Are you a data cruncher or a data phobe? Whatever your disposition, there's no getting away from the fact that data analysis has been, and always will be essential to every business and institution, even individuals.

Yes, individuals. Even going through our finances is the simplest form of data analysis. How much are we spending? How much do we owe? How much can we afford to put aside for a rainy day? What do we need to scrimp on to save more? etc. etc. Decisions decisions. All based on data.

Data determines the next, and what we envisage as, the best course of action. With the digital world exploding exponentially with more channels and touchpoints, information flows have increased. That means more data.

We've all heard about 'Big Data' There, I said it! But it's unavoidable. The ‘BD’ thing has been a challenge for many businesses. How to manage a lot of data, interpret it and act on it!

Think about your business. You have a website that may (or may not) have goals assigned to events. You may use social channels and emails that direct users back to your website or to a landing page in your domain. You may publish videos, white papers, infographics and more.

Now let's break that down into the data pouring in and remember that someone needs to analyse that data collection:

  • General website traffic
  • Bounce rates
  • Triggered goals and campaigns
  • Videos watched 
  • Social links clicked
  • Landing pages visited
  • PDF downloads
  • PPC Click-Through-Rate (CTR) / ROI
  • Email open rates and CTR
  • And more...

The list can go on but you get the picture. There's a lot of data analytics. Whether from your own CMS, Google Analytics, Matomo Analytics, Hotjar, Marketing Cloud, Mail Chimp etc. that data needs to be analysed to determine the best course of action to achieve your business goals. But imagine if you could have an idea of what future outcomes could be. That's where predictive analytics comes in.

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Predictive Analytics

Predictive analytics uses historical data, statistics and machine learning algorithms to make predictions on the likelihood of future outcomes. It's been around for a while but it's coming into its own for a number of reasons:

  • There's more data that can be analysed to gain valuable insights 
  • Computers have become faster 
  • Software has improved and is easier to use
  • Gaining a competitive advantage is harder and more important than ever 

So is predictive analytics a game changer? In short, yes. The reason is that businesses can use it to find solutions to difficult problems and to also uncover new opportunities that help gain a competitive advantage, in say, customer experience. Below are some uses:

  • Detecting cyber fraud:  There are loads of people that have been stung by a digital scam.  Predictive analysis can detect patterns that could lead to cyber security breaches and criminal activity. High tech behavioural analytics has the power to spot anomalies that may indicate potential fraudulent behaviour. This knowledge gives businesses a slight edge in combating fraudsters.
  • Marketing: Predictive analytics can help businesses create cross-sell opportunities, attract new customers, maintain loyalty, promote advocacy, and reduce churn. Algorithms can determine future customer responses to marketing campaigns, allowing marketers to produce awesome content most likely to improve the customer experience and influence user behaviour in real time. 
  • Streamlining operations:  Every business could do with being streamlined. The more streamlined it is, the more efficient it becomes. Businesses can use predictive models for forecasting inventory and managing resources. Airlines for instance, use it to set ticket prices. Hotels use it to predict guest numbers to maximise occupancy. 
  • Risk reduction: The best example of this is credit scoring. When you apply for a loan your credit data is checked to see whether you're worthy to be loaned to (or not). Predictive models use this data to determine your credit score. The higher the score the lower the risk to the lender, and the more likelihood you’d be approved. The irony here is that if you have no credit history i.e. you've never taken out a loan, then there’s no available credit data to analyse, hence your credit score is lower and you're more of a risk to lend to. But that's the way of the world. 

The vast majority of consumers have no idea what's really going on behind their browsing behaviour, clicks, submissions etc. And they probably don't care as long as they have a positive experience.

Businesses however must care. They use data science. They analyse consumers' behavioural tendencies with technologies like Customer Data Platforms, AI, Machine Learning, IoB, statistical modelling and predictive analytics to improve content and hone the customer experience so that maximum outcomes are achieved.

Predictive analytics has found its calling. As our digital experiences evolve so will the need to determine what we’re likely to do next to oil the wheels of industry. Scary thought? Or just another cog in an exciting future? Only time will tell…

Working with Codehouse

For over 15 years we've been helping our customers get the best from their digital projects, from web design and build to analytics and insights. If you have a project you want to talk to us about, get in touch whenever you're ready.