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No matter what industry you may belong to, your customers are an inseparable part of your business. Feedback from them can be an extremely valuable resource that, if dealt with right, can bring both benefit and efficiency. But how do we sift through, measure, analyze, and draw conclusions from feedback?
I mean, at Smartproxy, we, too, take our time to understand our customer base and their needs. Without it, we wouldn’t be much of a business. So, what is this sentiment data?
Customer sentiment data can be extracted from surveys, emails, texts, comments, reviews, etc. This data can show how your customers feel about your company, product, or service. It’s a great way to assess their emotions, problems, anxieties, and even issues they might have with your business. Sentiment data can also be considered as a KPI (key performance indicator) in teams that work with customers like customer support.
Sentiment analysis is the process of extracting qualitative data from various forms of digitally written text with NLP (Natural Language Processing), machine learning, and sometimes even AI. The aim of this analysis is to detect customers’ emotions regarding a business.
While the methods of sentiment data analysis vary heavily from company to company, it usually starts by pinpointing specific resources to target for information. It could be a blog, social media posts, forums, webchats, etc. So first, it’s best to research what form or platform your customer base prefers to share their opinion.
After that, it’s all extremely customizable – you can start with analyzing how your customers feel about your company, whether they trust you and the service, how useful they find your products… Honestly, there’s no end to the information that you can gain with the proper tools and techniques!
Customer support teams often play a key role in customer-business relations as they are on the front lines of communication with customers. If a customer support team has strong and reliable people, customers will pick up on that and may even disclose more information than just reporting a problem.
So if companies invest in a quality communication infrastructure, gathering specific information from chats and messages is much easier. Additionally, companies will be faster in dealing with issues early on.
Sentiment data is one of the most valuable resources that can help drastically improve existing products or services. In some cases, the information learned from customers’ feedback can even push companies to develop something new and better.
Having a great customer support team is awesome, but you can’t base your brand sentiment analysis solely on the feedback given to them. More often than not, particularly dissatisfied customers will leave extensive reviews on other websites or even competitor websites mentioning the challenges they might have faced with your brand. Relying just on feedback from internal reviews could greatly skew data and lead to bad business decisions.
So researching where your brand is getting mentioned can help identify any issues faster and with better accuracy. Also, it helps to understand your niche better and grasp what people expect from it. Or even identify security breaches if someone leaks sensitive information.
A good product idea might only sound good to you if you don’t research the market first – especially today when there’s such a variety of similar if not identical products. Before putting out anything new into the market, it’s important to spend time overviewing it, so, once again (surprise surprise), knowing the needs and fears of potential customers is key.
Of course, keeping an eye on your competitors, their products, and prices can be a strong indicator of finding your footing in the market. But people that you seek to attract usually know best what they need. So yes, you can do a sentiment analysis from people who aren’t your customers yet but belong to the specific demographic that you aim to attract with your product or service.
Dealing with sentiment data can undoubtedly lead to high-converting results. Still, it also takes a lot of time and effort to set up a sound system of gathering feedback, filtering it, and making the right decisions. And that’s only one challenge among a few bigger ones. Sentiment data is based on human emotions, which can be incredibly difficult to understand and deal with. Some other major issues might include:
No matter how advanced your software or application can be, the complexities of how people communicate their emotions will always be a tough nut to crack. Not to mention the extensive use of irony and sarcasm. Seriously, not every person understands when someone is being sarcastic or ironic, so how can a computer?
Some people leave great big walls of text when they write feedback messages, and that’s amazing. But not everyone is as gracious with their opinions. Particularly younger generations communicate with short phrases that can be interpreted both in a good way and a bad way.
Or they can mean something completely different altogether! I mean, leaving a “lol” comment next to a question asking whether a customer is satisfied with a product can mean they’re just that happy with the experience they had. On the other hand, they can drop a “lolz” because they simply don’t care enough about you wanting to improve.
Sometimes people leave false reviews and feedback either on a company’s page or on other pages on the internet. It can come from a person having a bad experience unrelated to the product and blowing their feedback way out of proportion.
