A few weeks ago, I wrote about customer analytics as the natural next phase of life for many good digital analysts. (For more on customer analytics, see this post from yesterday by my colleague, Adam Jenkins.) It was a significant departure from my past writings on this blog, most of which have been focused not just on digital-only analytics, but on practical items such as implementation tips and product updates. I feel strongly about where we as an industry are headed, so at the risk of boring those of you who are looking for Adobe Analytics tips and tricks, today I’d like to continue sharing my thoughts and observations around a particular tenet of customer analytics which might be my personal favorite: visual query.

What is visual query?

Imagine a continuum based on how easily an analyst can quickly and iteratively probe their data for insights.

At the far left end are traditional query tools, like SQL. Ultimately, you can get whatever you need, but after writing a query (depending on the size of the data set and other factors) it can take hours or days get a result. If you have a follow-up question (which analysts are great at thinking up), you have to write another query and, once again, wait for it to return. This cycle continues until the analyst exhausts his or her patience and/or obtains the desired insight. But it’s painful to do analysis this way; it isn’t conducive to exploration at all. The analyst thought process is more iterative than this.

In the middle are a bunch of tools that seek to facilitate better analysis in a few different ways. For example, they might improve speed of iteration by applying “structure” around the queries that the analyst is making. A tool like Ad Hoc Analysis (formerly known as Discover) in Adobe Analytics fits this mold. Digital analysts love Discover because they can very quickly create new segments, drill into their data, and create customized data tables and visualizations. The analyst can move from thought to thought much faster than they can by writing database queries manually, and the tool offers flexibility far beyond basic reporting. But even here, the analyst must still wait (albeit just a few seconds) for data to return. Furthermore, a number of restrictions limit the way that the analyst orders the behind-the-scenes queries that return their data to him or her.

As we approach the right end of our continuum, we are also approaching “speed of thought” for the analyst. This is visual query—a concept where selecting and exploring data natively changes the query on the fly, and results begin to return in real-time, even as the query continues to retrieve data. Why do we call it visual queryBecause you are querying the data visually rather than by writing a statement, and because the data updates based on the query right there, visually, across everything you’re viewing. The analyst can do virtually anything here; selecting a row or a series of rows filters the entire data set (whatever data tables and visualizations are loaded up) by changing the query. Building a segment or defining a cohort is as easy, literally, as clicking a mouse. The analyst is free to move from thought to thought literally as quickly as they come to mind, to easily move back up the “question tree” and pursue a different branch at any time, and to really explore across multiple dimensions, metrics, and visualizations simultaneously.

Let’s say I’ve got four data tables open on my workspace. For simplicity, let’s pretend that they map nicely to reports that you’re familiar with: a Visits table (by day), a Products table (showing Revenue), a Campaigns table (showing Revenue), and a Countries table (showing AOV). In the Visits table, I simply click and drag to select seven days. Instantly, the other three tables begin to update to reflect this selection. Now I’m only looking at data for that one week. Now I select a particular campaign the same way. Again, the tables update. That’s interesting; a product that I hadn’t expected to see has a lot of revenue tied to that campaign on those seven days. I select the product and de-select the campaign. Now I am looking at all the campaigns for users who purchased that product during those seven days. And on and on, across all of your dimensions and metrics, cross-channel paths, any associated visualizations and more. Everything can be used to change your lens on any data that you’ve got available to you, with multiple pieces of data available to you on-screen at once.

That’s visual query in a nutshell, and it enables analysis like you’ve never done before.

Why is visual query so important to customer analysis?

We could actually ignore the whole “omnichannel customer data” conversation for the purposes of this post; visual query doesn’t necessarily require multiple channels of data in order to be tremendously valuable to the analyst, as I hope you can imagine. If you want to query your data faster and more intuitively, visual query is for you—regardless of the data set you’re analyzing. But in the world of customer analysis, visual query adds particular value, and that’s worth discussing briefly.

I met recently with a major U.S. retailer who has been thinking about customer analysis. Since this is a well-known brand which has a tech-savvy audience, it’s safe to assume that they have what most of us would consider “a lot” of web data. Still, according to them, web data represents only about 15% of their total data store by size.  That means that 85% of their data store comes from other systems and channels: CRM, point-of-sale, call center, IVR, etc. But don’t focus on size; instead, think about how much more complex customer paths are than web visitor paths. That web path could be just a small fraction of the overall customer path. As you’ve heard elsewhere, the customer journey often neither begins nor ends on your web site. As a result, the range of questions that the analyst will have as he/she moves through omnichannel customer data is far less limited than in the digital analysis world. We’re not just concerned about whether a user moved from Page A to Page B, or whether a visitor purchased Product X because she saw Campaign Y; potentially every interaction that the customer has had with your brand, via any channel, is in scope. Thus, the ability to iterate quickly, always revising thinking and questioning, and to begin to get results immediately across multiple views into the data becomes critical.

Think about it: You don’t want to be limited by having to write queries and wait an hour or more for your next iteration, nor do you want to be roped in to even a flexible set of reports. Because you are now thinking across the whole customer journey, across multiple channels, the value of being able to explore and change your focus on the data (i.e., your query) with a single click in a data table, or on a column in a visualization, can become the difference between analysis in minutes and analysis in hours or days.

Simply put, visual query and customer data are a match made in heaven for the analyst.

What does visual query mean for how I should approach my world as a digital analyst?

I still believe that customer analysis will become a bigger and bigger part of your world over the coming years, digital analyst. As such, insist on doing customer analysis using tools that allow you to query the data visually. Adobe Analytics Premium has this capability today, and as I mentioned above, you don’t actually need to wait to merge customer data into your web data in order to take advantage of the increased speed, efficiency, and flexibility that visual query provides. You can enable more powerful analysis based on your web data today.

Ultimately, though, my goal is simply to make you aware, in case you were not already, that your world as an analyst will likely not revolve around basic, rigid reports in the near future as it may have done for many years now. In fact, the whole concept of a “report” changes when visual query is available. Not to worry; I believe there will always be a time and a place for reporting. Analysts can have their cake and eat it, too. Think of visual query as in addition to the standard reporting that your organization relies upon you to provide. (Not to mention that the reports, segments, audiences, etc. that you can create based on the results of your visual queries can help change the way your marketing colleagues use data.)

The future of analytics is visual. We see and hear this in a number of places. We’ve all recognized for most of a decade that compelling visuals help tell stories, and telling stories gets buy-in. I’m here to tell you that it isn’t just the outputs of analysis—the Excel spreadsheets, the PowerPoint presentations, etc. that will be visual—the queries themselves in the tools that you use will be visual, and the analyst will be far better enabled because of it.

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