Customer satisfaction is one aspect that is very difficult for business to meet consistently. It requires faster delivery time of products and services in order to ensure that the customers always get what they demand for. Being able to meet your customers’ demands is a means to measure the success of your business.
The traditional Software Development Lifecycle (SDLC) uses waterfall methods for testing and achieving faster quality and delivery time. However, with advancement in technology, the accuracy of the waterfall method is falling short. This is where the agile testing comes into play. In agile testing, development and testing can take place simultaneously instead of conducting it in phases.
Agile testing takes care of the requirements of end customers and testing teams. This way the customer requirements can be easily met. Instead of testing the codes after development, agile methodology conducts testing early and frequently. In addition to using agile methodology for accelerating your software release cycles, companies need to follow certain strategies for faster delivery time.
Companies often use agile testing methodology without taking care of their IT environments, workflows, culture, or architecture. This is a wrong way to go about. Faster software release cycles require better collaboration, flexibility, and transparency among the development and testing teams. The IT environment and workflows must be managed well so that teams get the right feedback and save valuable time in managing the testing methods.
Another means of saving the testing time is to automate the testing process. This way the long codes can be easily checked for mistakes and integrated with the expected outcomes. Automation prevents code defects and regressions. Automated testing helps reduce costs, compress long regression cycles and accelerate release time. Since the cost changes constantly, automated testing offers regulated feedback.
Companies face a challenge of reduced ROI due to high cost of maintenance of automated testing. The solution is to modify the automation architecture. Some companies have adopted a method to break down the large code into smaller pieces on which the teams start to work. This allows the team to properly define and maintain the interfaces.
Agile testing methodology may faster the delivery time of software release cycles; however, it comes with its own challenges and opportunities. In addition to agile testing, companies must also take care of their organizational structure, vision alignment, and team communication. Provided the company knows its end deliveries, the agile testing method can be of great help in meeting customer demands.
Read this to Advance Your Image Processing Knowledge
Today, automated image analysis has become an integral practice in multiple industrial and academic sectors. In order to analyze an image in detail, acquiring advanced image processing knowledge and skills can be highly useful.
Image processing as the name suggests is the process of editing images in a way to make them look more appealing and to identify the hidden details. Advanced image processing enhances the image in its best possible way. It is one of the most rapidly growing technologies used widely in medicine, forensic science, electrical engineering, and computer science domains.
Image processing can be carried out using two methods.
Analogue Image Processing
Digital Image Processing
This method is used for hard copies such as printouts and photographs.
This method is used for processing digital images with the help of computers.
Analysts use fundamental techniques of interpretation – Analog signals
Analysts use three steps for interpretation – Pre-processing, Enhancement and Display.
Due to the increase in the usage of digital mediums like digital cameras, computers, mobiles, etc., digital image processing method is used more often as compared to analogue image processing method. In order to understand image processing in detail let us look at the following steps.
1.Acquisition – The first step is to acquire the image from the source. It also includes aspects like scaling and color conversion. Color alteration enhances the image.
2.Image enhancement – This is a subjective phase, which may or may not be applicable for every image. However, this step exposes the hidden features of the image.
3.Image restoration – This step makes the image more appealing and may or may not be applicable for every image.
4.Color image processing – It deals with full color and pseudocolor image processing
5.Wavelets and Multiresolution processing – In this step, image is presented in various degrees for better image clarity.
6.Image compression – This step deals with image size and resolution modification.
7.Morphological processing – This step deals with extracting image components that can describe the shape of the objects in the image.
8.Segmentation Procedure – Segmentation is one of the most difficult steps of image processing. This step partitions the image into its constituent parts.
9.Representation & Description – This step transforms raw data into processed data
10.Object detection and recognition – In this step, one can assign labels to the objects detected after the entire image processing is completed.
Artificial Intelligence (AI) has proved to have several applications in image processing. For example, helping doctors in interpreting X-rays and MRI images by developing computer aided diagnosis systems. This is a breakthrough in medical sciences, making diagnosis simpler and easier to manage.
Image is simply a two-dimensional signal; however, image processing focuses on the details and hidden aspects of the image, enhancing its usability.
Several companies look for talented resources that possess both UI and UX design skills. The major reason is that having both the skills proves to be an attractive combo for the employer. Although having both UI and UX design skills may prove to be beneficial, there are certain unique differences between the two. In this blog, we will understand the differences between UI and UX Design.
UI stands for User Interface, which is a series of specific assets users interact with in order to experience a product or service. For example: screen, pages, and other visual design elements such as colors and typography, button, icons, etc.
UX stands for User experience which deals with the interaction and experience users have with a company’s internal products and services. Based on a user’s experience, the interaction patterns can be modified and made better.
Both these terminologies may seem to be similar, but they are not. While a good UI design helps to attract users, a good UX design helps to sell the products or services. While UI caters to only interfaces, UX designing caters to products and services in addition to interfaces.
UI designers are responsible for creating an attractive product appearance which results in branding and graphic development, customer analysis, and creating user guides or storylines. They work on developing UI prototypes and implementing it.
The UX designer is responsible for content strategy, customer analysis, and product strategy. They work on prototyping, testing, development and planning of overall user experience for company’s products and services.
3.Colors in Use
This is a unique difference between both the designers. UI designers tend to design the prototypes in full color. On the other hand, UX designers use only three colors in the prototype design – Black, White, and Gray.
