The good news is that investing in digital insight management doesn’t have to cost money; the better news is that this investment more than any other unlocks the value of digital analytics within the enterprise, facilitating the shift from analytics as a cost center to analytics as an incremental profit center.
Regardless of the exact challenges you face when it comes to translating raw data into actionable recommendations, the development and application of a Digital Insight Management plan is the first step you need to take. A tailored plan will give senior leadership both confidence and reasonable benchmarks against which to measure progress towards overall better use of the digital measurement investment.
Tip #1: Clearly define ownership for analytics
Give your organization a chance to succeed with digital analytics and assign a senior, analytically minded executive ownership over the people, process, and technology required. Empower him or her to create and manage the execution against a Digital Insight Management plan and, assuming he or she is successful with that effort, provide compensation commensurate to business value created.
Tip #2: Plan to add analytical expertise
Plan to hire at least one experienced analyst if you don’t already have one. Unless data and information is clearly presented in a business context and coupled with clear expectations for how the data should be used, the most common case is decisions made without support from analytics. The real value of digital analytics isn’t unlocked until human effort is applied during the creation of insights and recommendations. Almost without exception, Digital Insight Management requires experienced analysts and, if your organization is still not invested in analysts capable of translating data and information... stop reading and create a hiring plan.
Tip #3: Establish clear expectations for the use of data and information
Business people are often simultaneously spread thin and lacking in expertise when it comes to digital analytics and data. Given this, unless data and information is clearly presented in a business context and coupled with clear expectations for how the data should be used, the most common case is that decisions are made without support from analytics.
Tip #4: Learn from past experiences
One of the most common mistakes companies make, regarding the use of digital analytics output, is failing to effectively learn from past experiences. The best indicator that this is a problem is a lack of awareness about the impact of changes well after the change has been made. As changes are continually made, this effect is multiplied, and the result can be potentially catastrophic if you are not paying close attention. Fortunately, there are a handful of simple strategies for ensuring this does not become a problem: perhaps the easiest is having a structured process for keeping track of changes so that they are accessible to everyone else in the organization. Another, somewhat more involved, strategy is requiring your analysis to conduct a “look back” analysis between 30 and 45 days after a substantial change has been made. The key is having robust process and/or workflow in place to remind the analyst of the need to actually do the work.
Tip #5: Define a “hub and spoke” model for analytical support
Analytics and analysis is a concerted effort that requires specific expertise and systems knowledge. The effective use of data does not scale to the level of the modern business, if that expertise and systems knowledge is closely held by a small number of individuals. Web Analytics Demystified’s “Hub and Spoke” model creates transparent ownership, action, and accountability for data consumers and analytics systems users across the entire organization. By assigning a core set of responsibilities to a centralized analytics “hub”, designed to support a business-wide capability for analytics, companies are able to develop specific expertise for analysis and the generation of insights and recommendations. The analytics “hub” has the time to produce this higher-value output, because the organizational “spokes” take responsibility for developing competency on appropriate systems, such that they are able to produce and evaluate their own data and information.
Tip #6: Create a consolidated view of your data
One of the most frustrating things for analysts and data consumers alike is fragmented data - the end result of multiple systems collecting information on the various independent aspects of any consumer’s digital interactions with a brand. The best platforms will give you the ability to not only aggregate data but also create new metrics from multiple data sources. For example, combining “Average Page Load Time” from a performance measurement solution like Keynote with “Total Page Views” for the same page collected via Google Analytics, giving the reader a weighted metric to explore page performance impact for the most popular pages on the site. Jump-start your Digital Insight Management efforts by evaluating how you currently consolidate digitally collected data and, if you find you are not doing so effectively, research third-party platforms and solutions that facilitate the necessary data movement and aggregation required.
Tip #7: Agree on key measures of success
It is still common to find business units launching campaigns, sites, and social media efforts without a clear and pre-agreed series of measures of success. The result is often disagreement about the success (or lack thereof) of the efforts and, occasionally, a great deal of effort exerted by the analytics team in an effort to find some needle of insight in the proverbial haystack of data. Therefore, before launching a digital campaign or a digital project, define the scope, the metrics to measure, and the goal of these. Web analytics helps to see if the goal set is met effectively and allows for reaction in real time.
Tip #8: Define and support analytical workflows
Simply providing someone access to information doesn’t always result in the obvious or necessary action. So many exceptional insights and recommendations go unheeded by business partners, often times simply because the insights weren’t communicated to the right group of people, at the right time, in the right way. Analytical workflows leverage a system designed to create effective notification, communication, and opportunity for follow-up with specific data, information, insights, and recommendations. Most companies attempt to use email for this, and given the volume of email traffic it is hardly surprising that this strategy almost always fails. Companies should explore structured solutions, e.g. that provided by this paper’s sponsor Sweetspot. What’s more, good support for analytics workflow improves an organization’s leverage over the “Hub and Spoke” model described above. The strong adoption of this approach will pay significant dividends for complex organizations as their use of analytics and the teams they have supporting those efforts continues to grow.
Tip #9: Quantify and share your analytics-driven successes
Surprisingly, sharing the results of analytics efforts is something that most companies fail to do almost completely. A plan for communicating results is a must in any Digital Insight Management plan, because if analysts don’t share their successes, nobody will, and if analysts don’t share their insights, the business doesn’t learn. Therefore, conservatively quantify the dollar value of recommendations made, either theoretically or observed, if testing efforts are in place, and communicate that amount in a regular and structured way to executives and other business leaders. If nothing else, encourage your analysts and teams to keep track of the opportunities they are uncovering. Track potential revenue, operational savings, increases in conversion, decreases in abandonment and bounce, and any other metrics that you know to have a financial impact on your business. Aggregate these gains appropriately on a monthly basis and, if possible, work them into conversations with leaders in your business.
Tip #10: Develop your own Digital insight Management Plan
Every company will benefit significantly from having a clear, documented plan to enable Digital Insight Management. There is no substitute for having written documentation that describes where your analytics efforts are today and what you want your Digital Insight Management practice to look like in the next six, twelve, or twenty-four months. So, put a plan in place. Start with a “current state” audit to determine where you are doing well and where there are gaps, and then document the exact changes you need to make to enable a complete transformation to a company able to leverage big data, data sciences, and digital analytics and optimization designed to create value for your business, your shareholders, and your customers.
Web Analytics Demystified’s Eric T. Peterson 20-page white paper “Ten Tips to Better Leverage Your Existing Investment in Digital Analytics and Optimization” has been published in September, and actually wasn’t easy to summarize, due to the wealth of information and experience I found in it.
However, if the summary has been interesting for you, have a look at the entire paper at www.webanalyticsdemystified.com.
By Anjum Siddiqi