For an eCommerce store, product descriptions do what sales agents do in a brick and mortar store. Right product descriptions convince your customs to buy your products. Why do some products connect instantly with the customers? Let’s look at some points that will persuade your e-store visitor to make a purchase.
Self Explanatory Descriptions
It has more to do with the way the product is placed for the customer with the appropriate words and Images. Product Description is never about the number of words but how you put it across, self explanatory descriptions do the job for you. For Eg:
Here, in one glance the customer or a visitor to the e-store can make out the details of the product with all the relevant information.
Use Keywords Wisely
Use words in title and descriptions that a customer is likely to type while searching for a product helping in SEO rankings. Free keyword tools such as GoogleKeyword Planner and Keywordtool.io, or paid platforms like SEMRush and Ahrefs help to perform in depth keyword research.
You need to find a keyword with search results of 100-10,000 keywords which are marked as “low difficulty” or “low priority” and include them in your product descriptions. Placing keywords in product descriptions especially in product titles does increase your e-store search rankings. Look for ways to include keywords in the ‘Title’ , ‘Meta description’ , ‘Alt’ Tag and product description body.
Turn Features into Benefits
Be specific about your product. Making dry statements like “very good quality “that generalize your product doesn’t help. Putting across each product feature with its benefits brings out the credibility of the product.
For example: WOODBAY Men’s Grey Running Shoe
Product Description:
Breathable mesh and synthetic upper for natural movement
PHYLON midsole for optimum comfort
Crafted for simple support these running-inspired slip-ons feature textile mesh.
Make it easier for your readers to Imagine
A customer cannot touch and feel your products during an e-store browsing. You need to let customers imagine how they would feel having the product in their hands. Practice writing lines that intrigue the user with words such as imagine, discover, experience and explain to the reader the positive feelings of owning and using your products.
5. Show them positive reviews of your product
It builds trust among your customers. Ask you customer’s reviews about your product during browsing and also after a purchase. Most customers after a satisfying purchase would be happy to put a good work across. Indicate reviews and rating in each product description page to increase the visibility of the feedback.
6. Images and Other Media (Vidoes, Brochures)
Keeping text descriptions short and featuring your product through images, videos, graphic bullets, icons enables to get the right information to the customer.
Data Description writing services is a niche area that Altius specializes in, enabling it to showcase your products convincingly in order to connect with your potential customers. Altius understands your audience so that the most relevant information is pulled out about your products and business, and projected through skilled content description.
The world is digital more than it was a year ago, with Covid-19 pushing most human activities online. There is a huge surge in the demand for information online. Web pages, email, science journals, e- books, social media websites, news feeds provide a lot of data. In order to sort the data into information and make sure that it reaches the target audience fast is what text classification is all about.
According to IBM, 80 % of all information is unstructured and companies have hard time extracting required information from textual data with analyzing, understanding, organizing and sorting taking a lot of time.
As the CEO and President of Amazon, said in his annual shareholder’s letter, over the past decades that computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern machine learning techniques make it easier to do the tasks for which tracing the precise rules is much harder. – Jeff Bezos
This is where auto-classification comes in, as the name implies it is classification of text into categories. Tasks are automated using machine learning making the whole process super-fast and efficient. Artificial Intelligence applies machine learning, deep learning and other techniques that make tasks faster. AI has enabled IoT that uses technology to make smart Televisions to Flasks.
Reasons for Leveraging Text Classification with Machine Learning
Speed
Automating the process of analyzing and organizing data which is in the form of text results in much faster and efficient results. Reading and restructuring each text is time consuming for the human mind’s.Machine learning enables analyzing millions of texts at a fraction of cost.
Real-Time Analysis
Companies could use real – time analysis for critical situations to take immediate action. Text classifiers with machine learning can make accurate predictions in real time that can be used to make decisions right away.
Accurate Results
Machine learning with text classifications outputs accurate results consistently. Humans make errors due to fatigue, boredom and distractions that are overcome by text classifications.
