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Successful Approaches to Using Intent Data – Practical Tips from Priority Engine Users Heather Turner

Vice President, Customer Success

Share This Post How can I enable my sales team and increase their adoption of Priority Engine?

Boosting rep engagement is a challenge all marketers face, and engagement with Priority Engine is no exception. Priority Engine users have developed many different enablement strategies, all aimed at reaching the same goal: improve sales rep performance by using Priority Engine insights in their outreach. Priority Engine users suggest these methods to get your sales teams excited about and using Priority Engine:

Explain why the data helps them win – Priority Engine gets sales reps into the conversation quicker, easier and ahead of their competition. Critical insights into buying team contacts, research activity, and interests help reps reach out at the right time, with the right approach.

Run a contest! Healthy competition is always a motivating factor for sales reps. If they are incentivized to use the tool, they will. And once they start, they won’t want to stop!

Review usage reporting – TechTarget Customer Success Managers can provide reporting on user trends within Priority Engine. One successful customer used these reports to analyze the relationship between “time spent in the tool” and “BDR pipeline”. The positive correlation he found is a great illustration that encouraged reps to use the tool. Other users share this reporting with Sales Management to hold inactive users accountable.

What should I do with TechTarget data once it’s in my systems?

Every company has their own unique tech stack, and therefore their own approach to integrating TechTarget data into their tech stack. Here are some of the most common:

Send Priority Engine contacts directly to BDR teams for outreach while simultaneously nurturing them.

Move Priority Engine contacts into nurture stream(s) and only pass them over to sales reps once MQL status is reached.

Upload territory and ABM lists to determine which of those accounts are active

Identify “lookalike” accounts that are active and align with your ideal customer profile but haven’t made it onto your lists yet

Another popular use case is to leverage Priority Engine data to increase event attendance and ROI. Users upload pre and post-event lists on Priority Engine to confirm interest, prioritize accounts, and gain access to additional members of the buying team. They also leverage the Researcher or Account Headquarters location to target prospects for location-based events. This approach has been very successful for customers, helping them attract active buyers to events and to communicate with them on topics they care about.

What’s the best way to measure success?

The varied responses from Priority Engine users suggest there isn’t only one correct method to measure success. A few common practices are looking at sourced or influenced pipeline, though there are a number of variables that enable such reporting – timeframes, data points tracked, and specific KPIs identified up front are just a few.

The critical step in tracking Priority Engine success is ensuring contacts are brought into your systems with attribution tracking applied to TechTarget contacts. Source Codes and MAP/CRM Campaign IDs are key fields to leverage. Attribution methods and system capabilities can also influence reporting success.

TechTarget recommends creating a TechTarget Source Field to produce the campaign metrics and pipeline analysis associated with your TechTarget program. Your TechTarget Customer Success Manager can help you explore the right reporting and KPI tracking for your campaign.

TechTarget holds User Group Meetings at many of our locations worldwide. For more information on and upcoming event or any of these topics discussed, reach out to your Customer Success Manager.

Priority Engine, Priority Engine User Group, purchase intent

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How To Change Career From Data Analyst To Data Scientist?

People constantly deal with data, and Data Analysts look for more challenging roles after gaining expertise in their domain. Data Scientist is often considered one of the most lucrative career options. Though it requires expansion of skill set, several educational platforms offer insightful knowledge that favors change. Numerous data analysts have successfully taken the switch, and you can be the next!

The following steps will help you contribute to the company’s development and add to your expertise as you embark on your career as a data scientist:

Assessing the Skills Gap Essential Skills And Knowledge Required For A Data Scientist Role

Data scientists need to experiment with data, so the mindset of developing new ideas and research is crucial. Equally important is the ability to analyze the mistakes from past experiments. Adding to these, the technical skills and knowledge required to carry out the duties things are as follows:

Technical skills: Analytical Skills:

API tools like IBM Watson, OAuth, Microsoft Azure

Experimentation and A/B testing

Predictive modeling and statistical concepts such as regression, classification, and time series analysis

Domain Knowledge:

Postgraduate qualifications such as a Master’s or Ph.D. in computer science, software engineering, or statistics

Subject Matter Expertise

Soft Skills:



Curiosity and Continuous learning


Overlapping Skills Between Data Analysts And Data Scientists

Both data analysts and data scientists have to:

Data Manipulation, Processing, and Preparation: Data analysts perform the actions for transforming raw data into a usable format, while scientists are concerned with model training.

Automation: The analyst automates the data to streamline repetitive tasks such as processing and report generation. Scientists work to automate feature engineering and model deployment.

Analysis: Analysts explore and uncover insights through research, while scientists use statistical analysis for deeper understanding and interpretation.

