If you’re involved in data science and forecasting, you’ll have more than likely come across predictive analytics. This term is becoming increasingly popular in digital marketing and it’s use cases in marketing performance predictions. Here’s our guide to predictive analytics, how it works and how you can utilise this in your business.
What is predictive analytics?
Predictive analytics are a set of advanced analytics which predict future events that haven’t happened yet. The method utilises many techniques like data mining, statistics, modelling, machine learning, and artificial intelligence.
How does predictive analytics work?
Predictive analytics are produced by reflecting on past data to find trends and the data’s current position to make predictions about the future. This can be done using a wide range of methods with some of the most popular being data mining, statistical methods, and machine learning.
This is the process of forecasting future outcomes and make decisions based on your prediction.
To forecast, you look at historic data and find trends and relationships within it. You try to understand what drives those trends. Then you assume the same relationships will still hold in the future (unless you have a reason to assume otherwise) and use those trends to predict future data.
A simple example of this would be, looking at historic data, summer was (nearly) always the warmest season, therefore we can predict that next year summer will be the warmest season as well.
How to use predictive analytics
Predictive analytics are valuable to a business as it reduces the need to be entirely reactive. For example, an eCommerce company needs to know how much a product will sell to have sufficient stock available. Predictive analytics can help to predict sales in the future so product shortages can be avoided, and revenue can be maximised.
Examples of predictive analytics
There are various sectors of the workforce where predictive analytics are vital and are used to forecast many important factors. Here are some of the examples:
Aerospace – In this instance, predictive analytics are used to predict the impact of maintenance on the reliability, fuel use and availability of aircrafts.
Automotive – They are used to incorporate component sturdiness and failure into vehicle manufacturing plans. Also, used to study driver behaviour to develop better technology for driver assistance.
Energy – Predictive analytics are used within the energy sector to forecast long-term price and demand ratios. They are also used to determine factors to consider like weather events, failure to equipment, and regulations.
Financial services – Used in this industry to develop credit models as well as predicting financial market trends and the future effects of new laws and regulations.
Manufacturing – Best for predicting the location and amount of machine failures. Helps to predict the amount of raw material needed to be produced and delivered based on future predicts on demand.
Law enforcement – Predictive analytics can be used in this industry to look at previous crime data to establish and estimate what areas need more police protection at certain times of the year.
Retail – They allow for retailers to follow an online customer in real time to track whether giving them more information or incentives will bring a likely outcome of purchasing.
Digital marketing – In our case, we use predictive analytics to forecast how a specific campaign will perform under target and budget constraints. Find out more.
Pros and cons of predictive analytics
Are prediction analytics good or bad, you ask? Let us break down all the pros and cons.
Pros of predictive analytics
- Allows you to stay ahead within performance
- Everything within your marketing-based endeavours; such as content, social media and PPC, requires time to plan and organise, and predictive analytics can help with this. It will take all your previous campaign data and forecast what could fail and what will succeed – therefore you can spend more time on the areas that are beneficial.
- Saves a lot of time and resource
- Predictive analytics allows for marketers to spend more time on alternative tasks as it predicts which marketing methods will give you the highest returns.
- Stops budget wasting
- Predictive analytics eliminates the risk that is associated with spending marketing budgets. If you understand and know which channels will perform the best, you are more likely to succeed.
Cons of predictive analytics
- It can be complex and intimidating to adopt
- Due to the complex nature of predictive analytics, many businesses are too intimated to adopt it because of lack of full understanding.
- You need to spend a lot of time on it for it to work effectively
- Predictive analytics can be difficult and time consuming to set up but of course, it will eventually pay off highly.
Predictive analytics tools
There are several predictive analytics tools already available, including Acxiom and H2O. In terms of digital marketing, BOSCO™ combines your internal data with predictive analytics modelling to forecast where to spend budgets for maximum efficiency. Book a demo to find out how BOSCO™ can help your business.