Big data technology has been the basis for the Fintech industry. There is no disputing the major benefits that big data has created for the financial sector.
However, there are also new challenges that have arisen as big data has become more widely available in Fintech. One of the biggest changes is new regulations. Fintech businesses must make sure that any data scientists working for them are licensed and trained to handle tasks with the utmost sensitivity.
One of the most regulated aspects of the Fintech industry is AISP, due to the vulnerability of people that might have their data exposed by people working in this field. You need to make sure that you are properly licsensed if you intend to offer AISP services as a data-driven financial company.
Getting an AISP License as a Fintech Company Relying on Big Data
It goes without saying, Payment Services Directive (PSD2) opened the way for new Fin-Techs to launch. Unfortunately, PSD2 regulations are applied only in the European Union and the European Economic Area. Account Information Service Provider (AISP) is authorized to send and receive account data from banks and other financial institutions. This data can be used for various purposes and could be extremely beneficial for end users. At the same time, online service providers who have AISP licenses can stand out and amaze with better customer experience.
Not only Fin-Techs can apply for AISP license, in some cases banks, insurance companies and other organizations use it for several reasons. Institutions can have both AISP and PISP (Payment Initiation Service Provider) licenses at the same time and carry out related activity.
Typically, authorized AISP license holders with the permission of users can get private information on other platforms. This solution works for safer banking and financial transfers as much as registering and signing to other platforms in a few times faster way. AISP technology allows to receive user data but doesn’t allow to make any financial transaction. Received information is mainly used for analyzing separate service providers and monitoring all of them on one dashboard. Anyway, the AISP licensing process is strict and time-consuming.
To get AISP license, organizations must hold Professional Indemnity (PI) insurance. It can be applied for business or self-employed freelancers. This type of insurance covers any cases where any financial loss is made by the customer‘s or organization‘s fault. So it‘s good to know that even if something may go wrong, there will always be a third party who will compensate for the loss of money or data.
As mentioned before, PS Directive is a great opportunity to start a Fin-Tech business by creating and representing a brand new, never-before-seen service or by improving one that exists but could be provided in a transformed format.
The AISP license is not easy to get. First, you must fill in an application form. It involves the business plan, all the legal information about the company, and why AISP is necessary in your business. All the documents and agreements must be provided to meet strict requirements. Also, you must ensure that your system is safe enough and use the required safety protocols. Funds and personal information are too valuable to be lost. The only way to do it – a compliance demonstration.
Your idea of an AISP-based system must be fulfilled and prepared without skipping any step. AISP technology is being developed, but there are still many undiscovered methods of use that must succeed.
PSD2 opened the new page of EU/EEA online services. To regulate this sector was a great idea, and the most benefits are felt by end users. Traditional banks now have new-age competitors who correct the prices of some services and make traditional banking come along with the latest trends. Glad that in this situation the maximum benefit is to the end user.
Data-Driven Fintech Companies Must Get a License Before Offering AISP
Big data has been highly useful in the Fintech sector. More companies are using sophisticated big data tools to offer services like AISP. However, you need to make sure that your company is properly licensed first.
FinTech is about connecting with customers. They expect something different from classically understood banking. The more you know about your audience, the more you can offer them. It’s similar to prices – price optimization through machine learning is a great tool to grow your revenue. What can you learn from real-market examples?
Figuring out the best pricing model can be tricky. Especially with a newly developed product, when you have to convince people to set up accounts and trust you with both: data and money. That’s where machine learning algorithms come into place. By processing and analyzing big amounts of data, they can help you establish optimized pricing plans. How exactly?
Hire machine learning to make optimal pricing decisions
Solutions mentioned below will boost your product in real-time. They can help both: established companies and startups. Think of them as a multiple-step guide to designing your app with specific features and customer-centric solutions in mind.
This is how you can improve a pricing model:
Use machine learning to process data and discover services that need a boost. There are highly specialized FinTech applications that offer only one product; loans for example. There are, however, applications that are very popular and sell multiple solutions to the same audience. What product generates more money? Which solution is better? Do an A/B testing and find out. By going through data, you can figure out what works and what doesn’t. This solution can free up resources (money, employees’ time) to pursue more profitable features.Use automated pricing models to drive up revenue. The Boston Consulting Group created a study and it seems that revenue can be boosted up by 5% with this. The BCG believes that machine learning offers optimal pricing rules in revenue management systems. It also enforces contractual pricing.Generate insight on changing user’s behaviors through automated pricing solutions. It gives a highly valuable context on transactional data, providing the necessary perspective. One of the companies that offer interesting solutions is Vendavo. Their model and industry integrations work great with custom software development, powering your app. This combo of data and development solutions will help you make pricing decisions. Especially based on cross-border parameters.Use machine learning to figure out which customers are willing to pay for a product or a specific feature. You can pull information by linking spending or monthly fees in a software-as-a-service (SaaS) model with discounts, promo codes, etc. It’s especially valuable in the case of VIP pricing plans.Predict pricing impact with AI-powered user personas. Try to predict whether a first-time user or a paying customer will perform a certain and desirable action. Thanks to artificial intelligence propensity models, you can increase the customer retention and reduce churn.Use rule-based artificial intelligence (AI) models to establish the risk-to-revenue. Software development, specially dedicated to the B2C market, isn’t always fully predictable. Customers’ needs and the market itself change rapidly. Friction in user experience can be managed but what about mobile app development? You can use the customer even before you know about the issue. The price is not acceptable. The solution is brilliant, but underdeveloped. Microcopy inside the app doesn’t transmit the offers very well. User experience and user interface design are not attractive enough. Machine learning pricing algorithms won’t give you all the answers, but they can show you the right direction.
