Review platforms are a goldmine of valuable information that can be used in many different ways. At DataForSEO, we offer data from popular review platforms, enabling our customers to develop a variety of demanded solutions around it.
In the following paragraphs, we will provide examples of tools you can build with Business Data Reviews API and Amazon Merchant API reviews endpoints and calculate the cost of collecting review data with DataForSEO.
Review management tool
Reviews are essential when it comes to making a purchase decision. According to BrightLocal, 93% of buyers read reviews of local businesses to assess their quality, while 91% trust reviews as much as personal recommendations. What’s more, a product with at least five reviews is 270% more likely to be purchased than one with no reviews at all.
However, it’s not just reviews that matter — buyers also pay attention to owners’ responses to customer feedback. The same study by BrightLocal points out that 96% of consumers read businesses’ responses to reviews, with 40% saying they do it every time they search for a local establishment.
In addition to that, 53% of consumers expect businesses to respond to a negative review within a week. In case a company does it, buyers tend to trust it more: 45% of consumers say they’re more likely to visit a business if it replies to negative reviews. More importantly, companies that respond timely receive higher average ratings, which results in increased conversions.
Yet, most businesses don’t respond to customer feedback or do it selectively.
Their excuse is that manually managing dozens of reviews is highly inconvenient. Business owners have to keep in mind several logins and passwords, jumping from platform to platform to handle each incoming review.
However, there is no need to manually manage reviews today — many software providers offer review management tools that can dramatically simplify the work with reviews.
Let’s take a look at one of such tools — Grade us.
Among its review-related solutions, it offers a tool that enables companies to track customer feedback across more than a hundred platforms in one place, ensuring that no important review is missed.
Using Grade us, business owners can:
- respond to customer feedback;
- filter reviews by platform and rating;
- search reviews by keywords;
- add tags to reviews;
- share feedback in social media;
- get notifications about new feedback;
- import reviews to the platform from CSV and XLSX files;
- manage up to 100 campaigns.
While Grade us is undoubtedly convenient, it has two significant drawbacks.
1 First off, Grade us doesn’t display images in reviews, which may be a deal-breaker for some businesses.
Suppose a customer left a negative review complaining that the item they ordered arrived damaged and attached an image of it as proof. In this case, a business owner wouldn’t be able to analyze that review on Grade us and check whether it’s true. Instead, they would have to visit the platform where it is published, which takes additional effort.
2 Secondly, Grade us doesn’t display replies to customer reviews. When working with large volumes of reviews, businesses should be able to filter reviews with replies so that they don’t get mixed with unresponded feedback. Unfortunately, on Grade us, a user won’t be able to do that. They can only manually mark reviews with replies with the “Responded” label — but such reviews will still be displayed on the platform.
However, using DataForSEO’s review data, Grade us could solve both of these problems. Our API provides URLs of images attached to reviews, as well as owner responses to customer feedback.
With Business Data API, you can build a review management tool yourself, allowing your clients to monitor customer feedback across Google and Yelp (BBB, Trustpilot, and Tripadvisor are likely to be added soon as well).
Natural language processing model
In simple terms, natural language processing (or NLP) makes human language intelligible to machines. NLP models are trained on datasets — pieces of structured data they process and learn from. The more datasets are used to train NLP, the more accurate the model will become.
Natural language processing is widely used in:
- healthcare (virtual therapy);
- finance (automated credit scoring, fraud detection);
- retail and e-commerce (virtual assistants);
- cybersecurity (spam detection);
- marketing (chatbots, sentiment analysis), and many other industries.
With DataForSEO’s review data, you can train your own NLP model and build several tools around it.
Consider reading this blog post — it explains how to construct a relatively simple NLP model using basic Python programming skills and customer reviews data (reviews + ratings). In this particular example, the author uses a small CSV dataset that contains 11200 product reviews. Using DataForSEO APIs, you could get the same amount of reviews for only $0.84.
Since we operate in the marketing industry, let us provide you with a marketing-related use case of NLP.
Chatbots are artificial intelligence systems that enable customer engagement via messaging. They are highly valuable to businesses for several reasons: chatbots increase user engagement, improve lead generation, collect customer data, and, most importantly, reduce customer service costs.
Let us demonstrate how useful chatbots can be on a real-life example — Harry Rosen’s chatbot.
When entering the store, a help icon appears in the upper right corner. If clicked, the visitor is offered to start chatting with Hailey, the company’s virtual assistant. They can ask pretty much anything and expect a meaningful, human-like answer.
Using the chatbot, Harry Rosen solves three issues at once:
- It reduces customer service costs. Instead of hiring someone to provide customer support via chat and paying them, it uses an AI-powered system that can do the same work for little to no cost.
- It improves user experience. A user can get an answer to any question in less than five seconds (no customer support team can reply to inquiries that fast).