Visualization: The analysts make interesting visualizations of complex data for stakeholders while scientists communicate feature distribution, model performance, and outputs to stakeholders and collaborators.

Data Query: Analysts use data queries for specific subset retrieval, filtering, and report generation. The scientists act to extract data for model training and evaluation.

Programming: Analysts are not as profoundly familiar with codes as scientists; the former can write code snippets or scripts while the latter write the complete programs for the implementation and execution of machine learning algorithms.

Statistical analysis:  Data analysts validate hypotheses and understand relations with statistical analysis while scientists evaluate model performance, check significance and reliability, and interpret results.

Areas Where Additional Skills And Knowledge Are Needed For The Transition Practical Skills:

Ability to design experiments and A/B tests and understand its principles and methodologies for conducting valid and reliable experiments.

Working with large datasets

Implementation of data pipelines

Data storage optimization and retrieval

Technical Skills:

Knowledge of neural networks, hyperparameter tuning, and model optimization

Domain Knowledge:

Industry-specific knowledge and understanding of the internal functionality of the industry

Formulate data-driven solutions for core domains of the industry

Knowledge of business principles, market dynamics, and economics

Soft Skills:

Storytelling and communication

The project management ability to handle complex projects and multiple stakeholders

Will constantly learn and adapt to new technologies

Remain competitive and innovative

Source: CompTIA

Bridging the Gap: Skill Development Exploring Educational Resources And Learning Paths For Acquiring The Necessary Skills

Both offline and online platforms provide numerous quality resources, such as books in pdf format, worksheets for practice, and free access to tools and programming languages. The learning journey becomes relatively more straightforward by joining learning paths and taking certified online courses from quality educators imparting practical knowledge.

Importance Of Acquiring Knowledge Of Programming Languages Like Python Or R

Python serves functionality for data manipulation and analysis through libraries like NumPy and SciPy, useful for preprocessing, wrangling, cleaning, and analysis of data along with exploratory data analysis. It is also the go-to language for machine learning tasks through supporting libraries such as PyTorch and TensorFlow suitable for building data models. Also providing options for data visualization, Python is preferred for web scraping and data collection through its unique and extensive library set.

Significance Of Statistics, Machine Learning, And Data Visualizations Skills

The transition from data analyst to data scientist requires understanding of specific skills. Statistics provides a base for hypothesis testing and experimental design through information on designing experiments and formulating hypotheses. It evaluates the idea by finding the significance and validation of assumptions. Statistical modeling techniques such as regression, survival, and time series analysis are essential for building predictive models. These are significant for understanding factors having a role in specific outcomes.

Machine learning helps data scientists formulate algorithms and models for decision-making and predictability without programming. These algorithms are essential for historical data prediction, which analyzes complex patterns and relationships in data. It also allows image recognition, recommendation systems, customer segmentation, fraud detection, and categorization of new data as per the defined criteria.

Data visualization skills help convey the information in interactive and storytelling format, which is helpful in decision-making and driving action based on depicted data. The data visualization skills include the identification of outliers, trends, and distribution, thus guiding data scientists to deep insights, hypothesis generation, and detection of patterns and anomalies.

Role of online courses, boot camps, and self-study in skill development

Online courses



provide recorded or sometimes live lectures, quizzes, assignments, and projects. The comprehensive collection of classes helps develop skills based on the learner’s pace. Expert guidance and hands-on practical experience are suitable for upskilling and becoming familiar with real-world trends. 

Boot camps: 

They are intensive and immersive programs that encourage students’ transition to data science roles in a systemized manner, making them ready for the job. Inculcating job-based skills is similar to company training, which must include live interaction sessions with leaders in a specific field. Direct interaction, mentorship, and career support are often seen in boot camps contributing to better skills and opportunities for networking. 


Self-study is a practical approach requiring self-determination. It involves the self-organization of numerous available notes. However, it comes with a customized learning approach where candidates can formulate their schedules and work based on their strengths and weaknesses. 

Source: ResearchGate

Gaining Practical Experience Importance Of Hands-On Experience In Data Science Projects

Hands-on experience is crucial to achieving functionality, updates on current trends, and the ability to work with others in a specific field. The experience familiarizes the candidates with real-world problems, helps them understand data complexity, and allows time and opportunity to explore various techniques.

Ways to Gain Practical Experience

Internships: Regardless of the stipend, internships are a great source as they familiarize the candidates with the field and work. It helps in gaining insights while learning. Analytics Vidhya is hiring data science interns to help them accomplish their dreams.

Freelancing: An experienced and independent individual can go for freelancing, where they learn communication skills, use their expertise in analytics, earn money, and exhibit their work.

