This provides control over how you organize your money and keep track of multiple financial goals.
This also means that for the first time in banking, one can create tailored sub-accounts to save or allocate money for specific things.
So, your purchases and experiences: whether it be shopping for a wedding, starting a small business or planning a holiday, can all be clearly budgeted for.
Your ‘Spaces’, therefore, are set up as separate sub-accounts. It makes it even easier to maintain discipline over your daily spending. Some of you(referred to as penny-pinchers) are disciplined enough to be already doing this.
Spaces will help you avoid using tedious spreadsheets or pen and paper.
You are now given the flexibility of customizing ‘Spaces’ by choosing names, transferring money seamlessly between accounts with simple drag-and-drop motions. You can also open and close the spaces as when as needed.
Some ‘good to knows’
Spaces is available immediately to customers in all 17 markets that N26 operates.
In addition to your main account, you can create and delete additional Spaces as often as you’d like. The number of active Spaces at any one time depends on your account type.
You get 2 more Spaces with an N26 or N26 Business account; or up to 10 additional Spaces with N26 Black and N26 Metal.
Your primary account statements will include all transactions sent to a ‘Space’ or received from one.
Only your primary Space can be negative (if you are eligible for an overdraft). All other balances in Spaces cannot be negative: meaning you can only transfer what is available in a particular Space.
You can only use the balance from your primary Space toward your Mastercard transactions. Consider transferring money before making a purchase.
Drag money from one Space to another by dragging your finger from one Space to another. Once the amount is confirmed the balance is updated right away.
Welcome to the future of innovative and mobile banking!
– Click on the card to get your new* N26 account –
*The personal card is free of any monthly management fees and available in EU and soon in the USA.
We are living in exciting and innovative times with futuristic technology literally at our fingertips. But for the longest time, small to medium-sized businesses were not serviced by the latest tech trends enterprises have been able to benefit from.
That is, until now. This piece explores these technology trends and how they will impact business in 2018 and beyond.
So, what kind of things can this ‘smart’ tech do? Just 4 months ago, an AI machine managed to complete a University level math exam 12 times faster than it normally takes the average human.
How? Through the art of machine learning; where computers learn and adapt through the experience without explicitly being programmed.
Furthermore, Facebook made headlines earlier this year when their chatbots created their own language. Some ‘fake news’ stories claim that the engineer’s pulled the plugin a panic as they were getting too smart.
However, the truth is that for Facebook’s purposes the chatbots needed to stick to English rather than developing their own shorthand. Their machine learning chatbots did nevertheless, create their own language outside their explicit programming.
This evolving area of computer science is the future for service businesses, and it’s already affecting the way we live and work today. In fact, research firm Markets and Markets estimates that the machine learning market will grow from $1.41 billion in 2017 to $8.81 billion by 2022!
So buckle up because these technology trends will affect every part of your business, from marketing to operations all the way through to payroll and here’s how.
Marketing Gets Smarter with AI and Machine Learning
AI and Social Media Marketing
In April 2017, CRM software provider, Salesforce conducted a study of marketing leaders worldwide, and the results were mind-blowing. Respondents said they expect to see improvements in efficiency and advancements in personalization over the next five years.
More than 60 percent of marketers envision leveraging AI to create dynamic landing pages, websites, programmatic advertising and media buying.
With behavioural targeting methods, AI will be able to locate and start the nurture process. For example, a marketing stack that employs AI algorithms might learn that a specific buyer who checks into LinkedIn on Monday mornings has recently started looking for a new CRM tool.
The software can then suggest (or even create) targeted posts to be published on the days and times that they’ll see them.
Currently, savvy marketers that are using social listening as way to nurture leads don’t have the necessary enhancement of AI. This is, however, time-consuming, manual and not in real-time.
So how do you start to get ready for this type of future content marketing distribution?
Firstly, you will need to have your buyer personas (using the consumer black box theory ) well defined. Taking a solid look at your CRM will give you tons of hints for content that will get qualified leads to respond.
By taking a step back and analyzing your channel’s content (like emails, phone calls and social media messages) you will start to get the right kind of insights. The ones that will prompt a lead to take the next step into the second phase of your sales funnel.
For instance, a C-Suite executive might respond best to data-driven whitepapers and infographics to peak their interests, whereas a fellow marketer might be more suited for an interactive case-study or video.
The only way to get these kinds of insights is to do a deep dive into your CRM platform and conduct a thorough review of customer details – using semantic analysis to understand the level of buying intent behind the words your qualified prospects use.