- And last but not least, the chatbot removes interpersonal communication barriers. Shy individuals are often afraid to bother a customer support manager: with virtual assistants, the fear is completely gone.
That’s why chatbots are demanded in a variety of industries. And that is also the reason the chatbot market is growing at a rapid speed: Insider Intelligence predicts that consumer retail spending via chatbots worldwide will have reached $142 billion by 2024 — up from $2.8 billion in 2019.
Given that chatbots use natural human language (which can be found in any review), you can use DataForSEO’s review data to train your own chatbot and become a chatbot provider.
Or you can build another in-demand solution around the NLP model trained on review data — a sentiment analysis tool. This tool can help businesses in many ways, which is the topic of the following paragraphs.
Sentiment analysis tool
Simply put, sentiment analysis is the process of detecting positive, negative, or neutral sentiment in text information. Based on NLP models, such instruments analyze text data on a large scale, providing businesses with valuable sentiment insights that can be later used for:
- Improving product or service. By understanding the sentiment around its brand, a business can learn what its product or service currently lacks and use that information to improve its offering.
- Building insightful data-based marketing strategies. Sentiment analysis helps understand the motivation behind customers’ purchasing decisions. Using that knowledge, a company can build data-based marketing strategies.
- Enhancing customer experience. By finding out what clients dislike about their company (it may be too long response time from the customer support team, issues with delivery, or pretty much anything), business owners can work on the problems and deliver a smoother customer experience.
To better demonstrate how sentiment analysis can help businesses, let us provide a real-life use case.
Improved hotel recommendations for an international travel company
An international travel company wanted to improve its hotel recommendations for customers. In order to do it, they decided to analyze thousands of hotel reviews on popular review platforms. However, their goal was to go beyond star ratings as reviews and ratings are very subjective: one person’s 5-star rating can be another person’s 2-star rating. For instance, some people might complain about the noise level at a hotel because of the nearby bars, while others might choose to stay precisely because the hotel is close to the nightlife.
Thus, recommending hotels based on their reviews and ratings wasn’t a clever decision. Instead, the company decided to analyze the sentiment in hotel reviews using the Repustate sentiment analysis tool. Based on the semantic aspects of each hotel’s review provided by the tool, the company has managed to create a smart search algorithm that offers personal hotel recommendations depending on a traveler’s preferences. For example, a traveler might type something like “hotels with the best breakfast in Florence” and receive a list of relevant recommendations.
That’s exactly how sentiment analysis tools help businesses become better. No wonder the global sentiment analysis software market is expected to reach $4.3 billion by 2027.
Using DataForSEO’s reviews data, you can train your NLP model to analyze sentiment in text information and then transform the model into an in-demand sentiment analysis solution. The best part is – you don’t have to overpay for review data as we offer reasonable prices.
Cost of collecting review data with DataForSEO
The cost of using review endpoints depends on three factors:
- Number of reviews you need to collect daily.
- Number of requests you will send to the APIs.
- Task execution priority.
Let’s go through the points one by one.
Number of reviews
We charge for every ten reviews returned in the API response. Note that our system processes ten, twenty, or thirty reviews in a row depending on the selected review platform. You can find the details for each review source on our pricing page.
Number of requests
You will be charged for every separate request sent to the API. Therefore, it’s better to optimize the number of requests where possible. Note that you can receive a maximum of 4490 reviews per request: if you need, say, 5000 reviews, you have to send two separate requests.
Task execution priority
We offer two task priorities — Standard and High. If your tool requires delivering results as fast as possible, it’s better to use high priority with a turnaround time of up to 1 minute. If you don’t require delivering fast results, you can use standard priority. Its turnaround time is up to 45 minutes (usually, results are delivered much faster), while the price is twice lower.
|Priority||Price per 10/20/30 reveiws||Price per a task|
See our pricing page for more information.
With all of the above in mind, we can easily calculate the cost of running a review management tool powered by DataForSEO.
Suppose you have 25 clients, each of which receives 200 new reviews on Yelp daily. To collect them, you have to send 25 requests to the Yelp endpoint every day. Consequently, you will be paying for 25 requests plus 5000 reviews daily. Once again, the cost will depend on the chosen priority.
|Priority||Daily price of reviews||Daily price of tasks|
The total daily cost of the tool (high priority) will be $0.79, which is $23.7 per month.
The total daily cost of the tool (standard priority) will be $0.39, which is $11.7 per month.
Having review data at your disposal, you can build a variety of in-demand tools widely used by businesses around the world.
You can develop:
- Review management software that will help your customers handle multiple reviews coming from different platforms.
- NLP model that can later be transformed into a chatbot, sentiment analysis software, or other valuable instruments.
From our side, we will provide you with structured review data from major review platforms:
Try Business Data API and Amazon Merchant API for free today — create your account and receive $1 to make your first calls to the Reviews endpoints.
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