Analytics is pattern recognition
While applications are mostly about answering questions you know in advance, analytics are about answering unanticipated questions or about discovering questions you did not know you had. One analytics client said he uses analytics to ask ten questions so as to know which one to answer first. Specific analytics we discuss can help you also detect questions, unexpected patterns in your data, and compete in this way.
Before discussing what analytics can do, let’s review what’s driving opportunities for business improvement (and hence analytics). One reality is channel proliferation. Not only are customers seeing more direct channels in which to buy, but also more indirect channels are influencing those purchase decisions. How effectively are all these channels working for you? How effectively are you able to ask and answer questions on all these topics you need to know?
Your questions can go in myriad directions. You have questions on sales results, e.g. questions by customer type, by product type, by time of year, or season, or promotion or sales partner, event etc. Data are available not only on your internal channels, but often also are available on competitors’ results, where you can measure competitive revenues and market shares, with considerable granularity of detail on prices, products, customers, locations etc.
Channel proliferation also begs questions of user experience. By understanding the experience, you contribute understanding of how effectively visits translate into revenues. With growth in electronic channels, there simply is an explosion of places for the customer to experience your product, each of those collecting data. Customer behavior data, when stitched together, can help understand why transactions eventually were completed or not, and how those outcomes could be changed.
The bottom line is that there are almost too many variables to track, between big picture trends on revenues and competition, between experience data for attributing revenues to channel exposure, and all the competitive data – it is almost too challenging to lay out in a repeatable organized fashion.
Yet how well you use all the available data, both internally and externally within the organization matters. Adam Goldberg of Hipmunk observes, “today it no longer matters who owns the data, but rather who is doing something with it.” The data is there. The question is who is using it, and how quickly is it being put into practice, to improve their business? And how much room is there for competitive disruptors to create opportunity and take competitive share?
Enter the opportunity for automated business intelligence (ABI). ABI is a technique born out of the pain created by the explosion of data sources. The rapid growth of these variety of data sources does not change the importance of the core transactions data of the business, nor of the dashboards and metrics used to track performance. Not at all. Without certainty on, and focus on, what its core metrics are, a business cannot be successful.
However, ABI does introduce the ability to track hundreds if not thousands of discernible impacts measurable in these plethora of new big data sources; and it can provide direct and actionable insights on specific exceptions that might be too small to be captured in the core dashboards, but nevertheless valuable early indicators of competitive erosion or some other actionable improvements in the business.
What is ABI? ABI is a scalable technique to systematically and periodically scan core measures of your business and report back any variances in the traditional relationships between these measures. These measures can be from any part of your business, from sales (e.g. revenues, bookings, orders, etc.), to measures of experience (e.g. web clicks, complaints), to measures of operations (e.g. inventories, defects, labor hours, etc.). The simplest example of a variance is simply a variance versus trend in a single variable, for example total daily sales versus historical trend.
The key difference however is the amount of data consumed, and all the dimensions consumed on each measure. For something like daily sales, there might be dozens of relevant dimensions, such as product type, location, customer type, color, size etc. The ability to track specific sources of variance, without any human intervention is the difference.
The result of ABI is a curated set of insights that reflect any variances from historic performance found in the relationships among these measures. Curation here is key, as ABI reports back only on those variances which it believes to fit the persona of the target executive, even though the software may find hundreds of such variances.
ABI is a service offerred by Oplytix in conjunction with solution partner Outlier. ABI is another resource to help you identify exceptions in your business. It complements your existing reporting and analyst led ad hoc queries. It specifically delivers faster insights on data and patterns that had not previously been identified for dashboard or human tracking. ABI – a scalable process for putting all of your data to work, every day.
For a sample case study, and an example of what we could arrange in a proof of concept for you, read about the use case for ABI in the travel industry on our page for use cases.