Automated AI Experiments: A Key to Success in ECommerce

Posted by Monis Khan on Sep 19, 2019 1:37:10 PM

Automated AI is the holy grail for everyone who doesn’t know a thing about AI and machine learning. Take a closer look at the history of washing machines. The simple items such as washing clothes have been through more than a century of evolution. Washing clothes at once would be the longest endeavor of the everyday. With its automation, the human has availed itself liberty to do more innovative things. AI in ECommerceis going through a similar journey, albeit much faster than washing machines.

Automated AI experiments for success in ECommerce

What is Automation of AI Experiments?

An AI experiment is the one in which ECommerce companies treat their data with machine learning algorithms. These algorithms would help ECommerce companies, predict pricing, forecast demand, identify product recommendations, or predict customers likely to churn. An ECommerce company can assess the worth of their data in terms of incremental revenues earned through actionable insights learned using AI experiments. AI experiments involve three essential steps and automating these steps can help a non-AI expert ECommerce to use AI for their benefit.

Preparing Data and Choosing Data Models/ Algorithm

Once you know what you would like to see from your data using AI, you would need to select the machine learning algorithm and build a data model. This step requires an AI expert to be on the team, in the absence of automated AI experimentation capability. Preparing data takes the most amount of time; however, automation helps you prepare the data visually using a drag and drop UI.

Training and Tuning Data Models

The second important step in the process of running AI experiments is to train the data model. There are two machine learning approaches, supervised and unsupervised. Supervised machine learning would cater to most use cases of ECommerce, while unsupervised learning is useful in a few. Here it is critical to assess the effectiveness of the data and model itself. Most AI experiments fail due to lack of data quality while others due to the inability to tune the data models. The objective is not to achieve models trained showing high confidence scores but to get a confidence score that is acceptable to start-with. With the automation of AI experiments, training and tuning of data models become a two-click process.

Building and Hosting AI Apps/ APIs

Once the data model trained with your data, you are ready to run your experiments. At this stage, you need to build an application or API so that experimental data can be supplied to the model - an inference model. Building and hosting of the app require software engineering and dev ops teams to contribute to the absence of automated AI experiments.

What can’t be automated?

As an ECommerce business manager, your domain knowledge about your business is something that can’t be automated. AI looks at things from a mathematical point of view while you have to chip in to give the AI models a domain perspective. For example, to determine which data models will contribute to better learning of a customer churn model, could be part of your implicit knowledge. 

Why is Automation in AI Experiments Helpful?

The core value proposition of automation is that it saves time, and money to get more accurate results. In addition to the time and money, AI automation helps reduce the dependence of the experts required to handle complex AI experimentation.

Time to Fetch Actionable Results

It takes 6 to 8 months to get results from your AI experiments in the absence of an automated AI tool. Often the data scientists get too particular about the kind of data they need, the level of confidence score they want to see, the accuracy level of the results. All of this requires many months for each AI experiment to see the light of the day. With automation, the perspective is to run quick AI experiments, see the value, and then invest further time and effort on those experiments to make the outcomes better.

Lack of Skilled Resource

Hiring and retaining the top-level data scientists is one of the biggest challenges that the business managers today face. The long span of AI experiments require the business manages to invest in the data scientist resources, possibly to see them leave even before an experiment shows the results. The replacement is another issue. The new data scientist may look at an AI experiment in a different light and might need to redo everything with the newer approach. The businesses are at a more significant loss in all of this. Automation of AI experiments has addressed this challenge to a more considerable extent. You do not have to depend on the niche AI skill-set to see the initial traction that your data is capable of providing you. Automated AI experiments empower software engineers, business experts, and testers to launch AI experiments and experience the results themselves.

Shortage of Budget for AI

Experimenting with data has mainly been an area reserved for the massively funded ECommerce companies. Experiments of any kind by nature come with their inherent risks as well as returns. AI and data science overall command a high premium in terms of the overall budget. Budget is a big concern for growing companies when it comes to spending it on AI experiments. Automation of AI experiments creates value by offering the least expensive option to run AI experiments. Since AI automation platform tends to use AI to do AI, it excludes the use of expert resources, and that reduces its costs. Automated AI experiments are pretty much in the budget ($100 to $500 per month subscription fees) for growing ECommerce companies. When you get an option to try before buy, that’s even better to freeze your expectations from an automated AI investment.

How to Drive Automated AI Experiments in ECommerce?

The most sophisticated automated AI platform tends to use AI to do AI in their approach to automation. This helps the ECommerce companies to get a data scientist in the box, to experiment with their data. Not everything can be automated. So generally, the consultants from the automated AI platform get an understanding of your data and use case over call, email, or chat. They help you build your first data model, train it, and build apps. The automated AI platform provides an option to work on dummy datasets provided with the platform just to get acquainted with the platform features and functions. 

ECommerce companies can run automated AI experiments with the help of Datoin’s AI platform. Under a free trial of 30 days, you can evaluate the worth of your data with Datoin.

Tags: AI, Machine Learning, ai for ecommerce, Artificial Intelligence, AI platform, Automated AI Experiments, automated ai platforms, Automated AI, machine learning algorithms

About Datoin

For online businesses, E-Commerce startups Datoin is the best AI platform. Datoin is no code AI platform for business users who understand the domain and are not from tech or data science background. Datoin is designed with a vision that the online businesses of any size need not invest in building data science expertise to begin monetizing the insights from their own data to drive more sales.

Datoin looks at building a community of ECommerce startups, online subscription businesses  using advanced AI signals to sell productively.

ROI Centric Use cases:

  • Product recommendations, Engine 
  • Customer churn prediction
  • Predictive pricing
  • Dynamics discounting
  • Demand forecasting
  • Predicting List of Upsell/ Cross-sell ready customers 

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