5 Things you Must Consider to Maximize the Value of your Company’s Predictive and Machine Learning Initiatives

19 April 2018

predictive analytics and machine learning

Investigating company for insights is a well known and widely adopted practice. However, using predictive analytics and machine learning is the next frontier in analysis.

The ability to predict future outcomes is what sets predictive analytics apart from other analytics used today, such as descriptive that gives answer to question “what happened” or diagnostic analytics that explains why it happened. PwC’s Global Data and Analytics survey has already proved the growing importance of predictive analytics among businesses stating that “modern executives want decision-making to be faster and more sophisticated”.  

Machine learning allows computer programs to make predictions or draw insights based on identified patterns in data and also improve those insights with experience without humans explicitly telling them what to do. Machine Learning allows going beyond internal data, combining knowledge from internal and external to drive new insights that were not previously possible.

The main goal of predictive analytics and machine learning is to take the guesswork out of understanding customers so that companies could maximize every sales opportunity.

There is a lot of preparatory work that companies need to do before implementing predictive analytics and machine learning to ensure that they can leverage the technology profitably.

Be ready to “fail”

Predictive analytics and ML require a different thought process than BI, that is already commonly used. Predictive mindset requires questioning the results of the model and asking whether they make sense. It is important not to take the results as a given. If something seems to be correlated, it doesn’t mean it is causing some activity. Predictive analytics also entails dealing with probabilities and uncertainty. For example, a result might state that the predictive strength is only 70 per cent. In some cases, no pattern at all will emerge using machine learning. And such “failures” are completely okay.

Articulate the problem

It is very important to understand what type of problem the company is trying to solve with predictive analytics. All the stakeholders involved in the process of predictive analytics and machine learning implementation must be on the same page when it comes to defining the problem. For instance, the company decides to start with solving the problems associated with customer churn. The whole team needs to come to mutual agreement  on how they articulate churn.

Other business problems that can be addressed with predictive analytics:

  • churn prediction
  • campaign optimization
  • recommender systems
  • revenue forecasting
  • dynamic
  • credit scoring
  • customer segmentation, and many others.

In order to maximize the value of machine learning to the business you need an ongoing collaboration between product managers and data scientists, where it is the responsibility of the product managers to ensure that the problems being solved are the most impactful ones for the business.

Have proper data in place

Quality data is a key to a successful predictive analytics and machine learning project. In large companies data is coming from different sources within the organization. It is difficult to link together the data from CRM system and operational system that are developed by different providers. Meanwhile, companies need to make sure that they create a single point of view for each customer in an organization.

Choose suitable predictive tooling

One of the large parts of work data scientists do is choosing appropriate tools and models for every business problem they get to solve. Data Scientists rarely start the work form building their own models from scratch, since there are a lot of models available built in source languages such as Python, which includes a programming language as well as a number of machine learning libraries are available, for instance, scikit-learn, SciPy, LightGBM, SpaCy, or Keras . These tools also provide interfaces to integrate with popular open source statistical languages, environments and APIs. Experienced data scientists always look for the best ways of customizing existing models to get better results, or, of course, they build their own proprietary models working on ground-breaking problems.

Work on your model governance processes

There are many organizations where business analysts are utilizing machine learning. Given the right tools and , machine learning models can be built by business analysts as well as data scientists. But here we are talking about the easiest machine learning tasks that can be solved using pre-built out of the box solutions for churn analysis, basic customer lifetime value calculations or demand forecasting. Successful organizations must clearly define which models make sense for the data scientist to build and which make sense for the business analyst. For instance, it is counterproductive for an organization to let business analyst build image-recognition system for top-level clearance using deep learning. However, it might be fine for a business analyst to build simple marketing campaign model.    

For organizations overflowing with data but struggling to turn it into useful insights, predictive analytics and machine learning can provide the solution. However, it is worth saying that since predictive analytics and machine learning systems may seem complex and challenging to build, it is easy to get focused on pure technology, rather than on your customers’ needs. Don’t let it happen to your customer-centric business.

Work with InData Labs on your predictive analytics and machine learning project

Have a project in mind but need some help implementing it? Drop us a line at info@indatalabs.com, we’d love to discuss how we can work with you.

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