Follow the Customer

Automation Forges Clear Customer Journey Paths

Customer centricity is on the rise. Companies of all sorts are exploring ways to simplify the customer experience. And many companies turn to customer journey mapping as a way to understand the customer point of view to prioritize customer interactions to meet customer needs in a cost-effective way. 

Too often, however, journey mapping is a cumbersome manual process that takes time and resources to design and implement. Touchpoints are usually siloed and insight across channels isn’t integrated, leaving many care leaders to settle on mapping simple task journeys rather than optimizing the holistic customer experience.

There is another way. Advanced data and automation tools can be applied to improve the speed, accuracy, and customization of customer journeys. These tools create customer journeys based on actual data coming from  different sources available in the organization, including customer touchpoints, internal business support systems, and operations support systems.

Automated journey development nurtures better comprehension of valuable touchpoints during a customer’s experience. Remember, no matter how good a company’s planning and systems are, the unexpected can always happen. Processing, competition, and customer issues can impact the journey. You need to detect and understand what the customer is going through in near-real time, so you can quickly be proactive toward problems that arise. 

By integrating data tools, including advanced analytics, AI, machine learning, and robotic process automation with customer lifecycle management, value analysis, and ongoing customer engagement strategies, it’s possible to create an end-to-end, automatic approach to customer journey mapping. These journey maps provide visibility to  the entire organization about customer behavior and propensities, especially in customer engagement areas. 

Advanced tools leverage all the data available about customers to identify their existing real customer journey to understand the inefficiencies and reasons for unwanted interactions. This insight can then inform self-service strategies and other customer initiatives.

Setting insight into action
First, information gathered from direct customer interactions reactively identifies and helps the firm understand the reason for the call. Front-line associates can be coached how to address and solve issues in the most efficient way. Process automation plays a key role here, not just driving the associate action, but also improving the quality and the amount of data captured during the interaction.

A big issue is that companies do not always fully understand what the customer is going through. Obvious touchpoints include in-store activity, apps, IVR, website, and customer service. Yet too many companies don’t take the time to review the data comprehensively to understand what is happening in these touchpoints. 

Often, customers will not openly complain or address issues with a brand. This removes the valuable chance to address and remediate their problems and increase customer loyalty. More than often, a frustrated customer will just find a better service, leaving the company to wonder what went wrong. 

When a company initiates an automated journey exercise, it is doing the heavy work of tracking all the types of customer interactions and touchpoints that exist within a brand. This information is already available or generated by the organization, but it isn’t typically applied to understand what customers go through. It’s possible to take this information and generate value to improve knowledge of the customer experience.

This proved invaluable for a telecom company seeking performance improvement and proficiency. Its customer complaints represented a huge cost in terms of contact center operations. 

The transition toward machine learning was greatly needed for an industry like telecommunications, which services huge workforces and houses enormous amounts of data. Big data enabled us to use info that was previously impossible to take into consideration and analyze. For example, mobile telecom users call with questions about network quality,

disconnections, lack of coverage, dropped calls, etc. An automated journey mapping approach helped define the customer’s support journey to determine what issues would be best solved automatically and which would benefit from human response.

Automated solutions are best for repeatable transactions that are solved with the same steps each time. For the telecom, it determined that self-service tools could streamline simple journeys like router resets, as well as more advanced ones like credit adjustments using bots and business intelligence. It also developed automated proactive outbound customer communications for known issues even before customers noticed, explaining the problem and providing instructions to avoid or handle the issue. 

Human interactions should be saved for issues that require an advanced level of understanding, negotiation, reconciliation, and empathy with the customer. The telecom company empowered its associates to solve problems that don’t rely on scripts or stringent processes, including when to send replacement items, or how much money to refund customers. These decisions depend on the customer’s temperament and amenity to certain solutions, and should be handled by people who can judge what’s best for the customer.

A new path
A benefit of automated journey mapping is its dynamic nature. The tools constantly learn from continuous interactions. Companies can then proactively address root causes of interactions that we aim to eliminate, or fix issues that come up unexpectedly.

Oftentimes we see companies use journey maps to make a bad process easier, rather than eliminate the bad process entirely. Journey mapping shouldn’t be about making it easy for a customer to complain. The focus should be on preventing the reason for the complaint if possible or being proactive toward the customer on how to deal with the problem before the customer is aware of it. The sooner a company has the information about an issue, the easier it is to determine the root cause and eliminate it.

These essential analytics are funneled into the AI’s machine learning platform. It can tell you what is happening to the customer and why. The tools, along with expert customer strategists, refine and expand analytics for the AI to draw a better customer journey more quickly. Companies can now modify customer’s journeys, making them match the ideal customer experience and service. 

Time to think differently 
Too often, organizations still rely on manually processing information—you simply can’t talk about automated journey mapping if you’re still working this way. Manual processing can take months to complete, only to find out that the information gathered is irrelevant or has changed. Problems are never static, they evolve and adapt as clients interact with a brand that also changes over time. Automation keeps track of the flow of data, so you know real time what is happening to your customer base. Months of hard labor turn into weeks of seamless AI performance. 

Without advanced data insight, industries with large customer data repositories, like telecom, insurance, and airlines, will miss key insight in their journey development, and will likely pay for it. While there is still a need to rely on manual tasks for voice, messaging, and face-to-face interactions, automated journey development provides the data to bolster the initiative.

While it may sound naïve, plenty of companies bring in the latest tools and still cannot fully understand their customers’ journeys. They simply aren’t paying enough attention to what customers are really going through. Some businesses believe that with all existing systems and technology they have, the information and the knowledge will just be there, but this is not true. Key information about what customers experience may not be revealed easily without expertise, data, and tools working together to make it appear.

Each company has specific targets of what they’d like to achieve within customer journeys: revenue, customer spending, reduced costs, market share, or all of the above. With automation, be transparent with the goals you have in mind. Design the journey to focus on the problems you need to bring to light. In short, be clear about the issue your organization needs to solve and how you are prepared to do this with the tools you have. 

The result of putting together these capabilities inside an organization is a huge improvement in customer experience by reducing the effort required from clients and customers alike. Of course, this scales into an enormous cost savings because of a deep and sustainable improvement of the operations. AI and other tools, combined with a focused customer perspective, can keep the scale manageable while enabling better experiences for customers.