In 2015, your customers demand an intuitive and seamless digital experience. How can you ensure that you deliver against this expectation? Consider the following scenario: Your firm creates a new digital product that is expected to dramatically enhance both its user and customer experiences. Everyone acknowledges the product will be a game changer. The development team works day and night to ensure it meets product delivery milestones dates. The product manager actively markets to and engages her customer base, fostering the expectation that this new product will be life altering. The big day comes when the product is launched to great fanfare and perceived success. At this point you may be asking why I say “perceived success”. I’ll answer that question with another question: what actually defines success for this product? Is it an expected uptick in revenue? Or perhaps it’s a specific target for customer adoption? Maybe it’s related to the total number of products sold since launch? I would challenge that, in today’s world of ever changing customer preferences, these traditional metrics are not sufficient to measure and enable sustainable success. Digital analytics offer valuable insight into customer behavioral data, measuring how (and allowing inferences into why) customers interact with a firm’s digital products. In turn, these behavioral observations influence key strategic decisions to enhance, refresh, or potentially retire a product. How can you ensure your firm effectively leverages digital analytics to create and manage its product portfolio? You can start by establishing your digital analytics foundation.
Digital Analytics 101
IBM recently estimated that humanity creates 2.4 quintillion (a quintillion is one billion one billion) bytes of data every day and that nearly 90% of the data that exists in the world today was created in the last two years.
24,000,000,000,000,000,000 bytes created every day
The vast majority of this growth can be attributed to the creation and expanding reliance on digital systems. Many firms view digital analytics as a decision enhancement platform driven by tools and technology. While that is true to some degree, the tools and technologies are not responsible for the value derived from digital behavioral data. They are simply the mechanism through which the data is collected and distributed to the analyst team(s). As with any analysis task, the actual value is created by the analyst’s interpretation of the data set delivered by the technology platform and their ability to subsequently craft an effective story around the data results. Thus the discussion shifts from the selection of the right technology platform (important…but not critical) to the creation and implementation of organizational processes required to effectively support a digital analytics program. Said in another way, you need to ensure your processes align with your firm’s analytical maturity such that they recognize the time and effort put into and the value derived from digital analytics. Here are three process related questions to consider when evaluating your firm’s digital analytics capabilities:
- Does your software development process (regardless of the methodology) include the evaluation, design, and implementation of digital data collection code for all customer facing functionality?
- Are there decision points within a product’s strategic roadmap that rely on digital data to determine the right direction to take?
- Is there a set of core digital metrics (vetted and agreed to by leadership) available for application to all digital product development work?
If the answer to any of the above questions is “no”, then work to establish a solid, sustainable foundation must be prioritized to ensure your digital analytics program has the best chance for success.
Let’s focus on the third question above, as it’s likely to be the easiest to address. What core set of metrics need to be established in order to ensure each digital product is evaluated on an objective, consistent, and actionable basis? The answer largely depends on the functional components of the digital product, however the below quantitative measures of digital success can be applied to most (if not all) product development endeavors.
Quantitative Measures for Digital Analytics
| Measure | Definition |
| Conversion Rate | How often are users/visitors of the digital product actually completing the desired action (e.g. buying car insurance)? You need a target goal here to make this metric actionable, and this is the only true KPI out of the group. |
| Unique Visitors | How many people access the digital product in a given time frame? |
| Visits | How many times did people visit (denoted by the placement of a cookie in the user’s device) the digital product within a given time frame? |
| Device/Device Type | What makes/models of devices (expressed as a %) are used to access the digital product? |
| Page flow/Pathing | What is the typical page or feature flow for users that access the digital product?
i.e. They start out in the home page, but where do they go from there? And then from there? Etc. This helps flesh out pages/features/functionality that may be ineffective, unexpectedly effective or perhaps not needed at all. |
| Bounce Rate | On what page/feature, and at what rate, are users exiting the digital product? Do they even make it to the desired action page? |
| Task Completion Rate | Are your users able to do what they expect to be able do in your digital product?
This data is often collected outside of your digital analytics platform, and is best facilitated via an in product survey (where applicable). The responses represent powerful data that measures how effectively your product meets your customer’s expectations. |
By evaluating each of the digital success measures individually, we understand the particular action or intent of the customer at that specific point in time. However, as we begin to analyze the relationships between these quantitative measures a holistic picture of your customer’s behavior and preferences begins to form. Once these behaviors and preferences are categorized and assigned value, product strategy decisions based on these analyses can be made.
Evolve with your results
The road to digital analytics success is ever evolving. Today you may feel confident that you have the right strategy mapped out, only to find that tomorrow the landscape has changed so dramatically, you have to go back to the drawing board. However, it is critical that you adapt your digital analytics strategy as the state of this domain and the behaviors of your customers change. The ability to be effectively responsive and to quickly integrate change based on objective analysis will serve as a primary differentiator between firms that realize success and firms who fall behind in today’s digital world. How are you measuring your digital product performance today?
