Customer Centric Product Development with Twitch’s June Dershewitz

Are product-centric organizations tone-deaf to customer needs and desires? How can you balance the two? June Dershewitz, Director of Analytics at Twitch (an Amazon company), explains how these mindsets are not mutually exclusive and when combined, can be incredibly powerful in driving both the bottom line and customer happiness. She offers three steps to take to adopt a similar approach, starting with setting clear, quantifiable goals, ensuring a balance between product- and customer-centric approaches to align everything with the company’s core values, and using data at each step in the innovation cycle to ensure product optimization.

from Ambition Data

Creating Serendipity with Data: Interview with Jeff Steward

The fifth floor of the Harvard Art Museums is home to the Lightbox Gallery, a minimalist space with nine LCD monitors arrayed on the wall as a single large viewing plane.  I had no idea this space existed until my host and fellow ASC member Jeff Steward invited me to experience this venue before our interview.  Used for exploring digital art on a larger scale, the Lightbox Gallery is a small area of R&D for a Museum that is also home to the Forbes Pigment Collection and world renowned conservation labs.  

We descend to the lower level where Jeff’s team explores different uses for technology in the Museum.  An original (working) Atari 2600 is on display opposite a VR station for exploring augmented reality.  Jeff picks up a small plastic kylix that was printed from a 3D data file of the real object.  He’s quick to mention that the data is a small sample of what the Museum offers to the public…and that it took about six hours to print.  We settle into a conference room, and what follows is an edited version of our interview:

Jeff what are some of the key measures for a museum of this size and what do you see as the role of data and analytics?

It’s a very large question that drives right to the business of running the Museum.  And it can be difficult to connect the value of analytics to these operational measures.  But we have an obligation to make use of the collection and the big question is whether a piece of art is earning its keep.  Managing the Cost of Ownership is big task for a museum of our size.

What does it mean to “make use” of the collection?  What are the different ways we can experience the Museum?

We have 250,000 works but at any time only about 1,700 are on display.  Art is handled by our staff in storage, experienced by museum guests, or viewed by students and faculty for research purposes.  These are just a few examples. And our visitors include people in and outside the Harvard community who may be casually interested or actual conservators.  

Wow…that’s a lot of art behind the scenes?!

Yes…yes it is!

Does the Museum collect data on these different Users, and if so how does it employ these data?

We have a ton of “log” data from our Collection Management System.  Whenever a work of art is handled in storage, or viewed by students or researchers for study we keep very detailed records of these touch-points.  That said, we haven’t fully explored all of the uses for these data and there is a real “void” in the data once a work has been installed in a gallery.  We’ve compensated for that somewhat on the Web. The Museum has a history going back at least 10 years of maintaining a Web page for every work in the collection.  We have been able to use Google Analytics to track things like Visits and Page views, and we started tracking events on the site as well…such as when someone reserves the Study Center online.  This is actually the only way to reserve the Center. So we do have some insight into how people are exploring the collection online and we use these data to influence search rankings on the site.  We can promote or demote a work in the rankings based on the historical traffic its page has received.

What’s your best story of how data was used to directly influence the Museum’s operations?

Our Conservation group met with security and gave them slips of paper and instructions to record every “touch” they witness to the collection.  Every time someone bumps into a sculpture, accidentally touches a painting or even if they notice some new paint flecks on the floor. After a while a lot of data was collected and the Conservation team used this information to decide where to hang the art.  They moved those black strips of tape you see on the floor based on the “touch” data that had been collected by security. There is a mobile by Calder in the gallery, and next to this work was sign saying “the slightest breath will set it in motion.”  As you can imagine, this work received the most marks from security and the sign was edited to change behavior and prevent damage to the art.

What are some of the ways data and analytics are changing how we experience the collection?

The pigments from the Forbes collection are a reference collection used by conservation scientists globally.  The pigments are cataloged in our database and will eventually be available to the public. One of our goals is to associate pigments with the art that actually contains them, so that once a conservator has done the analysis and found a shade of crimson in a work, for example, that association is available to the public.  So in theory one day you could find online all of the Van Gogh’s around the world that share a common pigment.

Today we make collection data available via a public API, part of what is essentially a scholarly search interface into the collection.  The data is available, but not especially accessible to a more general audience. So I am experimenting with using basic machine processing to do face detection, text detection, auto tagging and auto captioning.  It’s fascinating how wrong some of the tagging and captioning is, but you can think of the computer vision service as just another set of eyes looking at the art even if that vision is flawed. Visitors are hoping for serendipity…odd quirks in the data you don’t expect but that are super interesting.  So the thought is that computer vision is building more perspectives into the data to ultimately support non-scholarly interfaces.  The Museums have a long term commitment to collecting and cataloging the data, but we cannot support lots of interfaces into the collections.  We can support those with the interest and ability to build interfaces themselves.

Jeff Steward is the Director of Digital Infrastructure and Emerging Technology at The Harvard Art Museums in Cambridge, MA.  jeff_steward@harvard.edu.

Thoughts for those getting started

Previously, Jason, Jon, and Jim discussed the unique paths and experiences in their career that brought them into the Digital Analytics field.  They discuss the advice they would give to those about to enter college and those about to start their careers and who are interested analytics.  They talk about the experiences that have played a significant role in getting them to the point they are today and what they have learned along the way.

from 33 Tangents

The Customer as the Asset with Anthony Choe, Founder at Provenance

There is no such thing as an average customer. While it’s easy to say the customer is the most important asset, few have spent time quantifying what that truly means.  Anthony Choe is one of those few. As Founder at Provenance, a progressive consumer private equity firm, Anthony uses Customer Lifetime Value as the primary lens for evaluating businesses. He explains how predictive CLV provides precision and allows both the investor and company to have a singular focus on the customer among all the data noise. With the amount of data and marketing tools available ever increasing, he believes CLV principles are timeless. Optimizing around CLV will always be the best answer, regardless of channel, regardless of the marketing message, regardless of shifting landscapes.