4 Steps to Deliver Actionable Insights with Analytics

Data is the new currency. But, its “spending power” is only as good as a company’s ability to obtain actionable insights from it. While there’s been an explosion of digital data – and web analytics platforms are getting smarter – many companies’ “spending power” remains low.

As our customers strive to unpack actionable insights from their data, they have a tendency to dive headfirst into the features of Adobe Analytics. Instead, we recommend starting at the thirty-thousand foot view: looking at your company’s highest level revenue-driving and cost-saving business objectives. By using a four-step approach to loop these business objectives back down to Adobe Analytics features, you can use the product in a more systematic and meaningful way.

STEP 1: Identify top-level digital objectives

At this initial stage, there are two key components to map out:

  1. Top-level key business objectives (KBO’s) that comprehensively cover all streams of revenue and efficiency.
  2. The digital-specific actions required to drive these two measures of value.

For example, one global leader in the hospitality industry we worked with had three focus areas with KBO’s for each area. These included:

For each KBO, we identified specific actions they needed to take to achieve the KBO. In the case of “optimizing value by growing bookings,” the hospitality company needed to act upon specific value drivers such as improving the navigability of the site and deploying personalization across the site.

Overall, by establishing a comprehensive list of KBO’s and digital initiatives, the company built a foundation upon which to structure their analytics approach. And, every analysis executed using Adobe Analytics is now in support of these over-arching objectives.

STEP 2: Identify key analyses and journey steps critical to achieve these objectives

Even after identifying actions to drive KBO’s, you have only set the foundation. In addition to aligning on internal objectives, it’s imperative to rally around the customer-facing point of view and pinpoint the journey steps you want to optimize.

While journey mapping and aligning touchpoints to phases is a common practice, what organizations do far less frequently in a structured, consistent way is create a master list of questions that would help uncover customer actions and preferences. The tendency is to instead preemptively decide on what customers need, without “listening” to their behavior first. This is not far off from the macro tendency to jump straight to Adobe Analytics product features without really having a basis for discovery.

Companies should craft questions that address how to improve the user experience as well as how to act upon value drivers like improving website navigability or personalization.

Looking again at our hospitality client as an example, we see that in each stage of the guest’s journey they ask key questions that are both customer centric and enterprise centric. For instance, in the research stage, one key question the company asks is: “From which types of pages on my site do visitors drop off?” This questions both helps them solve for “how do we make the logistics behind planning a trip that much more seamless for the customer?” as well as “how do we remove obstacles that prevent bookings to drive up conversion rate?”

The performance tracking that comes with each type of analysis might be owned by distinct business units. For example, the paid media group may own traffic source analyses and an e-commerce group may own site performance analyses. However, having one consolidated view of all analyses that occur across the customer journey will enable you to get a more currency from Adobe Analytics, including cross-functional alignment on what data is being analyzed, alignment on KPI’s, and a mechanism of accountability to address open questions.

STEP 3: Determine the best environment to execute each type of analysis

Traditionally, we’ve seen organizations, especially those with large, complex businesses, look at channels and journey stages in silos. This not only prevents a single view of the customer but results in inefficiencies from a web analytics standpoint – different business units often have varying levels of understanding of how to leverage a vendor software such as Adobe Analytics. This leads to inconsistencies in adoption across the organization, or sometimes duplication of analyses in cases where the organization has owned platforms on top of Adobe Analytics.

If you instead start with the master list of customer-driven questions that stretch across the journey, you can take a top-down approach to identifying how to maximize your analytics platforms, and who should be enabled to use the tools. In situations where the enterprise also has an in-house analytics platform, the first step is to determine which big buckets of analysis should you execute in which environment.

Keeping with the example of our hospitality client, in the case of conducting a traffic source analysis, using Adobe Analytics as the primary analysis environment allows for a full guest journey view – from awareness to guest booking. This is because the tool allows a user to track where site visitor traffic is coming from and what that same traffic does once they come onto the site. Additionally, the hospitality company should consider dependencies, such as needing consistent campaign classifications imported into Adobe Analytics, as analytics platforms are only as good as their implementation and data collection governance.

Overall, it’s also important to consider not just the Adobe Analytics-specific capabilities, but also the restrictions when determining where to execute your analysis. In the case of a service/reservations analysis, for instance, because the data contains personally identifiable information of customers, a client-owned environment is a better choice.

STEP 4: Review current adoption of Adobe Analytics and address gaps

Having aligned on business objectives, asked the right questions, and determined where you will be executing the discovery to answer those questions, you’re now equipped with a structured approach to using Adobe Analytics product features and functionality.

For instance, one national retailer we worked with to put this methodology to practice, was able to identify gaps in Adobe Analytics adoption that ultimately indicated uncaptured revenue-driving and cost-saving value.

In this example, the company evaluated the level of adoption of individual Adobe Analytics features and mapped these features back to the business objectives. Note that this process doesn’t end up as a 1:1 mapping of feature to business objective – there are some features that are used as means to multiple ends. One example is the Fallout Report. The Fallout Report is a tool that allows you to understand where visitors are abandoning the site. However, when mapping this feature to its business objectives, the retailer could see that it was useful for two business objectives – growing site traffic and growing site revenue. Had the retailer not started with a value map that clearly pinpoints the business objectives, they might have ended up with a whole bunch of fallout rates that they didn’t know what to do with.

From a traffic-driving perspective, understanding where shoppers are bouncing from the site might allow you to determine which visitor actions should trigger retargeting on owned and paid media channels. Cart abandonment is a trigger that is widely adopted by eCommerce players, but without analyzing fallout across the full journey, you might miss the opportunity to recapture visitors who have high purchase intent but never quite make it to the cart addition phase.

Identifying moments of fallout also help us drive revenue from the site by uncovering potentially broken or poorly structured site flows. For example, identifying increasing fallout rates overtime for a segment of visitors who use site search functionality could indicate a reason to explore improvements to the discovery tool. A drilldown on search queries by one sporting goods retailer revealed a trend of frequent searches amongst unsatisfied visitors for a cycling gear brand that the retailer didn’t carry. This discovery eventually influenced the retailer to begin to offer the brand on their website.

Increase your Adobe Analytics spending power

The list of business-specific use cases across Adobe Analytics features goes on and on. Eventually, you want to get to a place where the above logic is applied to all business objectives across the value chain. The goal is to have an understanding of which features map to which objectives, which features are currently underutilized, and how to capture lost value through client-specific application of the features. In this way, you are right where you want to be – combing through the analytics platform and understanding what you need to do to maximize the investment you’ve made. But, having started with the end goals instead of the means to the end, you are now better able to use the tool, drive impact, and improve the customer experience.

Special thanks to the contributions made by Adobe colleagues Katie Cook, Digital Strategy Associate, Rishabh Dayal, Principal, Digital Strategy Group, and Albert Bahar, Head of Digital Strategy, Travel & Hospitality.