Predictive Analytics – How to Tell the Customer’s Story

What are Predictive Analytics?

Predictive analytics utilise statistical techniques including predictive modellingmachine learning, and data mining to make forecasts about future events.

For businesses, predictive analytics can exploit patterns found in historical and transactional data to identify potential risks and opportunities.

The predictive models created then guide decision making for future product development and marketing campaigns.

Predictive Analytics can, in addition, detect customer issues and fix them before they become problematic.

Tell the Customer Story

The discoveries of Data Mining and Data Analysis can be understood in terms of the customer story.

We can learn how the customers are interacting with a site and how they are responding to particular marketing campaigns.

Specifically we can understand the path they are taking to complete a conversion and identify the dead ends or bottlenecks that are preventing them from completing an action.

This is where the storytelling begins.

The ultimate goal is to understand exactly what the customer wants.

When simplified predictive analytics essentially boils down to a single yet solid theory, which is…

  • What is the Customer Story?
  • What Customer Story Does the Data Present?

Here is a TEDTalk from Joseph Pine taking us through the functioning style of today’s consumer minds.

 

 

Having listened to Pine speak about consumers on these terms we can begin to understand the perception of data experts as storytellers.

“Data are not just the numbers but pages from each of our online biography.”

Predictive Analytics and Marketing

Large businesses are already adopting big data analytics.

They are able to better aggregate, analyse and take action on the massive amount of data they generate.

The outcome being better decision making.

In an article on Big Data and Business IntelligenceAIRS present the following case analysis of the use of predictive modelling in large scale businesses

 

advanced predictive analytics

 

The graph illustrates that it is the task of the Data Scientist, the Business Analyst and the BI Expert to understand the consumer story too.

In marketing terms it is becoming more and more apparent that it is the work of the Marketer too.

The more efficient and detailed the analysis of the data sets, the more comprehensive the understanding of the customer psychology.

And we can take this further still…

With enough data provided daily and with enough analysis, predictions for the next set of customer behaviour become intuitive.

Through a deeper understanding of customers desires comes much opportunity for optimising marketing campaigns and developing future products.

 

“Having someone along, to deal with one’s quest is what every human wish they had.”

Now we have it…..

Anything Is Possible

With predictive analytics  we can understand what customers will need for a better life in the future.

By creating a picture of the trail that consumers leave, online businesses can now provide life enhancing products and problem solving solutions.

 

advanced predictive analytics

Guideline for Predictive Analytics:

  • Use the Data to perform Predictive Analysis and establish data sets of customer behaviour
  • Identify problems customers are likely to encounter in the future
  • Design the Wireframe of your solution/product
  • Run it on your existing customer base (with minimum interference to their interactions)
  • Establish 95% Statistical Significance (Can’t Bid On The Remaining 5%)
  • Go Global

2 Steps for Successful Predictive Marketing Analysis

1. Decoding User Behaviour

Understanding how customers behave is the engine that drives better product development.

Primary user behaviour analytics, such as clicks, demographics and focus regions are fast becoming  insufficient.

With the aid of Machine Learning (ML), it is possible to derive data insights that can deliver problems solving solutions before the problems arise.

It has given rise to a new set of deep personalisation techniques that includes:

  • Advanced Segmentation
  • Hyper-Personalization
  • Giving Omnichannel Experiences
  • Cross-Device Betting
  • Predictive Segmentation

and much more.

Predictive Analytics in Practice

So how do all of these techniques work in practice?

Let’s take a look at how Adobe applies personalisation to one of it’s sales funnel

Below is a typical homepage example from Adobe’s website:

 
applications of predictive analytics

 

Consider that a user that is interested in Mobile App Development now browses through the site.They may watch a few video tutorials on App Development, scroll through a number of Mobile App based features or read a relevant blog article.Then when they return to the homepage they are presented with highly personalised content like this: 

benefits of predictive analytics

 

This is how Predictive Analytics or Web Analytics Intelligence functions when applied to telling the customer story. 

Companies, such as Adobe, are now able to forecast user interest based on their interactions with the site and then present user focused content based on their behaviour.

 

2. Optimise Features/Products/Campaigns

As in the example from Adobe, insights can be acquired from real-time tools and Content can then be adjust to correlate with the customer story.

If the data illustrates a particular issue, such as a lack of functionality on the platform or a problem with one of the site’s features then, further testing can be made followed by the implementation of a solution.

 

best examples of predictive analytics

 

Venture Harbour discovered an issue with the webforms on one of their websites.

Using a Predictive Analytics Tool they discovered that users were dropping off at the point of sign up. They were then able to improve their sign up process and massively increase their lead opt in rates as a result.

Other tools, such as RapidMiner, GraphLab and IBM Predictive Analytics can extract precise user behavioural data and improve the existing customer flow.

Another scenario can involve conducting external user analysis for BETA testing products.

 

Predictive Analytics – Looking Ahead

A recent report from Gartner forecasts that by 2018 50% of consumer product investments will be focused on customer experience innovations.

Customers are unlikely to remain interested in duplicate innovations that require much understanding.

Instead the demand will be for results driven solutions that offer simple and actionable remedies for complex issues.


 

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