Analytics Vidhya

provides a guide to step forward in this direction. 

Data Science Competitions: This help brings forward candidates’ competitive edge and exposes their ability to work under pressure. Also, working on the innovative bend of mind, candidates must participate in

data science competitions


Hackathons: Hosting numerous Data Science Competitions in Analytics Vidhya’s

Data Hack

, buckle up to prove your worth. There will be numerous competitions to participate in, along with networking opportunities with leaders of Data Science.

Joining internships, regardless of stipends, is the most appropriate approach to gaining experience. It requires cracking interviews and proving yourself to enter the field. Academic research projects, freelancing, or consulting work must also be looked forward to becoming familiar with real-world trends and requirements in data science. Collaboration, data science competitions, and hackathons provide the right platform for practical experience.

Significance Of Collaborative Projects, Internships, And Industry Certifications

Collaborative projects in data science fill individuals with diverse perspectives and the art of working in a team. It expands the knowledge base and ability to collaborate with other field experts. It exposes the candidates to alternative approaches and creative solutions and adds to the skills of different fields or industries relevant to the job role. The networking opportunities are the most significant benefit.

Due to certificate awards and performance reviews, internships are complete proof of working in the corporate world or field. It helps in professional development through interaction with experts and supervisors enlightening the candidates about possible career paths and opportunities.

Industry certificates are the best way to validate skills and knowledge base. It helps in closing the skill gaps and gaining recognition by employers. It also increases networking and knowledge through ongoing industry learning and renewal programs.

Leveraging Transferable Skills Identify The Transferable Skills From A Data Analyst Role To A Data Scientist Role

There are overlapping skills expected in the data scientist role that can be transferred when transitioning to a data scientist position. They are data manipulation, preprocessing, transformation, and cleaning. The ability to analyze, visualize and interpret data can be transitioned too.

Relevance of Skills like Data Cleaning, Data Exploration, and Problem-Solving

Data cleaning: 

It is required to adhere to the high data quality achieved by removing incomplete mess and errors. It serves as a foundation for analysis and modeling. Data cleaning is also crucial for gaining deep insights into the data and is responsible for the trustworthiness and representation of the information. It helps in minimizing the risk of flawed decisions and incorrect conclusions. 

Data exploration: 

It is required for clear data understanding, pattern identification, insights, and derivation of relationships. It familiarizes them with the structure and variables of datasets. Data exploration also aids in the title of features that impact the target variable and analyzes data relationships with variables. It also contributes to data visualization by uncovering anomalies, outliers, and trends in data. 


Data scientists deal with repeated multiple experiments where the most important thing is to analyze the problem leading to discrepancies in results. The essential skills guiding them to solutions are analytical approach and problem-solving skills. It is also helpful in dealing with industry-based challenges. 

Importance Of Effective Communication And Storytelling In Data Science

These non-technical skills of data scientists are essential to connect with stakeholders. Data scientists also handle teams of juniors where the insights or interpretations coupled with decisions must be communicated. The clarity in how, why, when, and where helps understand and builds trust in the process and leader.

Building a Professional Network Benefits Of Networking In The Data Science Community

Data scientists need to focus on networking as it benefits through:

Continuous Learning: The different people in the industry hold distinct expertise while working on their projects. Communication with them enlightens one about the current trends and technologies.

Innovation: The working knowledge of interdisciplinary fields contributes innovative ideas to cutting-edge research. People from different fields can work together to solve existing loopholes and increase their areas of expertise.

Resources: Gaining familiarity with different domains increases opportunities. One can also utilize other software and databases creatively for their functionality while gaining resources through communication. It effortlessly benefits the workability of individuals while saving time.

Guidance: Connecting with experts, mentors, and professionals is one of the best methods for direction in career choices, work, and technical challenges. It also exposes individuals to various experiences, challenges, and opportunities, paving paths for professional development.

Widen Perspective: Learning the works, methodologies, and methods to tackle different projects widen the perspective arising innovation.

Explore Networking Opportunities Through Industry Events, Conferences, And Online Communities

Owing to the numerous benefits of networking in data science, multiple methods exist to increase connections. The industry events and conferences invite numerous field-based personalities and experts, including professionals, researchers, industrialists, practitioners, and educators.

The tech conferences, meetups, and user groups focusing discussions on data science, AI summits, and world conferences are good sources, regardless of the online or offline mode.

Online communities allow global connectivity from the comfort of home. Bridging the gap between time zones, these are a good source of collaboration with expert individuals in the field.

Further, online communities also include hackathons, open-source projects, online courses, and webinars that help actively engage the community and share knowledge and skills.