How to achieve your goals?
According to McKinsey, the estimated AI-based pricing solutions can have a global worth of $259.1B to $500B, globally. According to Mordor Intelligence, the AI market in the sector will grow from $7.27B in 2019 to over $35.4B by 2025. Those are, however, numbers you can’t use. As previously mentioned, 5% is something real. How to get to that? Use these factors to drive your decisions:
customers’ personasoperating costs and preferred marginsseasons and holidaysother, especially unforeseen, economic variables
Also, focus on something called a dynamic price. It’s adjusting prices, usually for a number of products, to react to the competition’s strategy. This model assumes frequent changes. It’s risky, unstable, and leads to churn. We have broken down the differences between price optimization, dynamic pricing, and price automation with machine learning.
Price optimization without machine learning is incomplete
Massive amounts of data and machine learning can generate pricing recommendations but you still have to base decisions on experience. Machine learning can and will give you a lot to think about, it can also free you from many mindless business operations. It can also be faulty.
As well as software development, which requires real specialists. Financial software development company can save you a lot of trouble and create a performing digital product worthy of your customers’ attention. Care to join the future?
Learn how genetic algorithms and machine learning can help hedge fund organizations manage a business. As well as bolster investor confidence and improve profitability.
As a hedge fund shareholder, you certainly want the best for your organization, right?
For instance, you want to generate effective AUM, NAV, and share value reports to improve investor confidence as a manager.
Or enable your company to produce maximum profits as a trader or employee, etc.
Well, it doesn’t need to be that difficult.
This article looks at how genetic algorithms (GA) and machine learning (ML) can help hedge fund organizations. For instance, to manage a business, boost investor confidence and increase profitability.
Let me walk you through these.
Modern machine learning and back-testing; how quant hedge funds use it
1. Manage funds and make investment decisions
First off, hedge fund companies require sound investment decisions to enable profitability.
As such, over 56% of hedge fund managers use AI and ML when making investment decisions. And their percentage is expected to increase sharply over time.
They do so since investment algorithms are effective as they aren’t affected by opinions, emotions, and judgments like their human counterparts.
“Most of the hedge fund managers surveyed are leveraging advanced algorithms and human judgment to deliver smarter investment decisions.” This is according to Barclay Hedge founder and President Sol Waksman in his July 2018 statement.
2. Perform quantitative analysis
Similarly, hedge funds often use modern machine learning and back-testing to analyze their quant models. Machine learning has done a lot to help them improve financial trading. They do so to ensure that they’re in top form or of the highest quality.
Here, the models get tested using historical data to evaluate their profitability. And their risks before the organizations invest real money.
According to Insight FactSet, hedge funds can use ML to find patterns in data. As a result, it allows models that explain stock performance based on different factors, such as company activity and pricing.
Besides that, integrate advanced back-tests to test their algorithms from time to time to ensure that they’re in tiptop condition.
Methods of Algo-trading, machine learning tests, back-tests
These methods typically observe price shifts, channel flare-ups, moving averages, and associated technical indicators to decide. For instance, issue a purchase command when an asset’s price rises. And give out a sell order when the asset’s price falls.
However, they do not call for price forecasts or predictions, making them easy to implement.
3. Mathematical Model-based Strategies
Unlike trend-following approaches, these methods use time-tested and proven mathematical prototypes to enable combination-based trading.
Here, a method instructs a quant model to buy or sell stock when a specified mathematical condition is met. Or withdraw or deposit particular sums of money. An example of such is the delta-neutral trading approach.
Machine learning tests
As a routine, hedge funds usually test new and incorporated ML’s to determine their effectiveness to maintain competitive advantage.
To do so, they typically use the following evaluations:
1. Pre-train tests
They’re mainly carried out early on when developing a new ML to identify bugs to avoid needless training.
Tests that check an organization’s ML model output shape to ensure that it corresponds with the labels in its datasetEvaluations that check for label disclosure between an ML’s training and validation datasets, etc.
What makes the pre-tests unique is that they do not require trained parameters.
2. Post-train tests
The primary aim of these tests is to cross-examine the logic gained during training and showcase how the models are performing—as behavioral reports.
