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3 Ways In Which Big Data Has Changed Financial Trading For Good

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3 Ways In Which Big Data Has Changed Financial Trading For Good

Big data is one of the latest internet-powered developments that has caused an enormous impact across nearly all industries over the last couple of decades. The term big data alludes to the colossal amounts of information constantly collected by search engines and websites as people continue to use the internet for various purposes. This data consists of all kinds of information like numbers, images, tables, audio and video files, and every other possible type of information there is. As a result, big data analytics involves utilizing different analytical methods to obtain value from this colossal amount of information so that individuals and organizations can make better-informed decisions regarding all kinds of life and work events.    

And whether you realize it or not, big data is making a significant impact on the global financial markets as well. As the exponentially increasing complexity and data generation are dynamically changing the way various industries are working, it’s significantly changing the financial sector for good. To make things clearer, in this article, we’ll review the three ways in which big data has altered the financial trading industry to help humans make better and more prudent decisions when trading.

How Big Data Is Taking The Financial Industry By Storm

Every day, the world is creating roughly 2.5 quintillion bytes of data. This whopping amount of data represents a fantastic opportunity to leverage this information in various ways by processing and analyzing the ever-growing sets of valuable data.

Nowadays, both finance and trading demand a lot of accurate data on display to make the best models and predictions based on real data analysis. While in the past, these numbers had to be arranged, categorized, and analyzed by real people, presently, this entire process is automatically calculated by machines and intelligent NLP-powered algorithms from start to finish. And because computers can go through the data and accurately process it and analyze it at a vast scale; as a result, much more precise and up-to-date stock or options selections and models can be made thanks to real-time analytics and the possibility to compare historical option prices, implied volatility, risk metrics, and others, all derived from the big live data that support the most sophisticated trading systems and platforms.

To get an even better perspective of how big data has changed the world of trading, continue reading to uncover the main ways it’s doing so.

The Creation Of Financial Predictive Models

In this day and age, the analytics behind the financial industry is no longer just an examination of the different prices and price behavior. Instead, they generate much more helpful information, including trends and everything else that could impact the financial and trading sectors.

These analytics are more precise and include more data that permits better predictive models to be created. These models can help traders make better financial predictions and effectively minimize the risk of making bad financial trading decisions.

Allowing For Real-Time Analytics

In case you didn’t know, algorithm trading is something that’s particularly buzzing around the financial sector right now. In fact, machine learning has taken such a giant leap forward, allowing computers to make much better decisions than a human would. Furthermore, machine learning-powered algorithms can finalize trades much faster and at frequencies that people would never be able to achieve.

This is all possible thanks to real-time analytics, as computers can incorporate the best practices and minimize the number of mistakes that could end up being caused because of inherent behavioral influences that would typically impact humans. In addition, this real-time analytics can effectively maximize the investing power that individuals and companies have by creating a leveled playing field where more parties have access to the correct information.

Provide Next-Level Risk Assessment

Big data is also essential for many actuarial processes. For example, financial institutions can use data analytics to develop better predictive models to identify better the risks associated with lending and precisely project the foreseen expenditures through insurance policies. This could be very well implemented in trading scenarios, where traders can use predictive models to assess the risk connected with investing in a given stock, option, or feature.

1645077374 56 3 Ways In Which Big Data Has Changed Financial Trading

Final Thoughts

As you can tell, big data impacts how financial trading transactions get carried out in numerous ways. For example, it helps to make faster and more accurate trades, effectively reducing the risk of each trade while maximizing the profitability of trading strategies and scenarios.

Nevertheless, it’s also noteworthy to say that big data analytics can’t perfectly predict market scenarios all the time. Like anything else in this world, big data has imperfections like the incompleteness of data patterns. But, in total, big data analytics present far more benefits than disadvantages to financial trading, which is precisely why it’s becoming an inevitable necessity for financial trading.


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Here’s Optimizely’s Automatic Sample Ratio Mismatch Detection

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Here's Optimizely’s Automatic Sample Ratio Mismatch Detection

Optimizely Experiment’s automatic sample ratio mismatch (SRM) detection delivers peace of mind to experimenters. It reduces a user’s exposure time to bad experiences by rapidly detecting any experiment deterioration.