Showcasing Your Skills Importance Of Creating A Solid Data Science Portfolio

A solid data science portfolio is an excellent way to showcase the technical skills and expertise gained through different opportunities such as internships, employment, research, projects, or other methods. Exhibiting the courses, educational qualifications, practical application of knowledge, and references serve as an identity and spokesperson of an individual. Providing the mode to stand out from the crowd, the data science portfolio serves as an exhibitor of the success or failure of tasks, providing the candidate with an opportunity to explain their valuable learnings from them.

Explore Ways To Showcase Your Skills Through Projects, GitHub Repositories, And Online Platforms

These three serve as great sources to showcase skills and share the works. To share the skills through data science projects, select the relevant tasks that fit your career goals and highlight the gained expertise. Ensure a clear definition of the problem statement for clarity and a logic-based choice of the approach used to overcome the challenges. It includes methodologies, algorithms, techniques, and using different tools. The project documentation must be clarified by incorporating flowcharts, graphs, and pictures per the requirement. Have proper indexing for more straightforward navigation and precisely communicate what is required and intended directly. Project the impact and results with efficiency while avoiding fake and error-based consequences.

Create the GitHub repository to display the data science projects exhibited in an organized manner. Add the readme file in each warehouse and summarize the projects comprising objectives, methodologies, key findings, visualization, results, and any other relevant detail, if present. Use the version control feature to find the changes and collaboration with other individuals in the field. Ensure adding credits to the collaborators. You can also add the links to projects created on Jupyter Notebooks in the Readme file on GitHub for better interaction and visibility of work.

You can also showcase your works on online platforms such as blogs, portfolios, communities, and Kaggle. Platforms like Medium allows data science blogs or finding other relevant online portfolios for expressing your contribution to the field. Leverage the power of data science communities like Reddit, DataCamp community, or Data Science Central for sharing, discussion, and feedback from others. Use LinkedIn to showcase your works or participate in Kaggle competitions for engagements and seminars.

Source: GitHub Docs

Highlight The Significance Of Demonstrating The Impact Of Your Work Through Case Studies And Data Storytelling

The demonstration through case studies and storytelling helps to communicate the value and relevance of data science to a broad audience, irrespective of technical knowledge. It helps increase familiarity with the topic, understand the impact of problems on different audiences, and develop innovative solutions benefiting humanity. It helps professionally by adding value and impact to portfolio and profile while applying gained skills in data science.

Data storytelling enhances communication skills by simplifying complex problems and making connectivity interactive. It contributes to higher engagement, further easing and introducing problem-solving, the immensely valued approach. It aids in connectivity and relatability with the listeners, leading to successful sessions.

Navigating the Job Market Insights Into The Data Science Job Market And Its Requirements

Data science jobs are rapidly increasing, and its market size is expected to grow at a CAGR of 26.9% from 2023 to 2027. In 2023, the market size is estimated to be about 70.376 USD Bn. Besides increasing demand, you must also consider the growing application of the field in different industries, which helps to find a job as per the candidate’s interest and specialization. The list includes technology, e-commerce, finance, healthcare, and marketing.

Source: Grand View Research

Strategies For Tailoring Your Resume And Cover Letter For Data Science Roles

Your resume and cover letter speak on your behalf and are the primary deciding factor in judging your suitability for the role.

Research the Job Requirements: The foremost thing is to thoroughly understand the job description to identify skills, responsibilities, and qualifications necessary for the role, type of programming languages, tools, industry knowledge, and algorithm. Find if you are the right fit for the position and possess the exact qualifications significant there.

Highlight Relevant Technical Skills: Showcase your relevant technical skills in your resume. Adding education, extra certifications in courses or programming languages, and job role keywords will help select company resumes.

Showcase Data Science Projects: Add a distinct project section mentioning detailed information on the works or your contribution. Ensure to state the quantity or impact of the result on the company in terms of increase in revenue, savings, accuracy improvements, or other such data.

Demonstrate Analytical and Problem-Solving Skills: Exhibit a section stating skills. Enlist your soft skills, such as analytical and problem-solving skills. Relate the same with examples in a crisp manner.

Tailor your Cover Letter: Focusing on a cover letter, perform detailed company research before beginning the writing. Highlight the technical skills necessary for that role and list your unique qualities or abilities to impress the recruiter.

Quantify Achievements and Impact: Mention the achievements on your resume. State the quantitative effect of your accomplishments and the impact caused due to the same. Use numbers or ratings to display the same and the most direct effect it had on the company.

Proofread and Edit: Ensure to proofread the resume and cover letter. Look for grammatical or spelling errors in the company or person’s name. Validate the described qualities or characteristics that match the job role and company. Edit in case of requirement for any changes.

