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Chain of Demand

How Predictive Analytics Can Change Your Business

We have been seeing it everywhere, buzz words from machine learning, big data, and predictive analytics. But, while these words have been tossed from right to left, entering and exiting from our ears, the questions remains just what and why are predictive analytics and machine learning so important to our business?

 

Simply put, these technologies are what helps you as a business become closer to the psychology of your consumer.

 

As everyone knows, the advent of the Internet and the advancement of computing power has allowed the world to witness the sheer power of what predictive analytics and machine learning can do. Just as the field suggests, making sense of data is a science, and in that, more than any other time in history, businesses are able to better understand what it is our customers truly want.

 

While there may be a few who wonder just where this all came from, predictive analytics has existed since before. Whether it be from calculating our credit scores to payment history, these insights are something businesses have meddled with from way back.

 

However, up till now, there was either not enough access to a variety of data sources (due to the lack of the Internet) or our machines were simply not strong enough to make powerful decisions. Well, all of that has changed.

Why Predictive Analytics Is Critical for Your Business

Besides better understanding customer analytics with greater detail, there are a plethora of other benefits that investing in predictive analytics and machine learning solutions such as Chain of Demand can do for you.

 

  • Lower costs – Due to the sheer speed of predictions that machine learning algorithms help with, this can streamline the decision-making process. Furthermore, with solutions like Chain of Demand that are geared toward retailers, a business can optimize prices and prevent inventory waste by predicting demand for specific products before the order.
  • Fewer resources – Similar to the point above, predictive analytics has the ability to calculate and recommend action that helps to save on costs, as well as time.
  • Faster results – With the help of a multitude of data points, the right machine learning algorithms have the ability to notice future trends. For example, with traditional forecasting, the average time it takes to make predictions is 6 weeks and average accuracy in identifying best-worst sellers is 55%. However, with algorithms like our own, it only takes 2 weeks or less to make predictions, and the average accuracy rate in the best-worst sellers is 85%.
  • Greater marketing efforts – By examining analytics and seeing deeper insights that would have otherwise been unseen by the human eye, the marketing team can plan and execute their marketing campaigns more effectively.

 

With all these benefits that harnessing the power of predictive analytics has brought for businesses, it is no wonder interest to use ‘big data’ has grown over the last few years. This has been shown to be evident from a graph by O’Reilly that shows data from 11 large Meetups targeting business analysts and business intelligence users’ interests (above).

The Different Use Cases for Predictive Analytics 

Over the last few years, there have been plenty of companies that have seen the upside of implementing predictive analytics into their business. All of the points mentioned above give further support of the reasons why but understanding how it is being used in the world today could give better insight into companies looking to shift their focus this direction.

 

According to Forbes, the number of companies that have been adopting big data analytics had risen from just a mere 17% in 2015 to a whopping 53% in 2017. One of the primary early adopters were seen to be from the finance and telecommunication industries. While most companies that have been adopting big data analytics and harnessing its power are of larger scale, many companies are looking to use this for data warehouse optimization, customer analysis, and better predictive decisions.

 

How Big Data is Affecting Travel & Hospitality

While IT and finance companies remain the early adopters to this type of technology, since 2018 roll around, travel management, airlines, hotels, and other companies related to the hospitality has also been finding ways to make use of the big data boom. Over 65% of travel-related businesses have begun to put focus into their data analytics team, as illustrated by The State of Data in Travel Survey 2017 report.  

 

Additionally, with the rising trend of the digital consumer, it is only natural that with each new customer, there will be a trail of data left behind, from the point of purchase to preferences in what type of area they would like to. Online travel agencies (OTA) and a myriad of booking platforms are helping gather more information about the way people travel, and in turn, will ultimately result in greater use of big data analytics. As a report by Accenture notes, “Powered by AI, the next wave of solutions will gather unprecedented amounts of data from disparate systems via the multiple touchpoints the traveler has with providers.”

How Big Data is Affecting Media & Entertainment

For those who spend their long and tired nights watching Netflix or on YouTube, we’ve already begun to witness the power of machine learning algorithms and data-driven alterations being made before our eyes. Recommendation systems to predict what we may enjoy watching or what we should watch next are just a few of the example of how artificial intelligence, and in turn, big data is shifting consumption of media.

 

As mentioned above, one of the things that allowed the motion pictures titan Netflix was able to leverage was analytics to better understand what their customers wanted. Based on their analytics of what genre was most popular to which customer, this helped them gain a better grasp on which types of content to release. This knowledge, in combination with their machine learning recommendation engine, served as the driving force of their success, as they discovered that over 70% of the viewer activity was impacted by what was suggested.