This deterioration is caused by unexpected imbalances of visitors to a variation in an experiment. Most importantly, this auto SRM detection empowers product managers, marketers, engineers, and experimentation teams to confidently launch more experiments. 

How Optimizely Experiment’s stats engine and automatic sample rate mismatch detection work together

The sample ratio mismatch actslike the bouncer at the door who has a mechanical counter, checking guests’ tickets (users) and telling them which room they get to party in.

Stats engine is like the party host who is always checking the vibes (behavior) of the guests as people come into the room.

If SRM does its job right, then stats engine can confidently tell which party room is better and direct more traffic to the winning variation (the better party) sooner.

Why would I want Optimizely Experiment’s SRM detection?

It’s equally important to ensure Optimizely Experiment users know their experiment results are trustworthy and have the tools to understand what an imbalance can mean for their results and how to prevent it.

Uniquely, Optimizely Experiment goes further by combining the power of automatic visitor imbalance detection with an insightful experiment health indicator. This experiment health indicator plays double duty by letting our customers know when all is well and there is no imbalance present.

Then, when just-in-time insight is needed to protect your business decisions, Optimizely also delivers just-in-time alerts that help our customers recognize the severity of, diagnose, and recover from errors.

Why should I care about sample ratio mismatch (SRM)?

Just like a fever is a symptom of many illnesses, a SRM is a symptom of a variety of data quality issues. Ignoring a SRM without knowing the root cause may result in a bad feature appearing to be good and being shipped out to users, or vice versa. Finding an experiment with an unknown source of traffic imbalance lets you turn it off quickly and reduce the blast radius.

Then what is the connection between a “mismatch” and “sample ratio”?

When we get ready to launch an experiment, we assign a traffic split of users for Optimizely Experiment to distribute to each variation. We expect the assigned traffic split to reasonably match up with the actual traffic split in a live experiment. An experiment is exposed to an SRM imbalance when there is a statistically significant difference between the expected and the actual assigned traffic splits of visitors to an experiment’s variations.

1. A mismatch doesn’t mean an imperfect match

Remember: A bonified imbalance requires a statistically significant result of the difference in visitors. Don’t expect a picture-perfect, identical, exact match of the launch-day traffic split to your in-production traffic split. There will always be some ever-so-slight deviation.

Not every traffic disparity automatically signifies that an experiment is useless. Because Optimizely deeply values our customers’ time and energy, we developed a new statistical test that continuously monitors experiment results and detects harmful SRMs as early as possible. All while still controlling for crying wolf over false positives (AKA when we conclude there is a surprising difference between a test variation and the baseline when there is no real difference). 

2. Going under the hood of Optimizely Experiment’s SRM detection algorithm

Optimizely Experiment’s automatic SRM detection feature employs a sequential Bayesian multinomial test (say that 5 times fast!), named sequential sample ratio mismatch. Optimizely statisticians Michael Lindon and Alen Malek pioneered this method, and it is a new contribution to the field of Sequential Statistics. Optimizely Experiment’s sample ratio mismatch detection harmonizes sequential and Bayesian methodologies by continuously checking traffic counts and testing for any significant imbalance in a variation’s visitor counts. The algorithm’s construction is Bayesian inspired to account for an experiment’s optional stopping and continuation while delivering sequential guarantees of Type-I error probabilities.

3. Beware of chi-eap alternatives!

The most popular freely available SRM calculators employ the chi-square test. We highly recommend a careful review of the mechanics of chi-square testing. The main issue with the chi-squared method is that problems are discovered only after collecting all the data. This is arguably far too late and goes against why most clients want SRM detention in the first place. In our blog post “A better way to test for sample ratio mismatches (or why I don’t use a chi-squared test)”, we go deeper into chi-square mechanics and how what we built accounts for the gaps left behind by the alternatives.

Common causes of an SRM  

1. Redirects & Delays

A SRM usually results from some visitors closing out and leaving the page before the redirect finishes executing. Because we only send the decision events once they arrive on the page and Optimizely Experiment loads, we can’t count these visitors in our results page unless they return at some point and send an event to Optimizely Experiment.

A SRM can emerge in the case of anything that would cause Optimizely Experiment’s event calls to delay or not fire, such as variation code changes. It also occurs when redirect experiments shuttle visitors to a different domain. This occurrence is exacerbated by slow connection times.