Explore Job Search Platforms, Professional Networks, And Recruitment Agencies For Data Science Job Opportunities

Finding a job is comparatively more straightforward with numerous online platforms. Quality job search platforms include LinkedIn, Indeed, Glassdoor, and Dice. These platforms provide regular updates on different job roles among multiple companies. The platforms offer job alerts to one’s preference for direct updates.

Professional networks and communities provide mentors and connections, providing the right opportunity and guidance to find a suitable role. The communities are available on professional networks such as LinkedIn groups and Kaggle. Connections and personal networking are also possible at meetups and conferences. Recruitment agencies also help in finding the right job roles. Common examples of such agencies include AlmaBetter, Hirist, Harnham, and Korn Ferry.

Source: FactoHR

Why Are Companies Hiring More Data Scientists?

There are multiple reasons leading to the increased hiring of data scientists. It includes excessive data generation and holding confidential information significant for the company’s growth. Processing and interpreting the same is possible by data scientists only. These guide the company to data-driven decision-making, helping make more informed, evidence-based decisions and improving efficiency.

Moreover, data scientists leverage the data to better understand customers’ behavior by understanding their preferences, behaviors, and experiences. Further, data is helpful for risk management and fraud detection to increase operational efficiency and cost reduction.

Source: Interview Query

Upskilling and Continuous Learning Importance Of Ongoing Learning And Upskilling In The Field Of Data Science

Staying updated through the abovementioned methods helps to inculcate efficient problem-solving skills and develop innovation and creativity. It helps to adapt to the industry’s needs and improve performance through the availability of new functionalities.

Success Stories and Advice

Multiple candidates successfully transitioned their careers to Data Science. Success is not limited to the right and deserving income; instead, it expands into career development, happiness, mental peace, satisfaction with their career choice, and proper use of their abilities.

Learning and acting as a data scientist from a petroleum engineer wasn’t easy. The love of mathematics and the opportunity for candidates to deal with data pushed

Jaiyesh Chahar

to change the direction of his career. Having initial knowledge of Data science from his field of job, he took action to learn coding and statistics. Finding a job as a fresher in a new area posed a challenge; however, industry-specific knowledge came to his rescue, helping him land a job with exciting projects. 

Holding experience in Software Test Engineering and Quality Assurance,

Bindhya Rajendran

has worked on real-time data and maintains industry-specific knowledge in equipment manufacturing. She was introduced to analytics through her compulsory training module, where the promising aspects of the field captivated her interest. Taking steps in the right direction, with accurate situation-specific guidance from the founder of Analytics Vidhya, she aced her career choice and is currently working at BOSCH in a Data Analytics specialist position. 

Begin the transition from Data Analyst to Data Scientist by understanding the data science path. Read blogs, books, and online resources to introduce yourself to the field.

Make a table specifying your skills and knowledge and the required ones for transition. Select the relevant ones to learn and choose the preferred courses from Analytics Vidhya. Find the system suitable for your timeline. It includes learning Python, R, Apache Spark, SQL, and Data Visualization tools: Tableau and others.

Familiarize yourself with Machine Learning algorithms such as regression models, decision trees, support vector machines, and gradient boosting. Gain experience by working on projects under your professors or mentors or through internships. Else, be creative and start a project yourself for learning.

Strictly work to expand your network, make connections, and remain in touch with people in your field. Actively look out for opportunities to participate in concerned events. Build an online portfolio.

When you feel satisfied with your learning and experiences and gain enough substantial certifications to prove your caliber, begin the hunt for jobs. You must focus on justifying yourself in the resume and cover letter.

Courses to Take to Become a Data Scientist

Concerning the importance of constant upskilling or transitioning your career from Data Analyst to Data Science, Analytics Vidhya has covered you in every situation. We offer multiple courses concerning the same:

A Comprehensive Learning Path to Become a Data Scientist

It is a beginner-friendly course with an orderly list of resources and course contents. It comprises assignments for testing and serves the prime purpose of upskilling oneself. 

Data Science Career Conclave – Transition to Data Science

It is the right course if you are confused about transitioning into a data science career. It covers essential topics such as different roles and the most suitable for you, panel discussion, methods to build digital profiles, and how to meet the requirements of hiring managers. 

Data Science Immersive Bootcamp

It is a job-guaranteed training program with a record of 100% placement of batches. It has also led to a 250% salary hike and guides you to interview preparation coupled with flexible learning. 


Data Scientist is an intriguing, rewarding, and fascinating profession with constantly evolving requirements of talented and skilled individuals. The ability to work on complex data problems with an analytical and problem-solving mindset and practical approach helps one reach the top in the long run.

