Data and digital analytics
- Analysis methodologies (Inductive reasoning, funnel analysis)
- Analytics development (new metrics & analysis schemes, KPI’s)
- Client servicing (Presentations, Business cases, Reporting)
Original post (illustrating the Data and Digital Analytics concept) :
Today is wrap-up time. Before dealing with practical use cases and comment on data-related news, I shall conclude my introduction series with its third part, analytics. And now, you have your data set, well-organized (patterned) and trained (enriched), ready to go. You only need to find the proper tactics and strategy to reach your goal, i.e. get them to talk and find the solution to your issues or validate your assumptions.
How is this analytical work? Sometimes complicated. Often critical. Always consuming.
Let us first ask ourselves a few questions:
- Is my software adapted and scaled for reaching my business goals? → needless to say, although… a nice database requires an efficient interface
- What type of tools and techniques may I use for getting the best out of them? → data drill down and funnel analysis is not equal to random search
- What are the patterns within my data? → how to reach global conclusions from exemplary data excerpts
- By the way, do I know why I am storing so much data? → small data sets are often more useful than big (fat and unformed) data mounds
In fact, and even though one may build databases hundreds of ways, using very different tools and techniques, there are only two ways to analyze data, Mine Digging, and Stone Cutting.
1. Mine Digging
Mine Digging is the typical initial Big Data work. Barren ground, nothing valuable to be seen, there may be something worth digging, but you are not even sure… This is often what Big Data offers at first look. Actually, a well-seasoned researcher will find out if something interesting is hidden in the data, like an experienced geologist deciphering the ground would guess if any stone could be buried in there. Still, excavating the data to reach more promising levels is a lot of work, often named “drill down”, an interesting parallel to the mining work… And something will be found for sure, but more often stones of lesser value, a Koh-i-noor is not always there, unfortunately… This huge excavating work I have named Mine Digging.
I have taken an example from a previous Market Research experience to illustrate this.
Digging in the data is cumbersome. No question. You seek for hours for a valid analysis angle before finding one. And then suddenly, something. A story to tell, a recommendation to share, a result of some previously taken action to validate. A gem in the dark.
The attached chart shows the example of a given retailer (A) compared to its competitors in a specific catchment area. Its underperformance being so blatant, it was heading unfortunately to a shutdown; however, we found additional data, and suggested an assortment reshuffle and a store redesign which finally helped retailer (A) to catch up with town average in less than 18 months.
This example shows how one may drill data down to the lowest level (shop/town) from a full retailer panel, so as to find meaningful insights
2. Stone Cutting
Stone Cutting has more to do with on-going analytics, especially those taken from existing software, be they for digital analytics, data mining, or even semantic search. In this respect, one already has some raw material in hands, but its cutting is depending on current conditions and client wishes… The analytic work, in that respect, is to find out how to carve the stone and give it the best shape to maximize its value. This refinery work is what I name Stone Cutting.
I have chosen an example from the web analytics world to illustrate this.
When optimizing an e-commerce website, one very quickly knows the type of action that triggers improved conversion; the analytics will then “only” provide some marginal information, e.g. what this specific campaign has brought to the company’s business, its ROI, its new visitors and buyers. Very important for the business, for sure. Vital even.
The attached example shows for instance that the efficiency of the banner-ad impression (click-rate per impression at more or less 5 per thousand) is stable up to 5 impressions, beyond this point, additional impressions are less efficient.
Information straight to the point with results also immediately actionable.
So two ways of analyzing data, one requiring heavy work and a lot of patience, the other one rather using brains and existing patterns, but both are necessary for efficient analytics, from breakthrough discoveries to fine-tuned reporting. Diggers in the South-African mines and cutters in the Antwerp jewelry shops are two very different populations, but both of them are necessary to create a diamond ring. For data analytics alike, a global in-depth knowledge of the analytical process is required, so as to offer the best of consultancy. So, let me remind you , my dual experience in marketing and operations is a real nugget that you can get easily either on a part-time or full-time basis.