2. Force-bucketing

If a user first gets bucketed in the experiment and then that decision is used to force-bucket them in a subsequent experiment, then the results of that subsequent experiment will become imbalanced.

Here’s an example:

Variation A provides a wildly different user experience than Variation B.

Visitors bucketed into Variation A have a great experience, and many of them continue to log in and land into the subsequent experiment where they’re force-bucketed into Variation A.

But, visitors who were bucketed into Variation B aren’t having a good experience. Only a few users log in and land into a subsequent experiment where they will be force-bucketed into Variation B.

Well, now you have many more visitors in Variation A than in Variation B.

3. Site has its own redirects

Some sites have their own redirects (for example, 301s) that, combined with our redirects, can result in a visitor landing on a page without the snippet. This causes pending decision events to get locked in localStorage and Optimizely Experiment never receives or counts them.

4. Hold/send events API calls are housed outside of the snippet

Some users include hold/send events in project JS. However, others include it in other scripts on the page, such as in vendor bundles or analytics tracking scripts. This represents another script that must be properly loaded for the decisions to fire appropriately. Implementation or loading rates may differ across variations, particularly in the case of redirects.

Interested?  

If you’re already an Optimizely Experiment customer and you’d like to learn more about how automatic SRM detection benefits your A/B tests, check out our knowledge base documentation:

For further details you can always reach out to your customer success manager but do take a moment to review our documentation first!

If you’re not a customer, get started with us here! 

And if you’d like to dig deeper into the engine that powers Optimizely experimentation, you can check out our page faster decisions you can trust for digital experimentation. 

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How to Use Email Marketing Automation to Encourage SaaS Adoption

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How to Use Email Marketing Automation to Encourage SaaS Adoption

SaaS adoption refers to the process that earns your product a permanent place in your user’s workflow. This happens when you empower your audience to extract useful value from your solutions.

Email, a tried and tested communication tool, plays an essential role in helping brands relay their product’s value to their customers and educate them on how to make the most of it.

However, smaller teams might find themselves at a crossroads, balancing the need for personalized communication with the scale of their user base

Email marketing automation offers a practical solution by ensuring that each message is tailored and timely, yet sent out with minimal manual effort.

In this article, let’s look at five tips that will help you build robust email marketing automation that will motivate your audience to adopt your tool and make it a part of their daily lives.

1. Segment your audience

Audience segmentation is crucial for personalizing your emails, which in turn, can significantly boost SaaS product adoption. Remember, a message that resonates with one segment might not strike a chord with another.

The key to effective segmentation is understanding where each customer is in their journey. Are they new subscribers, active users, or perhaps at the brink of churning?

Here are some actionable steps to segment your audience effectively:

  1.  Analyze User Behavior: Look at how different users interact with your SaaS product. Are they frequent users, or do they log in sporadically? This insight can help you create segments like ‘active users’, ‘occasional users’, and ‘at-risk users’.
  2.  Utilize Sign-up Data: Leverage the information gathered during the sign-up process. This can include job roles, company size, or industry, which are excellent parameters for segmentation.
  3.  Monitor Engagement Levels: Keep an eye on how different segments interact with your emails. Are they opening, clicking, or ignoring your messages? This feedback will help you refine your segments and tailor your approach. Plus, consider setting up small business phone systems to enhance communication with your audience.

2. Create campaigns based on behavior

Sending behavior-based campaigns is pivotal in effective email marketing. By focusing on performance metrics such as open rates, click-through rates, and engagement times, you can gauge the effectiveness of your emails and adjust your strategy accordingly.

You can also use digital signage to entertain or make customers aware of something new – product or service, through a digital sign.

Different types of email campaigns serve various purposes:

  1. Educational Campaigns: These are designed to inform and enlighten your audience about their problem. They can include tips, best practices, and how-to guides. The goal here is to provide value and establish your brand as a thought leader in your industry.
  2. Interactive Campaigns: These campaigns encourage user engagement through surveys, quizzes, microblogging platforms, or feedback forms. They not only provide valuable insights into user preferences but also make the recipients feel heard and valued.
  3. Onboarding Campaigns: Targeted toward new users, these messages help them get the value they seek from your product as soon as possible. They can include step-by-step tutorials, video guides, or links to helpful resources.

4.Re-engagement Campaigns: Aimed at inactive users, these emails strive to reignite their interest in your SaaS product. They might include product updates, special offers, or reminders of the benefits they’re missing out on.

