This makes sense – there is a fundamental lack of experience in organizations on how to use data correctly. This lack of experience makes organizations over-reach, trying to solve their data-gap in a software-only approach (BI). Most of the time, the software they purchase is a generation or two ahead of where their actual data, the oxygen that feeds their BI platform, is. Furthermore, there is a lack of experience in how to build the models that interprets the data. This results in a tangible lack of return on the capital invested. After all, what is the purpose of data? I present these primary use cases: DATA SHOULD BE USED BY RETAIL ORGANIZATIONS TO MAKE STRATEGIC DECISIONS AND BE THE FOUNDATION OF THE CAMPAIGNS THAT MAKE BUSINESS GOALS A REALITY. This implies that there have to be processes within a retail organization that identify the ‘right’ data, collect / aggregate it, interpret it, then finally, apply it. Mapped to most organizations, it would look something like this
|Layer of work||Who’s responsible|
|Identification layer||In most organizations, unfortunately no one is responsible for this. The extent of identification is usually dependent on a google analytics tag that collects a lot click-stream data, but ignores any product or customer data that resides in ERP’s, CRM’s, Invoices, etc.s|
|Aggregation layer||Again, for most organizations this is just google analytics or omniture.|
|Interpretation Layer||These are the actual people in the organization – those that need to look at data (or the reports), and make decisions on strategy and campaigns.|
|Application layer||This is typically technology, where digital campaigns and transaction occur.|
A Company’s Data Maturity Journey
|A Company is..||…when they are able to||…an example would be||…which leads to campaign effectiveness of|
|1. Metrical||…when they are able to only collect and aggregate metrics or KPI (i.e. GA main reports).||
||Very low. Since this company is only looking at metrics, their strategies and campaigns rely purely on metrics, not insights. In this case, companies will simply
|2. Informational||…when they are able to partially cross dimensions, not just metrics.||
||Medium. Now this company has more information about their customers. They are able to segment the type of people that were buying the shoes, and how often they are making repeat purchases. This leads to some better strategies.
|3. Knowledgable||…when you are able to cross product, channel, and customer data||
||High – truly relevant campaigns. Wow, this company now has real knowledge about their customers, the products they buy, and the channels they bought from.
|4.Transformational||…when you are able to predict, to at least 40% accuracy, the purchase habits of your customers.|