Analytics 101 for the Marketer – Deconstructing the SPIRAL Framework

  • 18 Feb 2016
  • Posted by Anand Sambasivam

In our last post I introduced to you the SPIRAL Framework that can help marketers plan their technology roadmap. But a framework is directional in nature and the nitty-gritties of the individual phases are critical for ensuring outcomes. Commencing this week, we shall look at each of the SPIRAL framework’s individual components in greater detail. In this post we go through some considerations that marketers need to keep in mind to manage the 1st and 2nd phases (S & P phases) of the SPIRAL approach to putting your customer-centricity on track.


S: Spot and identify “Marketing Aligned” consumer information.

P: Pool and manage multiple data sources into a single storehouse.


The S & P elements are probably the most crucial aspects of the methodology since they are really the bread board or the foundation over which the rest of the circuitry can be wired on.

The depth to which you get to in designing and thinking through the ‘Spot’  & ‘Pool’ phases sets the direction in terms of the data elements that are critical to you and how they are structured together as a conceptual data model. Although slightly technical aspects, they are simple enough with some spitballing alongside a data analyst.

The infographic above depicts the behavioral aspects of a consumer business that could be of potential interest to a marketer in a consumer scenario. I have taken the liberty of drilling down into some of the data elements that make up a particular behavior. While some of these are perhaps less pertinent to your business, there are probably others that impact your marketing efforts more significantly. The data elements need to be carefully traded off based on the business value that each step can bring in, vis-a-vis the investment that is required to get these in place.

  1. Where does this data flow in from and why is it important to me?
  2. How can we store it so that it can be accessed on a regular basis?
  3. How can we get to this data at real time?
  4. What is the aggregation mechanism to build intelligence on this data?
  5. How do we effectively use this to “power” our marketing message with relevance and timing?
  6. How does the interplay of behaviors matter to my business and what does it tell me about a consumer’s mindset?
  7. How do we use this intelligence to be proactive about our marketing efforts?

Having identified the ways and means of getting the individual data elements in place, the next step is the Pooling phase (P phase). The structure in which the data resides is the key and the following considerations need to be in mind while designing the databases and integrations.

Here’s a quick design check list that can help arrive at a consensus to building the right data structures and zero in on the right technology to use. While some of the key aspects are covered below, the simplest and quickest approaches are often the best ways to demonstrate quick wins before investing in a complex & time consuming data engineering efforts.

Remember to choose only those options that will provide the maximum business value and build that piece or pieces to demonstrate value before charging at the windmills.

In the upcoming posts we will delve into some aspects of how the integration concepts can be thought through, end to end. Keep watching this space!