One Ring That Rules Them All

Devangshu Dutta

January 10, 2017

In this piece I’ll just focus on one aspect of technology – artificial intelligence or AI – that is likely to shape many aspects of the retail business and the consumer’s experience over the coming years.

To be able to see the scope of its potential all-pervasive impact we need to go beyond our expectations of humanoid robots. We also need to understand that artificial intelligence works on a cycle of several mutually supportive elements that enable learning and adaptation. The terms “big data” and “analytics” have been bandied about a lot, but have had limited impact so far in the retail business because it usually only touches the first two, at most three, of the necessary elements.

Elements in Operationalizing Big Data and AI

“Big data” models still depend on individuals in the business taking decisions and acting based on what is recommended or suggested by the analytics outputs, and these tend to be weak links which break the learning-adaptation chain. Of course, each of these elements can also have AI built in, for refinement over time.

Certainly retailers with a digital (web or mobile) presence are in a better position to use and benefit from AI, but that is no excuse for others to “roll over and die”. I’ll list just a few aspects of the business already being impacted and others that are likely to be in the future.

  1. Know the customer: The most obvious building block is the collection of customer data and teasing out patterns from it. This has been around so long that it is surprising what a small fraction of retailers have an effective customer database. While we live in a world that is increasingly drowning in information, most retailers continue to collect and look at very few data points, and are essentially institutionally “blind” about the customers they are serving.
    However, with digital transactions increasing, and compute and analytical capability steadily become less expensive and more flexible via the cloud, information streams from not only the retailers’ own transactions but multiple sources can be tied together to achieve an ever-better view of the customer’s behaviour.
  2. Prediction and Response: Not only do we expect “intelligence” to identify, categorise and analyse information streaming in from the world better, but to be able to anticipate what might happen and also to respond appropriately.
    Predictive analytics have been around in the retail world for more than a decade, but are still used by remarkably few retailers. At the most basic level, this can take the form of unidirectional reminders and prompts which help to drive sales. Remember the anecdote of Target (USA) sending maternity promotions based on analytics to a young lady whose family was unaware of her pregnancy?
    However, even automated service bots are becoming more common online, that can interact with customers who have queries or problems to address, and will get steadily more sophisticated with time. We are already having conversations with Siri, Google, Alexa and Cortana – why not with the retail store?
  3. Visual and descriptive recognition: We can describe to another human being a shirt or dress that we want or call for something to match an existing garment. Now imagine doing the same with a virtual sales assistant which, powered by image recognition and deep learning, brings forward the appropriate suggestions. Wouldn’t that reduce shopping time and the frustration that goes with the fruitless trawling through hundreds of items?
  4. Augmented and virtual reality: Retailers and brands are already taking tiny steps in this area which I described in another piece a year ago (“Retail Integrated”) so I won’t repeat myself. Augmented reality, supported by AI, can help retail retain its power as an immersive and experiential activity, rather than becoming purely transaction-driven.

On the consumer-side, AI can deliver a far higher degree of personalisation of the experience than has been feasible in the last few decades. While I’ve described different aspects, now see them as layers one built on the other, and imagine the shopping experience you might have as a consumer. If the scenario seems as if it might be from a sci-fi movie, just give it a few years. After all, moving staircases and remote viewing were also fantasy once.

On the business end it potentially offers both flexibility and efficiency, rather than one at the cost of the other. But we’ll have to tackle that area in a separate piece.

(Also published in the Business Standard.)

Customer segmentation – Learning from the Vedas

Devangshu Dutta

April 23, 2009

Advertising Age recently carried an article titled “The Death of Customer Segmentation”, by Michael Fassnacht.

He questions the traditional marketing hypothesis that the better we segment consumers, the better we know what is relevant and the better we can market to them.

Fassnacht argument is that:

  1. Segments are becoming more volatile [totally agree!]
  2. Consumers are never part of just one segment [fashion companies discovered that a few years ago, and began marketing to “purchase occasion segments” rather than plain-old consumer segments defined by demographic and static psychographic profiling], and
  3. Consumers are preferring to choose what information would be relevant and of interest.

