Monday, March 2, 2020

Definition of Digital Ecosystem





Gartner has a succinct definition:

A digital ecosystem [is] "an interdependent group of actors (enterprises, people, things) sharing standardized digital platforms to achieve a mutually beneficial purpose."


www.gartner.com/imagesrv/cio/pdf/Gartner_CIO_Agenda_2017.pdf

Monday, October 9, 2017

How Image Search is Changing Shopping

Since Google Image Search was introduced in 2001 many of us have found it useful in a variety of ways. According to Wikipedia image search results are based on the file name of the image, the link text pointing to the image, and the text adjacent to the image. If you use the Google Images search box, you can drag and drop an image to search and that is kind of fun. Other search engines also offer image search but this post will focus on the impact of image recognition on shopping and how we are getting there.

What is Deep Learning? How is it Affecting Marketing?

Talking about AI-driven advances in image search immediately brings us to the subject of deep learning. Its development is explained in the Nvidia blog as

                       Artificial Intelligence  >>  Machine Learning >>  Deep Learning

Deep Learning layers for dog
Deep Learning Layers

The post explains that what we can do at present falls into the  category of “narrow (specific) AI”. Examples include image classification on Pinterest and facial recognition on Facebook. Machine learning uses massive neural networks, running huge amounts of data through a network until it learns to recognize the item with near perfection. The “layers” shown in the dog recognition graphic are at the heart of deep learning. Speech recognition and image recognition are both products of deep learning technology. The impact of voice technology was discussed in my recent post on voice search. Image recognition is, of course, the enabler of image search.

Image search is interesting in a general sense, but the recent presentation I developed on Social Commerce opened my eyes to some of the ways image search—fueled by AI—could offer new shopping opportunities. That presentation focused on Pinterest, clearly a leader in the social shopping space, and on Facebook whose sheer size makes it impossible to overlook its initiatives in social commerce.

The clear leaders in the image search space are, not surprisingly, the leaders in voice technology—Amazon, Apple, Google and Microsoft. You could probably add Chinese web services firm Baidu to that list, but less is known about the current status of its AI.

Amazon has been in the space for a considerable time, using images as one input in its recommendation engine. Over time, it has added AI/deep learning to its use of images. Amazon recently made news when it opened its deep learning framework DSSTNY to other developers as an open source platform. There was speculation in the trade that Amazon was using this as one way to catch up with the technology of its more recent rivals, especially Google. Facebook also has made its facial recognition software open source. Facebook’s facial recognition services have been a special target for privacy advocates, a subject in itself. Apple is using facial recognition to unlock the iPhone X.

Google is making advances in deep learning AI research on several fronts. In 2011 it introduced Rank Brain (discussed in Chapter 10) as part of its search process. At present, it also uses deep learning in a number of ways:

•    To help categorize images on the web to improve search results. It also allows image enhancement, essentially filling in the blank, when images are missing detail.
•    Google Cloud Video Intelligence allows videos to be segmented and analyzed for content and context with automated summaries provided. It can be used to search for various types of meaning including suspicious content. See a video demo on YouTube.
•    Language recognition technology is essential to Google’s growing line of home assistant devices.
•    Google also uses deep learning to improve recommendations on YouTube, thereby keeping users on the site longer.
•    It is enhancing Google Image Search with Pinterest-like suggestions about related content. For instance, it will highlight when a recipe is available for a particular food image.

Remember—it’s still early days for these technologies. Nevertheless, there are already useful commercial applications. The fashion industry with all its visual content provides some good examples.

How Image Search is Changing Fashion Shopping

Tech CEO Ron Palmeri says the smart phone camera is on its way to becoming “the keyboard” because it can capture so much more information than words alone. Palmeri said he imagines a time when a shopper takes a picture of a desired item and rather than typing in a keyword to find it, they upload the image to a search engine that spits back a number of similar items at a range of price points or even items customized to a specific price point, if the user has integrated financial data into the model.

