Codependence: 4 Examples of How ML and API Change Businesses

APIs and Machine learning technologies are massively taking over the world without the majority of us noticing. Have you ever logged in the website with your Twitter or Facebook account? Bets are high you did. Maybe you ordered Uber through the ride-sharing app or searched for the restaurants on the Google maps? Then you must know – they are all based on API, meaning that their functioning is dependent on the data pulled from “donor” – a technology that opens its source code for integrations or building new extensions right on it.

There are over 50,000 public web APIs that large corporations are currently sharing with the vast of the world, including Facebook, Google or Dropbox. Such API sharing is not giving out the corporate assets, they offer only the part of the code containing special requirements and protocol of connection establishing.

This is an equivalent exchange where brands build their projects based on existing technology codes without having to develop own from scratch, and large corporations are getting lots of new tech features free of charge. Look at it from company’s point – you’re buying a fully developed tech commodity and run it under the White Label, focusing on your business. Such move allows a company to save money and time resources instead of reinventing the wheel over and over, let alone the outcome of independent tech creation is often far from perfection.

Let’s take a look on the couple of cases where companies managed to update their models of work fundamentally using ML and API and turned their businesses into highly effective mechanisms.

Analyzing big data & customer behavior

Japanese-owned American international network of supermarket stores called “7-Eleven” experienced severe financial hardships until the company wasn’t sold to the holding which implemented serious transformations inside the chain through API integration.

From that moment the chain was meant to be completely reshaped with flexible cloud-based technology based on API. As units became closely integrated within the chain, it helped the company to obtain information coming from thousands of cash registers connected to the Internet. For now, the large array of big data that furtherly gets represented in the reports is used by the company staff who can interpret the behavior patterns of the customers, predict it relying on gathered information, and quickly adjust the new product assortments. As a result, “7-Eleven” chain managed to grow capitalization to billions of dollars per year.

For every company, API can automate not only complex but also relatively simple operations through the cloud computation system such as ticket generations and mailing. Such task as analyzing and predicting consumers’ behavior, however, will always remain to be the subject of change. Luckily, API-based technologies make a good mediation for changes that hold a great flexibility potential to be adjusted accordingly even if the market and tastes of your customers shift rapidly.

Decision-making automatization & cost saving

The growing volumes of operational data don’t make business processes easier to handle or more transparent, especially in the advertising sphere where the traffic grows daily, and the number of its attraction mechanisms does quite the same.

In such circumstances, manual advertisement makes no sense as placements may take indefinite amounts of resources for the inventory search, negotiation, payment, and actual placing. Programmatic allows brands to search for the target audience in the right context, take into account the received information about users and close deals automatically basing on this information in seconds.

This edgy advertising technology is currently in high demand among companies that connect to the API-based White Label solutions in order to capitalize on the high revenue opportunities programmatic features. This way, for instance, the revenue of ad tech providers which acquired programmatic algorithms from SmartyAds has grown up to 97% last year.

The other major tendency on the programmatic market is that companies are more willing to bring technologies in-house. This approach doesn’t only help to cut off unnecessary costs typically paid to agencies, it also removes the middlemen along the process that helps to ensure security customers’ personal data.

Improving & individualizing customer service

Every visitor that browsers website in search of the product is unique, everyone has their own preferences and tastes, and, most importantly everyone develops their own path to reach the desired product.

This fact is now kept in mind of million retail giants like Alibaba. Over 500 billion people use Alibaba today because at the right moment the company decided to guide every user individually from visiting to purchasing stage by applying machine learning algorithms.

The banners are also adjusting to each customer to provide the precise offer and on-site search returns the most suitable results. Their chat-bot Ali Xiaomi implemented with API can sort out the majority of technical support issues.

API and ML can greatly contribute to both offline and online business optimization, having such technologies onboard means opening the gateway to the greater opportunities for the company in the future as they can be suited and tailored to specific requirements anytime.

Retaining the customers & winning the market

For the digital product or service strengthening and popularizing own API means the business will be more influential and successful in retaining the customers in the future. For instance, the bigger market share of your app’s API means the more developers will be connecting to it, and in the long run, it will be troublesome for your competitors to win over the market.

The prevalent service with API will be used and integrated into other websites and services guaranteeing their customers are drawn into the funnel along the way. Evernote is successfully implementing such strategy, almost in any app or website you can find functionality that shares the content directly to the digital notebook.

Retaining the customers is also impossible without customer satisfaction, and the good quality brand communication is the key to this. But how can company listen to all of the opinions and feedbacks left on the Internet? Analyzing the content of billions of user-generated blog posts today became possible with machine learning technologies that search and interpret social web content in order to find out the moods in the crowds.

The last word

The In-house and White Label solutions are the next big things if the tech evolution. The companies use them neither because of the hype around them nor because they want to look edgier, these technologies are successfully fueling ambitious startups that have limited resources for developing their own tech stacks. While White Label decisions allow joining the most revolutionary technologies through API, the In-House gets chosen more frequently by the companies that strive for independence and control over all their internal processes.

About Author: Oleksii Oliinyk embraces COO position at SmartyAds, before this he also worked at the company as a product manager. Oleksii managed to gain a professional experience in the variety of areas, including software development, business analytics, and project management. 7+ years spent in IT industry gave Oleksii a profound knowledge and in-deep understanding of the power of modern technologies for substantial business growth.

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