This difference can be prominently seen in their designing styles specially in the usage of assets like icons, buttons, pages, images, drop down lists, text fields, checkboxes, etc.
The functioning of the two roles differ because of the different tools used by the UI and UX designers.
For UI designers, designing images is of utmost importance. They tend to use the best tools for creating images such as, Flinto and Principle. Both these tools offer the ability to sketch, which comes handy for developing images.
UX Designers look for tools that help them modify and improvise user experience from time to time. This means, they must be able to test and preview projects from time to time. Mockplus is one such prototyping tool that is helpful during the testing process.
Both the roles may be distinct, but they complement each other. However, it is important to understand the differences between the two roles in order to use them wisely. In conclusion, let us summarize all the differences.
Takes care of how things look
Takes care of how things work
UI elements include icons, drop down lists, text fields, buttons, and more.
UX elements include visual design, usability, interactive patterns, and more
Uses full colors for prototyping
Uses White, Black, and Gray colors for prototyping
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
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
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.
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.
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
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.
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 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).
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
Voice of customer
Resources for Text Classification
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.
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:
Google Cloud NLP
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.
It is not at all about taxes but it is a step towards better revenue. Taxonomy is a Greek word for the laws of ordering. Taxonomy is an effective process of eCommerce data presentation which provides structure to product data and specifies the relationship between them. When the products are organized in a structure, customers can find the products in the least possible clicks.
It is arranging the products in a hierarchy that puts products into categories. Each product in a category will have its own attributes (such as color, size, etc). Thus helping customers to easily navigate through product listings and helping them find exactly what they want increases the chance of a successful purchase.
Improved User Experience
Taxonomy allows products to be arranged in a hierarchical order in which products are grouped into specific subsets. This allows the product catalog to be presented in stages making it more manageable.
Category tree, which helps group products by nature, is called the breadcrumb trail.
Home->Tools-& equipment -> Power Tools -> Sanders & Sander ->Accessories -> Orbital Sanders
Value list which is a predefined set of possible values such as 230 V, 1000-2000 min speed, etc.
Faceted Search Results
Taxonomy enables search results that get the most contextual content to the user. A Forrester research report found that when retail sites are not properly showcased they sell 50% less than better-organized sites. When searches come up with unexpected results, 47% of users gave up after just one search, and only 23 percent tried three or more times.
Taxonomy takes into consideration what matters most to your customers. It helps to understand who your audiences/customers are to meet their specific needs. Building a taxonomy that takes into consideration what a shopper needs, to make a good buying decision, what kind of people will be using the site, which taxonomic terms connect with common people helps immensely to increase traffic to your website. Customer-focused taxonomy connects with regular shoppers online resulting in more purchases.
Strong product taxonomy makes it easy for search engines to pull out the required product information and present it to the customers. It makes it seamless for customers to navigate, search, choose a product, or find an alternative from the recommended choices, add products to the cart, and checkout resulting in higher revenue for the e-store.
Altius specializes in product developmental services and comes up with a master category table with high-level industry standards that cater to B2B and B2C sectors, creating a site that is easy for search engines to find your products and for customers to make purchases.
Catalog Management is the process of managing your eCommerce Product Catalog to ensure the integrity and quality of the product data across different sales channels. A product catalog is a detailed list of inventory of a store, which consists of product images descriptions, specifications, price, and more. The process of organizing, standardizing, and publishing this information to multiple sales channels is what product catalog management is all about.
Companies need catalog management to keep up with the huge stock of SKU’s (Stock Keeping Unit) for different sales channels. It is hard to maintain the integrity of huge quantities of product data and time consuming to keep track of the update in product data which results in incorrect and improper product data representation.
Common challenges of eCommerce Catalog
Product types make SKU management more complex because products vary by attributes like size or color. Products come in a different part or as a single individual part, keeping track of the hierarchy of product parts can be hard to manage.
Managing across multiple channels
When selling across different channels besides the company’s own website like public market places such as eBay, Overstock, Amazon creates a challenge for cataloging the products in the sense that each product is formatted in a different manner while listing, so it becomes hard to keep track of often resulting in publishing duplicate or incomplete product data.
Dealing with third party data
Data from different sectors like distributors, manufacturers, and suppliers come in all sorts of formats. It is a humongous task to transform each and every detail into a single internal standard format. It requires large teams who spend weeks compiling, updating, and publishing the product data which could also result in the wrong and inconsistent data for customers.
Capacity to handle infinite data
As your e-store gets established, it will have more product data coming in. It will get hard to organize data that is not in control. Expanding the product catalog becomes a challenge which in turn affects the day to day activities of the e-store stunting its revenue growth.
Consistent Data Quality
All of the above challenges affect the quality of the data. Maintaining your product data while simultaneously expanding the catalog, communicating with suppliers, and publishing on multiple sales channels will be a hard task. With inconsistent product attributes such as wrong spellings, missing information, wrong descriptions, or attributes, it can be overwhelming to maintain a consistent standard.
These are the reasons why you need to invest in a strong data management system like Altius InRiver PIM that centralizes the data with a single view with an efficient and consistent way of managing the product data across multiple channels.
Altius InRiver PIM saves investors and e-business owners from spending enormous time cleaning up product data enabling to list rich, high-quality product data across all of their channels.