There’s been a lot of hype surrounding predictive analytics, but not many real-world examples to prove its worth.

So, we thought we’d dig a little deeper to discover more about this mysterious method. We have an inkling it will help improve CRO efforts in the future, so hopefully this will help you stay ahead of the curve.

What is predictive analytics?

Predictive analytics is much more scientific (and accurate) than peering into a crystal ball, or guessing an outcome. But don’t worry, it’s relatively simple to get to grips with, here’s our definition:

Predictive analytics: Analysing behavioural data to predict customer responses

There are three stages to the predictive analytics process:

  1. Data collection
  2. Analysis
  3. Implementation

OK, this might be oversimplifying things - as the analysis/implementation stages use a set of sophisticated algorithms and prediction models to predict the probability of an outcome – but you get our drift.

Predictive analytics removes all emotion from the decision-making process, as everything is based on hard data. This makes it much more accurate and reliable than a marketing manager hazarding a guess at what customers will do next.

How can you use predictive analytics?

You can use predictive analytics to optimise many areas of your business, however most companies (74%) tend to use it for sales and marketing.


Predictive Analytics Use Cases

Here are just a few instances where predictive analytics could come in useful:

  • Determining price – predict the top price your customer will pay, and present that on your site to optimise revenue
  • Customer queries – resolve problems proactively and offer relevant advice
  • Delivering content – support the customer journey, with timely and engaging content
  • Inventory management – stock products according to customer behaviour
  • Minimise fraud – detect costly fraudulent purchases by analysing browsing/purchasing patterns, as well as payment methods

There is a host of possibilities when it comes to predictive analytics. You’ve probably already encountered them, without even realising.

Two popular examples are Amazon’s product recommendations and Netflix’s content suggestions.

Both use predictive analytics to assess past behaviour (including purchases, likes, ratings), similar customers' behaviour, and your virtual shopping cart/watch-list to create highly-personalised suggestions.

Predictive analytics has been super-successful! According to a recent Forbes Insight survey, 86% of businesses who have used predictive marketing for at least two years have seen an increase in ROI as a result.

However, despite this apparent success, only 13% of businesses consider predictive analytics to be a critical part of their business intelligence strategy. Why? Well, it’s still a specialist tool at present, so not everyone understands how to use it or its capabilities.

However, we think this will change very soon!

How does it tie into CRO?

Predictive analytics helps look at all the different variables to generate the desired engagement from the customer”, and it does this by tracking and understanding customer behaviour. Marketers can then make changes based on this information, and consequently increase conversions and profits.

Sounds familiar? It should – it works in pretty much the same way as CRO!

Predictive analytics adds another layer to existing CRO data-gathering techniques. By forecasting what the customer will respond best to, predictive analytics gives CRO-nerds something precise to test, and the best chance of success – it’s a win-win!

Predictive analytics can also help CRO when it comes to audience targeting and segmentation. It brings together data from multiple sources, to determine a personalised promotion strategy that will work for an individual customer or segment.

It’s based on concrete data, so you can be sure there will be a strong chance of a positive outcome. It requires a lot of testing to achieve this, but it’s definitely worth it!

Ultimately, it also means marketers may contact fewer leads than they used to, but they'll get better results as they'll only target highly-engaged audience members.

A great example of this was put forward by PredictiveAnalyticsWorld, (catchy name). 1 in 3 US high-school seniors looking to go to college were using the same educational portal. The marketing team decided to use predictive advertising to match promo offers to their existing traffic.

They ended up getting a 25% increase in response rate – approximately $1 million of ad revenue every 19 months. Now, could you turn that down?

One more thing – this relationship isn’t a one-way street. CRO can help predictive analytics get a better rap. Remember, we said earlier that not everyone believes in its power? Well, improved conversion rates are a great way to illustrate the effectiveness and ROI of predictive analytics.

How to bring predictive analytics and CRO together

So, now you know why CRO and analytics should come together, here’s a little on how to get your predictive analytics system up-and-running alongside your CRO strategy.

Choose the right implementation method for your business

There are several predictive analytics tools and platforms that can be integrated into eCommerce sites. Here are a few of the most popular:

  • Site plugins – Springbot, Canopy Labs, Custora
  • Dedicated platforms – R, KNIME, Prediction IO
  • Full featured suite – SAS, Predixion, SAP

Choose one that will work for you and your company. Consider the amount of time you can commit to data management, your budget and the features you require.

Make sure you understand what predictive analytics is – in detail!

Having the right platform does not guarantee success – you also need the right know-how to back it up.

We’ve given you the basics in this blog, but you’ll probably need a data-scientist or analytics pro to come in and help you get to grips with it. You could even set up an in-house position dedicated to analytics data.

Without the proper understanding, you won’t be able to make the most out of predictive analytics, and it’s likely you’ll encounter many errors along the way. Seek expert help where possible.

Have a goal in mind

When you set out on your predictive analytics journey, you need a specific objective – otherwise you’ll have lots of data and no direction.

This goal should match up with your CRO objectives, as it should simply involve adding another layer of insight. Think about what you want to improve and apply this to both areas.

Double check the quality of your data

Your data needs to be of top-notch quality if you’re going to gain accurate and insightful answers about customer behaviour.

Take your time, ditch the junk and consult experts if you need to. It’s really important you get this right in the early stages, so don’t jump in head first before cleansing your data.

Compare your predictive data to data collected through CRO efforts (including insights gained from any tests you've run). From there, you can make a truly informed decision about your next step to bring more users down that conversion funnel.

Don’t stop!

Collecting data is an ongoing process – you’re always going to be able to improve on what’s gone before.

Keep analysing and testing new hypotheses using CRO techniques. Companies that are unwilling to test is one of the factors holding predictive analytics back, so embrace it! Unleash its potential by finding a test frequency that’s right for you, and work from there.

What does the future have in store for predictive analytics and CRO?

Forrester believes that the daring duo of predictive analytics and CRO will lead to a world of “hyper-individualised experiences”. And we totally agree – especially when we know that artificial intelligence is going to make its presence felt very soon!

AI will automate many different systems within marketing, including CRO and personalisation.

Tests will be carried out and changes made based on user behaviour patterns, without any human interference. Predictive analytics will play a key role in this, providing more data to inform the AI about the decisions it makes.

Any changes will be made in real time, creating an effective, engaging site that will encourage conversions right up to the point of sale.

We also think that predictive analytics technology will become more accessible. It’s currently a little pricey, but as the tech improves and demand grows, alternatives for smaller businesses will come through.

So that’s it – a rundown of everything you need to know about predictive analytics. Use it to create a highly-personalised site that speaks to the user’s specific needs and you’ll go far!