Machine learning and eCommerce performance
Can machine learning help to overcome the performance?
According to the Microsoft Corporation report in 2015, that showed people lose concentration on the web after 8 seconds. Ideatarmac present a text about future machine learning and finding solutions that’s affect eCommerce today. 1
Page loading time is most important for an eCommerce site. Unfortunately, consumers tend to care more about the speed as the goal is to reach faster on to what they are looking for. Having a web presence is not just enough but the speed at which the web can load makes a greater impact on the business, customer satisfaction, and revenue.
Website performance has an immense effect on conversion rates. Akamai’s research shows, every 1-second delay in eCommerce site can lead to at least a 7 percent decrease in conversion rate2.
fig: impact of site loading time
In this article we will take you through the several aspects of Machine learning:
- Problems with traditional monitoring and human intellect
- Breaking traditions with Machine Learning
- Getting started with Machine Learning
Problems with traditional monitoring and human intellect:
For longer times eCommerce companies have been using Monitoring tools and their dashboards. Basically, to address the performance issues and to manage the decline in conversion rates and user satisfaction.
Although the purpose is achieved, the fundamental problems are:
- Monitoring tools and Dashboards: These were never designed to troubleshoot, so it brings only the myopic view of the problem. Analysis and interpretation of this blinkered data are left to humans to troubleshoot and eventually fix those issues.
- Siloed systems: Not all the monitoring systems include business data to derive the impact of a technical issue on your business. Out of ten issues, prioritizing this is impossible when you have no real-time data to draw any kind of inference.
- Immense data: Analysis of immense data is beyond human intellect. More features in monitoring systems generate a massive amount of raw data. The reality starts sinking when it turns out to be non-resourceful and meaningless. Eventually, it leads to the question of whether to rely on this marginally enhanced intuition after huge capital investment.
The traditional process demands an ample amount of effort and the equal amount of errors that something unavoidable.
Breaking traditions with Machine Learning
Although Artificial Intelligence (AI) and Machine learning have revolutionized, they never can completely replace support engineers. However, incontestably they are going to transform the way engineers approach operations, performance, and ROI through issue resolution in the eCommerce industry. The combined application on the website creates a better functionality and user experience.
When people talk about machine learning or automation, they are primarily talking about the ability of machines to learn from data. The data that is derived to fulfil the objectives based on data-driven reasoning.
Users are often facing slow page responses and are not even aware of it. The machine learning algorithm finds the reason and also detects the symptoms across the eCommerce architecture. It generates a report on probable root causes, troubleshooting protocol, and the business impact of it. All this in a matter of a few seconds.
And that’s not all! It also looks into user journey, demographics and analyses various patterns to suggest threshold values of slowness; a threshold at which your customers may walk out on you.
fig: changing IT operation management
Machine learning can help you to achieve the true potential of your eCommerce business with remarkable intellect.
McKinsey’s survey showed that the adoption of AI in the standard business process has increased by nearly 25 percent year-over-year3.
Getting started with Machine Learning
Machine learning becomes more prevalent. New technology will help businesses in more efficient and productive through its enhanced accuracy.
There is a lot that can be done with Machine learning and Automation
- Resolving catalogs of Problems: Frequently made decisions, routine IT operational issues or consistencies should be automated with machine learning algorithms. If an issue or technical process is complex, the use of Machine learning helps to create a decision support system.
- Focus on data but not dashboard: Collects feasible data and this data can troubleshoot any performance issue. For example, some of the data that could help with troubleshooting problems are
- Real user monitoring
- Monitoring 3rd party tools
- Web data (consumer behavior) for business impact
- Infrastructure Monitoring
- Code performance data
- Right machine learning solution: Focus on the area of IT operations, that is flexible to integrate any data source. It can create optimized custom algorithms based on your use cases.Remember, pick the solution that can fix your operations and performance issues by combining:
- Machine learning skills specialized in performance issues
- Helps you resolve issues identified
Thus, making the solution responsible for what it offers.
- Useability: Let the algorithms learn the behavior of your website over a period. More data shows a better output/ prediction of the system.
There is a lot that can be done with machine learning and patience when it comes to business. It can take care of your routine issues while your team can focus on more complex and demanding tasks. Informationssystems will not replace human efforts, but will increase the technological advancement.