Using Predictive Analytics to Sell Smarter

Work smarter, not harder. We’ve all heard that one, right? 

Well, the same is true when it comes to shopping, although it should perhaps be modified to ‘buy smarter, not more.’ 

But in the age of consumerism it seems we actually spend a lot of time, and money, buying more but not necessarily buying better. This issue isn’t just relegated to the retail sphere, across almost every sector, the internet has opened up incredible opportunities—it also allowed a lot of grifters to create websites, products and services that look legitimate, but are little more than verisimilar junk.

Many people think the issue comes down to consumerism versus minimalism, demonizing the age of stuff and pushing to reduce consumption. The reality is, people do need to buy stuff and procure services and it can be hard to find the right fit, so people turn to places where they can find cheap, accessible products. Then, every spring they go on a Marie Kondo kick and chuck out a bunch of the accumulated stuff that never quite fit their need.

A myriad of small startups with rapid growth and uniquely tailored services (like Etsy) are proof that people do want high-quality goods and products that fit specific needs and wants, even if it costs more. 

People don’t want to buy junk, but often they either have no choice or are overwhelmed by the sheer volume of products on the market.  

This is where predictive analytics can really shine. 

In a general sense, predictive analytics refers to a variety of statistical techniques from data mining and machine learning to analyze current and historical data to make predictions. 

However, from a business standpoint, predictive analytics refers to taking aggregated data about a product (or products) and using this data to create new or better solutions. Predictive analytics can also be used to identify risks and opportunities.

Some examples of predictive analysis that we encounter every day include credit scoring, actuarial science behind insurance premiums, customer relationship management, cross-sales and health care (to identify at-risk patients). 

This technology is also useful for child protection: to flag high-risk cases, in disaster planning and currently in our fight against COVID. 

Clearly, predictive analytics usefulness has a huge scope, one which businesses, consumers, governments and the general public can leverage. 

As the network of the internet of things grows, we provide more and more data for companies to use to better their products and services. In doing so, we create a ripple effect that actually helps reduce the creation and consumption of goods that only halfway meet needs. 

Leveraging predictive analytics allows businesses to tap into the specifics of what customers want and allows us, as consumers, to actually declutter.

 

A good example of this is Amazon. 

Amazon is already using predictive analytics to create personalized product recommendations based on buying patterns—but you didn’t need me to tell you that—you likely already have a saved cart of things that have simply ‘popped’ up since Amazon thought you ‘might be interested in them’. 

Not great if you’re trying to stick to a budget, but nevertheless, good use of this technology. 

Ultimately, the retail industry sees nearly 4 trillion in sales annually, so it’s no surprise companies are using predictive analytics to ensure they have what their customers want.

And, it’s not just the retail biz where predictive analytics shines. 

 

  • Healthcare companies can use it to create more personalized care. 
  • Entertainment industries use it to curate content. 
  • Manufacturing companies can predict maintenance for industrial equipment. 
  • Cybersecurity companies can use it to help detect fraud. 
  • HR departments at any large company can use it to predict employee growth and aid in hiring.

 

The cool thing about predictive analytics is that you don’t need to be an industry titan to make use of it. 

Since many of today’s POS software systems are already great at gathering customer data and integrating with other systems, it is more about understanding predictive analytics and being able to apply it to your business model. 

Plus, using predictive analytics can help any business stand out from the crowd and give your customers what they need before they even knew they needed it.

With a global population of 7.8 billion, there is a huge world of opportunity, but as we move into a future where we must be more environmentally responsible, predictive analytics can allow businesses to ethically thrive.

 

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Morven Watt

Morven Watt

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