What is Predictive Analytics, and why every business should implement it?
As of January 2019, there were over 1.94 billion websites on the internet. Over half the world’s population now have access to the internet and are using countless devices to do so. In 2019, the number using mobile has exceeded those on desktop with a small percentage utilising tablets as their preference. What does this mean? With so many options, businesses face an astounding amount of competition to gain customers. What worked traditionally will no longer be a market leading strategy as demands, trends and technology changes by the day.
Small to medium sized businesses need to find ways to keep up with the competition that won’t break the budget. While artificial intelligence (AI), machine learning and analytics might sound like buzz and hype to these businesses, it is that technology which will help them to succeed. In this article, we will look at one of those fields known as predictive analytics. Business leaders no longer need to be highly technical or have a degree in mathematics to reap the benefits of their data.
We will help you understand what predictive analytics is, why businesses need it, who uses it today and how it works.
What is predictive analytics?
There is a stigma that AI is only available to the tech giants like Google, Amazon and Facebook. However, the truth is that most tools for Big Data and cloud technology are now freely accessible to anyone. With a well-structured strategy, businesses can find cost savings in upgrading their infrastructure while being able to benefit from the insights and analytics in ways they never have before. Data that they previously couldn’t tap into suddenly becomes available in real-time with the potential for immense processing power to make business decisions through predictive analytics.
Predictive analytics is about using data, statistical algorithms and machine learning frameworks to identify the likelihood of future outcomes based on historical data. Whereas business intelligence teams have always worked towards telling you what has happened in the past, predictive analytics is focused on telling you what is likely to happen in the future.
For businesses without big resource and technology budgets, there are plenty of tools out there to deliver predictive analytics. That said, many applications of predictive analytics are designed to improve efficiencies, reduce costs and generate income. Even with some initial cost, firms can find ways to get a return on investment (ROI) from their predictive analytics strategy. This will become clearer when we look at use cases later in this article.
Why is predictive analytics important now?
The theories of predictive analytics have been around for a long time. Just think how many years you’ve been watching weather forecasts on television for as an early example. It has become more important now as everybody has access to it. In using predictive analytics, businesses can gain a significant competitive advantage in areas such as income generation, reduction of operational costs and business efficiency. There are four key reasons why this has become even more important in the last decade.
- Exponentially increasing volumes of data through omni-channel customer experiences. Businesses now have enough information to be confident in their predictions. For example, they will be making decisions based on millions of records and not hundreds as was the case during the AI Winters of the 1970s.
- The cost of computers is now a lot cheaper than it ever has been before. The same applies to storage structures like the cloud which as very scalable
- The tools associated with predictive analytics have become much easier to use. By that, they are not just easier for experts in the field, but even non-technical people can pick up the necessary skills.
- With so much online traffic, the economic conditions are tough, and businesses must find ways to differentiate themselves simply to keep in profit. Not changing isn’t really an option.
Applications of predictive analytics have shown the impact it can have. We’ll come onto those later in the article.
How does predictive analytics work?
We won’t go into huge technical detail, but predictive analytics uses and application of AI known as machine learning. Machine learning builds statistical models and algorithms that teach computer systems to perform tasks without explicit instructions. It does this using existing and historic data to train the models. As more data is added, the models can continue to learn without human intervention.
There are a few commonly used methods and two core types of models known as classification and regression. Classification is generally used to determine the likelihood of an event happening, usually tagging it with a 1 or a 0. For example, you might have an algorithm built to work out if it is going to rain tomorrow. The model will process all your existing data and label the next day with a 1 if it is going to rain or a 0 if it is not.
Regression techniques move away from labelling and predict actual values. For example, it could forecast how much a customer is going to spend in their lifetime based on the data in their profile. It does this by estimating the relationships between several variables to reach a conclusion.
There are a huge number of statistical techniques within classification and regression methods. Some you may have heard of are decision trees, k-means clustering, linear and logistic regression, gradient boosting and time series mining. The choice of algorithm depends on what you are trying to achieve through predictive analytics. Data Scientists will often spend their time testing several different methods to attain the best level of accuracy. There are systems that can automate that process as well such as Data Robot.
Business benefits of predictive analytics
There are cases for using predictive analytics in virtually every industry. The main benefits can be split into five categories.
- Business productivity can be improved by using predictive analytics for proactive responses. For example, data from sensors in factories can collect information about the performance of machinery. Analysts can use this data to receive alerts as to when a machine is likely to need maintenance rather than waiting for it to break down. Staff don’t waste time fixing items unnecessarily and there is no machine downtime.
- Marketing campaigns can be far more targeted using predictive analytics. Traditionally, marketing teams might bulk send the same email to every customer. With predictive analytics, they can gain insight from their communications and understand which customers are likely to engage with them. For example, those who prefer offers get an email with an offer and those who always like free delivery get a shipping discount. Targeted campaigns improve conversion and revenue while improving the customer experience.
- Social media is a prime example of predictive analytics. How many times have you seen a Facebook Ad and thought how much of a coincidence it is that you are interested in it? The ad you see is no coincidence and is based on data gathered from your profile and cookies, depending on what you have authorised. Facebook Ads can be used by all sizes of businesses and when targeted well, is very cost effective.
- Recommender systems are becoming more commonplace in retail and entertainment in particular. Predictive analytics can match customer data to a cluster. A cluster is a group of people who appear to share similar characteristics e.g. age, salary, location. If other customers in that cluster have bought a specific product, the same item can be pushed out to new customers in the segment. Amazon would be using this kind of algorithm when recommending products for example.
