Business leaders need to understand what they are going to get in return for their efforts and investment - what the structure of success "looks like" before deciding to move forward.
Last month I discussed a new analytics paradigm for a successful 360-degree "convergence" framework that provides deeper and more actionable information than we can get from the various analytics platforms today.
This month let's focus on the primary elements needed to enable the new paradigm to thrive. The expense of setting up and configuring disparate converged solutions places heavy demands upon analysts and support staff. Business leaders need to understand what they are going to get in return for their efforts and investment - what the structure of success "looks like" before deciding to move forward. For example, when business leaders do not understand the value coming from hiring more analysts, or have decided it is more cost effective to outsource analytics to third parties, it's hard to imagine "convergence analytics" ever taking root there.
One way to find out if businesses are ready for convergence analytics is by creating a rubric based on five questions; most of the answers aren't too hard to come up with using Facebook, LinkedIn, and Twitter searches.
Figure 1: Fashion Retailer Matrix of E-Commerce vs. Brick and Mortar features driving online to offline purchases. Source: Reproduced with permission from L2ThinkTank.com.
Figure 2: Organizations where the number of analysts is over 1 percent of the total head count. Source: WebMetricsGuru Social Intelligence.
I answered the first question for my rubric by using LinkedIn. Gap, Lacoste, Michael Kors, Nordstrom, and Macy's have large enough analytics departments to support converged analytics solutions (see Figure 2 above).
But finding out how "siloed" the analytics teams are (Question 2) is not as easy, as it requires a qualitative discovery process to get the information and the same could be said for ascertaining how visible analytics teams are to the executive suite (Question 3). Executive presence in emerging media channels is easier to come up with (Question 4) using specific keyword searches in Followerwonk/Twitter or other social listening tools.
Figure 3: Is the corporate culture analytics driven? Source: WebMetricsGuru INC.
I answered Question 5 (see Figure 3 above) using Facebook Advertising data to calculate the number of employees of each retailer in the L2 study who are interested in some aspect of marketing automation, and who have a college degree (in red - this filters out interns), comparing the former to the number of employees who are identified as analysts, based on their LinkedIn profiles.
Caveat: I used disparate data to try to provide some "convergence analytics" of my own! In an imperfect world, coming up with workable answers often means assembling fragments of data from disparate sources.
Figure 4: Fashion brands most likely to succeed when implementing a converged analytics solution. Source: WebMetricsGuru INC.
Once the right rubric was applied to the L2 data points, Nordstrom, Macy's, and Bloomingdales emerged as the organizations within that dataset that could benefit and support a convergence analytics platform, if they chose to develop one. Case in point, Nordstrom has its own data Innovation Lab and uses BlueDay platform from The Yacobian Group to increase its in-store profitability by 3 to 7 percent. Macy's has 1.38 percent of its employees involved in some aspect of analytics, and according to L2, Macy's is also very effective at driving in-store sales, though it is less effective than Nordstrom at linking its online to offline e-commerce capabilities.
In closing, the right rubric can help organizations gauge their own readiness for convergence analytics, while, at the same time, vendors can identify the organizations most likely to succeed with converged implementations they propose.
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For over a decade Marshall Sponder has influenced the development of the digital analytics industry with his WebMetricsGuru writings that focus on social media metrics, analytics and media convergence. He also possesses considerable in-house corporate experience with roles at IBM, Monster.com, Porter Novelli, and WCG while continuing to work with start-ups. Marshall is a Board Member Emeritus at the Web Analytics Association (DAA) and teaches Web Intelligence at Rutgers University and Baruch Business College. Marshall is the author of "Social Media Analytics: Effective Tools for Building, Interpreting, and Using Metrics," published by McGraw-Hill in 2011.
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