Think of Application Program Interfaces (APIs) like toolboxes. They’re sets of different commands, codes, and protocols used to build software applications and make applications talk to each other. We can’t see them, but they’re there in the background of everything we do on a computer.
API stands for application programming interface. As it’s a rather complicated concept, let’s break it down by looking at each of its parts.
In the simplest terms, APIs is a set of methods that govern how one application can talk to another.
One of the biggest challenges many companies are facing in today’s world is developing their services to work across all the different platforms their customers are using, from Androids, iPhones, websites, social networks etc. To help with these challenges, and to reduce development costs, while improving the experience for your customers too, you need to use APIs.
APIs have become extremely important in today’s digitally connected environment. APIs are used to accelerate multi-channel strategies, improve internal processes, and build consistent, cross-channel customer experiences.
Automations (also known as "business rules" or "automation rules") allow you to automate common tasks on particular subsets of cases.
One of the most common uses for automations is to build workflows that help move cases their arrival in the support queue through to a speedy and satisfactory resolution. You build automations by first specifying a set of criteria and then defining an action the automation should take.
For example, you could build an automation that would look for any cases assigned to your sales team that are set to high priority, and then email the sales team lead with a notification about the case.
Powerful automations are one of the biggest advantages to working with a customer service platform like Kayako.
Definition: A standard or point of reference in measuring or judging the current value or success of your company in order to determine your future business plans.
The process of benchmarking your business to evaluate your current success can be quite involved, requiring the collection, analysis and comparison of mounds of data on everything from your recent sales growth to production capacity. However, you may want to start the process by simply sitting down, looking around and asking yourself if your business seems to be where it should be right now. Your gut-level intuition of how the business is doing may be more valuable than even the most detailed analysis.
Then take a look at your sales. You probably won't have to consult any financial statements or even think for more than a second or two to recall your business' sales for the most recent month and year. Sales revenue is the most common measure of a business' size and level of success. However, don't stop after you look at your total sales figure. Break that figure down as much as you can. Looking at your sales by lines of business, product lines, individual products, varieties of individual products and price points of individual product varieties can be far more useful than just knowing that sales are up.
Next, you might want to take a look at exactly how profitable you are. And it's not enough to just know whether you are or are not making money overall. You should also look at your current profitability in light of several ratios, including gross margin (sales minus cost of goods), return on equity (profit divided by net worth), and return on investment (after-tax net profits divided by total assets).
And profits aren't the only way to measure a company's success. You should also be aware of how much your company is worth. One way to do this is to check out an updated balance sheet. That figure at the bottom for net worth, representing assets minus liabilities, is a good indicator of whether you've built value in your business--and if you have, how much.
Don't stop your valuation checkup with your balance sheet, either. There are a few other ways to measure value. One of the most important valuation techniques is based on expected future cash flow, or how much cash the company should be able to throw off for you in the next several years. Businesses are typically valued as a multiple of their future cash flows, but different industries and types and sizes of businesses use a variety of indicators. To find out what rule applies to your industry, check with your trade association.
Next, take a look at your market share. Try to break down your markets and products as finely as is practical to get a realistic view of your market share. The results can be an accurate indicator of the most likely direction you should head in to achieve growth.
You should also consider your employees. Having a work force of skillful, motivated employees is essential to a small company's ability to deal with globalization, shorter product cycles, evolving information technology, and the other challenges of modern business. At the same time, the pressures of competition mean that no company can afford to have more employees on its payroll than it needs.
When it comes to employees, the basic question you're trying to answer is this: Do my employees have the capability to carry out the work that is and will be required of them? You'll have to look at a variety of factors. Some key factors used in measuring work force quality include: number of years of education of a typical worker, average length of time a worker has been with your company, and average length of time a worker has worked in your industry. You may also look at defect rates, turnover rates and absenteeism records to determine the quality and motivation of your work force. Work force quality can't be expressed as a single number, but it's a key variable in plotting your company's future growth.
Location is your next element to evaluate. Entrepreneurs in fast-food and similar industries know that these businesses aren't just about providing good products, good service and good prices. They're also about real estate because the companies with the best locations tend to have better sales than their competitors, all other things being equal. Location is also important for companies in industries from transportation to health care. Here are five factors to use in evaluating your current location:
Before you can decide where your company's going, you need to know what your current capacity is. Here are some questions to ask to help you figure it out:
Different businesses will have different answers to these questions. They'll probably have different questions as well. For instance, a travel agency may have very little limitation on supply when it comes to the airline tickets it can sell to its customers. However, there may be significant limitations on average productivity per employee for that agency. So take a look at your capacity and try to measure it in the way that makes sense to you. The measurements you make will come in handy when you're studying how to grow in the future.