- Predictive analytics can help reduce business costs. An example might be in a call centre environment. Data can be used to forecast the number of calls a business will receive each day and ensure only the requisite number of staff are on shift. Other examples might be predicting system failures to proactively stop them or analysing patterns to negate cybercrime.
- Predictive analytics can give you a competitive advantage. Only you have access to your own data and it can tap into patterns about why consumers choose your brand. In knowing what they like and how they behave, you can get the right products to the right people at the right time.
- In some industries, predictive analytics is vital for reducing risk or detecting fraud. A good example is in lending whereby data can give an indication into the potential risk of a consumer wanting a loan.
How are predictive analytics used in practice?
Predictive analytics are being used in just about every industry. Below are some of the key examples to be aware of.
Financial Services
Predictive analytics is used to detect fraud and understand the potential risk of credit. As an example, Commonwealth Bank completes a fraud check on customers within 40 milliseconds of a transaction being initiated. Trading is a prime candidate for predictive analytics to forecast the best stocks and shares to invest in at any given time based on their likely future. Insurance companies use predictive analytics to decide how likely a customer is to claim and rate premiums accordingly.
Retail
In retail, predictive analytics is used to work out the best offers for different customers, the channel they like to use and the price point they are comfortable with. AutoTrader use data from their 40 million monthly visitors to understand their patterns in behaviour and create a customer journey and propensity model to maximise revenue. Predictive pricing platforms will combine your own data with that of competitors alongside market demand to recommend the right thresholds for your products.
Healthcare
Predictive analytics could be a game-changer in healthcare. Trials in Hong Kong have shown that data gathered through patient records and images can diagnose some forms of cancel faster and just as accurately as doctors. There have also been trials around predicting the number of free beds required at a point in time to ensure availability and care.
Manufacturing and Industry
The clear use case in manufacturing comes from what we call the smart factory. Using sensors to monitor all aspects of the supply chain, factories can improve productivity and efficiency while reducing costs. The global auto parts manufacturer Hirotec use predictive analytics to prevent system failures through real-time analysis of data to spot anomalies in behaviour. They achieved a 100% reduction in time manually inspected their machines. This more than accounted for the cost of equipment.
Education
In education predictive analytics can be used to create early alert systems of students who look like they could be falling behind the curve. Pastoral teams can reach out proactively rather than needing to wait for a student to fail. On a similar note, the masses of data collected can predict from a very early stage which students are likely to graduate. The continuous feedback opportunity of predictive analytics means students can get better support than they have ever done before. In August 2019, it was reported that 1,400 education institutions in the Georgia State of the USA are investing in predictive analytics.
Agriculture
In agriculture, we are entering an era of digital farming and predictive analytics is a key part of the strategy. Data collected using drone technology can be used to do pest modelling, optimise soil conditions and nutrient movements all with the aim of improving yields. If data can be used to improve production without a farmer needing to walk 20 miles per day over land, there are clear cost and revenue benefits. To this end, agri-tech companies like The Climate Corporation and Gamaya already offer data-driven agricultural insights that take soil type, seed suitability and local weather patterns into account.
Almost every industry has a use for predictive analytics. The examples above only touch the surface of the benefits with law, construction, sports, entertainment and public sector companies all having cases for the technology. Within these industries, there are sub-sectors that have their own unique way of using predictive analytics. Two examples of this come from the worlds of plastic surgery and dentistry.
Predictive Analytics in Plastic Surgery
There are a few ways that predictive analytics could be the new best friend of plastic surgeons. First, using data on existing patients could help surgeons to predict what type of surgery they might want next. Patients, after a successful procedure, are likely to return for supplementary surgeries that compliment their first. For example, Botox and dermal fillers are two that go perfectly well together. Predictive analytics can aide cosmetic surgeons by upselling the right services to their patients.
The key to returning customers is success. A study by Galanis et al in 2013 showed that the reason most people fear cosmetic surgery is due to the risk of failure. Predictive analytics can help surgeons by forecasting the outcomes of their procedures using existing data. For example, it could simulate a facial aesthetic surgery before the patient undertakes it to negate their fears.
Predictive analytics can forecast the time it will take burn victims to fully heal, through analysing the depth and surface area covered. This could be quite ground-breaking in a space that is typically very difficult to determine final results.
From an acquisition point of view, predictive analytics can be used to segment patients into the types of surgeries that they are likely to be interested in. For example, a research paper by Li et al in 2016, showed that patients aged between 19 and 34 accounted for 76% of surgical procedures and female patients are more likely to undergo Botox. In this case, clinicians can set clear target markets.
Predictive Analytics in Dentistry
In dental practices, AI and predictive analytics can be used to forecast the likely treatment paths of patients. For example, it could take their medical records and map them against existing data, detailing to patients what services they need. For example, if a patient has lots of fillings, it can tell them when they are going to need to have fillers.
With AI having the ability to take unstructured data like images and turn it into business decisions, dentistry is ripe for disruption. This is known as computer vision. For example, if a dentist takes jaw x-rays, these can be converted to data and modelled against millions of other images in real-time. The options for treatment can then be presented accurately based on real examples.
The larger the practice or network, the more data there is to work with, so the better the insights will be. For example, if in a large practice, you see a lot of patients with bone loss, you may consider buying an expensive dental laser. A solo practitioner who sees a similar trend might work with a specialist who already owns the equipment. Predictive analytics can help you find the best solution.
Summary
Big Data, machine learning and predictive analytics have gone from being a nice to have business function to a must have deployment to ensure businesses remain competitive. At a time where the technology has become affordable and scalable, small and mid size businesses can now integrate predictive analytics into their strategy in the same way that the tech giants do and do it on a shoestring budget.