Regular upskilling is a crucial factor that helps in one’s professional development. Analytics Vidhya brings you numerous courses regardless of your experience level. Helping you reach your dreams and achieve your goals, we are the helping hands leading you at the peak of your career.

Frequently Asked Questions Related

Baidu Pay Per Click: 7 Tips For A Successful Campaign

Baidu is the most popular search engine in China, with over 400 million users. When Western marketers think of entering the Chinese market, Baidu is often the first marketing channel they consider, especially for paid search marketing.

I’ve had the opportunity of working with many Western companies that are experienced with Google AdWords, but new to Baidu. I’ve prepared seven tips for doing well on Baidu based on the most common issues and questions I’ve come across.

1) Localization is Key

The process of localization is a lot more than translation of content. The business strategy and marketing campaign should be considered from the ground up. If you’re going to be entering the Chinese market, you should be prepared to adjust your business to China, even to the point of coming up with products tailored to the Chinese market.

2) Provide Real-time Support

If you’ve ever spent time poking around Chinese websites, you probably noticed that they love chat boxes! Chinese consumers expect to be able to speak to a human very soon, if not instantly. The most common methods of communication are chat box, QQ messenger, and phone.

Chinese Internet users use real-time chat to get information, but also just so they can reach out and make a connection with a human being. When adding real-time support to a campaign, we typically expect to see an increase in conversions of three to four times. In fact, for e-commerce type sites, I think real-time chat is a must.

Real-time chat functionality isn’t totally new to English-language marketers either. For example, it’s widely used on hosting websites.

3) You’re Probably a ‘David’, Your Competitors are ‘Goliaths’

In most cases, the foreign company entering China will be facing competitors that have a much bigger ad budget and are much more experienced with the Chinese market. I’ve come across this scenario many times, especially with small businesses.

4) Flaunt Your Foreignness

What do you think when you read “Made in China”? Do you think “cheap”? “Low quality”? Perhaps you even think “unsafe.” Well, Chinese people tend to think the same thing.

This point may seem confusing since I’ve already mentioned how you need to do things the Chinese way in points one and two above. To clarify, this is what I mean: Adapt your product and service to China, but present a foreign brand image.

5) The Baidu Account Setup Process is Not as Simple as AdWords

Unlike Google, Baidu does not setup ad accounts itself. Instead, it outsources this task to a network of resellers. Some of these resellers are clueless when it comes to working with non-Chinese clients. Reseller regulations and authority also varies.

Advertisers do not need a .cn domain, or a Chinese government ICP license, or a Chinese phone number in most cases.

Baidu currently requires an initial deposit of 5,000 Chinese Yuan (About 800 USD) which will be used for account credit. Plus, there is a standard account setup fee of 1,200 Yuan, but there may be room for negotiation on that.

6) Search Marketing Agencies Are Different in China

If you hire a search marketing agency, make sure you know what you’re getting. There’s a breed of marketing agency in China that isn’t quite the same as their Western counterpart.

For one, the marketing agency may be a reseller itself, which means they’re working on commission from Baidu. They’re paid by the client to manage the account, but they’re also paid by Baidu, which provides motivation to increase expenditure.

Finally, they may have different views when it comes to working with multiple competing businesses simultaneously. Guess what happens when a marketing agency is on Baidu commission, and they’re promoting you and two of your competitors? They definitely have a strong incentive to ramp up the expenditure to the maximum possible amount.

7) Plan Your Web Analytics Integration

Two Approaches To Getting Free Tv Online

Since most of the TV shows are on what appears to be a three-month vacation, now might be a good time to seek Internet alternatives. I got the idea from a story last week in the New York Times about how one of its reporters has gone completely cold-turkey on their cable TV consumption.

It got me thinking about two guys that I know in their 20s that have taken completely different approaches to their digital entertainment consumption. Their approaches illustrate what we have to do to get our TV these days.

J. is single and a DirecTV subscriber, at $95 a month. C. is engaged but doesn’t pay for his TV programming. Like the NYT reporter, he uses his computer to send video to his TV from various Internet sources, using a HDMI to DVI cable. Both are relatively computer savvy guys. Both bought their TVs earlier this spring – this is C.’s first TV since his college days, and did so because he wanted to make it easier for him and his girl friend to watch shows both separately and together. J. has a second TV in his bedroom, and a bigger plasma display in his living room. Both guys have 10 MB cable connections for their Internet service.

