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If you’d like to know some tips on how you can best utilize big data to boost your earnings, here are some suggestions you can considerCompanies use big data to analyze their customer base and explore opportunities in their market space. Big data refers to technologies that store, analyze, and manage large and complex data. Through customer data, the needs of the consumers can be easily identified and catered to by providing improved goods or services. It also eliminates the guesswork for marketing strategists as they can quickly determine a customer’s purchasing behavior and use it as a basis for campaigns.
Ultimately, the goal of collecting and analyzing customer data is to know their profile, study their interests and product preferences, and guide them towards a successful sale or transaction with the company. In essence, big data is turned into marketing revenue through various methods.
If you’d like to know some tips on how you can best utilize big data to boost your earnings, here are some suggestions you can consider:
Employ Big Data To Enhance Marketing StrategiesCustomer data is an excellent tool to help you design effective marketing campaigns. By analyzing customer profiles, you can better understand your target audience. In effect, this understanding will help you curate a campaign that would attract attention, pique the market’s curiosity, and invite new and repeat business.
For example, you can utilize cookies gathered from the customer’s web activity to learn their interests, purchase histories, and general profile. With this information, you can tailor your subsequent campaigns in a manner that would best suit your target audience. This way, you can minimize strategic errors that may hinder the success of your campaigns. In addition, you can also help your enterprise save time, effort, and resources in your marketing strategies moving forward.
Create Data-Based Customer Engagement StrategiesYou can also use big data to design strategies that augment customer engagement. For instance, you can study how your target audience interacts with your brand and the factors that boost their engagement. You can also identify
how to increase customer value
through these interactions, online or otherwise.
Big data analysis can also provide you with crucial information to make adjustments where needed. For instance, you can observe which of your existing products gets the most and least engagement. This way, you can devise a plan to help attract more attention towards less-engaging items or realign your resources towards developing new and improved products.
Boost Brand Awareness And Customer Acquisition Through Big DataAs you collect digital information based on your customer’s responses on your online platforms, you can also determine how to widen your brand’s reach and boost awareness in other market platforms. One way you can do this is to increase your brand’s presence on the sites that your target audience frequents.
For instance, you can design an online campaign via social networking sites popular across your customer base. This way, it will be easier for them to engage with your brand and share your product information with their network. Also, while you promote brand awareness, you can improve customer acquisition by engaging with audiences connected to your current customer base.
Use Big Data As Basis For Adjusting Price PointsProduct prices can significantly influence a customer’s purchasing behavior. As such, it’s crucial to be aware of price movements in your market and how your brand stands against the competition. While offering lower-priced products may seem like the best way to beat competitors, it can backfire if the price adjustments are not justified. For instance, the customers may question quality or brand credibility if the prices are too low compared to other brands.
With these factors in mind, you’ll need to make price point comparisons using customer data that shows how product selections are influenced. You may see specific purchasing patterns that may help you point out the audience’s primary considerations in choosing a brand or an item. This way, you can make reasonable price adjustments that won’t hurt your brand and your revenue.
Meanwhile, you can also explore other options to make your prices more competitive. For example, you can consider adding discounts and freebies, which can help your products stand out from other brands.
Conclusion
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4 Tools To Analyze Affiliate Marketing Data
However, if you’ve never done affiliate marketing before, it might be a puzzle for you. As a result, it’d be prudent to take a professional course that helps you kickstart your new venture. There are numerous course providers you could choose from, but you should exercise your due diligence as not all of them are legitimate nor worth your time and hard-earned money. You can go now and read a review of one of such courses, as well as go over the reviews of other service providers, so you’d end up making the right choice.
Besides taking a relevant affiliate marketing course, you should also know about the tools you can use to analyze your data. That being said, here are four tools that help analyze affiliate marketing data:
Google Analytics (Web Analytics)Besides that, you can gain oversight of your site’s page views, users, and sessions, and use your collected data to project possible patterns for conversions, bounce rate, traffic sources, and user position. Web analytics tools are essential to tracking your traffic from search engines and social media platforms. Moreover, you can also know the online pages that led visitors to your site.
To proficiently know how to use this tool, you may need to do at least one affiliate marketing course to hone your skills, such as Partner With Anthony. You may read now a detailed analysis of the said course so you can determine if it’s the best one for your needs.
2. MailchimpIf you’re a digital marketer, email marketing is one channel you can’t afford to overlook. However, to be effective in your email marketing campaigns, you must create an email list. Mailchimp is a free tool that gives you a certain number of emails to send for free. However, it’s believed that the allocated number is sufficient for affiliate marketers. It’s a common app used by millions of people globally because it can be integrated with many other applications to connect with other marketing channels.
