AnalyticsBridging the Cross-Channel Analytics Divide

Bridging the Cross-Channel Analytics Divide

No lone study of web analytics will satisfy everyone in your organization; the only solution to coming up with a prioritized list is analysis – deep cross-channel analysis. Here’s a sample process to start bridging your online-offline analysis.

you-cant-do-everything-you-want-to-onlineThe post-holiday season is a great time to reflect on the year that was 2011, both from a personal and professional perspective.

For many of us, the first quarter of the year means planning for the year ahead and prioritizing against a variety of opportunities competing for time and resources.

Unfortunately, no lone study of web analytics will satisfy everyone in your organization; the only solution to coming up with a prioritized list is analysis – deep cross-channel analysis.

At this point, you may be recollecting all those articles about calculating true campaign ROI, or perhaps reminiscing on an old economics class you attended that reviewed concepts such as “cost of goods sold” or “break-even analysis” or “first-in, first-out accounting.” If you’re just starting out, your cross-channel analysis doesn’t have to be that complicated.

Here’s a sample process to get you started in bridging your online-offline analysis:

  1. Categorize your products and/or services by profitability. There’s no point in doing a deep-dive analysis on low-profit products. Note: try to find someone in finance to help you out with this one, as many of those aforementioned economics concepts will come into play here.
  2. Compile a list of KPIs from your web analytics solution. Append this data to your product categories from step 1. Note: if you don’t have much of a sample size to work with, you may have to abandon analysis on even the most profitable items (or revisit your analytics implementation).
  3. Look for a primary key that can be leveraged to identify prospects and customers online and offline. This doesn’t have to be internal account numbers or customer IDs, it can be anything that will get you closer to matching online behavior to offline activity. Note: it’s not uncommon that you may have to translate a web analytics ID to a CRM ID to an offline account number.
  4. Match online (web analytics) data and offline data. Try to leave no stone unturned when it comes to offline data; investigate customer interactions in-store, over the phone, via snail mail, etc.
  5. Aggregate your findings. Compile a report that offers insights detailing online behavior and offline activity that identifies key opportunities where:
    • Online success equates to offline success.
    • Online success online equates to offline challenges.
    • Online challenges equate to offline success.
  6. Generate a list of optimizations you can execute to enhance the online contribution. Include a list of requests from other channels in your organization.
  7. Share your findings with key stakeholders in your organization. Execute toward a common goal.
  8. Revisit your analysis every quarter. Ensure assumptions and optimizations align to your business goals for all channels.

The goal of your study should never be to “nail” the statistical analysis on the head, but rather introduce the opportunity for further study. The best analysts will urge stakeholders to ask more questions, and execute additional insights on specific business requirements. Online-offline analysis will always raise a few eyebrows and often is the cause for heated debate, but those are two very good things.


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