applicable to marketing. For instance, voice search is wholly dependent on NLP, as it uses complex algorithms to understand a user’s commands and discern the most helpful response.
How to Use Natural Language Processing in Marketing
By now, you’ve probably started to understand just how useful NLP is to marketers, but in reality, the use cases are likely more substantial than you’ve imagined! Here are some of the most relevant and fascinating.
Understanding Customer Sentiment
Whether you’re a household name or a tiny startup, you need to know when people are speaking about you online and what they’re saying.
NLP software helps by analyzing social posts, reviews, and user-generated content related to your brand. Hootsuite’s sentiment analysis tool, which analyzes the language used in brand mentions on social media, is a super simple example of how this looks in practice:
There are many more complex, dedicated tools that use natural language processing to monitor sentiment across digital channels, from social media and review sites to blogs and forums. Examples include:
- Social Searcher
- Social Mention
- Critical Mention
Sentiment analysis tools are powered by one of the following three types of algorithms:
- Rule-based: These use a set of manually determined rules to automatically predict the sentiment of a given social mention, review, blog post, etc.
- Automatic: Automatic algorithms rely solely on machine learning techniques to understand user sentiment.
- Hybrid: These systems combine both of the above approaches, often producing more accurate results.
Building Chatbots for Customer Service and Lead Gen
Why do people use chatbots? Well, as this study shows, there are a bunch of reasons. They’ve become a key customer service tool and an invaluable part of the buying process, helping people find quick answers before connecting to a real human for more in-depth discussion.
Natural language processing is the technology that powers chatbots. Without it, they’d be limited to extremely simple interactions. Sure, it’s normally pretty clear that you’re speaking to a bot rather than a person, but this doesn’t seem to be a problem for users. In fact, 54 percent would always choose a chatbot over a human if doing so would get them an answer 10 minutes faster.
Identifying Trends with Natural Language Processing
You’ve probably used a news aggregator or RSS feed before to find regular information about a specific brand, product, or topic area. Well, NLP takes things a lot further by finding that information, then summarizing all the key points in just a split second. That’s invaluable if you’re trying to identify the next big trend in your market.
Scaling Content Creation
Artificial intelligence is capable of writing fiction and plausible news stories, so it’s no surprise that it’s also capable of much simpler content creation tasks.
I’m not saying you should turn your whole content marketing strategy over to robots. For now, at least, you’re best leaving anything more creative in the hands of humans.
What about content creation at scale though? Say you’ve got a huge e-commerce site with thousands of products; creating descriptions for all those individual pages would be a copywriter’s worst nightmare!
That’s where AI-driven content, underscored by natural language processing, becomes invaluable. Indeed, e-commerce giant Alibaba has already introduced an AI copywriter capable of handling all that labor-intensive writing. Clothing brands like Dickies and Esprit use it to create Chinese-language product descriptions.
Leveraging NPL for Voice Assistants
About a quarter of US adults own a smart speaker.
While we’ve barely scratched the surface when it comes to realizing the marketing potential of these devices, there have been a few standout examples. Amazon Echo users were given the chance to explore the dystopian setting of the TV show Westworld, while Netflix promoted the second series of Stranger Things by allowing Google Home users to “chat” with the character Dustin.
Of course, as I’ve already discussed, none of that would be possible without natural language processing to translate speech into text, semantically match that text with the device’s knowledge base, then provide a helpful answer.
NLP Marketing Case Study: Tenable Doubles Conversion Rates
While the phrase “natural language processing” might be new to a lot of us, the technology itself has been around for a long time. So it’s no surprise that brands are already using it to deliver impressive results.
One great example is the cybersecurity company Tenable. It was facing two big problems with its sales process:
- Leads were taking too long to reach a sales development representative (SDR)
- SDRs faced a bottleneck in engaging with leads outside office hours, or at points in the day when they were busy or away from their desks
“If you’re not following up with them, there’s a good chance that a person’s going to say ‘I don’t even remember filling out that form,’ or, ‘I don’t even remember going to your website,’” noted Matt Mullin, Tenable’s Senior Director of Global Marketing Operations and Technology.
By implementing a business development strategy that placed smart chatbots front and center on its website, the brand saw a 30% upturn in the quality and length of conversations with prospects, while conversion rates doubled.
Uses for Natural Language Processing Besides Marketing
NLP didn’t start as a marketing solution, and its use cases extend way beyond marketing. Here’s just a handful of other uses for the technology:
That’s right: NLP isn’t just about marketing, it’s been helping us fight the pandemic. Alibaba Group’s R&D institute, the DAMO Academy, built an NLP-based system capable of using chest scans and deep data to diagnose Covid-19 infections in just 20 seconds, with 96% accuracy.