Applications of Text Classification
Emotion Analysis
It involves an automated process of scanning texts for positive, negative or neutral emotions. It is also called sentimental analysis. Emotion Analysis covers a range of applications like product analytics, brand monitoring, customer support, market research, workforce analytics, and much more.
Topic Labeling
The topic is studied carefully for clubbed for related subjects. It involves rearranging of data according to the related topic, for ex: sorting out the latest news of the hours, organizing customer reviews by its topic or clubbing together
Language Detection
Language detection is an important element of text classification; it is the process of classifying text according to its language. These text classifiers are used for routing purposes (e.g. route the related customers to according to the services they are looking for).
Purpose Detection
Text classifiers are used for detecting the purpose of customers from their conversations like phone calls, email, chat and social media posts that is used to promoted customized products or for product analytics
For example, the following classifier was trained for detecting the intent from replies in customer’s chats. The classifier tags the customers as Interested, Not Interested, Unsubscribe, Wrong Person, Email Bounce, andAuto Responder etc.
This technology is used in applications such as:
Social media monitoring
Brand monitoring
Customer service
Voice of customer
Resources for Text Classification
Datasets
Dataset to provide examples for training the classifier – We need training data that will guide your text classifier. An efficient classifier depends on the right data that best represents the outcome that you are looking for. Gathering the right data is the key. E.g.: you want to predict the intent from particular data sets like chats on social media, you need to identify and gather such data exchanges that represent different intents so as to predict the outcome. If you feed your algorithm with another type of data, it is not going to give the desired result.
Training data can be found internally and externally. Internal data generated from apps and tools that we use everyday such as CRM, chat apps, help desk software, survey tools etc. External data include data available publicly on the internet, on social media sites or public data sets.
Some publicly available datasets that you can use for building text classifier
Reuter’s news dataset
It contains 21,578 news articles from Reuters labeled with 135 categories with varied topic, such as Politics, Economics, Sports, and Business
20 Newsgroups: It is a popular, widely accessed dataset that consists of 20,000 documents across 20 different topics.
Datasets for Sentiment Analysis
Amazon Product Reviews: A well-known dataset that contains around 143 million reviews and star ratings (1 to 5 stars) spanning from May 1996 – July 2014.
IMDB reviews: It is much smaller dataset with 25,000 movie reviews labeled as positive and negative from the Internet Movie Database (IMDB)
Twitter Airline Sentiment: With around 15,000 tweets about airlines that is labeled as
Labeled as positive, neutral, and negative, this dataset is very handy
Other Popular Datasets
Spambase: This dataset consists of 4,601 emails labeled as spam and not spam
SMS Spam Collection:spam detection dataset that consists of 5,574 SMS messages tagged as spam or legitimate.
Hate speech and offensive language:Dataset with 24,802 labeled tweets organized into three categories: clean, hate speech, and offensive language.
Tools
A tool for generating and consuming the classifier- Once the classification categories are defined, the labeled data is fed into the machine learning algorithm and it is called supervised classification. The algorithm is set up to take on the labeled dataset, making sure that it generates the desired output. Example of supervised classification is spam filtering where the incoming email is automatically categorized based on its content. Other examples are Emotion Analysis, Topic Labeling, Purpose Detection, Identifying emergency situations by analyzing online information etc.
Some of the resources used in the different phases of the process, that is transforming texts into vectors, training machine learning algorithms and using the model to make predictions are:
Open Source libraries
Open source libraries are available for developers interested in applying text classification. Python, Java, and R offer a wide selection of machine learning libraries that are actively developed with a diverse set of features, performance, and capabilities.
SaaS APIs for Text Classification
Software as a Service (SaaS) for text classification is for people without any knowledge in machine language. SaaS don’t require machine learning experience and even people who don’t know how to code can use and experience the power of text classifiers. Some of the SaaS solutions and APIs for text classification include:
MonkeyLearn
Google Cloud NLP
IBM Watson
Lexalytics
MeaningCloud
Amazon Comprehend
Aylien
Supervised Classification
Supervised Classification is where the computer imitates human actions. The classifier has to be trained to identify emergency situations with accuracy from millions of text lines which could be from email text or online conversations.