3. Invariance tests
They’re typically carried out to explain the sets of perturbations used. In addition, the tests are implemented to examine the consistency of the model predictions.
Hedge funds usually carry out back-tests on historical data to:
Test if the quant models are working effectivelyEvaluate previous trading days or go through historical data to train new modelsCollect statistical data regarding the likelihood of the opening gaps getting closed within trading sessions
That said; some of the commonly used historical data analysis methods used by hedge funds include the use of:
Hedge fund organizations mostly use a Strategy Test to test and boost their trading techniques (Expert Advisors) before engaging in business.
This reduces the chances of making losses during actual trading.
Here, an Expert Advisor with its initial variables is first run on history data during the testing phase. And subsequently, run using different parameters during the optimization phase to identify the most suitable combination.
This reduces the chances of making losses during actual trading.
As you can see, genetic algorithms and machine learning are being extensively used by investment organizations, such as hedge funds to improve profitability.
You can also tap into this revolutionary field by implementing similar strategies.
Understand and implement ML when making investment decisionsLearn and use Algo-trading approaches
Routinely test your system’s ML capabilities
Doing so can significantly revamp your hedge fund business.
If you enjoy working with data, or if you’re just interested in a career with a lot of potential upward trajectory, you might consider a career as a data engineer. But what exactly does a data engineer do, and how can you begin your career in this niche?
What Is a Data Engineer?
A data engineer’s job is to take data and transform it in a way that makes it easier or more useful to analyze. For example, imagine that you’re working for a company that’s creating self-driving cars. A central server is likely collecting tons of data from multiple sources; onboard measurements from the self-driving car, feedback from the driver, and external sources of data are all feeding into the system.
The company is interested in creating solutions that allow them to analyze car performance, customer satisfaction, road safety, and other concepts. Your job as a data engineer would be designing, creating, maintaining, and improving the systems to do it.
How to Become a Data Engineer
If you like to think logically, if you have a love of engineering, if you want to solve problems, or if you just want a sustainable career, data engineering could be the best path for you. So what does it take to become a data engineer?
Get a bachelor’s degree. It’s not strictly necessary to have a bachelor’s degree to begin working in data engineering, but it certainly helps. Some employers will specifically look for candidates to have a four-year degree in computer science, data science, software engineering, or a related field. If you have a bachelor’s degree in a non-related field, like English, that may help – but you’ll need to make up for that lack of degree with an abundance of experience. Master your software engineering skills with small projects. It’s a good idea to polish your software engineering and coding skills with small projects. You’ll need to be very acquainted with SQL, a foundational programming language in the realm of data science, and be at least somewhat familiar with other languages and frameworks like Python, Spark, and Kafka. Your small projects should also help you better understand things like machine learning, database architecture, and data mining – as well as commonly used platforms like Amazon Web Services.
In addition to boosting your skills, this step will help you assemble a portfolio of work to show off your talent. Depending on where you’re applying and what role you’re seeking, this could be crucial in helping you get hired. Join hackathons, groups, and other networking opportunities. Get involved with the community and start networking. Join hackathons, data and software groups, and try to meet other professionals in the industry whenever you get the chance. This is a great opportunity to learn new things, put your skills to the test, and have fun doing it. Plus, you’ll make a plethora of new connections, which may ultimately direct you to new job opportunities. Start applying for entry-level jobs. At this point, you’ll be ready to start applying for entry-level jobs. Try not to get too hung up about the job title, pay rate, or working conditions – what’s important at this point in your career is getting your foot in the door. If you’re not happy with this position, you can always move onto something else – and that transition will be much easier now that you have some experience.
Just make sure you’re professional and polite at all times during this process to maximize your chances of getting hired and preserve your connections; that means everything from sending a thank-you email after your interview to leaving on good terms. Earn new certifications. Getting an entry-level job isn’t the end of your journey as a data engineer; in fact, it’s barely the beginning. Whether you get hired immediately or not, your next objective should be earning new certifications to boost your skills and credentials. Talk to your mentors and other people connected to the industry to find out the best certifications for you – and the most relevant ones for modern employers. Pursue higher education. Most data engineers ultimately spend more time in education, pursuing a master’s degree, or even a PhD to fuel their career. This can be difficult to manage if you’re also trying to hold down a job while studying, but it’s worth it to maximize your career-long earnings and open the door to better opportunities.
The path to becoming a data engineer isn’t always straightforward, and you may have trouble getting started in this relatively new field. But once you have the skills and background necessary to be successful, you should have a bright career path ahead of you; demand for data engineers is unlikely to abate anytime soon.
Analytics technology has been invaluable to modern marketing. The market for web analytics is projected to be worth $9.11 billion by 2025. The utilization of analytics and big data in the marketing industry has played a massive role in this robust growth.
One of the most important benefits of analytics in marketing is with PPC marketing. More companies are using analytics to expand the reach of their PPC campaigns and improve their ROI. PPC marketing would be infeasible in 2021 without analytics.