Identifying & Analyzing Competitors
Every business uses some degree of competitor analysis to inform strategic direction. However, in an increasingly globalized world, it’s not always obvious who your biggest rivals are.
You might think you’re competing with the brand down the road, when in reality your customers are being poached by a company on the other side of the planet.
Again, NLP has a solution. Tools like Zirra (and many others) are capable of automatically mapping the competitor landscape, creating a list of companies ranked by how closely related they are to your brand.
Lenders use credit scoring to understand whether an individual or business is a safe bet for a loan or some other form of borrowing.
However, that’s not always possible in emerging markets, where key records may not be as readily available.
Now, brands like Lenddo are using natural language processing to make lending decisions based on non-traditional data sources that encompass an applicant’s entire digital footprint, from their browsing habits and social media usage to e-commerce transactions and even psychometric profiling.
For years, recruiters and HR teams have been using technology to scan resumes and cover letters for certain keywords.
NLP is a logical extension of this. Rather than fixating on specific phrases, it’s capable of analyzing and extracting the information that’s most relevant to the specific role.
That allows employers to automate the lengthy process of sifting through CVs, safe in the knowledge that those who make it through will be up to the task.
Natural language processing certainly sounds advanced, but it’s based on the old-fashioned marketing principle of understanding our customers better.
Rather than directly asking your audience what they think about your brand or product, what challenges they’re facing, or what their goals are, NLP helps you to discern their feelings, motivations, and opinions from the words they use.
NLP is another step toward removing the guesswork from our marketing decisions, enabling us to reach the right people, at the right time, with the right messaging.
How are you planning to use national language processing in your marketing strategy?
The post How Natural Language Processing Affects Digital Marketing appeared first on Neil Patel.
Even during COVID-19, you can still get financing via crowdfunding. But you should know these recession crowdfunding terms.
Do You Know These Recession Crowdfunding Terms?
Crowdfunding can seem to be a bit of a mystery. Why are people willing to part with their cash in this particular manner? There are a lot of recession crowdfunding terms thrown around all the time and they can sometimes get confusing. So consider this your primer on some basic crowdfunding terminology.
Because even if you do not think you will use this method of fundraising, you will probably encounter it all the same.
But before going any further, does crowdfunding ever actually, you know, work?
Crowdfunding Success, in a Nutshell
For some companies which crowdfund, the rewards are great. According to Crowdfunding Blog, the single most successful crowdfunding campaign was for the Pebble Time Smartwatch. And that was as of November of 2018. But before you run out and buy one, note that they are now a part of FitBit.
As in, they went out of business in July of 2018. And this is a business which raised over $20 million in 2015. That is no typo. And in point of fact, Pebble holds three of the top six spots in the biggest crowdfunding successes of all time. Together, these three crowdfunding campaigns took in a staggering $43.39 million. This is about $8 million more than the town of Huntington, New York (population 203,264) budgeted for highways in 2018.
Hence there is one thing that should be clear to all. Runaway crowdfunding success is no guarantee whatsoever of actual success.
But now it is time to get to the recession crowdfunding terms themselves.
Important Recession Crowdfunding Terms: Project
A project is what you are asking for money for. Projects can take a few months or even years. The more complex your project, then (usually) the longer it will take. The person starting the project is generally called the project runner or the project creator.
Projects can be for goods or for services.
What frustrates you the most about funding your business in a recession? Tell us in the comments.
Important Recession Crowdfunding Terms: Donors
The people who donate to the project are called donors. Or sometimes they are referred to as contributors or backers.
On rare occasions, they may even be called investors. However, such a word connotes a far different relationship. Many crowdfunding platforms shy away from such a term. And this is for good reason. It is because investors and investments may come under the purview of the SEC. The Securities and Exchange Commission exists in order to protect investors. This is in ways not current available to donors 0r other contributors to the success of businesses.
Hence, unless the crowdfunding platform is specifically for investing in companies, more like angel investing, you are not too terribly likely to see the investor.
Important Recession Crowdfunding Terms: Campaign
The act of requesting money on a crowdfunding platform is called a campaign. This is the soup to nuts of crowdfunding. So it covers everything from the first pitch to the final collection or perk distribution.
