5 Tips for Website Redesign that one should never ignore!

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How to Create a User-Intent SEO Strategy

Search engines exist to provide users with results that are relevant to the search query. Smart SEO campaigns are built on an understanding of how your audience searches around your industry, products, and services.A key point here is to understand the intent behind a given keyword search. A user wants to find specific information, and search engines have advanced algorithms and large amounts of traffic they analyze to determine which results are the best matches for a keyword.In this article, we take a look at how to understand and categorize keywords based on intent to provide a solid foundation for your search engine optimization Understanding searcher intent secures a good chance for you to show up in front of people at the exact time they have a requirement. Unfortunately, commercial terms are highly competitive across the paid and organic search. For most businesses, there are other opportunities for branding and targeting customers higher in the marketing funnel. We just have to develop a greater understanding of the intent behind search keywords.The first step here is to understand the three categories of search queries:Navigational search queriesDo you ever type “Google.com” or “Facebook.com” into your browser? Or do you just type “Google” or “Facebook” directly into the address bar? This is a navigational query, a search performed with the intent of going directly to a specific website, or even a page on a site.Informational search queriesFrom a business perspective, we are looking to identify queries that relate to the product or service your company provides. Then develop content that answers these questions or provides information on certain topics. This will position you in front of your potential audience and build brand awareness.Commercial or transactional search queriesCommercial queries relate to the desired action that you would like a prospect to take. These keywords are all highly commercial in nature and therefore high-value. It is important not only to target these keywords but to ensure you focus on helping the visitor complete their goal once they arrive at your site.Ambiguous keywords and personalized resultsNot all keywords are crystal-clear in their categorization or intent. Take “software development” for example. If a user searches for “software development,” what exactly are they looking for?Some free tutorials? Development Company? The searcher may not be entirely sure and may simply be starting to investigate a topic.Where intent is not always clear, Google will typically show a page of results that cover a wide variety of potential answers. Mapping searcher intent to keywordsUnderstanding searcher intent allows you to build more strategic lists of keywords. This then helps us understand how to best target a given keyword and where that keyword fits into our overall SEO and strategy.Ad Words CPC and competitionAlso include Ad Words cost per click, Ad Words competition and keyword difficulty in your keyword spreadsheets to help you get a full picture with regard to the potential value of the keyword.How to clarify searcher intentSearcher intent is not always clear, and it is easy to classify keywords as commercial when in fact they may be more informational in nature. Your SEO campaign then becomes about promoting those content pieces to help build organic visibility.Content strategy and clarity of intentUnderstanding the intent behind the keywords you target simplifies the entire SEO process. Trying to rank content where the intent of your message is different from the intent of the search term is doomed to failure.Creating content that ranks well and converts users requires a crystal-clear understanding of what the searcher is looking for. Once you understand the intent, you can review the content that ranks and look for opportunities where you can improve the content that is already ranking. For more details, you can get in touch with our experts at sales@sundewsolutions.com or call us at +91-98367-81929.   

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7 Ways to Use YouTube Marketing to Improve Your E-commerce Sales

In this competitive landscape, the key to success is creating high-quality content for the viewers. New forms of marketing are evolving, and the industry is diversifying across a plethora of other options available to the marketers to grow their audience and build a pipeline of interested prospective customers.The influence of video on e-commerce business is undeniable. An affirmative experience with a video ad increases brand association by 139% and purchase intent by 97%. If you haven’t experimented with video yet, then it’s the time. YouTube has become one of the largest search engines in the world. If you have spent time in search engine optimization (SEO), you should be giving some time to your video strategy, too. For businesses, understanding how to leverage YouTube marketing for e-commerce sales is key to building stronger brand visibility and driving higher conversionsLet’s look into the different ways to use YouTube Marketing to improve your e-commerce Sales:1. Use short video clipsVideo length matters, especially, when it comes to sharing on social media platforms. Usually, YouTube videos which are under two minutes in length get most views from the users. Instead of creating random videos, use precise content that targets your niche customers at different stages of the customer journey.2. Create a branded channelTo improve your e-commerce sales create videos of your product and services but never offer a direct pitch for sales. Videos look more professional when it comes from a verified account with your store name; it creates awareness of your brand.3. Be consistent with content productionFor YouTube marketing, it’s best to be committed for the long term. Create content strategically if you can post consistently, and then come up with a multi-pronged strategy to engage viewers.4. Use analytics to understand what worksYouTube offers profound insight into your video performance through analytics. Take full advantage of it. The data is comprehensive enough for you to know exactly what works and what doesn’t work.5. Use annotations to keep your audience engagedAnnotations are a YouTube feature that let you put text and links over your videos. Interactive footnotes can give you a boost in subscriptions.6. Advertise with AdWordsYou are able to advertise on three domains: as a pre-roll ad before the videos that people watch; as a banner ad when you’re browsing the web; or as a promoted video when people search for similar videos on YouTube.7. Promote your content across all platformsTreat your videos as you would do for a blog post, and propagate it through all your social media channels like Facebook, LinkedIn, Instagram, Twitter and you can also blog about the same.Incorporating a video into your e-commerce business strategy will make visitors stay on your website longer and increase the attention span, which will enhance the organic reach and grow your conversion rates.YouTube Marketing may cost you more time and money in the beginning, but it’s an investment that will easily pay off tenfold. By carefully placing these videos on social media and your website, you’re giving potential customers all the information possible in order to get them to purchase your offerings.