Palmeri’s firm, Layer, supplies the search technology for Nordstrom’s Trunk Club. The Twitter box describes the service and a posting shows one offer. The postings tend to show accessorized outfits, but a customized tux was the most recent post when I visited. Note that a personal fashion advisor is available on the mobile apps. Trunk Club actually started in 2009 as a service for men who didn’t like to shop but its expansion to women’s fashion and the importance of the smart phone camera is putting the emphasis on mobile.


Trunk Club Offr
Trunk Club Twitter Offer



@TrunkClub
 Trunk Club Twitter Page
















It’s about more than just selling; the importance of the images is influencing design at Nordstrom. “We are trying to understand how one pair of jeans plays out against another pair that was released in another season,” explained Justin Hughes, vp of product development and design at Trunk Club. “We want to get really granular and understand what really works.”

In a related development, Nordstrom is rolling out a line of stores with no inventory. The first store in Los Angeles has no merchandise, but it offers a fascinating array of services. See this post for a picture gallery that would stimulate a lively discussion about the future of retailing in the digital age.

British fashion supplier Apsos finds 80% of its UK traffic coming from mobile and 70% of its orders. Customers average 80 minutes each month on their mobile apps. No wonder Apsos is adding image search to its mobile apps! A customer takes a picture of an item or pulls one in from, say, Instagram for the search. The app returns similar items with reasonable accuracy, according to one reviewer. The results that don’t match so well may actually give customers more shopping ideas she says. There is plenty of room for ideation; Apsos currently has about 85,000 items in its image database.


the Find it on eBay demo
  Play the demo   https://vimeo.com/226972601
We've all probably spent time searching for an item among the multitude available on eBay. Well, eBay will soon have an app for that! The app will allow the user to search not only for products but also for specific features of the product. The user can also choose the color. Play the description from the link in the caption.

Target has taken another route. The retailer is adding Pinterest’s Lens search technology discussed in the Social Commerce presentation) to its mobile app. Users take a picture with their smart phone and the search will find similar items for sale at Target. “The Pinterest partnership quite literally helps us shorten the distance from when our guests have an idea to when they’re ready to make a purchase,” said Rick Gomez, Chief Marketing Officer for Target. He adds that this technology will help understand what customers are looking for and therefore improve their merchandise planning.

What is the Future of Image Search?


There are a number of implications to be drawn from this discussion. As far shopping is concerned, many other visually-oriented categories like food should quickly be active in the space. The technology will continue to grow more powerful because of the players discussed here and other powerful ones, IBM for example, whose activities in deep learning were not discussed.

Brands have choices in terms of how to become a player in image-based shopping. Some will make a quick entry by partnering with a technology firm—Target and Pinterest, for example. Others will carefully build their own back-end processes, Apsos and eBay for example. Burberry is said to have engaged in massive image creation for a trial on Pinterest when it introduced its new Cat Lashes mascara. These activities are already beginning to impact the physical retail space as Nordstrom's new store illustrates.

Finally, search marketers will be pardoned if they emit another long sigh. Image search looks to be another disruptive development in the search space.

Look for all sorts of fascinating developments in the months and years to come!

Wednesday, September 20, 2017

Is Social Commerce the Next Disruption?

According to Statista only 18.9% of U.S. internet users had purchased something on a social platform at the end of 2016. Social commerce has been slow to take off but recent developments, especially in visual search, are likely to accelerate its growth. This brief presentation gives background and examples from Pinterest and Facebook.

It would be a good addition to the social media chapter or it could be used as a stand-alone. There is teaching commentary and additional resource links on the Notes panes, so be sure to use those to make it easy to introduce this new subject.




Friday, September 8, 2017

Value Chain to Value Ecosystem



Your students may have difficulty making the leap from the traditional channel of distribution (Figure 2.1a) to the value ecosystem (Figure 2.1b) without understanding that the value chain is an intermediary step.

Traditional Channel of Distribution  > Value Chain  > Value Ecosystem


Most are likely to be familiar with Porter's value chain concept and this graphic helps make the marketing application more specific. You might want to add it either before or after the current slide 3.