Hot tip: Starting to run your analysis now and developing strong personas will be key to implementing AI algorithms to your social media in 2018 and beyond.
Marketing and Machine Learning
Put simply, machine learning is about understanding data and statistics. It’s a technical process where computer algorithms find patterns in data, then predict probable outcomes. An example is when your email determines whether a particular message is spam or not depending on words in the subject line, links included in the message, or patterns identified in a list of recipients.
It’s the perfect example of how machine learning can be applied in marketing to optimize for successful campaigns.
Businesses can also use machine learning to up-sell the right product, to the right customer, at the right time. In 2018, marketers will continue to rely on machine learning to understand open rates when it comes to email. This way, you know exactly when to send your next campaign to increase click-through rates and ROI.
The next big thing? It might sound small but ticket tagging and re-routing can be a massive expense for small businesses – costs that can be saved with machine learning.
Having a sales inquiry automatically end up with the sales team, or a complaint end up instantly in the customer service department’s queue, is going to save companies a lot of time and money, and this is all being made possible with modern technology.
Here’s what else to expect in 2018:
E-Commerce Reaches New Heights
You’ve been shopping for a new pair sunglasses on Amazon, then before you know it, your Facebook feed is filled with multiple eyewear ads and related trends for summer.
This is machine learning.
In fact, this example of analyzing data based on a user’s purchase history or online shopping behaviour is the future for e-commerce.
Retail companies are also tracking what ads or images you’re most likely to stop scrolling on, in order to target you with specific content. For example, if you always click on ads that contain happy women and some text, then a machine will log this as preferred content so that you are only targeted with ads that fit this description.
Machines can also track what time of day you are most active on Facebook, Instagram, Twitter and/or Pinterest, in order to present these ads to you at an optimal buying time.
Then when it’s time to purchase, machine learning is applied to reduce the risk of credit fraud in small businesses.
How? Machines learn from historical datasets that contain fraudulent transactions and can identify patterns that represent a typical fraudulent transaction.
Similar to the way spam emails are detected and deterred. Machine learning will start to affect other parts of your business funnel as well, just take a look at the rise of Chatbots.
There was a time in which chatbots were only thought of as manmade pests on the Internet. Through machine learning, they are getting smarter and businesses are embracing them en mass.
In 2018 and beyond, chatbots will play a key role in the future of customer service. Why? Chatbots can help achieve a faster customer service resolution, as well as provide quick histories of each customer for impeccable customer service.
There are some key benefits that chatbots have over solely human interactions:
Giving 24/7 customer service: The great things about machines? They don’t sleep! Coupled with the fact that chatbots are getting sophisticated enough to recognize human emotions such as anger, confusion, fear and joy. So should a chatbot encounter negative sentiments from the customer, they can seamlessly transfer to a human to take over and finish assisting the customer.
The era of being ‘on hold’ is gone: A huge barrier to providing excellence in customer service is long wait times. How many times have you tried to get customer service from Comcast (or any TV/Internet provider) and you are getting progressively more frustrated with the wait times? This can all be eliminated with chatbots!
Quick access to customer data makes service more personal:One thing that humans will never be better at than chatbots is quickly digesting customer data and history to provide context to customer questions.
Chatbots excel at collecting customer data from support interactions. They can serve as virtual assistants that can feed customer data to your customer service officers so they have a full history of each account quickly.
The final trend we’ll explore is Automation and how it affects businesses today.
Automation now and beyond 2018
Though Machine Learning and AI are hot topics in the tech world, it is not to a point that small to medium size businesses can leverage it in the immediate future. But there is still hope for them with automation.
Powered by the Cloud, this type of technology has already revolutionized Marketing and Sales workflows and interactions but it is also starting to touch the various other parts of a business. For example:
Once you win an important sale, you’ve got to deliver the product or service you’ve promised to the client. What does that process look like for most businesses now? You all will have a kick-off meeting and hope to cover all the promises that marketing and sales have given to your client.
However, with the use of operations automation and a powerful CRM, you will be able to read the interactions and see all the various touch points a client had with your company before that kick off call even happens.
This will give your service businesses a head start in providing great client relations and managing expectations. This category of SaaS products is called Service Operations Automation, or ServOps for short.
If there is one data-entry heavy department it would be Accounting. The problem is that as humans, we are fallible and much slower at data entry than a machine. Innovations with bank feeds, rules-based categorization and integrated payments have dramatically reduced the workload of clerical and bookkeeping staff.
This gives business owners more timely access to accurate financial information for their businesses.
