Choosing the right earning style for your business


Most business owners eventually reach the same point: you realize cash back isn’t cutting it, and you want to start earning points or miles instead.

But once you make that switch, a new question comes up pretty quickly: what’s the right approach to earning?

There’s no one-size-fits-all answer. But most strategies fall into two camps: simple and consistent earning, or more complex bonus-category optimization.

Simple and consistent

If you’re too busy to sink a lot of time or planning into earning travel rewards, a card with a simple earning structure can be a good choice. The brand-new Capital One Venture Business and its bigger sibling, the Capital One Venture X Business, are good examples of this. Every time you make purchases with these cards you’ll earn 2 miles per dollar spent.

The only exception is booking travel through Capital One Business Travel, where you’ll earn at higher rates (even better).

The upside to this structure is the “set it and forget it” mentality that allows you to focus on running your business. You’ll never have to worry about which categories your spending falls into, or whether an employee used the less-optimal card with a merchant. And, if you don’t mind booking through Capital One Business Travel, it’s a simple and lucrative way to earn some extra Venture miles.

The downside is the lack of other bonus categories where you can really pile up the points. And, specifically with Capital One’s transfer partner network, the lack of a major U.S. airline partner can be limiting.

Yes, you can use points with foreign airlines to book domestic flights on U.S. carriers (and there are actually some good options for this, like Finnair Plus and Air Canada Aeroplan). But that may ultimately make the simple strategy feel more complicated than it should.

A bit more complicated

On the flip side, there are a number of credit cards that feature a variety of bonus categories tailored to small business owners. The obvious benefit here is to increase the pace you earn points or miles. But a strategy of this nature requires a bit more attention.

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You’ll need to make sure you’re spending more in those business categories than a category which only earns 1 point or mile per dollar, such that your effective earning rate exceeds the 2 miles per dollar you can earn in the previous example. Chase has a number of cards that fit the bill for small business owners, including the Ink Business cards and the relatively new Chase Sapphire Reserve for Business℠ (see rates and fees).

Let’s use the Sapphire Reserve for Business as our example here. While the $795 annual fee on this card is higher than the Venture X Business’s, it also has a variety of bonus categories that let you stack up points, like:

  • 8 points per dollar spent when booking travel through the Chase Travel℠ portal.
  • 5 points per dollar on Lyft rides (through Sept. 30, 2027).
  • 4 points per dollar when booking flights or hotels directly with an airline or hotel chain.
  • 3 points per dollar on social media and search engine advertising.
  • 1 points per dollar on all other purchases

Comparing both strategies

Let’s take a theoretical $10,000 in business spending and compare these two earning strategies.

Spend $10,000 with virtually any merchant other than Capital One Business Travel, and you’ll earn 20,000 Capital One miles.

Spend that same $10,000 on your Sapphire Reserve for Business card, it could look something like this:

  • $2,500 in spending on social media: 7,500 points
  • $2,500 in direct airline or hotel bookings: 10,000 points
  • $5,000 in spending on everything else: 5,000 points

This scenario gives us more points (22,500), but if your spending habits don’t align well with the bonus categories, you could earn less than you would with the straightforward double points on everything.

Bottom Line

There’s nothing wrong with simple and straightforward, especially when it comes to earning miles and points. But there are so many bonus categories across various small business credit cards (think utilities, cell phone bills, digital and software subscriptions and a number of other categories businesses invest heavily in) that it may be worth seeing if a more complicated card can help optimize your spending.

Or, consider a combination of both, where you use one card to target your largest recurring business expenses and let a simple double-miles-on-everything card cover everything else.



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What is SAS?

SAS stands for Statistical Analytics System. It is a software system developed to accommodate complex analytics, data techniques and other mathematics, but is mostly used by big companies, especially in the banking, health and insurance sectors. SAS is not open-source, this is not free but it is not affordable either, and this is the greatest deterrent to business owners and start-ups that would have been able to do so.At present SAS is expanding its platform to include emerging technologies like AI and machine learning tools as well. Moreover, it also provides services related to custom intelligence, risk management and identifying, big data functionalities, etc. 

Why SAS?

Since SAS has been developed primarily for industrial and commercial purposes, this may not be the greatest option for beginners or solo data analysts to discover except if their main objective is to think about working in an industrial environment and to have new skills to be more competitive in the current industry. For all those who wish to learn SAS computing for free, a free version of SAS known as SAS University is available for educational purposes only and not for industrial applications. 

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Features of SAS:

The exciting features of the SAS are:

  • SaS is not a free platform or even an open source.
  • It integrates the functionalities or capabilities of AI and machining learning techniques.
  • SAS comes with high data security and stability.
  • Moreover SAS provides excellent customer service, technical support and maintenance services as well.
  • As it is compatible with cloud platforms, commands can be easily processed in the cloud.