Mailchimp is a one-stop shop that helps you connect with your clients and other interested parties. It’s a powerful tool for customer data analysis and for designing an effective email marketing campaign.
It’s an easy-to-use tool where you start by picking a template depending on your goal and preferences. After selecting your template, the next step is to add your well-curated and designed content. After that, choose who’s supposed to receive the email from your contact list. You can also add more email contacts. Once everything is set, you can send the email to your recipients.
3. ForensiqIf you’ve been active in online marketing for a while now, it’s no doubt that you’ve heard or encountered scammers. As a result, you may need a platform that ensures your affiliate program’s safety through fraud detection. Forensiq is a fraud detection platform that generates essential data concerning the authenticity of traffic reaching your site.
In effect, it can scrutinize user details across your browser and ascertain whether the IP address, device, and behavior are from a familiar customer. Nowadays, there’s a lot of traffic coming from bots. Thus, you always need to authenticate every user.
4. Hootsuite Final ThoughtsAnalytics: Turning Data Into Dollars
Web site analytics provide valuable information about who is coming to your site and what they do once they get there. At the simplest level, a counter that displays the number of visitors to a page is a source of Web analytics, but analytics when interpreted properly, is much more than that. Knowing information about your site visitors and the actions they take can help you determine some important factors.
Many Web hosts provide a degree of analytical reporting as part of their service, but generally these are lacking in the amount of information they provide. For a single-page fledgling site they may provide enough information, but for a multiple page catalog you’ll need more. It isn’t necessary to pay large amounts for a service, though, and free services such as the highly-rated StatCounter provide an excellent basis from which to begin your analytical journey. Generally speaking, they all provide similar data, which can be used in the following ways:
By closely monitoring the keywords people use to find your site it is possible to adapt your service and your site to match their needs more closely. If searchers entering specific search strings are visiting your site then you know the kind of information they are seeking. It is possible to alter and optimize your pages, your products, and even your offers to increase revenue and performance.
Second, these statistics also offer a method of determining which of your visitors are more likely to purchase. In most cases, the more specific a search term or the more targeted a link, the more likely a person is to eventually purchase through your site. Because the visitor path displays the way a visitor found your site and shows if they made it to “checkout” or not, you can soon determine the most responsive sources of traffic.
A good way to encourage people to spend more money on your site is to offer them “related product” links. On product pages you can place links to related items that they may be interested in and the visitor path information is the ideal way to find the perfect combination of products.
Less than 5 seconds
Between 5 and 30 seconds
Between 30 seconds and 5 minutes
Between 5 minutes and 20 minutes
Between 20 minutes and 1 hour
Longer than an hour
Hopefully you will witness a small percentage of people that remain on your site for less than five seconds, but in reality it will depend on how targeted your traffic is. Well-targeted traffic is more likely to remain on the pages of your site whereas poorly targeted traffic will leave if not immediately interested.
Take a closer look at visitors that leave quickly and determine where they came from. If a PPC ad is providing you with visitors that only look at the resulting page and then leave, that’s an area you should spend time studying. Letting a poor-performing PPC campaign go unevaluated is one of the quickest ways lose money.
System statistics includes information like the browser that each of your visitors uses, or the resolution of their screen size, the operating system they use, and even whether their browser is Java enabled. This is invaluable for ensuring that all of your visitors are able to effectively view your site and provides you with potential reasons for any unusual activity on your pages.
While Internet Explorer is usually the browser of choice for 50 percent or more of site visitors, that still leaves a lot of shoppers using Firefox and other browser alternatives. If your site has only been tested in Internet Explorer and does not display correctly in other browsers, you could be losing as many as half of your potential customers.
This article was first published on chúng tôi
How To Turn The 2023 Mac Mini Into A Capable Windows Gaming Machine
Can you game with the 2023 Mac mini? If you’re willing to install Windows 10 using our Boot Camp tutorial, and you have an eGPU at your disposal, the answer is yes. Watch our video showing you how to transform the 2023 Mac mini into a legit Windows 10 gaming machine.
The setupFor this tutorial, I’m using a 2023 Mac mini with a 6-core Intel i7 processor. I upgraded the Mac mini RAM from 8GB to 32GB, and I attached a 1TB Samsung’s T5 SSD for the purpose of housing large games.
Instead of using macOS, I installed Windows 10 using our 2023 Mac mini Windows Boot Camp tutorial. This provides me with substantially more gaming options, as Windows remains miles ahead of macOS as far as game title support and (non-Metal) performance is concerned.
