We are living in the middle of a global revolution right now. TToday's business world is more interconnected than ever, not only in terms of technology., but also in the number of partners that companies usually deal with.
External partnerships have become essential to building and operating a business today for the simple reason that they often lead to increased productivity and create new possibilities for both parties..
The number of partner networks is also increasing. a IBM Study entitled "Evolution of the API economy" highlighted that the 70 percent of companies want to expand their external partnerships.
As an example, An overview of a digital business ecosystem in the travel industry can be found in the image below. This is made up of:
- Offer side: This includes providers such as hotels and flights.
- Demand side: This includes services such as airlines and travel agencies..
- Partner network: This connects the supply and demand side and can include entities such as supply banks., stock exchanges or markets.
- Third party apps: This can include companies such as payment providers, CRM, etc.
Regardless of company or industry size, one of the common points that exist in all external associations is that they share data with each other. As you can imagine, sharing data with external partners in any capacity adds additional complexity and vulnerability to both institutions. In particular, the APIs that tie these partnerships together are incredibly valuable, but they are also very error prone, downtime and cybersecurity threats. Due, when one of these providers does not meet a defined SLA, end users are affected, revenue is lost and data breaches occur.
As API usage and the size of these partner networks continue to grow, detecting these flaws and threats within a reasonable period of time becomes a non-trivial task even for the most advanced technical teams.
The traditional way of monitoring metrics associated with partnerships, as an example, the traffic, referrals and income, has been feeding them all through IT monitoring or an Application Performance Monitoring system (APM).
Despite this, The problem with these traditional approaches is that the monitoring machines and the company's monitoring KPIs are fundamentally independent systems and should be treated as such.
In particular, business metrics associated with external partnerships can often be much more volatile than monitoring machines. Not only that, but the KPIs can also be influenced by external forces, like seasonal human behavior. This means that, unlike machines, business metrics simply cannot be measured and examined in absolute terms.
Another key difference between monitoring association KPIs and monitoring systems is measuring the topology or relationships between these metrics.. When we monitor machine data, we can often identify clear relationships between them. When it comes to monitoring business KPIs, Besides, there are often millions or hundreds of millions of metrics to track. This large volume of data means that many of the relationships between the KPIs will be non-linear., which makes them much more difficult to identify.
Fortunately, this is exactly the problem that machine learning helps us solve.
Before discussing how machine learning can be applied to association monitoring, Let's first review some common types of partner networks that many companies are already part of..
Types of partner networks
Let's analyze the two main categories of partner networks (affiliate networks and programmatic advertising) and the API technology they depend on.
An affiliate network acts as an intermediary between publishers (In other words, the affiliates) and commercial affiliate programs. As you can imagine, managing large affiliate networks with hundreds of accounts is an exceptionally complex task. One of the challenges these large networks face is automating their follow-up processes to avoid lost revenue and turnover..
Affiliate tracking systems require account managers to track myriad metrics, as the volume of traffic, conversion rates, the return on advertising investment and many others. To manage these factors at scale, Affiliate networks are now turning to autonomous anomaly detection solutions that provide real-time alerts to network incidents. As an example, if the answer identifies drastic changes in metrics, like a drop in referrals, this could mean that the account is at risk.
The result of implementing a machine learning system for affiliate monitoring means that, instead of constantly monitoring changes in these metrics, account managers can focus on higher value tasks, like relationship management.
Another common partner network that many companies rely on today is a programmatic advertising platform.. Programmatic ad networks connect advertisers with publishers, where advertisers bid on inventory (In other words, advertising space) in real time, also known as real-time bidding (RTB).
These partner networks connect companies that manage huge advertising budgets in a fully automated way., which means that the platform also needs to monitor countless metrics, including prints, clicks and conversions from partners. The number of metrics these ad tech platforms are tracking each day can often run into the hundreds of millions and are indispensable for both supply and demand..
As you can imagine, a poorly tracked metric or network error can lead to millions of dollars in losses in seconds.
To solve the challenges faced by programmatic advertising networks, Artificial intelligence-based monitoring systems have become essential.
Machine learning is particularly suitable for this task, since it can handle a large amount of network data, extract meaning from data and identify potential incidents in real time.