Important Recession Crowdfunding Terms: Donor Levels
In general, donor levels refer to the amount of rewards which are on offer for a particular size donation. Note: I will get to rewards in a moment. Your donor levels might look something like this:
- $10 fountain pen (100 available)
- $20 includes $10 level plus a tee shirt (50 available)
- $50 includes $20 level plus a framed picture (30 available)
- $100 includes $50 level plus dinner with the project runner (10 available)
- $500 includes all other perk levels plus a new car (2 available)
Donor levels are limited by your imagination and your capacity for handling complexity. After all, five separate donor levels mean you are keeping five separate lists. If you are well-organized, then this is possible. But it is not easy. Five separate donor levels are plenty, particularly for people running their first campaigns.
Truthfully, you will be a far happier person if you cut the number of donor levels to no more than three.
Of course, time and budget should be considerations for anyone. But that is not just the case for crowdfunding.
What frustrates you the most about funding your business in a recession? Tell us in the comments.
Important Recession Crowdfunding Terms: Rewards (Also Known as Perks)
One basic about crowdfunding for creative projects is that you will need to provide incentives for your donors to open up their wallet. Crowdfunding to help someone with their medical expenses is a different animal. So let us get back to crowdfunding for business funds.
This is where perks come in.
Your rewards can be nearly anything. But it can quite literally pay to have them relate directly to your project.
For example, if you are crowdfunding to get enough money to back your new smart phone invention, then your rewards probably should not be your grandmother’s blueberry muffin recipe. And this is no matter how wonderful it may be. Instead, you could base your rewards around your invention. So this could be everything from offering a case to an extra battery or charger. Or you might even offer an app which only your donors can download.
A Word to the Wise about Rewards
Rewards are a very real part of crowdfunding and they can often be a part which project creators do not take into consideration. Sometimes, we think a product will go to market in, say, a year. But circumstances change, and now one year turns into two. So be it – this sort of thing happens all the time.
But it is an issue if your perks are dependent on your product going out the door. So if you need to fulfill perk promises to 10,000 people, you will likely find you need to do one of any of these things:
- Delay your product launch
- Hire someone to do fulfillment for you
- Offer alternative perks (if you can)
Reneging is not an option, and it can get you on the wrong end of a lawsuit if you are not careful.
A fourth option is delaying perk fulfillment. Not every donor will go for that.
A For-Instance on Perk Level Complexity
Sending out so many perks is a major task. It can take months to get everything out the door.
Why does it take so long? Consider the degree of complexity. Let’s go with an easy number: 100. So let’s say you have 10 separate perk levels and they each have 10 slots. Once an eleventh person wants a certain perk level, they just plain can’t have it, as it’s gone. Are you with me so far?
Your ten separate perk styles may be of differing weights. So this means they will have different shipping costs. If any of your 100 donors are outside of the United States, then you will have to pay more to ship to them as well. Plus of course you have to make sure all of the addresses are complete and correct.
It becomes even more complex when your perks do not fit into such neat little buckets. This is where you have, say, eight perks. And you might have anywhere from 12 to 1,000 people who are supposed to be getting them. Plus some people may have donated twice and are waiting for two separate perks. Or maybe even more.
See how ugly and difficult this can get – fast?
What frustrates you the most about funding your business in a recession? Tell us in the comments.
Getting Around This Problem
The easiest way to get around these issues is to offer intangible perks. In our smart phone example, the exclusive app would fit the bill nicely. Your best bet is to make the intangible perk good for the largest number of donors possible.
Hence if your lowest level is $10, and you have 100 of those slots, then you could just give 100 people a download code. This is a lot faster than figuring out postage for all of those donors. Plus, with an intangible perk, technically the number of perks is effectively infinite. But scarcity gets people interested, so you might not want to make the downloads never-ending.
For the more tangible perks, leave them for far smaller groups, such as the 25 people who are at your two top donor levels. Mailing to 25 people is far easier than it is to mail to 10,000 people. And this is so even if the mailings are difficult.
But I Don’t Have Intangible Perks!
No? Then what do you call a coupon sent in email? See, there are ways to offer intangible perks even when the entire business operation is very, very tangible. Coupons have been around, seemingly, forever. People will gladly print them off or carry them in their smartphones for scanning.
Or there can be discount codes, which are virtually the same thing, except with no designing of a coupon to be cut out or scanned. Amazon, for example, gives these out all the time. And the vast majority of backers will know exactly how to use them.
Important Recession Crowdfunding Terms: Takeaways
There is, of course, more to recession crowdfunding terms than this. But these should at the very least get you started. And as always, if you have any questions, please feel free to ask them in the comments section of this blog post.
In Part 2, we will talk about types of crowdfunding and types of platforms. There’s more to this unique form of financing than just recession crowdfunding terms.
The post Recession Crowdfunding Terms You Should Know, Unveiled appeared first on Credit Suite.