C. watches a combination of shows from various Web video sites, such as Hulu and Boxee and some on air TV too. He works in the financial industry, where he has Bloomberg TV streaming to his desktop PC as part of his job. J. works in sales and has some downtime during the work day, where he also watches TV on his PC, but only those Web stations that aren’t blocked by his employer. SpikeTV is his favorite. C. likes the Netflix streaming option, J. hates it – “if I wanted to watch ten-year old movies, I would just find them for free online.”

J. is a big computer gamer and has an Xbox and connects other gaming consoles when his friends bring them by. “The Xbox was difficult to setup to find my digital media,” he told me, much worse the Playstation 3, which easily found and played the majority of his video files that he has downloaded to his PC. It is ironic that a Microsoft gaming console connecting to a Microsoft Windows PC is more difficult to configure than a Sony console connecting to a Windows PC. C. runs on a Mac.

Curiously, the two guys also differ on how they watch movies. J. hasn’t been in a movie theater since 1996, and is proud about that record. Instead, he has downloaded hundreds of movies illegally from a file sharing service, and makes copies of the videos for all of his friends. C. goes to the theaters once every two months but says that it can get expensive, especially at big-city ticket prices.

C. has about 30 GB of music on his PC, most of it illegally downloaded. His last CD was purchased from a store about nine years ago. J. bought his last CD in 1996., and also has several gigabytes of stolen music on his computer. “There is no point in downloading a clip from a legal site,” he told me. “In the time it would take me to listen to the commercial and the first 15 or so seconds, I can find the entire MP3 song online and have it on my hard drive.”

Second, the system isn’t spousal friendly, at least not for my generation. When I checked to see about my wife’s favorite local TV station, they didn’t have any stream that I could watch from their Web site. HGTV’s Web site is also miserable, making finding a show more of an Easter Egg hunt, and I mean that not in any good way. I know free-TV isn’t ready for my wife yet. C.’s fiancée is happy with their free-TV setup, but it has taken her a while to get used to the arrangement.

Third, while the TV producers and networks are trying mightily to avoid another Napsterization of video, they have yet to succeed. They have experimented with copy protection and that seems to be on the wane, and are now concentrated on streaming. Some episodes are available for sale on iTunes.

One thing that is clear is that broadcast networks “must-see TV” is so over. Both guys don’t watch much in the way of sports or news programming. Both watch shows on their schedule, not the networks’.

Finally, the number of add-on devices and gotchas is still mind numbing if you want to deal with the Internet channel. For ABC’s shows, you need to download a player and not use Safari. Netflix has the best and widest streaming support but you’ll need a computer, a supported Blu-Ray DVD player, Xbox or PS3, or their Roku device. Some current shows don’t show up for days or weeks online. Others only have excerpted clips.

Speaking of Roku, I bought mine a month ago and unlike J., am happy with the Netflix choice of those older movies, especially the ones that have been upconverted to HD status. There is little interruption in the video streams, even with a Wifi connection to my network. And Roku continues to add other services, such as Pandora roll-your-own music channels, to make it easier to get content to my living room.

We certainly have come far with free TV — it wasn’t all that long ago that we were using videotapes and buying DVDs, both things that seem so quaint now. Streaming video gets better and better as our Internet pipes improve.

But we still have a long way to go before the Internet can replace the cable DVR. Certainly, Hulu is worth taking a look at and seeing if you can find your favorites and queue them up to watch on your computer. And as Netbooks and used Mac Minis are around $300, there isn’t much friction in having one of them connected to one of your TV outputs. The big remaining issue is having to deal with the various software pieces to try to play the videos.

David Strom is an expert on Internet and networking technologies who was the former editor-in-chief at Network Computing, Tom’s chúng tôi and chúng tôi He currently writes regularly for PC World, Baseline Magazine, and the New York Times and is also a professional speaker, podcaster and blogs at chúng tôi and

How To Change Network Priority In Windows

If you have both Wi-Fi and Ethernet, you might have noticed that Windows automatically switches to Ethernet when connected. This happens because of the automatic network priority that Windows assigns to all its network adapters. Generally, this is good. However, if want to force a particular network over the other, then you are forced to disable the other network adapter.

For instance, when there is a problem with the broadband connection, I try to use mobile data via USB tethering. However, most of the time Windows refuses to use the new connection due to its lower priority when compared to the regular desktop Ethernet adapter. To fix the issue, I have to change the network priority.

If you have multiple network adapters and want to force one network connection over the other, then you should consider changing the network priority rather than disabling the adapter. Here’s how to do it.

Note: to set the priority, we’ll use positive numbers that are higher than zero. The higher the number, the lower the priority.

Change Network Priority via Adapter Settings

Generally, Windows uses priority numbers above 20, so anything under 20 priority should be fine. If not, you can always change it to something lower.