Companies that intend to use analytics in PPC can find that it drastically reduces the CPA of lead conversions, while also increasing the number of leads as well. Lead generation is one of the most important roles that marketing plays in your business strategy, so you shouldn’t overlook the benefits of using analytics to accomplish it more effectively. Existing customers on their own won’t help you to grow your brand: you need to find new customers and leads to keep your business in motion.
Most (if not all) marketing activities can be effective ways of generating leads, but in this blog, we’ll be focusing on using analytics to improve your strategy for generating leads through Pay-Per Click (PPC) advertising.
Is an Analytics-Driven PPC Strategy the Best Approach for Lead Generation?
One of the main goals of marketing is to generate leads, so it stands to reason that most marketing channels are effective methods of lead generation. Those with a lengthy customer journey might find content marketing to prove particularly effective, as it creates more touchpoints to build up a relationship with a new lead. Many retail brands see great success on social media, as it gives them the chance to show off new items in a format that can be creative and fresh when done right.
PPC on the other hand, can prove to be a highly effective lead generation strategy for any business or budget. You’re ensuring that you’re appearing when users are searching for things you offer, and you can more easily control your message for different stages in the marketing funnel, helping to ensure you’re generating qualified leads. However, as with any marketing strategy or campaign, your success will be dependent on how well you’ve thought through your activity before beginning.
This is why analytics is so important in PPC marketing. You have a lot at stake, since you are investing such a large amount of money. Therefore, you are going to need to use big data and PPC together to get the best return on your investment.
5 Ways to Use Data Analytics Ensure Your PPC Campaign is Generating Leads
So, how can you ensure your campaign is doing everything it can to give you the most lead generation opportunities? Can data analytics help you get more out of your PPC campaigns? Below, we explore 5 key ways you can guarantee success.
1. Campaign Organization
Setting up your PPC campaigns for lead generation success means that you need to have a real handle on your campaign organization. For those new to PPC, structuring paid search ads can be quite daunting. With multiple layers of the campaign to work through and various targeting metrics to consider, it’s easy to get it wrong and waste your budget. Luckily, there are plenty of online tools, and of course experienced marketing agencies, to provide you with a helping hand.
For those starting out, the simplest way to structure a PPC campaign to ensure that it generates leads is to organize it in line with your goals. This means creating ad groups that are specific to the results you want to achieve. For example, you may want to drive traffic to a specific landing page, in which case you would use a targeted PPC ad group linking solely to this page. Setting up your campaigns in this way also gives you greater control; allowing you to adjust your budgets on a micro level and thus see incremental improvements to your ROI – especially if you use multiple groupings.
Data analytics is going to be very important for this stage. You are going to need to outline your overall strategies and make sure that they are backed by data analytics. Analytics technology will help you better organize customer demographics and estimate the profitability of different market segments, so you can decide which to focus on.
2. Extensive Keyword Research
Another way to ensure you’re generating leads through PPC is by completing extensive keyword research to identify as many relevant opportunities as possible. Keyword research is a crucial part of any PPC campaign; you want to ensure that you’re appearing whenever someone searches for the products and services you offer.
As you’re evaluating target keywords, make sure that they are both relevant to your business, and achievable. This way, you make sure you’re appearing in the search engine results pages (SERPs) whenever someone is searching for things related to your business, but you aren’t pushing yourself into more difficult and competitive waters.
Part of your keyword research will also involve highlighting the keywords that are irrelevant and perhaps even harmful to your brand to highlight as negative keywords. There will naturally be searches you don’t want to appear for: a business in another sector with a similar name, harmful terms, or even highly competitive keywords. Knowing which keywords to avoid is as much a part of your keyword research as knowing those you want to go for is.
New advances in analytics technology have made keyword research much more effective. You can use analytics tools that work with third-party keyword research platforms to estimate the monthly search volume of various keywords. You can also use analytics to evaluate trends to estimate the future cost of various keywords and gauge the potential profitability of given keywords based on projected conversion rates.
3. Draw Attention with Catchy Copy
Nothing draws in new leads like powerful copy, and the same is true in PPC. Eye-catching, interesting headlines and descriptions for your PPC ads help you to stand out from the crowd in the SERP, bringing you more clicks and generating quality leads. Consider what your USPs are compared to your competitors, and be sure to detail any special offers you might have on. This (alongside audience insights) will help determine the content you should be including.
Writing effective ad copy can be a difficult task: you’ve only got a limited number of characters to get all the key information across in a way that draws in new leads. A full-service digital marketing agency will be able to support your copy creation, and will also bring expertise, audience insights and industry best practices to support your lead generation strategies.
Analytics can be surprisingly useful in developing better copy. A lot of marketers are now using AI technology to automate content creation. This is a lot more effective if you merge your AI content generation technology with analytics tools that can see how various content has performed in the past.