Research, done by Xero, (a popular financial software provider) suggests that by 2020, automation will be commonplace in accounting. A significant number of finance professionals will be using the next level of analytical tools to help them add value to business models across the globe.
Finally, the Cloud and Automation have come to the Payroll and Human Resources sector. These important areas of a business too often suffer because small businesses aren’t big enough to afford a full-time HR department.
What’s the alternative? Having only part-time efforts of founders and principals which can often lead to serious risk to the business.
With new automation technology, compliance is automated by platforms. The effort of keeping time-off approvals in sync with PTO balances and payslips becomes a thing of the past.
In the near future, we will see the rise of great technology, powered by the Cloud, Automation, AI and Machine Learning.
This truly is the start of the Golden Age of Information Technology and it is time for businesses to take a hard look at their organizations and find ways to start integrating these tech trends.
This is a slightly shortened re-blog from a marketing post on Tenfold.com *
*This post was guest-authored by Tara Callinan and Jenneva Vargas from Accelo
In economic terminology, the term “utility” has not much to do with multifunctionality nor completing specific useful tasks.
It does in context, relate to the level of satisfaction or “completeness” one derives from the consumption of a product or service. For example, there is only so much pizza one can eat before feeling ill from satiety.
On a broader and more macroeconomics spectrum, our utility levels will also help determine how resources are allocated and consumed.
The concept, a brainchild of Daniel Bernoulli, has so many relevant connotations. As humans, we individually have a maximum biological boundary which when reached, signals absolute satisfaction. This in economic terms is called maximum (total) utility.
Total utility as the complete satisfaction that you can get from consuming all units of a specific item.
Economists are more interested in the changes in levels of utility or what is referred to as the marginal utility.
We will return to its application to the economy.
Incidentally, utility has no formal unit of measurement – though some have coined the term “utils”. These so-called utils equate a number to utility levels in a controlled sample experiment.
Understandably it can be quite a feat to quantify utility as it is based on human behavioural preferences. The closest we got to quantifying such was via the marketing concept of the consumer black box.
As an illustration, the concept can be applied to something as basic as eating a delicious meal.
Depending on how hungry you were, you would derive the highest utility from the first few bites of your meal.
As you progressed and depending on your appetite, each additional fork, spoon, handful more would provide fewer levels of satisfaction. As you reach your stomachs capacity (inch towards satiety) your utility diminishes.
This can be applied to the taste of the meal. It specifically explains why we tend to eat something sweet after a main (savoury) meal.
The appreciation of ice cream when you are starving would diminish quickly as you concentrate on filling up your stomach. This as opposed to enjoying the taste.
When applied properly to the running of an economy, governments and policymakers can determine which goods and services yield the most maximum utility.
This helps them to consequently direct expenditure to identified priority areas (products/services).
It is a long term concept
Education, for instance, may not provide immediate utility (gratification) for scholars and pupils. However, when appropriately harnessed, it could yield higher levels of satisfaction as individuals enter the job market with better remuneration packages.
Tweaking education curricula, taking into consideration levels of utility to whip up interest for the good of the individual. This should, therefore, be a prime focus for legislators.
Inputs such as maximum times that students can concentrate and the length of study for a degree, diploma or course should be offered without compromising the substance.
Without a doubt, there would be considerations, at a micro-level to assist in enhancing both marginal and total utility in the education sector.
The concept of utility is a lot less ubiquitous as we think and relates to the unsavoury phenomenon of megalomania and why we have greed.
When the level of satisfaction or self-gratification diminishes quickly, you find that it takes longer for those experiencing lower levels of marginal utility to reach a plateau of pleasure.
Drug addiction, sexual appetites, and fetishes would then kick-in in such cases where people upgrade the “product or service” that they have already maximized their utility. At that stage, another level of fulfilment would be sought.
Utility applied to finances
It also explains why people lose a lot of money gambling or investing in stocks. The satisfaction of gaining more for a little outlay based on your decisions, will often drive you to take more risk until a level of risk aversion kicks in.
High-risk equity investors “called whales” are now delving into the Crypto market to maximize their utility. They are diverting their funds from property and stock trading into digital currencies like Bitcoin and Ethereum.
The saying too much of a good thing is inevitably bad for you applies and can be countered by diversifying the things that deliver pleasure or satisfaction.
This is to ensure that you do not maximise utility on them too quickly and lose interest. Worse case, delve too deep into the dangerous territories of addiction.
Economists need to be relevant, more than ever before. They also need to formulate a means to measure and quantify utility or provide “utils” for at least, the most common goods and services.
With such a strategy, policy-making, product pricing and the efficient allocation of resources would be more effortless.