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Anomaly detection and Anomaly detection methods: Why every brand needs it

Introduction

Unlike some decades back, the amount of information being collated and collected by companies is simply massive. When such data are analyzed and interpreted, it becomes insightful information that can change the course of any business’s growth. In recent years, many entrepreneurs have been able to reach their target audience quicker and more efficiently. They have earned more revenue than ever before. Several CEOs are expanding their base across different countries. Most of these impressive developments are emerging courtesy of the impressive use of data.

More excitingly, savvier business groups are gaining even better momentum by taking advantage of anomaly detection. This approach is about recruiting computers to assess collated data and see if there is an outlier that could bring an additional insight. Such an insight often turns out to be an action changer. And other times, many businesses use it to optimize their marketing efforts and cover any loopholes where they could be missing out on important potential customers. For business owners who are looking to learn more about anomaly detection and how it can influence your marketing effort productively, here is an extensive read for you.


What is an anomaly?

Considering the impressive and smart programs and management software businesses currently use, it’s far easier to collate information about every side of a business operation. The multiple metrics provide a massive dataset worth assessing to check a business’s performance. In most cases, the dataset usually follows a specific pattern to represent business as usual. However, a deviation from the “norm” could be noticed or an event that seems not to conform with the expected dataset pattern. This is referred to as an anomaly.

What categories of anomalies exist?


Anomalies in the business dataset are categorized into three groups as follows:

i. Collective anomalies

This involves a subset of data points in a set, which is an outlier to the complete dataset. In this case, the specific values are not anomalous in a contextual or global sense. However, analysts can see collective anomalies during the assessment of distinct time series. Also, when individual behavior in a specific time series dataset is combined with another in a different time series dataset, the anomalies become more pronounced.

ii. Point anomalies

Otherwise known as global anomalies, this is when the deviation exists beyond the dataset as a whole. Point anomalies are common the fraud detection.

iii. Contextual anomalies

This category includes anomalies having values with significant deviation from other data points existing in the same context. For more clarity, an anomaly could be a norm in a context but exist as a deviation in a different context. Otherwise known as conditional outliers, they are often found in time-series data as the datasets are records of a specific amount over a period.


Background knowledge about anomaly detection

In simple terms, the steps taken in data mining for the identification of data points or events that completely differs from the normal behavior of the dataset are regarded as anomaly detection. With anomalous data, the analysis team can find out critical incidents. Such a situation could be a potential opportunity worth exploring. For instance, if there is a new development in a consumer’s behavior. This is important to assess as it could shed light on a new marketing approach to take on.

When a company network of systems crunches data from time to time, it’s likely to understand the expected metrics or common datasets, especially based on the variables supplied. This is anomaly detection’s strength as it allows the computer to detect marginal anomalies. For instance, if a business recorded a slightly slower sales rate compared to the previous months in which reports have been consistent, it is important to look at the cause and how to address such an outlier. The computerized anomaly detection enables the marketing team to get an alert when such a situation arises to adapt as appropriate.


Anomaly detection methods

From the early period to modern times, anomaly detection methods have evolved significantly. Below are the available anomaly detection methods as they evolve with time:

  • Manual anomaly detection: This is the use of employees to check the analytics and figures for any deviation in the dataset.

  • Statistical methods: Unlike manual anomaly detection, statistical methods can be used to check more massive data and even interpret it within a shorter period. For instance, the computer can use the method to calculate average values and notify the team about any outlier found.

  • Machine learning algorithm: This is a more sophisticated tool that assesses a massive amount of data and checks multiple variables quickly. A machine learning algorithm is best used when statistical methods are likely to lead to a false-positive result.

Why every brand need anomaly detection

From detecting deviations in application performance, product quality, user experience to sales applications, there are several ways to take advantage of anomaly detection. Learn about the most common ways that business experts often use anomaly detection below:

  • Anomaly detection is helpful for finding lapses in user experiences, such as a change in the customer support process or a faulty version release. Learning about these anomalies early can save a company several hundred dollars.

  • Anomaly detection comes in handy for monitoring network or data center performance. In this way, the company can proactively prevent any problem that may affect customer experience.

  • Anomaly detection helps track the sales funnel for deviations that may suggest an error. Also, the marketing team may find a new opportunity to enhance traffic.

  • Anomaly detection is useful for being notified of any website errors that can limit a user’s experience.

  • Anomaly detection is useful for detecting possible security issues. For instance, unauthorized personnel may try to access the data and could potentially jeopardize the company’s public trust.

Final note

How important or significant anomaly detection would have for a business depends largely on the industry and application. However, when applied adequately, it can save a company millions of dollars across the year in several areas of operation. Anomaly detection is revolutionizing the marketing niche and generating new opportunities. The onus is on a company to take advantage and secure a better chance of becoming more successful.



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