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Anomaly Detection Can Work Wonders for Your Business

25 February 2020
Anomaly detection for business

We live in the era of data, where everything goes digital and generates a lot of data. Nowadays, even small and medium-sized enterprises are producing a mind-boggling amount of data. To handle the technical aspect of this, companies are always on the hunt for cutting edge technology solutions.

Luckily, these solutions help enterprises not only effectively manage lots of data but identify anomalous changes or events that arouse suspicions. It doesn’t come as a surprise when a small company turns into a big one, it becomes a top target for scammers. To uncover anomalies and eliminate them, anomaly detection comes to the rescue.

Anomalies and Its Detection

In short, an anomaly itself is an unexpected change in the business workflow.
Anomaly detection techniques are used to detect data items, events or observations that differ from most of the data, and are hard to catch by a human. These abnormalities, if not detected, can potentially damage valuable data assets and make a bad impact on businesses. That’s why the insights gained from anomaly detection are of vital importance for business owners.

What Can Be Analyzed and How It Works

Depending on your business and the sector you work in, you should know the key metrics to keep an eye on. Anomalous data detection can be applied to analyze the following KPIs:

  • CAC (Customer Acquisition Cost)
  • AOV (Average Order Value)
  • DAU (Daily Active Users)
  • Web page views
  • Mobile app downloads
  • Transaction volume
  • Stickiness
  • Revenue per click
  • Cost per lead
  • Cost per click and so on

In fact, it helps create a baseline for normal behavior in your KPIs and measure the overall business performance. With this being made, it monitors suspicious events and actions in your system saving your time and effort. Once an abnormality is detected, it sends out alerts.

Anomaly detection in marketing


Why Your Business Needs Anomaly Detection

With the world making a phenomenal shift to digital, we can’t but keep pace with it. It doesn’t matter what domain you operate in or the size of your business, you are highly likely to generate more data in the coming few years. And at this point, you should be prepared to accumulate, analyze and interpret these large volumes of data. And to top it all, when something goes wrong, you will need to be able to trace the root cause and address it.

With the abundance of data and hundreds of KPIs to measure, you end up hinging on datasets. Which literally means that protecting its security is crucial for your business. And that’s where anomaly detection comes into play.

Anomalous Data Detection in the Clinical Environment

A good example of anomaly detection adoption can be the healthcare domain. As a matter of fact, abnormalities are related to caring treatment or even patient survival. That’s why it is obligatory to detect it in a timely manner.

Recent discoveries in AI (Artificial Intelligence), ML (Machine Learning) and deep learning have made automated anomaly detection for the healthcare sector real. For example, in clinical settings, abnormality detection can be implemented using a density-based clustering approach on patient careflow logs. A learned model enables the recognition of the anomalous patient’s careflow with reference to the normal logs. Used wisely, automated anomaly detection helps recognize changes in a patient’s condition and provide emergency assistance.

In simple terms, AI has the potential to enhance healthcare providers at the point of care allowing better care and diagnostics.

Anomaly Detection Aligned to Win Against Counterfeiters

The business of counterfeit goods is one of the biggest underground industries, a half-trillion-dollar problem and a detrimental threat to our society. As the world grows, so does the refinement of forgers to produce fraudulent copies. As manufacturing methods keep on improving and replicated goods becoming more persuasive, making it hard to understand whether it’s fake or not.

From clothes and cosmetics to the pharmaceutical and medical industry, it’s sweeping the globe and inflicting damage on vendors. Receiving shipments and finding counterfeits in it, vendors experience revenue losses. Currently, vendors have difficulty understanding whether they’re selling counterfeits or not, since QR codes are easy to fake. In this occurrence, it brings up the question of harnessing cutting technology in the war against counterfeit and piracy.

One of the latest AI developments that can combat this sophisticated issue can probably be anomaly detection analysis. Consolidating this technology will provide vendors with safe and user-friendly anti-counterfeiting tagging aimed at verifying the product’s origin. Specifically, deep learning models can be used to address this challenge. To put it simply, the process seems to be similar to face detection. The ultimate goal of this is to capture and identify one of a kind patterns but in this case for objects, not for individuals.

The process goes as follows:

  • A vendor takes a photo of a QR
  • A convolutional neural network searches for anomalous data
  • The result gets compared against the data in the database
  • If a fake is detected, a vendor gets a counterfeit alert

Detecting counterfeiters with QR codes


We can forecast a high demand for anomaly detection to beat the multi-billion dollar industry of counterfeit in the upcoming years. Applying abnormality detection to your business, you are killing two birds with one stone: you avoid money loss and mitigate damage to your brand’s reputation. Worth mentioning, that this innovative approach can be easily tailored to various industries and has the potential to win the battle against piracy and counterfeit sales.

Anomaly Detection for Monitoring Product Quality and Preventing Errors

Now, we see product companies enjoying the benefits of AI has to offer. They adopt innovations to improve the quality of their products and services and minimize human efforts.

Let us see how abnormality detection can serve product-based businesses in the following domains:

FinTech industry: security is crucial for any fintech business, as it deals with critical procedures like payments, transactions, transfers and so on. With anomaly detection, all the sensitive data is safe and sound. It incorporates data into a centralized system and identifies any real-time security vulnerabilities or breaches. Having anomaly detection in place, a company prevents a bad impact on their margins and customer trust.

eCommerce: monitoring sales funnel, bounce and conversion rates and other KPIs is a matter of paramount importance for every merchant. What’s more, it’s vital for product managers or marketers to identify pricing glitches and accidents in time, because it may cost them a fortune and millions of furious customers. Utilizing Machine Learning for anomaly detection helps track down these sort of issues before an eCommerce store crushes and clients get affected.

Detecting anomalies has the capability to capture opportunities to boost margin profits. Imagine, you’re running a campaign for a particular product, and anomaly detection shows that this campaign has higher conversion rates than any of the campaigns you’ve had before. This valuable insight can help you have a look at your business at a different angle and double your profits in the future.

In addition, you can reap the benefits of abnormality detection applying it to social media. It’ll inform you what products or services of yours are trending and being promoted by bloggers. Having this information in place, you’ll have the right amount of products in stock ready for sales.

Manual vs Automated Anomaly Detection

With the speed of technology evolution, manual anomaly detection seems like an old-fashioned option. There’s a glaring problem with it – it is inaccurate, time-consuming, and requires much human effort.

As for automated detection of anomalous data, it serves as a powerful tool to exert control over business systems and guards against any possibility of errors and fraud. Today, there is a whole list of options to cope with the wealth of data and detect errors automatically. Using supervised and unsupervised machine learning, you can empower your business by monitoring its security and averting any errors.

Final Thoughts

We can’t deny how AI is revolutionizing the way we live and do business. We are already making a profound shift from manual processes to automated ones. AI, and anomaly detection specifically, are creating value for businesses by freeing up time through automating manual processes in the workplace.

To beat up the competition in the age of digitization, leveraging AI should be the ultimate goal for every business. Taking advantage of the latest technology will benefit your business in many ways. In fact, you will not only save time and money through process automation but also strengthen the security of our business.

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