One of the reasons partner networks are so difficult to monitor is because of the underlying infrastructure.. As usual, connect via APIs or application programming interfaces, that act as software intermediaries so that two applications can communicate with each other.. One of the main challenges of monitoring APIs is not only that large amounts of data are transferred every second, but also the fact that they have such low visibility, which means that it is feasible that we do not always know when something has been broken within the protocol.
An API error can often result in downtime for many other applications that depend on it, what, how can you imagine, creates a snowball effect of potential problems. Specifically, gaps in API performance can affect user experience, disrupt workflows and seriously damage a brand's reputation. Due, monitoring APIs at all times is essential to avoid significant loss of revenue.
AI-based monitoring can be used for APIs by learning the normal behavior of each metric and subsequently providing real-time alerts on anomalous events and potential incidents. One of the advantages of a machine learning-based system is that you don't need to set exactly what to look for when monitoring the API.. However, the system learns to monitor normal functionality, performance, the correctness and speed of each API call alone.
The traditional approach to monitoring partner networks
Monitoring of partner networks has traditionally been done with the use of business intelligence dashboards (WITH A) along with a manual alert system if irregularities are detected. The first challenge with this approach is the obvious inability to scale, since a team of analysts can only monitor a certain number of metrics manually, which is certainly well below the hundreds of millions of data points created each day within partner networks.
Another issue with this approach is the latency or delay between an incident and team resolution.. This is known as the mean time to resolution. (MTTR) Y, how can you imagine, an increase of even an hour in the resolution time of your team can have a great impact on the cost of the company.
In conclusion, partner networks are, in essence, a relationship business. Then, if you are not as proactive as possible in finding and fixing problems, it is quite easy for these relationships to be irrevocably damaged. Anyway, problems in partner networks will always keep happening, but if your partners know that you are using a monitoring system based on artificial intelligence instead of a team of human analysts who simply “they observe” incident logs and trends, that can take a long time. way to build trust.
Automated partner monitoring with AI
Now that we have discussed some of the problems associated with traditional approaches to partner monitoring, let's review how an approach based on machine learning can automate and improve the whole procedure.
The first benefit of AI for partner tracking is speed. We mentioned previously that a company's mean time to resolution is a key metric for keeping costs down when incidents inevitably arise.. The fact is, machine learning algorithms can monitor hundreds of millions of data points per second., something a team of analysts can simply never do on their own.
In the following example, the anomalies were detected by the automated business monitoring platform Anodot, who helped the team solve them in an hour.
At the same time monitoring the data points, a machine learning based system can automatically learn the normal behavior of each metric individually, so it can detect even the slightest deviation from the expected behavior. With real-time alerts built into the system, this means your team is on top of and can respond to incidents much faster than traditional monitoring systems.
Another advantage of AI-based monitoring is scale. Regardless of the size of the team you create to monitor systems, Human capital returns are likely to decline in terms of the scale of data that partner networks have to deal with. Automated AI monitoring doesn't take breaks, either, so no matter what time of day it is, can be used to extract meaning from huge data sets so your technical team has a complete overview of what is happening on the network.
A final advantage of an AI-based approach is the granularidad provides. AI-based monitoring not only provides a real-time overview of the entire network, but also inspects the most granular metrics of the network.
As mentioned, even with a team of analysts monitoring the network, these metrics can easily get lost in the sea of data. In particular, if there is an incident on the network, an artificial intelligence-based solution can group anomalies and related events into a single alert, so that you do not receive “warning storms” for every little incident that happens. This ability to find correlations also means that you can identify the source of the incident., to location, device and browser.
As we have commented, a partner monitoring solution based on artificial intelligence has the ability to track every data point in a network at the most granular level, while providing an overview of the network as a whole. This means you can be as proactive as possible in getting the most out of your systems., find issues as soon as possible and identify the source to boost your resolution time.
Due, AI for partner monitoring enables you to build more trust and sustainable relationships within the network.
About the Author
Amit Levi is vice president of product and marketing at Anodes. Passionate about turning data into insight. During the last 15 years, is proud to accompany the development of the analytics market. Having held management positions in several leading emerging companies, Amit brings a wealth of experience in planning, development and delivery of large-scale data and analytics products to leading web and mobile companies. Product and data expert, its motto is “Good judgment comes from experience and experience comes from bad judgment”.