This last point is of particular importance, since electronic media – especially websites that customize themselves based on analysis of the users behaviour and history – are becoming more prevalent communication platforms. In fact, for the last few years “mass customization” and “a consumer segment of one” have been fashionable phrases thrown about in marketing circles.

Fassnacht quotes Amazon, Apple and social networking sites such as Facebook and MySpace to support his well-structured argument.

However, it may be a challenge for traditional retailers and brands to apply the learnings from these brands in their physical stores.

Going further and on a lighter note  – or perhaps not 🙂 – if we are to believe the philosophy of the Vedas, the Universe has a head start on “self-segmentation” and “customization of consumer experience” technology. According to it, the world and our experience of it is “Maya,” an illusion product of our mind, and we are free to create and mold it, and experience it as long as we hold the illusion.

If that’s the case, our modern techies and marketers have a long time to go before they climb that technology curve.

The original article is available here: The Death of Consumer Segmentation?

User Customization of E-commerce Websites

Devangshu Dutta

February 19, 2009

Online retailer Zappos is planning to introduce customizable web pages, and that has attracted all kinds of commentary – warm & welcoming as well as dismissive.

The big question is “what is the customization and how it is being offered”.

My rule is simple: web-page customization has to drive simplification of the shopping experience.

Changing skins, page layout, and other cosmetic stuff may keep novelty-seekers happy – for some time, that is. But the average user will find that it is just another thing too many on the already over-full to-do list.

Simplification of the user-friendly sort has to be heuristics and analytics-driven, and behind-the-scenes. It has to be driven by not just stated preferences (through options / settings, through drag-and-drop etc.), but unstated – by studying past behaviour in both purchase and browsing. Imagine if you had every customer stating their preference for a physical store layout. In fact does everyone even know what they really want?

The flip side is that this kind of monitoring may sound creepy and 1984-ish to some people. But probably those would also be the people who are blissfully unaware of the fact that in today’s world the only way to remain totally untracked is to not use any form of electronic / communication device at all, or to build each such device (hardware AND software) yourself from scratch. If you use social networking sites, and have “friend suggestions” on your page, you are being tracked!

There is also the balance to be kept in mind between the boundaries the customer defines and promotions that the retailer wants to drive. The consumer may want to control completely what reaches her; the retailer may take the view that there are incredible deals which the consumer just wouldn’t know about if she built impregnable walls around herself.

For those who’re interested in customization, the British Broadcasting Corporation’s (BBC) document from 2002 about their 2001 website redesign (“The Glass Wall”) is a great resource to refer to. It doesn’t seem to be available anymore on the BBC website itself, but copies are available elsewhere on the web.

DIG To Find Hidden Gold

Devangshu Dutta

October 16, 2007

BOOK REVIEW: HIDDEN IN PLAIN SIGHT: Erich Joachimsthaler

In the midst of extensive or frequent civil works, fluorescent high-visibility clothing contributes to the invisibility of the individual, and can serve as a superb disguise. Similarly, in the midst of extensive research and in-depth analyses, basic insights can go unnoticed.

Erich Joachimsthaler has plenty of examples in his book Hidden in Plain Sight to drive home the point that attention to stuff that is not so obvious to competition can lead to brilliant success such as Sony’s growth through innovative products (the WalkmanT, for one) that met unexpressed consumer needs. Conversely, an inability to spot this can bring even the leaders down, illustrated once again by Sony’s loss of leadership in mobile personal entertainment to Apple’s iPod.

The challenge for companies is to uncover the hidden opportunities by looking into their business from the outside rather than the usual inside-outwards view, and by accurately defining the ecosystem of demand. For most management professionals, this will be harder than it seems.

The exercise begins with the question, “Why didn’t we think of that?” This is intended to remind the reader of how the obvious escapes attention as we sink deeper and deeper into complex analysis and in developing ever more complicated scenarios. And Joachimsthaler sets out a framework that he believes can help larger companies to innovate in a structured way.