Video walkthroughSubscribe to 9to5Mac on YouTube now for more videos
As I noted in our 2023 Mac mini review, it’s the most versatile computer in Apple’s lineup thanks to its small form factor and I/O. Sporting four Thunderbolt 3 ports on its rear, the 2023 Mac mini has a seemingly endless amount of expansion options at its disposal. Considering that the Mac mini has terribly underpowered integrated graphics, an external GPU is an obvious choice to beef up the machine’s capabilities.
For graphics, I’m using the Razer Core X eGPU (review) chassis with an AMD Radeon RX Vega 64 inside. I opted for an AMD card because of its compatibility with both macOS and Windows. In a forthcoming post, I’ll show what it’s like to game with an Nvidia RTX 2080 GPU, which unfortunately is Windows-only.
For a console-like experience, I purchased an Xbox controller, which pairs nicely with Windows 10 machines via built-in Bluetooth settings. This is especially nice for playing games like Forza Horizon 4.
How to turn the 2023 Mac mini into a Windows gaming machineStep 1: Install Windows 10 on your machine using our Mac mini Windows 10 tutorial.
Step 2: Connect your eGPU to your Mac mini via Thunderbolt 3. As noted, I’m using an AMD RX Vega 64 inside the Razer Core X eGPU chassis.
Step 3: Install the latest drivers for the AMD RX Vega 64 using the AMD Radeon installer tool.
Step 4: Connect the Xbox Controller to Windows via Bluetooth settings.
Step 5: Download your game of choice, and configure it to your liking. In my video demonstration above, I was able to run Rocket League, Madden 2023, and Forza Horizon 4, all at full resolution (5120×2160) with high settings.
All of the games were playable, even when running my display at such a high resolution. The LG UltraWide 5K2K (review) features a maximum refresh rate of 60Hz, so it’s not really designed for gaming, but playing games in a 21:9 aspect ratio at such a high resolution is quite engrossing.
9to5Mac’s TakeThe Mac mini becomes a capable gaming machine if you’re willing to connect an eGPU and run Windows 10. I was pleasantly surprised at how well every game ran, especially when at native 5120×2160 resolution. The Mac mini isn’t a gaming machine by nature, but with the addition of an eGPU + Windows, I think it performs surprisingly well.
What do you think?
FTC: We use income earning auto affiliate links. More.
Can Big Data Solutions Be Affordable?
One of the biggest myths still remains that only big companies can afford Big Data driven solutions, it is appropriate for massive data volumes only and is expensive as a fortune. That is no longer true, and there were several revolutions that changed this state of mind.
Maturity of Big Data technologiesThe first revolution is related to maturity and quality. There is no secret that ten years ago big data technologies required certain efforts to make it work or make all pieces work together.
Picture 1. Typical stages, growing technologies pass-through There were countless stories in the past coming from developers who wasted 80% of time trying to overcome silly glitches with Spark, Hadoop, Kafka or others. Nowadays these technologies became sufficiently reliable, they eliminated childhood diseases and learned how to work with each other. There is a much bigger chance to see infrastructure outages than catch internal bugs. Even infrastructure issues can be tolerated in most cases gently as most big data processing frameworks are designed to be fault-tolerant. In addition, those technologies provide stable, powerful and simple abstractions over calculations and allow developers to be focused on the business side of development.
Variety of big data technologiesPicture 2. Big Data technology stack Let’s address a typical analytical data platform (ADP). It consists of four major tiers:
Dashboards and Visualization – facade of ADP that exposes analytical summaries to end users.
Data Processing – data pipelines to validate, enrich and convert data from one form to another.
Data Warehouse – a place to keep well-organized data – rollups, data marts etc.
Data Lake, place where pure raw data settles down, a base for Data Warehouse.
Every tier has sufficient alternatives for any taste and requirement. Half of those technologies appeared within the last 5 years.
Picture 3. Typical low-cost small ADP
Picture 4. ADP on a bigger scale with stronger guarantees
Cost effectivenessThe third revolution is made by clouds. Cloud services became real game changers. They address Big Data as a ready-to-use platform (Big Data as a Service) allowing developers to focus on feature development, letting cloud care about infrastructure. Picture 5 shows another example of ADP which leverages the power of serverless technologies from storage, processing till presentation tier. It has the same design ideas while technologies are replaced by AWS managed services.