Finally, you have to remember that a business is not a static entity. It's always growing, shrinking or just about to change direction and do something different from what it has been doing. One of the most important measures of how you're doing is determining exactly whether and how you've been growing. This affects your future prospects for growth. If you've been growing at double- or even triple-digit percentage rates every year, then it may be time to take a breather rather than go in search of faster growth. On the other hand, if your business has seen declining sales, shrinking markets and overcapacity, then growth may be something you won't be able to accomplish without radically repositioning your company.
Don't stop after looking at the top-line sales growth you've experienced. Also examine whether and how fast you've been adding employees, expanding to more locations and taking on new customers. Find out which products and services have been growing at the fastest rates. Determine whether new staff positions have tended to be in administrative functions or in production or sales. Evaluate all the new locations you've added. Are they in high-traffic spots with strong demographics that are increasing the average quality of your outlets? Or have you been growing without careful planning? Answering these questions will do a lot to guide your plans for future growth.
Bounce e-mail (sometimes referred to as bounce mail) is electronic mail that is returned to the sender because it cannot be delivered for some reason. Unless otherwise arranged, bounce e-mail usually appears as a new note in your inbox. There are two kinds of bounce e-mail: hard bounce and soft bounce. Hard bounce e-mail is permanently bounced back to the sender because the address is invalid. Soft bounce e-mail is recognized by the recipient's mail server but is returned to the sender because the recipient's mailbox is full, the mail server is temporarily unavailable, or the recipient no longer has an e-mail account at that address.
Bounce e-mail can be handled by a program when sending e-mail to a distribution list and most e-mail distribution list vendors include this capability. Such a bounce handler can retry later, unsubscribe the addressee from the list, or take some other action.
Some products and individuals have developed bounce e-mail handlers that recognize spam messages and return a bounce message so that the recipient will be taken off the list.
Some products and users use the term bounce to mean "forward a received note to someone else."
Software bugs are errors and failures in a software that cause unexpected results for users and may lead to problems further down the line. Multiple complaints about the same thing might mean your development team needs to implement some bug fixes.
But customer support managers and teams need to beware: not all bugs are really bugs. Promising fixes that you might not deliver can lead to even more frustration. Promise instead to investigate and follow up with them when you have more information.
A Help Center is your customer-facing support site – ideally your customers’ first stop when they run into a problem. Help Centers should include self-service articles, FAQs, contact information, and feedback widgets.
It should be very easy for your customers to contact your support team, through a variety of channels, from anywhere on your Help Center.
Articles that cover step-by-step instructions, commonly asked questions, reference materials, best practices – the list goes on.
Typically they live in your Help Center, and are aimed at helping your customers fix problems and use your products. Your self-service articles and any links within them should be maintained regularly (see Help Center Audit), or your customers will get frustrated using outdated content.
Chatter can be used as a company intranet or employee directory. Each employee has a profile page with photo and work-related information that explains what the employee’s role is within the company, who the employee reports to, where the employee is located and how to contact the employee. Employees can “follow” both people and documents to collaborate on sales opportunities, service cases, campaigns, projects and tasks. Like Facebook and LinkedIn, Chatter allows users to manage their feeds and control how notifications are received.
Another word for the client or customer who has raised an issue through one of your support channels. Typically, contacts can create an account on your help center where they can check the status of their open cases and communicate with your support team.
In Kayako, you can also tag and track these contacts by their needs and behaviors.
Frequently asked questions (FAQs) are a self-service format that provides answers to common queries and problems with the hope that customers can get rapid solutions to their problems.
If you have a limited number of topics you cover in self-service, then FAQs are often a good fit. If your content spans a lot of complex information, though, you may want to look at other formats for your self-service content. Having too many FAQs is stressful for the reader as it adds extra friction in them finding the relevant answer at the quickest opportunity.
Integration as a Service (IaaS) is a delivery model that puts system integration into the cloud. This paradigm facilitates real-time exchange of data and programs among enterprise-wide systems and trading partners.
In B2B (business-to-business) integration, IaaS allows partners to develop, maintain, and manage custom integrations for diverse systems and applications in the cloud. In this way, the enterprise can more effectively pursue process innovations without the need to constantly modify and maintain diverse and often incompatible application programs.
IaaS has gained favor among small and medium-sized businesses in the past several years because it facilitates low-cost, efficient, reliable B2B integration. IaaS allows enterprises of modest size to spend more of their valuable resources on the products and services that directly benefit customers. In addition, IaaS can streamline IM (infrastructure management) by minimizing the amount of unnecessary and redundant time and energy spent on it.