It uses functions, sampling techniques and methods like building a stack of multiple classifiers in a step by step result oriented process. Algorithms are given a set of data called the train data which generate AI models that are given untagged data that are automatically classified.
Unsupervised Text Classification
Unsupervised classification does not depend on external information for the process. The algorithms are formulated to discover natural structure in data. Natural structure is not what we think of as logical division. Similar patterns and structures data points are identified and grouped into clusters by the algorithms. Data is classified based on the clusters formed. An example is Google search. Here the algorithm makes clusters based on the search sequence that the user requests and outputs them as results to the user.
Every data point is embedded into the hyperspace. The data exploration helps to find similar data points based on textual similarity. Similar data points form a cluster of nearest neighbors. Unsupervised classification enables generating quality insights from textual data and is language agnostic since it is customizable as no tagging is required and can operate on any textual data without the need of training and tagging it.
Custom Text Classification
A lot of the time, the biggest barrier to Machine learning is the unavailability of a data-set. Businesses and individuals are looking to apply AI for categorizing data but the necessity of a data-set is giving rise to a situation similar to a chicken-egg problem. That is where Custom text classification comes in; it is one of the best ways to build your own text classifier without any data set.
Altius has come up with unique methods for text classification using algorithm structures that are able to identify customer emotions on a large dataset and come up with new categories or dataset. This allows for the algorithm to create its own data set which is used to work against the data clusters. This training methodology is used in multiple neural network algorithms to get better results from different datasets. It brings down the cost and time takes to build a text classification model, since no training data is needed.
Unifi-I enhances your e-commerce store with sub-second-page load speeds, giving the user a better e-shopping experience, which helps to retain customers and build a relationship. Converting your eCommerce sites to sub-second eCommerce websites accelerates your conversion by 15-30 %.
Let’s look at how Unifi-I can get more out of your e-store for your business.
Improved User Experience – Improves user experience such as navigation and searchability. Seamless skimping between pages without delay or search results or page clicks that output the results in instant, keep users engaged in your e-store.
Speed – Unifi- I enable faster loading of WebPages. The ideal response time to keep the visitor engrossed in your e-store is 0.1 sec. Unfi-I achieves a sub-second page load by making your website ultra fast and super fast which helps to load web pages faster making your e-store connect with your visitor instantly.
Increased Time on Site – Visitors spend more time with your e-store when it is easy, comfortable, and user friendly to surf through your e-store. With Unify, It results in more traffic, and more browsing actions get converted to purchases resulting in better conversion.
Right Technology and Tools – Unify-I integrates your e-store using front end technologies such as Progressive Web Applications (PWAs), Single-Page Applications (SPA), and AMP which decrease the page load time for speed optimization.
Better SEO Rankings – Improved page loading speed results in decreased abandonment rates and makes people come back for more. With higher SEO ratings due to increased traffic, your website will be recognized by Search Engines like Google, Bing etc.
Improved Conversion Rates – Faster page loads result in better conversion rates. Pages that are loaded in 2.4 seconds have a 1.9% conversion rate. Even a slight increase in conversion rate has a huge impact on the e-store revenue.
Advantage over competitors – With a modern front end using Single-Page Quick Ordering Web App technology Unifi delivers the competitive advantage in a faster page load that your e-store requires to stay at the top of the game. The website that loads faster will rank higher.
A study proves that 53% of web surfers exit from pages that take longer than 3 seconds to load. Even a one-second delay in website page loading time reduces the number of page views by 11 %. This goes on to show how page load speed is crucial to reach your customers and it is the most important aspect for a successful online presence.
Altius’s quest to offer perfect eCommerce Solutions have led to continuously assimilating, researching and analyzing customer’s business needs and the hindrances they face in running their e-store. Unify-I offers e-stores’s ease in running the platform with awesome navigation and speed to generate higher revenue.