Picture 5. Typical low-cost serverless ADP Worth saying that the AWS here is just an example, the same ADP could be built on top of any other cloud provider. Developers have an option to choose particular technologies and a degree of serverless. More serverless it is, more composable it could be, however more vendor-locked it becomes as a down side. Solutions being locked on a particular cloud provider and serverless stack can have a quick time to market runway. Wise choice between serverless technologies can make the solution cost effective. Usually, engineers distinguish the following costs:
Development costs
Maintenance costs
Cost of change
Let’s address them one by one.
Development costsCloud technologies definitely simplify engineering efforts. There are several zones where it has a positive impact. The first one is architecture and design decisions. Serverless stack provides a rich set of patterns and reusable components which gives a solid and consistent foundation for solution’s architecture. There is only one concern that might slow down the design stage — big data technologies are distributed by nature so related solutions must be designed with thought about possible failures and outages to be able to ensure data availability and consistency. As a bonus, solutions require less efforts to be scaled out. The second one is integration and end-to-end testing. Serverless stack allows creating isolated sandboxes, play, test, fix issues, therefore reducing development loopback and time.
Maintenance costsOne of the major goals that cloud providers claim to solve was less effort to monitor and keep production environments alive. They tried to build some kind of ideal abstraction with almost zero devops involvement. The reality is a bit different though. With respect to that idea, usually maintenance still requires some efforts. The table below highlights the most prominent kinds.
Cost of changeAnother important side of big data technologies that concerns customers — cost of change. Our experience shows there is no difference between Big Data and any other technologies. If the solution is not over-engineered then the cost of change is completely comparable to a non-big-data stack. There is one benefit though that comes with Big Data. It is natural for Big Data solutions to be designed as decoupled. Properly designed solutions do not look like monolith, allowing to apply local changes within short terms where it is needed and with less risk to affect production.
SummaryAs a summary, we do think Big Data can be affordable. It proposes new design patterns and approaches to developers, who can leverage it to assemble any analytical data platform respecting strongest business requirements and be cost-effective at the same time. Big Data driven solutions might be a great foundation for fast-growing startups who would like to be flexible, apply quick changes and have short TTM runway. Once businesses demand bigger data volumes, Big Data driven solutions might scale alongside with business. Big Data technologies allow implementing near-real-time analytics on small or big scale while classic solutions struggle with performance. Cloud providers have elevated Big Data on the next level providing reliable, scalable and ready-to-use capabilities. It’s never been easier to develop cost-effective ADPs with quick delivery. Elevate your business with Big Data.
About the AuthorHow Big Data Can Revolutionize Agriculture
Big data is becoming pervasive by introducing more sophisticated ways to exploit roots of technology. Not only user interfaces but also necessary tools have evolved drastically. Big data has made the world truly close, and yes, the customised data choice is a cherry on the cake. Big data tools and its results have entered into almost every segment of human lives. Just say the name and big data is there. Actually, data is everywhere, it needs to be handled professionally to take out gold from ash. The agricultural segment is the backbone of the Indian economy. Not only India, the existence of humanity is having a knot with the yield from the land. The world is changing, things are changing, the climate is changing, and humans have already adopted those changes. But the motherland hasn’t. According to a survey, the world population will be having a boom real soon by hitting around 47% growth by 2040. Now that’s a warning bell for human existence. The overexploitation of natural resources and lack of strategic decisions has led all of us to a situation where the balance of nature has shifted to a whole new different level. To tackle the future food crisis, technology has to be used to analyze and modify the existing agricultural practices. Here, big data comes into the picture. Let’s have a quick overview of the ways in which big data can be deployed to evolve agricultural segment.
1) Generation of Data Sets by Revealing Food SystemsData has enormous power to turn things upside down, but only when it is used effectively. The data can only be used wisely if it is converted into segregated data sets. The agricultural segment has a long list of attributes that can be taken into consideration for the proposed analysis and consequent result studies. Key attributes having the impact on the process output can be handpicked and used for generating data sets. These data sets will be used to produce a ground for all related activities. Every food systems have different structure and these can be easily analysed only and only if, the data set implementation is done.
2) Monitoring of the TrendAll the data relating to the history of specific crop disease or pest can be used to generate the data set and consequently monitoring of this data may lead to unfolding the trend in the agricultural field. Nowadays, predicting exacts things are nearly impossible. All the attributes have become so arbitrary that nothing can be guaranteed. But monitoring these attributes, for instance, the pest and crop disease history, data monitoring can be used to predict the future attacks on yield so that preparatory actions could be taken. This will not only save the stakeholders money but also the time investment. Thus, monitoring the selected attributes has an enormous importance in the implementation of big data.