 

 

This type of use of their data points to make predictions and recommendations was no different from the music streaming service Spotify. They too managed to utilize the information they received from their users to make greater decisions on creating a better experience for their listeners. They took this one step further to create Spotify for Artists, which allow the artists to examine the analytics for their songs. Such insight can help the artist cater more to their audience.

How Big Data is Affecting the Health Industry

Beyond catering to our consumerist behaviors, one of the more impactful ways that unleashing the power of predictive analytics has brought us is the healthcare industry. Those in the healthcare industry are using data points across the board to reduce costs of treatment, but also make predictions of epidemic outbreaks, as well as increase the quality of life as a whole.

 

For example, while in the past, many physicians would use their judgment to decide on when and what to treat their patient, the rise of predictive analytics now allows to make more data-driven, evidence-based decisions. This process typically involves aggregating clinical data sets to make the best decision, a process that has been noted to detect the smallest nuances which might have been ignored from the human eye.

 

 

Whether it’s helping the finance industry through identifying fraudulent activities or any of the other ways mentioned above, there is no doubt that predictive analytics is having a big impact on the world both presently and for the future. Besides the banking sector, which has invested approximately $20 billion in 2016, retail is another sector that has greatly seen a surge of a boon from big data. From an increase in sales to inventory optimization, there has been a list of benefits that retail has seen across the board.

The Changing Landscape of Retail

More than any other industry, the changing demands of technological forces have probably influenced the retail sector in the biggest way. With more consumers moving to digital to do their shopping, there has been an array of closures of stores across the globe, from small to big brand names. For this reason, it is

Facing Limitations – Why Everything is Not Gold

While the use of predictive analytics might appear like a magic formula that may lead to increased profits and better business, there are some drawbacks that every manager or CEO needs to keep in mind when inevitably implementing the right technologies.

For one, your predictions are only as good as how well you use them and the data you provide.

 

Just as stated above, predictive analytics does have the potential to create massive gains, but if there is not enough data, or the sampled information is incorrect, inaccuracies can occur.

 

One popular case of this was from the Hilary and Trump campaigns, where predictions were made that Trump merely had a 15% chance of winning, unlike Hilary which only reached 85%. In the end, everybody knows by now how that turned out.

 

From the case of Hilary versus Trump, it was discovered that most of the data from the polls were not reaching all the likely voters. For example, reliable news sources that were more in support of Hilary such as Huffington Post were seen showing a higher percentage in the likeliness of Hilary winning, which ultimately can skew the data.

 

Although politics and business are slightly different in the way data is collected and viewed, there are still many things to consider when attempting to harness the power of predictive analytics.

 

For example, many uninformed users may assume that the use of historical data to make the predictions will result in an accurate outcome. However, this is under the assumption that consumer behavior had not or will not change. Charles Duhigg, the Pulitzer Prize-winning American journalist wrote in his book The Power of Habit, commented while in people generally do tend to stick with strong patterns of behavior over time, they still do and may change, which means the models of predictions should be different with passing time.

 

Simply put, what worked before may not be accurate for the future. Thus, this is where the risk for error lies, as if the company does not input up-to-date data sources, entire predictions could be wrong.

The Old Versus the New

While there are plenty of reasons why investing in predictive analytics would be worth it, sometimes, for smaller companies, they simply do not have the necessary funds. Whether they do not have any data scientists or machine learning engineers in-house or are just starting off and don’t have enough data points, there will always be those that will choose to stick with the old, rather than embracing the new.

 

The use of artificial intelligence/machine learning and analytics for business intelligence may be new but are not by any means concepts that have just popped up. Businesses have been using statistics and analytics to give them insight since long ago. However, with greater computing power and more refined models to work with, there are definite differences in the output.

Just because you are focusing on big data, does not necessarily mean the results will be the same as working with custom machine learning algorithms. The table above outlines some of the biggest differences between traditional predictive analytics versus the more modern machine learning-data analytics that is performed today.

What You Can Do About It

The world is changing rapidly and with it, retailers cannot expect to rely on the same methods that have kept them going. Consumer behaviors have shifted with the advent of technology, and with AI developing year by year, it is imperative that brands act immediately before falling behind.

 

As mentioned above, not only do predictive analytics solutions save costs in the long run, it boosts overall margins through various way, from price optimization to inventory management.

 

Gone are the days where you as a retailer are stuck with piles of wasted stock never sold.

 

While the fear of change may be looming before you, in that transformation comes opportunity. From banking to retail, the world is only just beginning to witness the true power of machine learning and predictive analytics.

 

As the fashion trends of yesterday, it is only those who choose to meet the changing demands quickly that will hope to increase their business efficiently.

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