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Data Engineering Vs. Data Science - Key differences

Business Intelligence(BI) and Data Analytics are no longer buzzwords. Instead, enterprises are rapidly gravitating towards them to improve business performance. With adequate focus on data literacy, its collection, and data infrastructure, it is possible to accomplish results capable of enhancing revenue generation. Businesses generate a humongous amount of data today. This necessitates adopting intelligent and result-oriented data products to process the generated data and enhance its utility. The model "Data Science Hierarchy of Needs," suggested by Monica Rogati, further corroborates this. According to this model, Data Acquisition occupies the lowest level. It is succeeded by Data Engineering, Data Analytics, Business Analytics, Data Science, and AI (deployment and observability). Data engineering helps to connect data gathering with Data Science. Raw data cannot form the basis for building predictive models that establish trends and patterns. It needs to be converted into a usable or accessible form. This transformation is achieved using well-designed systems and pipelines. The designing, developing, testing, and maintenance of these pipelines and architectures fall under the purview of Data Engineering.Data Science deals with the extrapolation of knowledge and insights from transformed but noisy raw data, both structured and unstructured, and applying that knowledge to answer business-related queries for better decision-making and formulate metrics to improve implemented business processes.Data scientists achieve the above by using different scientific methods, algorithms, processes, and systems. Data engineers complement Data Scientists by providing them with the necessary framework and architecture. Data Engineering: Defining its scope and criticalityAnalysis of Big Data has completely changed the way of doing business. The collection and management of such a large volume of data require the development of an architecture that can handle structured and unstructured primary data and appropriately cleanse and transform it. The development and management of this data architecture is done by Data Engineers. They use different intricate methodologies to achieve this. Tools associated with the implemented technique range from AI to Data Integration. By choosing and employing the correct tools and techniques, Data engineers gather, clean and authenticate data to make it comprehensive and coherent for analysis by Data Scientists. Data Engineering is also important because it helps to refine SDLC(Software Development Life cycle), enhances data security, protect businesses from cyber attacks and cyber frauds and increase business domain knowledge. Its contribution to elongating the shelf-life of a business is indisputable. By converting unreadable data into readable form, Data Engineering empowers Data Scientists with secure data to generate accurate business insights. Data Science: Its meaning and definitionModern businesses are awash with data. With the expertise of professionals, it is possible to use available cutting-edge technology and tease actionable insights from the gigabytes of transformed data generated. These experts are Data Scientists. They add value to a business by providing enterprises with accurate analytics and insights for precise decision-making, deciphering trends to realign goals, improve workflows by focussing on its best practices and identifying growth and revenue-earning opportunities. Data Science is also used to provide quantifiable data-driven evidence, refine target audience and influence insightful talent acquisition. Data scientists are invaluable assets who analyze disparate data sources to generate meaningful insights that help businesses to grow, become profitable, and attain sustainability.Data Engineering vs. Data ScienceOften confused and thought to refer to the same thing, Data Engineering and Data Science are interwoven processes with distinct fundamental differences. Data engineering is the bridge that straddles the divide between data gathering and gaining value from data. It plays a critical role in the success of data science. Differences between the two primarily relate to:Data handling: Big Data can benefit businesses by creating multiple possibilities for improvement. An organization employs people skilled in Big Data management to maximize this advantage. Data engineers and Data scientists play a crucial role in this management.In the "Data Science Hierarchy of needs" pyramid, there is a clear distinction between the job roles essayed by Data engineers and Data scientists. Data engineers collect relevant data, transform it, and move it into pipelines so Data scientists can aggregate, optimize, test, and analyze it to generate real-time insights.  Data task classification: The work of a Data engineer is technically oriented as it involves three critical data actions, namely designing, building, and arranging Data "pipelines." They are Data Architects who design Big Data architecture and prepare it for analysis.Alternatively, Data scientists analyze, test, create and present data so enterprises can improve business decision-making and make it data-driven. Data engineers do technical work, while Data scientists are more business-oriented. Tools involved: Machine Learning(ML) and Deep Learning(DL) are to Data Science what ETL(Extract Transform Load) and ELT(Extract Load Transform) are to Data Engineering. ETL is the process of extracting, transforming, and loading the transformed data onto the original database.ML, a subset of Artificial Intelligence or AI, enables computers to forecast future scenarios automatically by using specific algorithms and existing information. DL uses artificial neural networks built on ML algorithms to allow the automatic learning of computers. Of algorithms and statistics: Data Engineering uses algorithms, but Data Science uses statistics. Algorithms comprise rules and processes that guide computers to carry out specific tasks. They deal with information retrieval, logical reasoning, and mathematical problems like calculus and linear algebra.Statistics involve the study and interpretation of numerical data. Other than using statistics to group, review, and analyze information, Data scientists also use it to apply quantifiable mathematical models to specific variables.ConclusionTo sum up, Data engineering plays a critical role in Data Science. But while they might occur together in almost all business applications, they are fundamentally different and require separate tools and skill sets for successful application.Data Engineering deals with data management, understanding, and extraction from big datasets. At the same time, Data Science is concerned with analyzing the cleaned and extracted data and using analytics to generate intelligent business insights. Together, they help businesses transition from average to excellent.