Journey analytics breaking down infinity

Denise Deveau, freelance journalist and frequent contributor to InsightaaS
Denise Deveau, freelance journalist and frequent contributor to InsightaaS

Just when you think you have mastered the breaking down of information silos within your organization, analytics throws additional wrenches into the works. Whether talking ecommerce or manufacturing, the plethora of data being generated by everything from smartphones to equipment sensors can strain even the most sophisticated of analytics capabilities.

A passage that is gaining popularity these days by the author of The Fault in Our Stars says. “There are infinite numbers between 0 and 1. There’s .1 and .12 and .112 and an infinite collection of others. Of course, there is a bigger infinite set of numbers between 0 and 2, or between 0 and a million.”

While this passage is discussing mortality, the notion of infinity within a finite realm can easily be applied to the analytics journey. In other words, for each data-related action, there are an infinite number of variables that can be contrasted, compared and parsed to find answers.

IBM has been putting its best foot forward on the analytics front in a number of quarters. The recent IBM Amplify event in San Diego, for example, provided an opportunity for the company to introduce what it described as “new cloud-based design and analytics capabilities” to help brands simplify the way they engage with customers. Under the umbrella of IBM Marketing Cloud, the company’s IBM Customer Experience Analytics is a platform that combines IBM Journey Analytics, IBM Digital Analytics and customer behavior analytics capabilities.

Labels aside, the whole impetus behind this approach is to cross the boundaries between enterprise functions (IT, marketing, design, etc.) to allow the business to follow activity from inception to execution and to actually quantify levels of engagement at every point along the way. A simple example would include data on a customer in a store looking at an item, checking comparable products via a mobile device, getting advice from family members and friends on social channels, and ultimately making a purchase using a tablet.

The most relevant aspect of all this is not so much understanding when an activity takes place. The more impressive part of it all is drilling down into the why, whether activity occurs in a retail or B2B transaction or around equipment performance or output in a production environment.

Deepak Advani, general manager for IBM Commerce, cited three types of analytics that are now coming into play: descriptive/behavioral data, interaction data and attitudinal data. “By assimilating these, we can get a feel of what customers are doing, how and more importantly, why they are doing it.”

A presentation spelled out the process for the audience:

  1. A retailer has developed and implemented a multichannel campaign to engage targeted customer segments. Analysis shows that while some channels show high conversion rates, the mobile campaign is falling well below projections.
  2. Drilling down into the data shows there is a high abandonment rate at the point of conversion – more specifically, three of five users were abandoning the transaction when entering the promo code on their smartphones.
  3. Going further back, following the most common paths that led users to the mobile site revealed that the problem customers had arisen on Facebook prior to users accessing their mobile app. Further investigation showed that the promo code cited in the Facebook promotion had an incorrect number in it, so over 4,000 of 9,000 mobile users entered a bad promo code.
  4. Armed with this information, the organization is able to target those 4,000 with an additional offer.

Achieving this level of insight is not without its challenges, said Jay Henderson, director, product strategy, for the IBM Commerce Business Unit in Boston, Mass. “Technology is making a lot more interesting things possible in driving customer engagement. But it has also created a lot of complexity that marketers and IT are struggling to manage. It’s more about ongoing dialogue versus shouting or sharing messages.”

As he pointed out, the customer journey is exploration, decision and purchase. “It’s a cycle that often repeats itself and doesn’t end with a purchase. A whole set of things need to happen downstream.”

A journey also implies there are infinitely more opportunities for things to go wrong, he added. “Take a really simple example. A mobile website has to be viewed on hundreds of devices with different screen sizes. The chances of creating an experience and things going wrong are pretty high. But with better visibility into where the struggles are, it’s simple to find and fix issues or understand their impact on the journey as a whole.”

This complexity is also bringing to light the classic struggle between IT and business within organizations. “There have been a lot of different attempts made to help bridge those two worlds,” Henderson said. “Whether you report to a CIO or CMO, you need somebody who deeply understands the discipline of marketing and how to apply technology to that.”

Mary Bunzel, worldwide industry leader, manufacturing for IBM Software views the analytics journey in a different light, but the end goal is the same: using new technologies to evaluate and understand opportunities and generate new revenue streams.

In a recent Fluke Measure of Innovation event, her presentation “New Frontiers: Industrial Internet of Things” also addressed the need for integration of data from multiple sources and platforms.

Given her playground is manufacturing, Bunzel naturally focused on smart manufacturing and the notion of condition-based maintenance on plant equipment. While it may sound pedestrian, the ability to overlay data from wide ranging inputs – from temperature and pressure to thermal imaging and prevailing weather conditions – promises to become a critical success factor for many operations.

“The focus on reliability hasn’t changed. Nor have the assessment tools,” she said. “What has changed is the need to use intelligent and analytics tools to uncover the process. In doing that you have the potential to create great insight into operations, quality and maintenance.”

There are countless benefits to be gained from being able to determine causality of failure even years after the fact. For example, what if an organization could connect product degradation back to an energy fluctuation during equipment startup or even environmental conditions such as temperature or airborne particulates? “Seeing how everything that is connected can impact product quality and putting that in the cloud is the Holy Grail. It’s no longer just theory,” Bunzel said. “With a single dashboard, managers can look for patterns and relationships people don’t normally see.”

Granted, this level of analytics capability looks impressive when being presented as a best case scenario at a conference. For most enterprises, however, it will take a building block approach to reach the level of execution on display at these events. Henderson advises looking to quick wins first, where organizations can apply a small effort and see immediate returns, while also developing understanding of where they want to go over the long term.

Where organizations are managing data across multiple channels, he suggests starting with one high value channel (e.g. mobile) to gain a deeper understanding of that aspect of the customer journey. “That may be a bit more manageable first step. Then you can add data from a complementary channel such as the website to achieve the next milestone.”

The thing to keep in mind is that the intent of all this is to get to the root cause of a problem, Bunzel said. “It’s not really about the software; it’s about the process.”


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