How to win in the customer intelligence era
The complex omni-channel customer journey
Today’s customer journey is more complex than ever. While no two journeys look exactly alike, one example of a common customer journey might look something like this:
Imagine you’re a tech company that sells a new application to help consumers improve their fitness. The journey – from discovery, to try, to buy – could span weeks or even months and include a half dozen channels. Perhaps most customers hear about your new fitness application from a friend and search for it online. Arriving to the website via a paid Google search ad, they browse the site for information and pricing before downloading the app on their mobile device. After registering for a free trial, they begin to use some of the free features. A few weeks into their journey, after a series of email campaigns and push notifications, they decide the app’s value warrants an upgrade to the paid version. Nice! But then, as with many fitness apps, usage begins to diminish over time and churn risk increases. In fact, you start to notice a lot of customers are using the support chat function within the app. Suddenly, customer churn begins to increase even more. Now you have a real problem.
In today’s experience economy, the customer experience is the differentiator when it comes to driving customer loyalty and wallet share. This means the customer should be the focal point of your company’s analysis. Unfortunately, that’s not the case with most organizations. While investment in analytics has grown over the past several years, most organizations are stuck in traditional analytical practices. Every team within an organization is using analytics to address channel specific objectives. The customer care team, for example, might be worried about net-promoter score (NPS) while the marketing team may be more focused on campaign conversion and ROI and the product team is focused on feature usage and daily active users. Unfortunately, this siloed approach to customer analytics often results in organizations making channel-specific customer experience decisions outside of the context of the full customer journey. As a result, customer experience suffers – and customer loyalty suffers along with it.
Today’s customer analytics approach
In the case of the fitness app example, channel-specific objectives and KPIs don’t translate effectively to designing successful end-to-end customer experiences. While the product team might report strong usage on key features, customer support is reporting increasing chat volume and low NPS because of specific challenges with payment options. And at the same time, marketing is targeting high-usage customers with a new upsell campaign that promotes the “premium” app version. This type of disjointed experience can lead to customer frustration and, ultimately, the decision to cancel their subscription.
Customer journey intelligence
For organizations to effectively design and deliver the best experience to the right customer within the context of their journey, they need to have good analytics. Understanding where the customer is in their journey, the type of journey they are on, and the intended outcome is critical to deliver the right experience at the right time. Journey analytics is more than aggregating customer data in a business intelligence tool. Customer journey analytics requires the ability to stitch together time-series data from multiple sources at a 1:1 level in order to identify patterns and customer preferences. But the payoff for doing this is significant.
According to McKinsey, satisfaction with customer journeys can potentially increase overall customer satisfaction by 20%, propel revenue by up to 15%, and lower the cost of serving customers by as much as 20%.
Customer journey analytics provides answers to incredibly complex queries that attempt to understand the customer’s behavior during the most important touchpoints of their customer lifecycle. Here are six questions Customer Journey Analytics on the on Adobe Experience Platform can address:
- Which combination of channels and touchpoints, both digital and physical, lead to the best outcomes for a specific customer segment?
- Which touchpoints lead to a customer contacting support?
- What is the most effective channel to interact with the customer?
- What other paths were chosen by other customer segments?
- What behaviors and preferences define the type of customers taking each path?
- What touchpoints, both digital and physical, lead to customer churn?
The current approach is broken
Today, most organizations are attempting to address these types of customer journey questions using a variety of tools that were built for a particular analysis purpose. Unfortunately, the current workflow to address customer journey questions is inefficient and resource-intense. For example, to answer what seems like a relatively simple question like the sixth one from above, the process might go something like this:
The challenge with this customer journey analysis workflow is that it requires significant time and data science resources. What needs to be addressed in minutes often requires days or weeks with dozens of lines of SQL code. At one large tech company, it’s estimated that each new business question cost them as much as $1,500 in time spent. Additionally, the time lost by traditional analyst workflows results in missed opportunities for experience optimization, which often results in lost revenue either through customer churn or lost opportunities.
A better approach to customer intelligence
Adobe Customer Journey Analytics was built for organizing and aggregating massive volumes of person-level event data for real-time customer journey analysis, and that sets it apart from more generic offerings. It also bypasses the reliance on resource intense BI processes that require significant time and SQL expertise. Instead, Customer Journey Analytics enables teams to interact with full customer journey datasets to ask and answer data questions as quickly as needed. For instance, despite having one of the largest BI and data science functions in the world, a large multinational tech customer turned to Customer Journey Analytics because their frontline customer experience owners required faster access to insights.
Adobe Customer Journey Analytics is purpose-built for interactive, omni-channel journey analysis. In contrast to other tools, capabilities such as attribution, segmentation, sessionization and pathing are built-in and enable users to quickly discover and act on customer insights.
Winning in the experience era
To win in the customer experience era, organizations must reorganize their analytical practices around the customers and their journeys. Businesses must evolve to using modern customer journey intelligence solutions to understand customer behaviors and friction points across the entire journey so they can design and deliver satisfying experiences in the moments that matter and keep customers coming back.