The New York Times archived much of their historical data in less than two days using an IaaS system developed by Amazon called Elastic Compute Cloud (EC2). Without the assistance of EC2 or a similar IaaS platform, the same process would probably have taken weeks.
IoT Cloud is a platform from Salesforce.com that is designed to store and process Internet of Things (IoT) data. The IoT Cloud is powered by Thunder, which Salesforce.com describes as a "massively scalable real-time event processing engine." The platform is built to take in the massive volumes of data generated by devices, sensors, websites, applications, customers and partners and initate actions for real-time responses. For example, wind turbines could adjust their behavior based on current weather data; airline passengers whose connecting flights are delayed or cancelled could be rebooked before the planes they are on have landed.
In another context, IoT Cloud can provide business users with much a much more comprehensive and integrated perspective on customers, without requiring technical expertise or the services of a data analyst. The platform can take in billions of events a day and users can build rules that specify events to act on and what actions to take. IoT cloud is data format- and product-agnostic; output connectors allow communication with Salesforce clouds or third-party services.
Salesforce.com is a San Francisco-based customer relationship management (CRM) and social enterprise software-as-a-service (SaaS) provider. The company launched IoT Cloud in the fall of 2015, at its annual Dreamforce user conference.
Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast future activity, behavior and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value, or score, on the likelihood of a particular event happening.
Predictive analytics software applications use variables that can be measured and analyzed to predict likely behavior by individuals, machinery or other entities. For example, an insurance company is likely to take into account potential driving safety variables such as age, gender, location, type of vehicle and driving record when pricing and issuing auto insurance policies. Multiple variables are combined into a predictive model capable of assessing future probabilities with an acceptable level of reliability. The software relies heavily on advanced algorithms and methodologies such as logistic regressions, time series analysis and decision trees.
Predictive analytics has grown in prominence alongside the emergence of big data systems. As enterprises have amassed larger and broader pools of data in Hadoop clusters and other big data platforms, it has created increased opportunities for them to mine that data for predictive insights. Heightened development and commercialization of machine learning tools by IT vendors has also helped expand predictive analytics capabilities.
Marketing, financial services and insurance companies have been notable adopters of predictive analytics, as have large search engine and online services providers. Predictive analytics is also commonly used in industries such as healthcare, retail and manufacturing. Business applications for predictive analytics include targeting online advertisements, flagging potentially fraudulent financial transactions, identifying patients at risk of developing particular medical conditions and detecting impending parts failures in industrial equipment before they occur.
Predictive analytics requires a high level of expertise with statistical methods and the ability to build predictive data models. As a result, it's typically the domain of data scientists, statisticians and other skilled data analysts. They're supported by data engineers, who help to gather relevant data and prepare it for analysis, and by software developers and business analysts, who help with data visualization, dashboards and reports.
Data scientists use predictive models to look for correlations between different data elements in website clickstream data, patient health records and other types of data sets. Once the data to be analyzed is collected, a statistical model is formulated, trained and modified as needed to produce accurate results; the model is then run against the selected data to generate predictions. Full data sets are analyzed in some applications, but in others, analytics teams use data sampling to streamline the process. The predictive model is validated or revised as additional data becomes available.
The predictive analytics process isn't always linear, and correlations often present themselves where data scientists aren't looking. For that reason, some enterprises are filling data scientist positions by hiring people who have academic backgrounds in physics and other hard science disciplines and, in keeping with the scientific method, are comfortable going where the data leads them. Even if companies follow the more conventional path of hiring data scientists trained in math, statistics and computer science, an open mind on data exploration is a key attribute to have for effective predictive analytics.
Once predictive modeling produces actionable results, the analytics team shares them with business executives, usually with the aid of dashboards and reports that present the information and highlight future business opportunities based on the findings. Functional models can also be built into operational applications and data products to provide real-time analytics capabilities, such as a recommendation engine on an online retail website that points customers to particular products based on their browsing activity and purchase choices.
Online marketing is one area in which predictive analytics has had a significant business impact. Retailers, marketing services providers and other organizations use predictive analytics tools to identify trends in the browsing history of a website visitor to personalize advertisements. Retailers also use customer analytics to drive more informed decisions about what types of products the retailer should stock.
Troubleshooting is a term for diagnosing and fixing a problem. Over the years, it has gotten a negative reputation because it became something of a catch-all term for the impersonal, automated customer service at the height of the 1990s dot-com boom.
Nowadays, troubleshooting is still an important part of modern customer support, but companies are moving their focus away from resolving tickets as quickly as possible and toward understanding customers so they can deliver personalized assistance.
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