3. A/B test before deployment

Rather than pushing a new campaign to your entire audience as soon as you draft the emails, A/B testing helps you know whether your messages are any good.

Here are some best practices for A/B testing in email automation:

  1. Test One Variable at a Time: Whether it’s the subject line, email content, or call-to-action, change just one (or a couple) element per test. This clarity helps in pinpointing exactly what works and what doesn’t.
  2. Choose a Representative Sample: Ensure that the test group is a good mix of your target audience as a whole. This way, the results are more likely to reflect how your entire audience would react.
  3. Measure the Right Metrics: Depending on what you’re testing, focus on relevant metrics like open rates, click-through rates, or conversion rates. This will give you a clear picture of the impact of your changes. Along with these steps, it’s important to use an SPF checker to ensure your emails aren’t marked as spam and increase the deliverability rate.
  4. Use the Results to Inform Your Strategy: Once you have the results, don’t just stop at implementing the winning version. Analyze why it performed better and use these insights to inform your future campaigns.
  5. Don’t Rush the Process: Give your test enough time to gather significant data. Adopt comprehensive marketing reporting solutions that give you a clear picture of your campaigns’ efficacy.

4. Leverage email templates

When managing multiple email automation campaigns, each with potentially dozens of emails, the task of creating each one from scratch can be daunting. Not to mention, if you have multiple writers on board, there’s a risk of inconsistency in tone, style, and branding.

Email templates are your secret weapon for maintaining consistency and saving time. They provide a standardized framework that can be easily customized for different campaigns and purposes.

They are also a great way to communicate with your customers. Another way to communicate efficiently with your customer is through best small business phone systems, which is especially efficient when conveying information about your product or service.

Here’s a rundown of various types of templates you should consider having:

  1. Welcome: For greeting new subscribers or users. It should be warm, inviting, and informative, setting the tone for future communications.
  2. Educational Content: Used for sharing tips, guides, and resources. If you are making this template to introduce online GCSE physics tutor services that you provide, you should be clear, concise, and focused on delivering value in your template.
  3. Promotional: For announcing new features, offers, or services. It should be eye-catching and persuasive without being overly salesy.
  4. Feedback Request: Designed to solicit user feedback. This template should be engaging and make it easy for recipients to respond.
  5. Re-engagement: Aimed at rekindling interest among inactive users. It should be attention-grabbing and remind them of what they’re missing.
  6. Event Invitation: For webinars, workshops, or other events. This should be exciting and informative, providing all the necessary details.

5. Use a tool that works for you

Email is more than just a marketing platform; it’s a multifaceted tool that can drive customer engagement, support, and retention. Given its versatility, it’s crucial to choose the right email automation tool that aligns with your specific needs.

When selecting an email automation tool, consider these key features:

  1. Intuitive Interface: Even your non-technical team members should find it easy to use.
  2. Robust Segmentation Capabilities: The tool must offer advanced segmentation options to target your emails accurately.
  3. A/B Testing Functionality: Essential for optimizing your email campaigns.
  4. Integration with Other Tools: Look for a tool that integrates seamlessly with your CRM, analytics, and other marketing platforms. Additionally, integrating a multilingual translation support can further enhance the tool’s versatility, allowing you to reach a diverse audience with tailored content in their preferred languages.

Popular tools like Mailchimp and ActiveCampaign offer free trials which are great for brands to take these for a spin before making a choice.

Wrapping up

Leveraging email automation makes it easier for SaaS brands to market their solutions to their audience and ultimately increase adoption rates.

Segmenting audiences, creating messages based on their behavior, testing emails before setting campaigns live, utilizing templates for speed and consistency, and adopting a tool that you are comfortable working with are essential email marketing automation tips to help you get started on the right foot.

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Marketing Team Reorgs: Why So Many and How To Survive

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Marketing Team Reorgs: Why So Many and How To Survive

How long has it been since your marketing team got restructured? 

Wearing our magic mind-reading hat, we’d guess it was within the last two years. 

Impressed by the guess? Don’t be.  

Research from Marketing Week’s 2024 Career and Salary Survey finds that almost half of marketing teams restructured in the last 12 months. (And the other half probably did it the previous year.) 

