IN DIGITAL RETAIL, DATA MATURITY IS A JOURNEY, NOT A LEAP
POST 1 OF 2: IN THIS POST WE TAKE A LOOK AT THE DIGITAL MATURITY CURVE AND ITS FOUR STAGES. IN THE SECOND POST, WE TAKE A LOOK AT HOW AN ORGANIZATION HAS TO WORK TOGETHER TO PROGRESS ALONG THAT MATURITY CURVE When Forrester recently asked 510 marketers on CRM projects, ‘what’s your biggest challenge is in improving customer experience,’ the top three answers involved data (see below). 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
IN THIS POST WE TAKE A LOOK AT THE DIGITAL MATURITY CURVE AND ITS FOUR STAGES. IN THE SECOND POST, WE TAKE A LOOK AT HOW AN ORGANIZATION HAS TO WORK TOGETHER TO PROGRESS ALONG THAT MATURITY CURVE
When Forrester recently asked 510 marketers on CRM projects, ‘what’s your biggest challenge is in improving customer experience,’ the top three answers involved data (see below).
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.|
When most companies say they want to be ‘data-driven,’ what they are really thinking about are metrics, but that won’t get them to where they want to be. When a Company bases strategies and campaigns on only metrics, they produce non-personal campaigns with various levels of success. To deliver campaigns that arecontextual and relevant, one needs knowledge. A knowledge driven Company is one that has a fundamental understanding of how channels, products, and customers impact its top and bottom line revenue. Let’s take a look at how most companies should hope to mature on the metric to knowledge journey, and the results it can produce:
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
There is no understanding of why those 1800 people bought those 2000 shoes or whyadwords drove 48% of sales.
|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.|
In my next post, we take a deeper a look at how identification, aggregation, interpretation, and application all play equally important roles in order to achieve a knowledge-driven organization which delivers contextual and personal customer experiences that ultimately lead to higher revenues.