4. Develop Highly Targeted Landing Pages
The ad experience doesn’t end when a user clicks on your PPC ad. To generate leads from your ad campaign, you’ll need to make sure the landing page users are sent to is tailored to the ad group you’re targeting.
Consider your landing page your big opportunity for converting visitors into leads. Your ad entices visitors in, but once they’re on the page, it’s the ultimate chance to seal the deal. Where possible, you want to be tailoring your content to the wants and needs of the audience you’re addressing. Try to keep your pages short and sweet, without too many distractions: the sole goal of these landing pages is to get visitors to complete the desired action, so don’t include anything that could prevent that.
Last, but certainly not least, you’ll want to maintain standard landing page best practices. Not only will this help push users to convert by giving them a great experience on site, but it will also satisfy search engines when it comes to determining your quality score.
This is one of the most important benefits of analytics in later stages of PPC campaigns. You can use analytics to see which landing pages have performed the best and optimize them for better ROIs.
5. Testing & Reporting: The Road to Continuous Improvement
Another major benefit of analytics is with testing your campaigns to make incremental improvements. With your PPC campaigns set up, you might be tempted just to leave them be. You’ve spent all that time researching and building, now should be the time to sit back and watch the leads flow in, right? Sadly, the work of a PPC manager is never done, and you’ll want to be monitoring your results and running A/B tests if you want to see continuous improvement.
Be sure to set up analytics reporting so you can see what activity is working, what’s bringing in new leads, and what isn’t performing as well. This information can then be used to optimize your campaigns to drive greater success. You can also add in new iterations of ads to be tested to see what gains you can make with tweaks in your copy or CTAs. To continue bringing in new leads, you need to continuously invest time and effort into testing and optimizing your campaigns.
Generating leads through PPC can be a highly effective strategy, and with these tips, you’ll be set to create and deliver well optimized campaigns. The most important things to remember are to keep an eye on your budget, and to keep testing to find the best ways to attract your target audience and bring in those leads.
There is no denying the fact that big data has become a critical asset to countless organizations all over the world. Many companies are storing data internally, which means that they have to be responsible for maintaining their own standards.
Unfortunately, managing your own data server can be overwhelming. You have to make sure that your data is going to be secure from breaches and preserved from potential data outages.
You can keep reading to learn more about how to manage a data server. This is going to be vital for your data management strategy.
Server Management Practices that All Data-Driven Companies Have to Follow
In today’s interconnected world, server management is an essential task for systems administrators, especially in companies that are very dependent on big data. As business data becomes more and more critical to the company operations, it needs backups on servers that are quickly and globally accessible.
The best server management practices will give you peace of mind knowing your systems are compliant and that your data will be secure while identifying problems before they turn into downtime. Moreover, you want the ability to work remotely without having local IT support available.
Understandably, data server management can be an arduous task, as it requires a hands-on approach from your end. If your server management overhead is biting off more than it can chew, you should consider serverless computing from a Platform-as-a-Service such as Akka Serverless.
The term “serverless computing” describes a model in which applications are run on cloud platforms and data can be accessed via connected devices or web browsers. Essentially, a serverless vendor handles the backend server management, freeing your team to focus on development and deployment.
To improve data scalability, service providers allow customers to pay only for what they use (“pay as you go”) rather than making an upfront commitment based upon expected usage patterns. In addition, developers can deploy their apps without worrying about underlying infrastructure or capacity planning. As you can imagine, this method also reduces deployment time.
That said, if DIY server management still feels like the solution for your business, here’s a comprehensive look at server management best practices to help your server set-up succeed.
What are some of the best practices when it comes to managing your data server?
Server management is vital for any company looking to reduce costly downtime. Whether it’s a small startup or a Fortune 500, these practices can be the difference between success and failure in today’s data-centric world.
Working with antivirus software
Antivirus programs are an essential part of any server’s anti-malware solution. However, individual users often have multiple security solutions running simultaneously, leading to conflicts and errors. For these services to work as intended, they need the appropriate management to avoid interfering with one another or causing damage. Redundancy in security can be wise, but it requires a more attentive eye than a single-software solution.
Monitor your drivers
Drivers are essential because they provide vital links between computer components, software programs, and the operating system. They are vital to any organization that relies on big data.
Since these tiny files control how an entire system operates, they must be regularly updated to avoid any problems or conflicts on a server. Furthermore, outdated drivers can cause compatibility issues with new hardware devices, which the team must consider when planning upgrades.
Many people do not think about writing policies on data management until after problems arise. Nonetheless, establishing policy is one of the most beneficial things that administrators can do. Keeping detailed records of previous issues and their solutions can save time and headaches in the future.
If not enough time is allowed between updates or different systems have different schedules for updating, you might inadvertently create gaps in your security measures. Use a clearly communicated update policy to avoid creating new vulnerabilities between patches.
While some hardware devices can be installed during a server’s initial set-up process, other equipment, such as new monitors or printers, often requires later implementation.
