Timing of Customer Journey Mapping

When should organizations practice customer journey mapping? Should it be done before the customer experience is built? There is a case for mapping the actual experiences after the fact and using the lessons learned to adjust the customer experience is strong. There is an emerging possibility of using real time emotional measures to adjust the customer experience based on the customer's reaction to the customer experience so far, which may include multiple touch points over time.

The Planning Approach:

Organizations that want to understand their customer experience and plan a better approach will use customer journey methods and technologies to actively create a better customer experience. This approach really works well with new systems and planned augmentations. The trick here is not to over-analyze, but to practically involve real customers, organizational representatives with a good dose of independent thinking.

The Audit Approach:

Organizations that want their analysis of the customer experience to be reality based, will use a measurement approach that gleans data from systems and people to create a near realistic view of the customer experience in context of the customers goals and persona. The challenge here is to think out of the box and imagine situations beyond the expected "happy paths" when designing a better customer experience. 

The Real Time Approach:

Organizations that want to adapt inflight, will gravitate to use technologies and techniques to measure the real time moods of the customers. This "on the fly" approach requires some newer emerging technologies such as AI enabled by emotion detection embedded in voice inflection, real time images and gestures or natural language understanding. This allows organizations to customize responses to individual customer situations in context. The challenge here is that the costs could get out of hand without creating some common responses over time.

Net; Net: 

The obvious answer is to use all three approaches, but that is easier said than done. The real time approach is still emerging and maturing. The planning approach might be too late if an organization has a legacy base that requires significant augmentation or change. Measuring what's going on might require significant instrumentation to gather the data. The answer depends on the situation at hand, but doing nothing is not an option. A customized mix is probably a solution, but this is not a "one and done" circumstance. We can't depend on getting lucky like the squirrel pictured above as we will all get wet with customer experience issues.

Dawning of Digital Disappointment

We have been seeing one article after another about how organizations are getting frustrated with Digital. It boils down to setting expectations. If you fell for the "digital will change your business model overnight", then I can understand why. Not every industry can expect a wholesale business model change overnight at this point in time. If your organization took an incremental benefits and learn approach as you progress digitally, you probably are still pretty happy even though you are not quite sure what the actual end game will be with digital. It may not matter because your organization is growing skills and competencies that will bode well for better customer experiences while creating greater effectiveness.

Let's Look at Rocket Mortgage:

Did Quicken Loans expect their product name to become more popular than their organization? Do they mind laughing all the way to the bank as they drew in more business even though they didn't really transform the mortgage business model while legacy applications of old still churn away under the covers? They added a very customer compelling front end leverage the best that digital offered at that time and put their muscle behind it. Customers received speedier and more visible results, but Quicken still does loans baby. This is practical application of digital in a focused way.

Let's Look at Taxi Service:

Many of  the taxi companies inspected themselves after the dawn of Uber & Lyft. They couldn't understand why customers would take a chance with unlicensed drivers just because there was an easy and visible app that linked you with drivers where you were. Once the traditional organizations realized they were in the transportation business that needed to wrap themselves around a better customer service model, many launched successful car services. Will it be enough to survive the jump start lead that Uber and Lyft have gotten? Time will tell

Let's Look at Industry 4.0: 

While the appeal of dynamic and customer adaptable supply chains whose complexity is buffered from the customer is great, there are incremental steps for new kinds of automation that do not require redesigning it all at once. More humanity laced automation in any piece of the supply or value chain will deliver benefits. Imagine a production line that watches workers and coaches them to better performance even if they are deep in the linked Industry 4.0 adaptable supply chain. While an end to end flexible supply chain that delivers custom product to the customer in the best way possible is still the goal

Net; Net:

Let's still keep our eyes and ears open for opportunities to disrupt with digital, but let's not forget that is not all "Turn Key". There is incremental value to mini-digital journeys that deliver benefit while making progress towards the ultimate digital destination. Maybe expectations need to be changed to match emerging reality.

Read more about incremental mini journeys and digital on ramps by clicking here

The Value of a Decision Management Framework

An organization’s ability to make excellent decisions will be a huge differentiator in today’s ever changing business environment. The days of leisurely decisions are numbered because of the speed and momentum of business. Also the amount of data, information that is available for any one decision is multiplying in an exponential fashion. More and more decisions will require the assistance from data science and combinations of algorithms with AI assistance. Aragon Research introduces it's Decision Management Framework(DMF). Click Here for the Research note. The basic dimensions are below. Click here for a free webinar on May 11th, 2018 with more details. 

While organizations will depend on people to leverage their experience and intuition in the best possible decisions, more and more decisions will be leveraging automated assists for human decision making. In the new digital world more decisions will be made in an automated fashion with significant consequences. In fact some decisions will be made at the edge without central control, so learning to manage these kinds of decisions that may be happening in an instant is essential.

Another factor being introduced in the much faster and smarter digital world is the ability to decide on making changes in the operational reaction. These changes could be implemented in procedures/processes, which in turn will help humans change. The ramifications of decisions and their downstream effects require a higher level of decision management.

Decision Management is an emerging discipline that needs a common base architecture, a shared methodology and a set of technologies. This research will seek to define a DMF that works for the large majority of decisions that will occur today and the foreseeable future. The resulting DMF will likely be used as a shared communication device for both business and IT professionals to describe the decision context, the decision components and the potential actions. The big reason is for better decisions consistently. 

Net; Net: 

Decision Management is an important emerging discipline so learning how to leverage a decision management framework will be well worth the effort. While decisions can’t always be purely methodological and scientific, having an organized approach is not only helpful in many situations and necessary in areas where there are lot of unknowns. This Decision Management Framework is important to keep in mind when making decisions and used as a communication device minimally. Technology markets are emerging to support these very frameworks and will be gain significant attention in the near future. 

Priority on AI Transparency?

Should we back off on AI to make sure it is transparent? Is there a way to balance exciting progress enabled by AI with the visibility necessary to make sure AI is not doing something wrong or immoral? AI is kind of a black box and what's happening inside is not only mystery to those using, but it is to the developers as it gets more sophisticated. Let's look at the some of the aspects of this issue.

The Case for More AI Visibility: 

How can we allow a black box to make important decisions? Can we really trust AI to make ethical decisions in a fair way? With programs, we can look at the coded logic and see the paths that can be chosen and watch the outcomes. With Decision Modeling Notation (DMN), we can see the decision and the results like programming. With AI we can't see the possible paths, reasoning and alternatives chosen from. Who can make sense of the inner workings of AI? Who do we hold responsible for the outcomes and ethics of AI? Would you trust AI with your life? AI is not flawless, so let's watch it closely.

The Case for Full Speed Ahead on AI:

Why should we slow down the benefits of AI while we wait for complete transparency? By a long shot AI algorithms are more accurate, by far, than human counter parts. AI can detect illnesses faster and can assist doctors with treatment plans. While some decisions are life impacting, there are a goodly number of decisions that are not life critical. Many AI investigations and actions can be logged and leveraged. AI should be tested like any other computer programming for a large number of possibilities. While the test beds for AI are difficult and sometimes near impossible, over time near perfection can be approached.

Net; Net:

Since AI will be involved with many decisions going forward, transparency will grow as an issue. If you want to hear more about decision management, AI driven or not, please sign up for a free webinar by clicking here  I believe that we can strike the balance by using AI to mine the audit the logs and actions generated AI activity. Let AI watch AI.