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Machine learning in everyday analytics

Intellicus > Data Science  > Machine learning in everyday analytics

Machine learning in everyday analytics

Organizations are capturing every bit of data that is coming their way. There is a variety of insights and patterns in this data that impact their business directly. Knowing these insights timely, and interpreting them correctly is important for any business to be able to make informed decisions.

Let us take example of marketing strategies of retailers. To be able to run effective advertising campaigns, it is important to get 360 degree demographic insights of their customers, and also study their transaction patterns. Customer interactions happen over multiple channels – in-store interactions, social media, online shopping, kiosks and more. Knowing customer behaviour across different channels and being able to correlate their purchase history with their demographic profile can reveal unexpected success for retailers.

If we are able to identify that a particular segment of their customers, may be as identified by age group, income, and time from last purchase, the exact segment could be targeted for immediate gratitude promotions on the right channels. It is in the best interest of the marketers to understand the patterns and correlations in the data generated by their customers. Unraveling these hidden patterns and correlations, can possibly change the direction of their business in future.

How do we identify these hidden wealth of information in our data?  This is where machine learning based analytics come handy.

Machine learning based analytics

Machine learning delves deep into your data, scientifically puts all the correlations into perspective. It creates models or a series of formulae, using the patterns of historical data and uses these patterns to calculate inferences for future. These inferences help you to connect the dots and take informed, futuristic business decisions.

The advantage with machine learning is that it can read tons of data and apply scientific and mathematical approach to study. It can handle data with very high complexity. It can identify patterns in high volume datasets with billions of data points with hundreds of attributes. It can track even the tiniest of the factor that could be affecting your business.

Predictive analytics with machine learning

The most beneficial application of machine learning is predictive analytics. Predictive analytics uses machine learning algorithms and statistical computing techniques to forecast future trends. It helps businesses identify opportunities, potential risks, and helps them to refine business strategies.

Consumer behavior and trends can change frequently. Machine learning can track, correlate and capture, even smallest of these changes. Predictive analytics can then use these learnings to give you a glimpse of their impact on the future.

In case of retail business, machine learning can be and is used for optimizing campaign cost and do targeted campaigns, altering pricing or allocating shelf-space and create strategies that deliver greater profit.

Is machine learning for your business?

Every business can benefit from machine learning and predictive analytics. Is it for your business depends on a sole important point: Does your business collect enough data?
Even simple sales and returns transactions data is a good point to start, if not all the mouse movements on an e-commerce site or eyeball movement in a retailer store.

The other factors of using machine learning is availability of compute power and data science skills. The compute process and selection of machine learning algorithms have been simplified by tools. Need of high-end machines is not essential for using these tools and need of a qualified data scientist is also not essential by simplified user experience.

Every businesses that has data can take advantage of machine learning based analytics to make strategic decisions and scale up their growth.

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