5 Machine Learning Areas That Can Accelerate Your Entrepreneur Journey

Machine learning (ML) is a discipline under the broader space of Artificial Intelligence (AI). ML is one of the most talked and sorted fields in the IT space. Everyone and every organization want to take advantage of the recent advancements in this field.

ML if explained in simple words, is a set of statistical algorithms put together to create a predicting model. ML predicts the future outcome by understanding the past data. ML can be based on supervised, non-supervised or reward-based models. And outcome (prediction) can be based on classification (e.g. true or false or good or bad or ugly) or continuous (series or range base) and clustering.

There are many ML definitions available on the internet, so I am not writing all of them here.

There are many applications of ML. E.g. weather forecast, video prediction (e.g. in Netflix), shopping patterns (e.g. Amazon), marketing response, etc. The list is literally endless and hence, let us only talk about the top 5 potential problems that ML can solve which has the potential to accelerate the Entrepreneur journey. 

Predicting / Validating the:
1.    Market segment – As you get into the market with your next big idea, you would like to understand and predict how the market would react to it. You can always apply human intelligence and make a logical prediction. But depending on the idea or business you are wanting to get into, there would be many parameters that dictate the outcome. It would be a mammoth task to consider all applicable features from a huge set of historical data. But by applying the right multi-class classification algorithm(s) to create a model, you can predict whether the response from the potential market segment will be good or bad or neutral. You can also predict size (range) using continuous / regression algorithms.
2.    Target demographics - It is important to understand and anticipate who, where and what your target personas (audience) wants. Predicting the right age range, gender, their likes and dislikes, preferences, geographical locations, etc. will play a role in the success or failure of the adaptability of your next big idea. And yes, while one can apply common sense to predict, but the scientific approach applied using ML will allow you to see many more potential outcomes and will consider the historical data in fullest.
3.    Marketing campaign outcome – Marketing the service offering or your product is super important. And you may spend a substantial amount on various online or offline marketing campaigns. But you can sense the benefit or return overspend only post or during the campaigns. But wouldn’t it make more sense if you are upfront able to predict the outcome of the campaign? You can do so by applying classification algorithm-based models to sense if reach or response will be high or low. You can employ a regression-based model to predict the response range (or whether lead conversion). You can then accordingly tweak the parameters of the campaign and upfront predict whether the response or probability of lead conversion is improving or not.
4.    Customer servicing – Need of the modern hour is to be proactive than reactive when it comes to servicing the customers. And if you would like to be proactive, predicting their response or reactions is key. We can employ ML to understand based on the historical data how typically a customer would react. While every customer is different but there are many common aspects where their expectations or reactions are similar. Hence, based on the varied expectations and service factors, one can predict a customer’s reaction. This edge provides you with an option to be proactive than reactive.  
5.    Core business levers – Everyone of us will have different idea and market. Depending on the business you are into, predicting the functional outcome can be also carried out using ML. These ideas are business or problem case centric. The core problem that you are trying to solve would be core to the ML model.


One common issue you will face is around quality data availability. Not only you need enough (quantity) data to train your model, but the spread of feature values and labels should be good. If not, the model may turn out to be biased over one outcome than the other.
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