Keeping a thorough inventory of what hardware is connected to a server and when it was attached will help an administrator find possible sources for problems should they suddenly appear.
Performing regular backups
No matter how careful administrators are, there will always be disasters that could damage or even destroy data. Having up-to-date backup files saved away to restore lost information maintains a server’s integrity and keeps operations running smoothly even after a disaster occurs.
When a server shows signs of slowing down, it’s time for the administrator to start thinking about upgrading some components. However, it’s important not to go overboard, as there should be enough room for existing programs and services to continue operating even as the team incorporates additional components.
If an administrator doesn’t know how much space a server needs, they can always conduct tests beforehand to determine what upgrades are needed and whether or not the server might require additional hardware.
In today’s world, servers are the backbone of your business. If you don’t have a reliable server management strategy, you will suffer from data loss and downtime that could cost thousands in revenue per hour. As such, you must employ the practices above to prevent that from happening.
Analytics has become very important in the field of marketing. A growing number of businesses are finding new analytics tools to streamline their marketing efforts in a variety of ways.
A lot of analytics tools are focused around improving social media marketing. However, analytics technology can be just as valuable when it comes to search engine marketing as well. Since SEO is such an essential aspect of content marketing, companies that take an analytics-driven approach to SEO will have a clear advantage.
Analytics as a Component of Modern SEO
As the need for people to share information increased, there was the innovation of the internet. It takes center stage as the primary tool to share marketing content with millions of internet users in modern times. The evolution is because of the increasing number of people who turn to the internet to seek information and solutions to their challenges.
Many factors have led to changes in the field of SEO. One of them has been advances in analytics.
On the other hand, businesses must rank high in search engines to gain a competitive advantage over rivals. The online traffic will peruse through the first items that appear on the search engines. Read on to gain insights on how you can make your businesses’ marketing content rank higher in the search engines.
Before you can create an analytics-based approach to SEO, you need to understand the fundamentals. Then you can use analytics to optimize accordingly.
Learn the Basics
The website is a vital component in having an SEO strategy for a business. It should be appealing to the visitors and make it easy to navigate through. Also, the site should have content that the visitors can use to purchase your products and services. The best plan is to have backlinks for content you share on various social media platforms. Integrating the backlinks checker will help to take advantage of the traffic from your businesses or other social media platforms.
When more individuals are accessing your site from the other digital media platforms, the algorithms in search engines will consider the website a high-priority platform. In turn, the website content will rank high in the search engines when online users search for items relating to the industry the venture operates in. Therefore, businesses can benefit from the link building tools to determine the words that will help your content rank higher in the search engines.
The Content needs to be High Quality
The terminologies in the content of a business are sharing on digital media platforms that will influence the ranking of the platforms. Search engine algorithms scan the materials on these platforms to determine the best relevant item depending on the online user searches.
The target audience for the content should be those who will need the business services or products. So, create content that will help the audience rather than focusing on marketing products and services. They will have questions and material about ways the venture can be beneficial to the website visitor. Also, there is a standard number of words that help your content rank high. Therefore, choosing a specialist in search engine optimization will help a venture rank their content higher in the platforms. They know what the algorithms search for and can make the material an organization shares on the digital media platforms appear among the first items in a search engine.
There are important analytics metrics that you have to evaluate to determine the quality of your content. One of the best applications of analytics in SEO is conversion rate monitoring. Dopson said that monitoring conversion rates is a valauble part of SEO and one of the most important aspects of content marketing.
Keywords are Critical in Search Engine Optimization
As explained earlier in the read, internet users will search for specific words when searching for content from a particular industry. The keywords o use in the online marketing content will help in ranking the material. Researching about the keywords will help you know the words and phrases to use when developing the content. Ensure they are relevant to what it is about.
Several search optimization tools help you identify the keywords that online users utilize for their searches. You can integrate them into the marketing campaign to make the strategy more efficient and attain success. However, using the keywords many times in the material can prove ineffective as the search engine algorithms will categorize the content as spam. You can use the keywords at least once on the title and not more than three times in the content. Ensure you include one in the opening paragraph of the content.
Improving your keyword targeting is arguably one of the most important benefits of analytics and data in SEO. You can use Google’s Keyword Tool to identify new keywords and assess their competitiveness in relation to search volume. There are other analytics tools such as Keyword Chef and Ahrefs that can also be very useful.
Consider the Time it takes for Pages to Load
A quality website loads faster. If the site takes a long time to load, it is vital to perform better for the users and be easy to access. For most instances, the media in the content will make it difficult for the sites to load.
Analytics is A Fundamental Part of Modern SEO
Businesses need to use the services of professional content creators to get ahead. It is a valuable investment. You need to make sure that your SEO team understands the importance of analytics to make the most of your SEO strategy.
There is no question that big data is very important for many businesses. Unfortunately, big data is only as useful as it is accurate. Data quality issues can cause serious problems in your big data strategy.
Customers won’t always directly tell you the information your company needs to provide better products or services.