Of course, the reader may feel differently, and quote George Bernard Shaw who divided the world into two kinds of people, the reasonable and the unreasonable, and credited innovation to the latter. Or one may agree with Henry Ford who, apparently, felt that customers did not really know what they wanted. He is reported to have quipped: “If I had asked my customers what they wanted, they would have said, ‘A faster horse'”

Yes, at the cutting edge, innovation may seem to be more about the innovator’s creative desire to do something different, and less about “meeting customer needs”. Yet, it is the unmet and, more importantly, unexpressed customer needs, that offer the greatest source of competitive advantage.

This is why innovation seems to spring more from small companies, or companies that are started up around a specific idea that is unique or new. In such a small company or a start-up, typically the founder/innovator/inventor is drawn from the same pool as the target customer. Therefore, while they may be addressing a need they feel acutely, the innovators are unconsciously plugged into their customer’s unmet/unexpressed needs. There are seldom any silos; the whole team is generally focussed on the one problem to be solved.

However, as companies grow larger, functional specialisation emerges — division of labour based on skill-set is deemed to be a more efficient way of doing things. The design folk design based on “trends”, the marketing folk market as they know best, and the manufacturing folk produce to specification and the “demand” generated.

With this speciality of skills taking over, there is a growing disconnect between their efforts to dig for insight and the gold that is “hidden in plain sight”. While data is available in abundance, real knowledge is scarce, and insight just gets buried in well-structured processes and hand-offs between functional silos.

This trend has only accelerated in the past 15-20 years with pervasive information technology that enables the mundane operational process to the most strategic. Never before have management teams been so focussed on information and analyses. As businesses grow, data warehousing and data mining are defined as the competitive cutting edge, pushed along by interested parties (including IT solution providers, but that is another book!).

However, in reality, excessive information is increasingly passed off as knowledge. An inward focus on the management team”s own objectives is often disguised as insight gained on the customer or the market. Functional specialists analyse the market, the latent needs and the gaps in their own way, and if the company is lucky to have some generalists, some of those dots get joined to form a more complete picture.

It is in reminding management of this reality that Joachimsthaler’s book provides a tremendous service. It presents a well thought out model named, curiously enough, DIG – short for Demand-First Innovation and Growth. The three elements laid out sequentially begin with a framework for defining the demand landscape, identifying the opportunity space within it, and then creating a strategic blueprint for action.

Joachimsthaler’s process to define the demand landscape requires managers to put themselves in the customer’ shoes – a process demonstrated with examples from Proctor and Gamble and Pepsi”s Frito Lay. Using the customer’s goals, actions, priorities (there’s the “GAP”), needs and frustrations, demand clusters can be developed and filled out with additional research. The strategic fit between these demand clusters and the brand can then feed into the next steps of identifying the opportunity space.

The filters, or lenses, as the author calls them, are the “eye of the customer”, the “eye of the market”; and the “eye of the industry”. At every step, assumptions and presumptions need to be challenged. Using these lenses, the sweet spot or spots and the growth platforms can be identified, and extrapolated into the strategy. On the downside, the book is clearly about a framework, which may have been best detailed in an article, rather than being stretched over a book.

The author does stress at one point that it is not about “brainstorming”, but about structured thinking. However, he seems to do this in a tone that suggests brainstorming as something vaguely distasteful due to the lack of directional structure.

While examples from the companies studied keep the text alive, yet in places one struggles to correlate the examples with the framework. Indeed, there may well be too much structure to this book, and not enough examples of how inter-disciplinary thinking and functioning can actually produce sustained innovation.

Understanding the model itself can be a fairly involved process. The best way to tackle it may be to approach it as a project, and use the DIG framework as a how-to guide for a real problem. If you are a structured, methodical, sequential kind of manager and possibly work in a large company, the book could provide tools to put that thinking to work for innovation in a team. On the other hand, if you are more of a “people person”, you may want to leave this book alone. [For more, here’s the book on Amazon.]