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What is Python?

Python is an open-source object-oriented programming language which has become exceptionally successful with data analysts and software engineers. Python is recommended as it endorses, among many others, organized, object-oriented and operational programming and incorporates current infrastructure.Python comes with libraries to support a variety of data manipulation functions, including data integration, information extraction, business intelligence, visual analytics, and artificial intelligence. The libraries of the python are: pandas, Numpy, tensorflow, matplotlib, etc.

Why Python?

The simple truth that Python is perhaps the most popular language between many software developers and project managers helps make it simple to master, interpret, and then use. Python provides a sleek comprehensible syntax that makes it more convenient for newbies because they don’t go into a lot of programming. This provides people an opportunity to plan mostly on learning the other operations of data science.

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Features of Python:

The attractive features of python are:

  • Python is easy and simple to learn programming language as it requires menial coding. 
  • It comes with more number of libraries
  • It comes with extensive support for many other operating systems like Mac platforms, Linus and Windows.
  • Python is a highly scalable, interpreted and fastest programming language.
  • Moreover, python comes with great features such as  visualization, data analytics, and data manipulation functions as well.
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Comparison between SAS vs Python:

Now let us compare the SAS and python in detail.

Python:Python, on the other hand, is quick to understand thanks to its simple function. However, instead of an interactive GUI like the one in SAS, Python has an IPython notebook that allows students to access code.

SAS:For individuals who are really experienced with SQL, mastering the fundamental SAS language is possible due to a growing Emphasis. Prior to actually writing code, an adult should first acquaint himself/herself with the SAS GUI interface. There is no need to have previous knowledge to learn SAS.

Python: Python becoming an open source platform and it is very much free to download it. However they won’t provide any tech support or guarantee documents for the users. It is mostly preferred by the small and medium sized organizations due to its flexibility and transparency of the systems.

SAS:SAS is a licensed option and is more expensive as well. This SAS platform is equipped with mutli[le features which can be used only after the purchasing and upgrades. Most of the big IT companies rely on it.

  • Data Science capabilities:

Python:In the field of data science, Python language succeeds in the analysis of complex data. Libraries also including Scikit Learn, Pandas, and NumPy, and Matplotlib for visual representation, end up making it an alternative for beginners who want to undertake a career in data science.

SAS:SAS also typically includes data science abilities, such as simultaneous data analysis, access to and strategic planning of datasets through an interconnected SQL database system.

  • Libraries and tools supported:

Python:Python includes many other libraries for web design, software development, data science and visualization, desktop GUI programming, as well as machine learning and AI frameworks. Python is therefore a great option for exploiting and envisioning huge amounts of data.

SAS:SAS provides a variety of built-in business intelligence, data storage, graphical and computational tools that make it a better platform for manipulating data, especially on stand-alone data centres or devices. Although SAS could be used to determine outcomes very well, it is not as great as Python in terms of data visual representation as it cannot create special statistics. 

Python:Python is a powerful device that is not restricted to data analytics and software engineering functionality, creating a broader market for individuals with Python tech skills.

SAS:For a long time, SAS held the largest market share, and in particular the organizational market. However, the economy is continuously shifting toward these open-source technologies, which is why Python has grown exceptionally in prominence.

  • Application advancements:

Python:Due to its open nature of Python, the introduction of innovative features and methodologies is fast compared to SAS. Although there are opportunities for sustainable development since they’re not well-tested due to their accessible ability to contribute.

SAS:SAS is introducing a new edition in the type of software releases or rollouts. As it is granted a license, all functionalities and updates are well tested. It’s much less likely to be an error especially in comparison to Python.

Python:Python has a fierce challenge with graphics bundles such as VisPy, Matplotlib. But, compared to SAS, it’s still complex.

SAS:SAS includes system graphical capabilities. But this is extremely practical. Making any customization is a difficult task to achieve. We need to comprehend the SAS Graph package rigorously to configure it.

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Python:Python is recommended by start-ups, small and medium-sized technology companies since it provides advanced features for handling large unorganized data sets at no cost. It even has AI and machine learning abilities.

SAS:SAS is mostly embraced by large corporations whose major worry is high stability, better security and devoted customer support, not the expense of the application.

Python:Python is continuously replaced with the latest features from the community, making the latest developments quicker than SAS.

SAS:SAS will only be amended when a new version is rolled out.

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Conclusion:

The technology is changing towards transmission. Second, tools like Python are flexible and most recommended for data science. SAS is much more appropriate to statistical analysis and business intelligence. For this reason, it would have been more beneficial for a beginner interested in exploring data science to understand Python. But adding SAS to their knowledge base would give newbies more possibilities.

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  1. Python Partial Functions
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  3. SAS Programming
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  5. SAS Vs Tableau



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