Three types of analytics and how you can use them in your business

Three types of analytics and how you can use them in your business

Analytics and ‘big data’ are the buzzwords of the moment and with good reason. The ability for organisations to use their data to create models that predict consumer buying behaviours, thus enabling them to make fact-based decisions, is incredibly powerful.

Smart businesses know that by using analytics they can gain a much deeper insight into the wants and needs of their customers to build real competitive advantage.

The greatest benefit that data analytics can deliver to small and medium-sized businesses is quite simply to better understand the customer experience. This helps keep customers satisfied and, in turn, improve customer retention.

Using analytics can also deliver on the bottom line for SMEs. A Harvard Business Review study in 2012 found that data-driven businesses increased their profitability by approximately 5% to 6% compared to their non-data-driven counterparts.

Big businesses may have jumped on the bandwagon early, but as the costs and amount of technology required to turn advanced analytics into meaningful information have shrunk, so too have the barriers to harnessing their power. This means it is much easier for SMEs to introduce analytics.

Here are three types of analytics that you can use in your business today:

 1. Video analytics

The majority of video analytic systems work in a series of processing steps. As a fundamental first step, the content needs to dissect what is happening in the video, frame by frame. Video analytic systems work on these two key concepts:

  • Motion detection: By examining each pixel in the frame, the video analytics software is able to pick up even the slightest movement.
  • Pattern recognition: Objects are distinguished within a frame. Specific objects/patterns can be programmed for recognition and will be recognised within the frame.

Once analysed, the system then qualifies these changes in each frame, correlates qualified changes over multiple frames, and finally, interprets these correlated changes. Should any change happen, i.e. an object is moved, goes missing, or new object added, the software immediately recognises it and sends out an alert.

For smart businesses, this means they can see how much foot traffic they’re getting in-store, where the most popular areas of the store are and focus their marketing campaigns in those areas.

2. Predictive analytics

This becomes useful when we start asking questions in future tenses. Who will respond to this campaign, and for what product and through what channel? What are the potential values of each customer and prospect? Who will stop subscription of your service, and when would that be.

In terms of complexity, this is the most demanding type of analytics, so smaller businesses either have to have the appropriate in-house statistical or machine-learning skills or rely on pre-packaged solutions and external expertise to understand and to make sure the models used fit the business purpose.

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