Why do marketing teams restructure so often? Is this a new thing? Is it just something that comes with marketing? What does it all mean for now and the future? 

CMI chief strategy advisor Robert Rose offers his take in this video and the summary below. 

Marketing means frequent change 

Marketing Week’s 2024 Career and Salary Survey finds 46.5% of marketing teams restructured in the last year — a 5-percentage point increase over 2023 when 41.4% of teams changed their structure. 

But that’s markedly less than the 56.5% of marketing teams that restructured in 2022, which most likely reflected the impact of remote work, the fallout of the pandemic, and other digital marketing trends. 

Maybe the real story isn’t, “Holy smokes, 46% of businesses restructured their marketing last year.” The real story may be, “Holy smokes, only 46% of businesses restructured their marketing.” 

Put simply, marketing teams are now in the business of changing frequently. 

It raises two questions.  

First, why does marketing experience this change? You don’t see this happening in other parts of the business. Accounting teams rarely get restructured (usually only if something dramatic happens in the organization). The same goes for legal or operations. Does marketing change too frequently? Or do other functions in business not change enough? 

Second, you may ask, “Wait a minute, we haven’t reorganized our marketing teams in some time. Are we behind? Are we missing out? What are they organizing into? Or you may fall at the other end of the spectrum and ask, “Are we changing too fast? Do companies that don’t change so often do better? 

OK, that’s more than one question, but the second question boils down to this: Should you restructure your marketing organization? 

Reorganizing marketing 

Centralization emerged as the theme coming out of the pandemic. Gartner reports (registration required) a distinct move to a fully centralized model for marketing over the last few years: “(R)esponsibilities across the marketing organization have shifted. Marketing’s sole responsibilities for marketing operations, marketing strategy, and marketing-led innovation have increased.”  

According to a Gartner study, marketing assuming sole responsibility for marketing operations, marketing innovation, brand management, and digital rose by double-digit percentage points in 2022 compared to the previous year.  

What does all that mean for today in plainer language? 

Because teams are siloed, it’s increasingly tougher to create a collaborative environment. And marketing and content creation processes are complex (there are lots of people doing more small parts to creative, content, channel management, and measurement). So it’s a lot harder these days to get stuff done if you’re not working as one big, joined-up team. 

Honestly, it comes down to this question: How do you better communicate and coordinate your content? That’s innovation in modern marketing — an idea and content factory operating in a coordinated, consistent, and collaborative way. 

Let me give you an example. All 25 companies we worked with last year experienced restructuring fatigue. They were not eager creative, operations, analytics, media, and digital tech teams champing at the bit for more new roles, responsibilities, and operational changes. They were still trying to settle into the last restructuring.  

What worked was fine-tuning a mostly centralized model into a fully centralized operational model. It wasn’t a full restructuring, just a nudge to keep going. 

In most of those situations, the Gartner data rang true. Marketing has shifted to get a tighter and closer set of disparate teams working together to collaborate, produce, and measure more efficiently and effectively.  

As Gartner said in true Gartner-speak fashion: “Marginal losses of sole responsibility (in favor of shared and collaborative) were also reported across capabilities essential for digitally oriented growth, including digital media, digital commerce, and CX.” 

Companies gave up the idea of marketing owning one part of the customer experience, content type, or channel. Instead, they moved into more collaborative sharing of the customer experience, content type, or channel.  

Rethinking the marketing reorg 

This evolution can be productive. 

Almost 10 years ago, Carla Johnson and I wrote about this in our book Experiences: The 7th Era of Marketing. We talked about the idea of building to change: 

“Tomorrow’s marketing and communications teams succeed by learning to adapt — and by deploying systems of engagement that facilitate adaptation. By constantly building to change, the marketing department builds to succeed.” 

We surmised the marketing team of the future wouldn’t be asking what it was changing into but why it was changing. Marketing today is at the tipping point of that. 

The fact that half of all marketing teams restructure and change every two years might not be a reaction to shifting markets. It may just be how you should think of marketingas something fluid that you build and change into whatever it needs to be tomorrow, not something you must tear down and restructure every few years.  

The strength in that view comes not in knowing you need to change or what you will change into. The strength comes from the ability and capacity to do whatever marketing should. 

HANDPICKED RELATED CONTENT:  

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Cover image by Joseph Kalinowski/Content Marketing Institute 

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