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.
- Is 1 percent or more of your company’s head count made up of analysts/business intelligence? Derived from LinkedIn searches on company name where “analytics” or “analyst” is in an employee’s profile. I reasoned that at least 1 percent of an organization should be working in their primary business roles as analysts in order to support a converged analytics implementation. In later columns, I will provide percentages of analysts to overall head count within several specific industry verticals.
- Are the analytics/business intelligence team(s) siloed? This is a qualitative answer and needs to be gathered via surveys or interview information, unless it is published elsewhere.
- Does the analytics team have high visibility to the executive suite? Derived from IR reports, ORG charts, and third-party sources.
- Is there a strong executive presence on emerging media channels? Derived from Followerwonk/Twitter, Facebook Graph Search, and LinkedIn.
- Does a significant percentage of employees have a strong interest in marketing analytics? Derived from Facebook Graph Search and Facebook Advertising – this is additional to the 1 percent head count search on LinkedIn.
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.
Marketers need to know what’s in their data and trim out the filler to provide continuous, data-driven ROI for their brands.
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