3) Impact AssessmentEvery system is designed with the consideration of risk analysis. Every wrong turn has to be considered before it is taken. The probable impacts and corrective actions for the same have to be defined. Same goes with the agricultural segment. Today, there is a number of unfortunate situations where the whole yield in the field is wasted due to some uncertainties. These things can be managed well if the impact assessment is done properly. For instance, if the impact assessment for pesticides is done at the very first stage of sowing the seed then the probable failure can be prevented. In any unfortunate situations, if the pesticide turns out to be dangerous, then the impact analysis helps to avoid the consequences. Necessary measures can be taken to avoid the wrong turns and help in taking corrective actions.
4) Data-Driven FarmingAs per the current scenario, decision makers are facing tremendous problems in predicting probable failure. Here, data is the saviour. Data can be used effectively to conclude predictions thus preventing them in taking risky decisions. Today, data sources including satellites, mobile phones, weather stations have contributed in making this possible. For an error proof analysis, the data quality and variance is a must thing. And the data source serves for both of the necessities. What to plant? When to plant? These basic questions can be answered very easily if the data backs it up. The dream of data-driven farming is slowly making its move and proving it with improved yields.
SummaryBig data is becoming pervasive by introducing more sophisticated ways to exploit roots of technology. Not only user interfaces but also necessary tools have evolved drastically. Big data has made the world truly close, and yes, the customised data choice is a cherry on the cake. Big data tools and its results have entered into almost every segment of human lives. Just say the name and big data is there. Actually, data is everywhere, it needs to be handled professionally to take out gold from ash. The agricultural segment is the backbone of the Indian economy. Not only India, the existence of humanity is having a knot with the yield from the land. The world is changing, things are changing, the climate is changing, and humans have already adopted those changes. But the motherland hasn’t. According to a survey, the world population will be having a boom real soon by hitting around 47% growth by 2040. Now that’s a warning bell for human existence. The overexploitation of natural resources and lack of strategic decisions has led all of us to a situation where the balance of nature has shifted to a whole new different level. To tackle the future food crisis, technology has to be used to analyze and modify the existing agricultural practices. Here, big data comes into the picture. Let’s have a quick overview of the ways in which big data can be deployed to evolve agricultural chúng tôi has enormous power to turn things upside down, but only when it is used effectively. The data can only be used wisely if it is converted into segregated data sets. The agricultural segment has a long list of attributes that can be taken into consideration for the proposed analysis and consequent result studies. Key attributes having the impact on the process output can be handpicked and used for generating data sets. These data sets will be used to produce a ground for all related activities. Every food systems have different structure and these can be easily analysed only and only if, the data set implementation is chúng tôi the data relating to the history of specific crop disease or pest can be used to generate the data set and consequently monitoring of this data may lead to unfolding the trend in the agricultural field. Nowadays, predicting exacts things are nearly impossible. All the attributes have become so arbitrary that nothing can be guaranteed. But monitoring these attributes, for instance, the pest and crop disease history, data monitoring can be used to predict the future attacks on yield so that preparatory actions could be taken. This will not only save the stakeholders money but also the time investment. Thus, monitoring the selected attributes has an enormous importance in the implementation of big data.Every system is designed with the consideration of risk analysis. Every wrong turn has to be considered before it is taken. The probable impacts and corrective actions for the same have to be defined. Same goes with the agricultural segment. Today, there is a number of unfortunate situations where the whole yield in the field is wasted due to some uncertainties. These things can be managed well if the impact assessment is done properly. For instance, if the impact assessment for pesticides is done at the very first stage of sowing the seed then the probable failure can be prevented. In any unfortunate situations, if the pesticide turns out to be dangerous, then the impact analysis helps to avoid the consequences. Necessary measures can be taken to avoid the wrong turns and help in taking corrective chúng tôi per the current scenario, decision makers are facing tremendous problems in predicting probable failure. Here, data is the saviour. Data can be used effectively to conclude predictions thus preventing them in taking risky decisions. Today, data sources including satellites, mobile phones, weather stations have contributed in making this possible. For an error proof analysis, the data quality and variance is a must thing. And the data source serves for both of the necessities. What to plant? When to plant? These basic questions can be answered very easily if the data backs it up. The dream of data-driven farming is slowly making its move and proving it with improved chúng tôi data has evolved the way things work. Now, it’s a turn for the agricultural segment. Many researchers are toiling their nights to make it more and more accessible, dependable and of course yieldable. Today, agriculture segment need to evolve to preserve human existence on the earth, and undoubtedly big data can help do this. The above-noted steps can be genetically followed to develop and implement procedures to yield good results. Hopefully, the near future will evidence the utopia in agriculture backed up with green evolution.
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