There’s an old maxim from the retail world: The customer’s always right. While so much has changed in recent months, this perspective remains vital. Whether rightly or wrongly, customers anticipate fast, reliable digital experiences, no matter what kind of stresses, changes, or demand spikes your teams may be contending with. No matter what, the customer’s digital experience must always be right. In this blog, we examine why delivering these optimized digital experiences is so critical and so challenging, and the key approaches to putting customer experience at the center of your operations.
The Criticality of the Customer’s Digital Experience
For some time, it has been through digital channels that business gets done. Everything from research to communications to commerce have grown increasingly digital. Interactions and transactions with customers, and the ways internal teams complete their work, have continued to get more digitized—and that was true before the onslaught of a global pandemic. Now, the urgency of digital transformation has only been magnified. Long-term prospects and even viability depend on an organization’s ability to deliver new digital services and accelerate innovation and adaptation to evolving demands.
At the same time, the reality is that there’s intense pressure to ensure each and every interaction is flawless, and that customer affinity, sales, and prospects for repeat visits can be eradicated by a single subpar digital experience. For example, a survey by 451 Research found 70% of consumers said they would switch brands or providers if the service they were using was slow or buggy.1
Therefore, one thing has to be front and center of any successful digital transformation: the customer’s digital experience. Put another way, the success of the most fundamental, large-scale digital transformation won’t amount to much if it doesn’t result in an ability to delight customers, every time.
The Challenge: Complexity and Data Volumes Obscure Visibility
For the teams that are responsible for the customer experience, it isn’t just the scrutiny and pressure that have increased. At the same time, the difficulty of tracking and optimizing service levels has also grown significantly in a very short time.
Today’s IT environments feature a complex mix of on-premises infrastructure and cloud services, as well as dynamic technologies like containers, Kubernetes, microservices, and software-defined networking (SDN). These complex environments continue to generate proliferating volumes of operational data. For IT teams, it is difficult to sift through all this data, understand what’s important, and respond quickly and efficiently.
This bourgeoning complexity makes it difficult to truly understand service levels, and more fundamentally, understand the impact on customers and the business. It’s easy to understand conceptually that the digital experiences customers have is a critical business success factor. However, when it comes down to mapping the specifics of infrastructures to the user experience, teams struggle. For example, a team can tell that the performance of an e-commerce server is degraded, but not know if that issue is translating to lost sales, and if so, what the exact revenue impact is.
Four Key Approaches for Optimizing the Digital Experience
As teams look to deliver optimal experiences, they’ll increasingly need to employ the following key approaches:
Embrace Artificial Intelligence and Machine Learning
Organizations continue to support growing numbers of digital properties. Given this growth, and the increasingly dynamic technology environments that support those properties, teams are contending with rapidly expanding data volumes. These data volumes threaten to overwhelm staff across a range of enterprises.
The imperatives for delivering optimized digital experiences, while contending with this explosive data volume growth, are significant, and a big driver for organizations’ adoption of artificial intelligence (AI) for IT operations, or AIOps. With AIOps, teams can more effectively sift through operational data, so they can find and fix issues faster, which addresses a critical need. As a matter of fact, 451 Research’s survey found that when it comes to the various types of organizational data to apply AI to, IT operations data was cited by the biggest percentage of respondents.2
Today’s teams continue to adopt new technologies, adding to the complexity that has to be navigated. With so many applications and services, and so much constant change in the underlying environment, existing teams struggle to keep pace with all the associated monitoring and management demands. Automation is, therefore, growing increasingly essential.
A 451 Research survey found that, currently, usage of automation is fairly low: 68% of respondents described their environments as mostly manual or highly manual.3 However, that appears set to change. The same survey found that interest in further automation investments is significant, with 74% planning an increase in spending in this category.
One significant area of early progress within many enterprises has been in the area of automated remediation. With sophisticated AIOps solutions, operations teams are starting to detect and circumvent potential issues, before users ever notice a problem.
Align Internal Skills Available
As organizations continue to be beset by rapid and radical change, an increasing strain is being placed on staff throughout the organization. Having the right staff, and positioning them to be successful, will be increasingly paramount. To meet the demands of modern environments, many organizations need to change internal structures, workflows, and roles.
In recent years, the move to DevOps has been widespread. A 451 Research survey inquired about the top reasons for making this move and uncovered these results:4
- Flexibility to respond quickly to changes: 43%
- More efficient use of personnel: 40%
- Faster software releases: 39%
As part of the move to DevOps, many teams are employing monitoring tools earlier in the development lifecycle, enabling staff to examine performance characteristics of code before releasing to production, which helps support faster delivery, while ensuring quality.
Site Reliability Engineering (SRE) is another approach seeing growing adoption. Through employing SRE approaches, organization seek to reduce the manual work associated with operations. AI and automation represent key enablers of successful SRE implementations. For example, teams are leveraging AI to automatically correlate alerts and speed root cause identification. Also, they’re employing automated remediation to reduce the burden of repairing common performance problems.
Align Customer Experience with Business Goals
As markets continue to grow more dynamic and competitive, a heightened premium is placed on decision making. Making strategic decisions based on trial and error, guesswork, and hunches is increasingly a recipe for disaster. However, a 451 Research survey found many strategic decisions aren’t being driven by data. While a majority of business leaders recognize the value and criticality of data-driven decision making, most still have work to do: Only 9% said “nearly all” strategic decisions are data driven.5
As part of becoming more data driven, teams need to establish capabilities to track and manage technology objectives within a business context. For example, instead of monitoring latency of a particular call, teams need to start tracking when shopping cart abandonments exceed a specific threshold. As part of this, it is vital to know at what point latency will lead users to abandon an application or shopping cart. By tracking performance against business goals, teams can prioritize anomalies that are most important and ensure the customer’s digital experience is at the center of all the work being done.
Particularly as the number of transactions and interaction types continues to expand, it becomes especially vital to enable teams to intelligently prioritize remediation efforts and investments based on business-level metrics.
BizOps as Complement to AIOps
While approaches like DevOps are helping to speed the delivery of critical new business services, it is this alignment with business objectives that remains a missing piece in many organizations. It is for this reason BizOps is emerging as a vital approach. An extension of DevOps and agile methodologies, BizOps is an approach that aligns IT and business leaders. This methodology helps put business outcomes at the center of everything, from value management to development to IT operations.
Now more than ever, the need to accelerate digital transformation and optimize the customer’s digital experience are urgent mandates. By establishing the four approaches outlined above, your organization will be best equipped to deliver against these objectives.