However, their conversations on social media, most frequently posted topics and words, and responses to survey questions can reveal information essential to your company’s performance. Are they pleased with your products and services? What are their interests? What influencers do they follow on social media?
Utilizing conversational analysis can reveal which business locations are excelling and which aren’t, which advertisements connect with customers, and what they think of new products. Conversational analysis can reveal what matters most to your customers and what influences their decisions most.
What is Conversational Analysis?
Conversational analysis is the study of qualitative data to gain insight into the thoughts, opinions, and decisions your audience makes. It relies on data to drive its AI algorithms. You have to ensure this data is reliable if you want to create a useful conversational analysis funnel. You need to recognize that both data quality and quantity are important.
By utilizing conversational data, a company’s audience can be studied. What social media influencers connect with customers? What experiences are customers sharing on social media? What can be gained from asking audience members the right questions during social media interaction?
Conversational analysis can help provide the answers to these and other questions about a company’s audience. Big data companies can provide the analysis needed to understand audience data.
Steps in Utilizing Conversation Analysis
Learning more about a company’s audience through conversation analysis follows a detailed process. Following these steps will reveal essential information about customers and their opinions and choices.
Define Company Goals
The more a business focuses on its goals, the more accurate the information that will be gained through conversation analysis. Conversational analysis can address objectives such as improving customer service or product quality, identifying customer wants and needs, or understanding more about customer demographics and backgrounds.
Conversational analysis can even help companies decide which objectives should be their goals. Audience data can point the way toward changes that are meaningful for the future of brands and businesses.
Identify the Audience
Identifying a target audience will help in understanding and utilizing conversations to gain insight. An audience could be customers at large or segments of an audience, such as women ages 35 to 50 or families with young children.
The more a company or brand knows about its audiences, the greater the chance they will serve them well and provide them with positive user experiences that are tailored to their wants and needs.
Identifying a target topic will make conversation analysis more focused, providing more detailed information about an audience’s opinions and the thoughts behind their decisions. Narrowing the scope of topics analyzed will provide the information about audience opinions and behavior a company or brand needs and none of the irrelevant information they don’t.
Audience research used to mean manually conducting surveys. Today, companies have tools such as web scraping APIs, which can collect audiences’ conversations to learn what’s important to them, their experiences and expectations with a brand, and what motivates them.
Analyzing the Conversational Data
Once audience data is gathered through conversation analysis, it should be studied. Big data companies can sort this information through charts, word clouds, graphs, categories, tags, or top authors. This analysis and organization will identify trends, challenges, and opportunities.
The information presented by analysis of conversational data will reveal a business’ strengths and weaknesses, opportunities, and threats. This allows companies to improve products and performance, understand their audiences better, and better serve their customers.
Benefits of Conversational Analysis
Businesses that do not analyze their customers’ conversations in surveys, posts, and discussions on social media platforms are, in essence, flying blind. They don’t have the insights into their audiences that conversational analysis can provide. Here are some of the benefits of conversational analysis.
Companies that pay attention to their audiences’ conversations and learn from them will better understand how to improve their products. If customers have suggestions for improvements, these companies are more likely to hear those suggestions and act on them.
Having data insight on the conversations of your brand’s fan base can show products’ strengths and weaknesses.
Analyzing conversational data with the tools provided by big data companies usually results in better engagement with customers, a deeper understanding of their wants and needs, and, ultimately, improved customer service.
Conversational analysis can show you how to serve your customers better, but companies have to be willing to act on this knowledge.
Interactions with audiences online add to greater awareness of customers’ conversations. Asking a customer more questions online, via a direct message, or by text or email can help businesses or brands learn more about them.
If a customer posts a positive review of your restaurant on social media, ask what they enjoyed most. If their experience wasn’t positive, privately message them to find out more about what happened and how you can make things right.
Knowing more about these conversations and posts results in a greater understanding of what the audiences of a business or brand want and greater empathy for them. A business’ communicator can use this information to make better connections online with audience members.
Conversational Utilization to Maintain Audience Data
Through conversational data analysis, companies will gain a greater understanding of what their audiences think about them and their products and services as well as what influences them. Knowing their wants and needs in products, their expectations of brands and businesses, and causes they believe in can help businesses in setting goals and making improvements.
Big data companies have the software and tools to gather conversational data from among various audiences and organize it to identify trends in qualitative data that will assist them in charting a course for the future.
The gathering and analysis of conversational data show a greater interest and responsiveness to audiences, with a focus on how best to relate to them and provide them with better products and services.
Big data companies can help businesses and brands in gaining deeper insights into their audiences’ online conversations that will give them advantages in serving and communicating with them better.
Big data has been instrumental in the evolution of marketing. More businesses are finding ways to create data-driven marketing strategies that are tailored to the needs of individual customers and customer segments.
