So far in our series of customer experience (CX) trends for 2018, we’ve highlighted the importance of cutting through the hype, as well as the top technologies CX leaders plan to use in 2018. There are two key pillars that make a CX strategy successful: technology and process. As we covered the former through two dedicated articles, we’ll now focus on the latter.

Investing in the right CX technologies is vital to ensuring proper use of organizational resources and achieving desired objectives. However, simply incorporating a new technology or upgrading an existing one isn’t enough to achieve Best-in-Class results, such as the ones highlighted in Aberdeen’s CX research. For that, companies must combine the right processes with proper technology utilization. Below are two process trends CX executives are increasingly focusing on in 2018 and beyond.

Customer journey mapping: In the CX Process Trends 2017 study, Aberdeen noted that CX leaders were planning to continue their focus on establishing the building blocks for omni-channel programs – defined as the ability to deliver consistent and personalized customer interactions across all channels. The same study also highlighted renewed emphasis on managing the entire customer lifecycle, made up of distinct journeys.

Aberdeen’s February 2017 CEM Executive’s Agenda 2017: Data-Driven Approach to Delight Customers study shows that 43% of companies currently have a process to map customer journeys. Data from that 2017 study also shows that more companies have plans to build customer journey maps. While journey mapping seems to be a priority for many CX leaders, many companies haven’t found ways to successfully manage this activity. For many firms, journey mapping is a one-time exercise, often outsourced to a third-party such as a CX consulting services provider who shares the maps with relevant stakeholders after a process that often takes several months.

Customer journeys, however, are dynamic; customer behavior evolves rapidly, and so do the related journeys. Therefore, to keep up with changing buyer behavior, companies must have real-time visibility into customer journeys. Only then will firms deliver truly omni-channel interactions.

Establishing real-time views into customer journeys is virtually impossible with one-time journey mapping assessments, as customer interactions often involve multiple touch-points, and firms have a wealth of data that must be analyzed to build accurate journey maps. However, using technology tools supported by machine learning and business intelligence allows firms to process vast volumes of data and build real-time views of customer journeys that employees throughout the business can use to do their jobs.

Automation: CX programs are getting more complex. Customer expectations evolve at a faster pace, and companies use more channels to interact with their current and prospective customers. In fact, Aberdeen’s October 2017 Omni-Channel Customer Care: How to Deliver Context-Driven Experiences study shows that 51% of firms use at least eight channels within their CX programs. Therefore, managing conversations across all these channels to deliver omni-channel interactions is no easy task. In addition, the resources available to manage CX programs often don’t grow in proportion to the complexity of customer experience programs. This means that companies must get more efficient in managing customer interactions.

Automation allows firms to drive such efficiency. For example, companies use data scientists to analyze self-service website visits and project the number of phone calls that the contact center will receive based on changes in the number of self-service interactions. This data is then used for scheduling agents. Automation decreases reliance on data scientists during this process by automating the activities involved in analyzing self-service data and creating or updating agent schedules. This means firms with automation reduce the time it takes to make critical decisions that impact performance, while also decreasing labor costs due to less reliance on data scientists.