6. Close all other Windows and restart the system to make the changes take effect.

That is all. From now on the higher priority network will be favored over the other active networks. If you ever want to revert back, just select the “Automatic Metric” checkbox in step 5.

PowerShell Command to Change Network Priority

If you want to, you can also use the PowerShell cmdlets to change the network priority. In fact, if you are comfortable using PowerShell, it is easier than the first method. All you have to do is find the interface index, and set the priority.

1. To change network priority via PowerShell, you need to have administrative privileges. Search for PowerShell in the Start menu, and select the “Run as administrator” option.

2. We need to find out the interface index of your target network adapter. To do that, execute the below command, and PowerShell will list all network adapters. You will see the interface index number under the “ifIndex” section. Note the interface index number of your network adapter. In my case it is 17.

3. Once you have the interface index number of the target network adapter, use the below command while replacing “INTERFACE_INDEX” with the actual interface index number and “PRIORITY” with the actual priority number that is equal to or below 10.

4. To verify the changes, execute the Get-NetIPInterface cmdlet. As you can see, the network priority has changed from 25 to 10.

5. Restart your system to apply the changes.

6. In the future, if you want Windows to take care of the network priority, simply execute the below command. Remember to replace INTERFACE_INDEX with the actual interface index of your network adapter.

Vamsi Krishna

Vamsi is a tech and WordPress geek who enjoys writing how-to guides and messing with his computer and software in general. When not writing for MTE, he writes for he shares tips, tricks, and lifehacks on his own blog Stugon.

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Tips For Using Online Proctoring Solutions

Due to the increasing health issue, everyone has been compelled to relocate their academics, talent tests, and certificates online.

Because we don’t know how long our online studies, training, and examinations will continue, corporate organizations, schools, colleges, and universities are using online proctoring software to make online assessments more manageable. But how do we police this new online environment? That’s where proctor management comes in.

What Is a Proctor?

A ‘proctor’ is a person who supervises an exam, and ‘proctoring’ is the act of supervising an exam or test of students, applicants, employees, or others.

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The Need for Online Proctoring

In the recent decade, the use of online exams and remote proctoring has increased at an unprecedented rate. Since the COVID-19 outbreak, remote proctoring has been the norm as a realistic and practical alternative to in-person proctoring.

With most educational institutions closed and exams looming, most institutions realized the value and potential of online exam proctoring.

It is in this situation that we turn to online proctoring. You can keep an eye on test-takers via their webcam, computer microphone, and desktop through this technology.

Whether timed or untimed, remote proctoring allows schools and educational institutions of all kinds to identify the test-takers, the absentees and keep track of any malpractices.

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Do’s and Don’ts of Online Proctoring

This is not so when you have to conduct tests online. You have to do everything, from verifying identities to keeping students in check, which can get very tedious quickly.

Once you have the software that works for you, you need to adhere to some rules and ethics. In this section, we will go through some of the best practices and do’s and don’ts of using online proctoring services that you should impress upon your students before they sit for an exam in this manner.

So, without further ado, here we go:

A webcam and microphone is required when a candidate takes a test online.

Pupils must be situated against a plain background. Ensure the presence of adequate lighting in the room. If you are unsatisfied, make them relocate to a different room.

The room must be kept as silent as possible at all times. Any noise at all must incur a penalty.

Ensure that the candidate is the only one in the room during the examination.

Before the examination begins, all candidates should have a sufficient number of sheets available. Nobody is allowed to get up to get more in the middle of an exam.

So much for do.

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Students should refrain from attempting navigation away from the exam window, as their examination screen will be proctored for the duration of the examination. If any suspicious activity is noticed during the exam, the exam will be deemed invalid and the results will be withheld. There can be no budging on this point.

Make sure that your candidates understand that they are not permitted to speak with anybody else throughout your exam.

Your candidates cannot use any digital devices, such as mobile phones or Bluetooth speakers, throughout the exam. These will be regarded as malpractice if observed in the test.

Instruct your students to refrain from using several devices at the same time because the system may terminate the exam for that candidate if it detects this behavior.

Before the exam begins, a practice screen is displayed. It is critical that the candidates do not navigate away from the test screen.

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Don’t Forget to Help the Student

Despite the necessity of online proctoring, please don’t forget that this is all uncharted waters for students.

They are not used to giving exams in a room, cut away from friends and family, with a stranger watching their every move.

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You can automate the process of conducting assessments with the correct proctoring software, starting with curating the exam, arranging test dates, evaluating the submitted papers, and finally revealing the final findings via the internet.

Because online exams are less expensive, faster, and more transparent, it’s a good idea to invest in AI-driven proctoring software to ensure that tests run smoothly and efficiently.

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