Every year businesses employ a variety of tactics in order to obtain data about their target audience, be that by using surveys, interviews, or focus groups. They gather customer demographics and psychographics and that data is then used for the benefit of both the business and the consumers. But how exactly do businesses use this data? There are numerous reasons, but here are a few of the most common.
Customer Data Helps Segment Their Audience
Most businesses will have a specific target audience that accounts for a larger portion of sales than the rest of their consumer base. That being said, they are not the only customers a business has. It’s not good business practice to tailor everything towards a single demographic, but it is equally poor practice to promote to everyone. If a business tries to please everyone, they will please no one. Gathering data from those that use their product will allow businesses to divide their audience into specific groups. This market segmentation is typically divided into demographics, psychographics, behaviors, and geographics, but can vary slightly depending on the company. This method of grouping splits the consumer base to target the highest groups of varying consumers.
Consumer Data Prompts Product and Service Changes
Every so often a product that you buy will change over time. Whether this change is big or small, it was most likely a result of consumer data. When businesses are able to use customer data to see what their customers think of a particular service or product, it gives them an idea of how customers view their brand as a whole. When enough people bring up similar complaints regarding a product or service it lets the business know that the product needs tweaking. This feedback can help improve products and services, which in turn makes the customer happy and elevates the company.
The Right Data Allows Businesses To Plan
Obtaining target audience data lets businesses chart out their future plans in more ways than one. It lets companies anticipate how they might spend their funding or where to divide their finances. This could help businesses to save money by avoiding dumping funds into dead-end paths. Another way is by viewing geographical locations and determining whether or not to open new business locations or possibly even moving the current one. Overall, businesses are basically given a roadmap that they can choose to follow or alter in any way that will benefit the future of the company.
Customer Data is Essential for Modern Marketing
There are many more ways that businesses use their consumer data to alter and improve products or services, but these are among the most practiced methods. They help businesses provide the best possible products for their customers and increase the chances of reaching a wider audience. It is vital for businesses to invest in customer data if they want to create a viable marketing strategy.
From vaccines and autonomous vehicles to language processing and quantum computing, artificial intelligence has progressed significantly and ensured its central position in the future.
Throughout the past, it took years and sometimes even decades to develop a new vaccine, However, just a few moments after the pandemic has taken over our everyday lives, vaccine candidates were undergoing human tests. This speed of vaccine development was also possible due to AI models that served researchers in analyzing vast amounts of coronavirus data. It was one of the most important ways AI helped against the pandemic.
Machine learning was able to sort through this enormous amount of data and predict which subcomponents are the most immunogenic ones. This was guiding researchers in designing targeted vaccines. More significantly, the success of AI technology in vaccine development surely will revolutionize the way vaccines are created from now on.
Fully Automated Driving
It’s not only in medicine where AI managed to provide outstanding findings and improve procedures. In 2020, we’ve seen autonomous driving technology continues with its development as the leading companies in the automotive industries continued testing driverless cars. In numerous cities, now it’s also possible to get a robotaxi service. Fully automated driving enables riding without a human driver is one of the necessities when it comes to scaling and commercialization of autonomous driving, so there’s no doubt there will be more breakthroughs in the near future.
Applied Natural Language Processing
Last year, natural language systems have advanced significantly when talking about processing aspects of human language such as sentiment and intent. Other achievements involved generating language that is aligned with human speaking and writing patterns, and visual understanding. All of these natural language models are adding to more accurate search results and more valuable chatbots and virtual assistants. Overall, it’s expected that these natural language models will lead to better user experiences and create incredible value for companies.
Quantum computing was huge in 2020, and even if you weren’t across this area, it was almost impossible to miss all those great updates in this field. From its initial period, quantum computing had enormous potential to supercharge AI applications when compared to binary-based classical computers. It can be used to run a generative machine learning model via a large dataset, making the model significantly more accurate and useful.
Other advanced technologies like deep learning algorithms are also vital for the development of quantum computing research. The company Baidu achieved several technical breakthroughs during the past years which are quite promising when it comes to bridging AI and quantum computing.
In 2020, we also saw the launch of several artificial intelligence chips customized for specialized projects. As ordinary processors are able to support various AI tasks, these AI-specific processors are modified with specific systems that are optimizing performance for tasks such as deep learning. As we’re seeing more and more AI applications appearing, any potential increase in performance or reduction in costs has the opportunity to unlock additional value for businesses that operate a wide range of data centers for commercial cloud service, and easily facilitate the internal operations of the business.
A Seat at The AI Table
If you’re interested in AI development and consultation services, there are plenty of companies which already established themselves as trustworthy partners in the AI community. Depending on what your business objective and industry is, you can find a way to implement AI into your business and improve your performance, reduce your costs, and even concur new markets.
And don’t worry if you don’t have the best knowledge about these technologies. With a good AI company, you will be able to translate your ideas into valuable technology for your business and your customers. Research the market, check a few portfolios, and start the interviewing process. Once you have found a partner for your AI project, they will guide you throughout it and provide you with all the knowledge you will need to turn it into a success story.