How To Build a Predictive And Adaptive Customer Lifecycle For The Next Decade

The time has come; some key industries will be disrupted in the next few years at an unexpected speed. There is no time anymore for a slow and iterative change. From e-mobility to Carbon reduction, to the rise of robots and AI, there is a slew of threats coming our way. And one of the biggest challenges is an increasing demand for efficiency combined with a need for accelerated innovation.

The lack of human capital will become the first barrier to innovation, and therefore to growth, because employees stack into old fashioned, ineffective processes and get paid to sustain the status as is, instead of using the most valuable capacity they have: their capability to innovate.

So what is the answer? Well, this is a wake-up call. We have to get out of our cozy comfort zones to make radical efficiency changes. Or in other words, we need to make it fit or die. You need to unleash the potential of your employees to innovate processes and become flexible to harvest chances out of the disruption instead of being disrupted.

One part of the answer is customer lifecycle automation — it is not the answer to all challenges mentioned above, but it is a very important point, which I focus on below.

The Importance of Customer Lifecycle Automation:

Customer lifecycle automation should be a part of digital and corporate strategy to achieve a competitive advantage and be armed against disruption. The main goal of this is to make your business partially-automated and customer-centered, to maximize flexibility.

This means creating a somewhat automated loop where the starting point and end point all come back to the customer. To achieve this, it’s essential to involve the customer in the transformation of your company, in surveys, but also in workshops and other initiatives. There’s no more room for developing processes out of internal necessities and models that the company thinks it can benefit from that the customer doesn’t directly benefit from as well. In the long run, these will only turn into weak points.

That brings us to the automation side of things. To be automated, means to enable a process that is fully automated from customer back to the customer. It also means being able to run a business partially with AI on a near real-time basis.

So once we’ve understood what both of these important concepts mean, it’s time to develop a strategic framework where your company breaks down its objectives into Key Actions and Key Results (OKR). Key results should be broken down into measurable figures, which can be forecasted from the beginning to see how effective the strategy is implemented and if internal barriers prevent an achievement.

End-to-End Customer Lifecycle Analysis:

It’s important to analyze any existing?end-to-end customer lifecycle processes across all process levels, from one to five. To really understand where the gaps are, you need to analyse these levels because data supply chains are mostly generated on levels 3 to 5 and besides that you can see key parts of an organization’s (mal)function. Modern BPM tools can help to visualize and support finding gaps easily.

Challenges You May Face:

Due to traditional organizations, vertical silo processes might exist, and horizontal processes might not be transparent. Processes are controlled from the perspective of the internal area of responsibility, but not from the perspective of the customer.

Different business areas use the same terminology, but different meanings — the confusion about what is really meant by a certain figure must be solved. Many process steps do not generate data, or the generated data stays in a database not connected to any data supply chain, every step in a process must be digitally tangible.

Data Supply Chains:

Identify as many data points according to the process landscape to understand where data is generated and relates to each other. Data mining is possible if good data substance is available.

It helps to understand where processes take a long time, where there are gaps, where is the weakest link in the chain. Discover or create the uninterrupted data supply chains along with the processes. The processes are the muscles of your organization while the data supply chain acts as the blood.

Process and Data Supply Chain Optimization:

A new responsibility must be defined, from the vertical responsibility towards a horizontal responsibility, following the end-to-end process with the goal of a customer centrically optimized process and respective objectives. Starting point of the process modelling is the customer. From there the process is developed into the company.

You need a process owner as a customer advocate with a real understanding in person of customer needs. You also need to make field visits, look at real on-site actions of users streamlining all process steps to achieve a customer value. To further increase this value, identify the factors that drive the customer lifetime value in the processes and in the data.

Define smaller and greater changes that increase customer lifetime value and implement them into the processes, reduce all aspects that are reasons for customer dissatisfaction, as that reduces the average customer value, again get customers opinion to validate.

Define parts of the processes which the user or customer could do by themselves through easy digital configuration or self-service.

Customer Life Cycle Data Supply Chains:

Leverage existing and historical data and sort them according to the processes of the customer life cycle. Define new data points and relations between data to discover the end-to-end data chain with no breaks or gaps. Define in that data chain data points to measure the increase of customer lifetime value. Check the data flow from end-to-end in order to create expected customer lifetime value and the connection to your OKRs.

Challenges You May Face:

Now you are in a field of personal careers, and many arguments might come up. These might include: why this transformation is bad for the company. But clearly, personal interest is standing as the main barrier against the real objective: the sustainable growth and the survival of the company through the next decade. You will need a strong backing from the highest court in the company.

KPI and objectives shape the processes. Processes are often steered from the perspective of areas of responsibility and their vertical restricted objectives. This transformation is also a change of responsibility and goal setting in the company.

New processes are often rejected under pretexts because, in reality, it is often about changes in responsibilities, commissions, and areas of influence that result from a process change. A clear focus on customer value and customer centricity can help to steer the discussion in the right direction. So therefore it will help going back to the set of OKR in the beginning.

Start with the areas where the business indicated most of the value, in that way you can deliver early results, and integrate the business.

Digital Innovations:

Shorten and simplify processes where possible. Invest in customer self service through multi-channel following the rule “anytime everywhere.” Think of new business models like a purely digitalized product offer, where all is managed by the customer itself through a fully digitalized and standardized offer framework.

Integration of AI helps to anticipate and reduce human interaction in terms of uncertainty. Over expectation or under expectation of AI and a wrong understanding how much data and process foundation is needed to leverage automation through AI is a main challenge in the beginning of AI integration.

Reduce the individual processes as much as possible, and integrate new digital solutions into them. To increase speed, use a cloud approach and build a secure API. Combine and arrange existing standard software like hyperscaler’s web services in the cloud or other SaaS Software and use new technology enabled approaches for single purposes.

Define a highly secured central data lake in order to reuse existing data in many use cases.

Build these radical new disruptive processes in a parallel entity and let them grow to prevent long and exhausting internal change. Optimize the new world and let it grow until it can replace the old business partially, fully, or let it run in parallel. Explore the disruptive business approach by yourself.

Measure and Learn

OKRs:

Whenever a part of the data supply chain is freshly implemented, the data generates first insights. Check how this part of the customer lifecycle is performing and does it achieve the OKR expectation. New roles like data scientists are necessary in order to analyze and interpret the data correctly over a long run.

Predictive Analytics:

Start a new continuous improvement process to optimize the new process landscape based on the new data, even in a stage when only parts of the new digital ecosystem are implemented. In that way, you can take action directly. Based on the historical data and new data, analyze the impact of single data points to the customer lifetime value and create a prediction model that allows you to run scenarios instantly on any potential business question. Start to make data-driven decisions and take adaptive actions. A company that masters this skill will be ahead of all other companies in the industry.