• August 24, 2020

Is machine learning right for your business?

Is machine learning right for your business?

Is machine learning right for your business? 1024 576 DataLit

written by Marco Belmondo (Chief Marketing Officer at Datrix group)

Benefits of machine learning: is it right for your business?

Machine Learning (for short ML) is what everyone is talking about at the moment. But apart from its indisputable sci-fi flare, can it be successfully applied to your business? And what would concretely be the benefits of machine learning?

What is Machine Learning?

Machine Learning is a branch of Artificial Intelligence, the one that tries to make computers learn just as humans do. But what does “learning” mean? In very simplistic terms humans learn from experience, i.e. from data that they gather from the outside and that they use to make generalizations, extract patterns, construct complex conceptual structures. In a certain way computers, when doing machine learning, do the same thing: they use enormous quantities of data (training set) to abstract patterns and then make inferences and predictions using statistical methods. This may seem simple, but machine learning is at the moment one of the most promising fields of Artificial Intelligence: Machine Learning is what lies behind automatic suggestions by Amazon or Netflix,  face and speech recognition, automatic customer care chatbots, fraud analysis systems, self driving cars, and much more. 

What are the benefits of Machine Learning?

Machine Learning excels in extracting patterns from really big set of “historical” data (and in fact one of the reason of its recent super fast growth is the availability, thanks to the internet and also to the internet of things, of huge sets of “big data” – i.e. data that are too big, too rapidly changing or too diverse for a traditional relational database to handle – that can be used to train the ML algorithms), so machine learning will be incredibly useful every time you have to analyse some data set  (e.g. customers shopping behaviour, linguistic structures in written texts, stocks performance over time and given certain conditions, etc). 

The right Machine Learning algorithm can then automatically make predictions on future outcomes (e.g. infer what a certain customer is likely to buy, output a written text that is likely to have a high popularity into a certain target audience, etc). And all this is done with a speed, a precision and a depth that no human could achieve. 

Of course the first question you should be asking if you think of adopting a Machine Learning solution is if you have enough good quality data (at least thousands of data points)  to train your algorithm. Data should be coherent (not scattered sets, coming from different sources and that are too difficult to put in relation), relevant to your business problem and “complete” (i.e. including more or less all that is to the problem: if there are unexpected changes in the domain that you want to deal with the algorithm probably will not work anymore). If that is the case, you can move on, look for a ML model that is apt to you and start profiting from the benefits of machine learning.

Let’s have a look at some practical application of Machine Learning to real life businesses.

Programmatic advertising

Displaying ads only and exactly to the customers you look for (and maximizing the monetization of your ad spaces) is something that every business craves for. With machine learning technologies this is easily done: whether you are a retailer, a publisher or an advertiser DataLit.AI has ready made solutions that allow all businesses, even the smaller ones, to have their part of the benefits of machine learning. In particular publishers with DataLit header bidding technology can monetize their websites in ways that without ML would have been impossible, while retailers can monetize the data or their non-buyer users, competing in this with giants like Amazon or Uber Eats.

Advanced analytics and customer segmentation

You may have data, even lots of data, but be unable to use them to identify customer segments, because your data sources are siloed or because you don’t have enough and sufficiently skilled resources to analyze them. This may result in lack of monetization and in a poor customer experience. With ML based technologies such as the ones provided by DataLit.AI you could identify interests at a granular level and predict user behavior and purchasing intent, greatly improving both monetization and customer experience, delivering an almost personalized experience to each user.

Customer lifetime value prediction

Calculating the customer lifetime value (CLV) of a customer it’s not a difficult task, but when it comes to predicting it things mess up significantly. Given that you have enough historical data about your customers, this is a typical ML problem. Once you know in advance if the CLV of a certain customer risks to be particularly low, you can take actions to increase it before it’s too late.

Product recommendations for e-commerce

Once you have granularly segmented your customers you can deliver personalized recommendations to them: both things are nowadays done with the help of ML models. DataLit with the help of its advanced AI systems can deliver the advantages of machine learning right to your business by identifying and targeting customer clusters.

Text datalization and automatic summarisation

Think of transforming all the written documents of your company (emails, reports, presentations, memos, financial reports, posts on company website and social networks, contracts, even print materials…) into data that can then be used for financial analysis, legal studies, sales and marketing insights, etc. PaperLit, part of the Datrix group, has developed cutting-edge machine learning solutions that allow your company to efficiently use, monetize and make easily read and scanned through (through automatic summarisation) all the written texts of your company. 

Smart recruitment and human resources

Many companies already use automatic HR algorithms to select between the hundreds of cvs and a smaller subset of fitting profiles. Also, machine learning can be used to assess the performance of employees, how good they are at a particular job and the areas where they could excel.

Of course, these are only a few of the many possible ways in which machine learning models could benefit your business. And thanks to companies like DataLit these technologies are now available to anyone, not only to giants like Amazon or IBM but even to smaller businesses.

Real world AI: where algorithms shine

On the contrary on specific tasks involved with automating or optimising things (for example examining enormous loads of data and taking conclusions from that) AI can achieve results that humans cannot even dream of. So AI is used in medical research, a field where with its ability to examine at a light speed thousands possible design decision can really speed up new drug development; big e-commerce companies like Amazon use AI to optimize their warehouses and also to calculate how many drivers they are going to need at a given moment to fulfill the orders; financial companies use AI to scan through thousands of online transactions to identify possible frauds.

AI and humans: a reciprocal advantageous cooperation

But does this mean that AI taking over will end in machine learning replacing humans? On the contrary it seems that currently the most advanced research areas point to developing systems that help humans in doing their job better and with less effort, freeing them for more creative, human-needing issues. Of course some jobs will be lost, but more will be created and even more will become less burdensome and labour intensive.

An excellent example of this interaction human-machine is customer care: could you imagine how much a customer care agent could benefit from an AI that in real time recognizes what the customer is saying and proposes to the agent some possible solutions from which she can choose, improving greatly also the customers’ experience? Also, customer care AIs help agents improve their surveys and response rates, or understand the best time to engage community members…in this way humans are free to focus on more relevant and sophisticated aspects of their work, aspects that have to do with understanding, empathy, dialog and that only humans can truly deal with.

Another example is the ability of AI algorithms to analyze customers’ data on retailers and publishers websites with a level of granularity that highly skilled humans can achieve with only tremendous effort. The results of this analysis can then be used by human decision makers (advertisers, publishers or retailers) to better plan their campaigns or to monetize their websites in ways they had not thought possible before.

This is what Datalit.AI does: thanks to AI technology, DataLit.AI can predict users behavior profiling websites visitors with a precision unreachable without using AI tools; it can offer the most desirable ad space (the header) to multiple demand partners and then maximize the revenue by let them bid on it; it can help retailers sell their ad space by capitalizing on their non-buyer users (identified through the algorithm). Here it’s clear that AI is not overcoming humans but on the contrary is helping them in doing a better job in a shorter time. Of course the ability to use the results of AI elaborations remains at the moment an exclusive human ability, even though we cannot set certain boundaries to what the future may hold. 

Human role in human/machine interaction has changed: machines are no longer used only as a tool, they can solve problems. Humans have to define problems that the machines have to solve and to move on using the and building on the solutions they propose. So it’s perfectly clear that even a technology such as the one that DataLit.AI makes available is of no use if there isn’t someone who clearly defines a business problem or opportunity. AI without human intelligence is (still) nothing.