Predictive Analytics for Marketing in 2020 and Beyond

Predictive Analytics for Marketing in 2020 and Beyond

There’s no doubt that predictive analytics will play a bigger role in marketing for 2020 and beyond.

With the data that has become available since the dawn of the information age, the time has come for businesses to turn to data analytics, particularly predictive analytics.

However, businesses are facing challenges with big data. 95% say their data is unstructured and needs to be managed, and Forbes reports that more than 150 zettabytes (150 trillion gigabytes) of data will need analysis by 2025.

So how can you benefit from predictive analytics? The first step, is understanding it. 

What is Data Analytics?

Data Analytics is the science of mining and analysing data to make significant conclusions. This is an invaluable tool for businesses since it can reveal critical signals and trends that are otherwise lost in the mass of data. 

One of the top benefits for businesses is predictive analytics. It answers the question, “What could happen?”. This is an area of data analytics that involves data collection, machine learning, and prediction of what could happen in the future, based on historical data. 

Data can also be used for descriptive and prescriptive analytics.

Descriptive Analytics uses data to provide insight into the past and answer the question, “What has happened?”. Prescriptive Analytics uses algorithms to advise possible outcomes and answer, “What should we do?”

Who is Using It and How?

Companies are turning to predictive analytics to make use of the big volumes of data they’re generating and improve decision making.

Big data, augmented intelligence (AI), A/B testing, and quantitative analysis are just some of the trending topics surrounding this profitable business strategy. 

In fact, this innovation is no longer a trend in some businesses. It has become an established part of the operations. Predictive analytics has been around for years.

It’s used inside our social media and video recommendations, personalised e-commerce suggestions, spam filtering, credit card approvals and more. 

For example, YouTube uses it to recommend new video matches in order for people to stay and keep coming back to their platform, thus generating more ad revenue. Amazon is using it to personalise the homepage and other recommendations for each customer to increase the likelihood of repeat sales. 

The Field of Marketing 

The field of marketing is not left behind. Because of data analytics, marketers are now beginning to avoid the term “sales funnel” and instead, embracing terms such as “sales flywheel”, “sales cycle” and “lifetime value of a customer.”

Marketing doesn’t end during the closed-won stage anymore, but rather it repeatedly revolves around customer’s historical data.

However, “Australian organisations lag behind the rest of the world when it comes to extracting value from their data analytics efforts,” says Byron Connolly, Editor-in-Chief of CIO.

Connolly lists “risk-averse cultures, reluctance to experiment, and lack of recruitment and training of data specialists,” as reasons why Australia is lagging behind.

Why Use Data Analytics?

Why should marketers consider predictive analytics? Here are the top reasons:

  1. Determine which lead or set of leads have the highest possibility for a close deal
  2. See which customer or segment have the highest possible ROI so you can focus your marketing efforts
  3. Asses customer responses, their most-likely purchases or cross-sell opportunities
  4. Evaluate what future behaviours are possible based on historical data so you can prepare your strategies and supplies

Other than marketing, businesses can also benefit from predictive analytics in detecting fraud, improving operations, and reducing risks.

How to Integrate Analytics?

The question now is: “How can I incorporate this into my business?” In order to augment predictive analytics, there are four elements you need to establish:

  1. What are the metrics you’re trying to capture?
  2. How can you make sure they’re reliable for future forecasts?
  3. What software, agency or technology are you going to use?
  4. Who are the right people to work with?

One crucial element is knowing who to recruit as your Data Engineer or what agency to tap into that can work with your team.

The Data Engineer’s responsibilities include designing, developing and maintaining recommendation programs for your current and potential customers.

Additionally, strong skills in a related programming language or marketing software are imperative in the data analytics game. 

The Challenge

To gain a competitive edge, your business must start investing or reviewing its data analytics strategy now. Establishing the four elements of data analytics and working with the right people is crucial to succeeding in 2020 and beyond.

If you have any more questions or want to know more about how predictive analytics can help your company, drop us a line here or call us on 02 66 87 91 76.