Another instance is when people are not informed enough about something. Let's take the well-known subject of a refund policy. If a person ends up losing money, they'll more often leave a negative review rather than actually look up the policy to learn that, in fact, they were wrong. And once that ball starts rolling, other customers with similar experiences could join in. And this can become a huge problem because it won't matter at that point if your company has a well-defined refund policy.
Finally, even though frowned upon, the practice of leaving fake negative reviews about competitors or their products still exists. Even though people are much more informed and likely to look up whether or not something is true, it can still skew your sentiment analysis results and throw you off track.
Customer reviews, opinions, and experience can be a true treasure trove of invaluable information that can kick off your business. But it’s an incredibly grueling and time-consuming process. Gathering any kind of information in bulk online is. That’s why web scraping is such a popular method of extracting massive amounts of data.
There’s a reason why web scraping is becoming more and more popular among non-IT specialists. Freelancers, business owners, marketers, SEO specialists – they’re all turning to this data gathering method as it allows them to locate the required information and extract it in impressive quantities.
If a company has a huge customer base that is also active in giving feedback, web scraping becomes a must-have step. Sure, it has a learning curve and will require specialized knowledge, but there are options for that as well.
Scraping software can be built from scratch and highly customized to fit your specific needs. If you are wary of coding, you can buy ready-made scrapers that, get this, don’t require any coding knowledge. Like our very own No-Code Scraper, for instance.
Whether you choose to build your own scraping tool or buy it, with its help, you can extract vital information and build knowledgeable databases. These databases can serve as the basis on which you’ll be able to build not only the understanding of what your customers need. You’ll also have a growing pool of insights that you can use to improve existing products and services. Or better yet, create something new!
Web scraping makes the data world go round faster, that’s for sure. But it wouldn’t be half as effective without proper proxies. Even though gathering publicly accessible data online for legitimate purposes is perfectly safe and legal, some websites track scrapers and crawlers. So if your scraper is going all Rambo and making multiple connection requests at a time – you’ll end up fighting myriads of CAPTCHAs and IP blocks.
Proxies work as an intermediary between you and the internet. There are different types of proxies, each one better suited for a particular task, but for web scraping, rotating residential proxies are one of the best choices. These proxies offer a network of fast, secure, and unique IPs from household devices. Besides, with a rotating session in action, you’ll be assigned a new IP with every new connection you make, becoming almost unblockable.
If you’re more experienced in this area, datacenter proxies might be a better choice for you both practically and financially, as they’re cheaper than residential proxies. Sure, they’re made up of artificial IPs that can be detected and blocked easier, but if you’re working on small-scale (and large scale, too, if your targets aren’t so sensitive) web scraping projects, and you know what you’re doing – power to you.
Proxies shield you from blocks, speed up the web scraping process, and help deliver the results faster. Armed with these tools, you can choose multiple ways to gather different customer sentiment data.
As awesome as your customer support team can be, sentiment data and proper ways of analyzing it are crucial in understanding your customers. Every bit of genuine feedback helps, especially the negative ones, as they pin-point existing pain points that need to be addressed.
And with each resolved issue, your company can climb a bit higher both in the trust and popularity meters.
Ella’s here to help you untangle the anonymous world of residential proxies to make your virtual life make sense. She believes there’s nothing better than taking some time to share knowledge in this crazy fast-paced world.
To put it shortly, it’s a specific branch of artificial intelligence that deals with programming systems to understand and analyze human language. You can think of NLP as a sort of bridge that allows humans to communicate with computers. If you’d like to learn more about this topic, we have a great detailed blog post dedicated to the application of computer sciences to process language.
Since the most common way people communicate their feedback, both good and bad, is in the form of a comment or email even – the main data that’s used is text. And it’s really useful, as it allows program software to search for specific keywords which makes it that much easier to get valuable information in a fraction of the time it would take to do it manually.
Sure, there’s always the risk of not capturing all feedback due to typing errors or different expressions when talking about the same thing. But it’s still an efficient way to filter and extract specific information.
Probably the best method would be the hybrid approach. This method is the most modern and advanced one yet as it allows us to look at sentiment data not just from the NLP side but also gives an element of customization. This leads to more data and increased accuracy of the data.