Food Processing – Supply Chain Conflicts and Food Security (Video)

This is a recording of a talk at the ASSOCHAM’s 8th Global Food Processing Summit in New Delhi, India. I touched upon the inherent conflicts in the food supply chain we need to be aware of before formulating policies and practices, and urged everyone to look at food security from the point of view of sustainability and risk-management.

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One Ring That Rules Them All

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.)

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Patanjali – from Yoga to Noodles

The Patanjali Group has created an Indian FMCG giant in a very short span of time on the back of a three-pronged strategy:

  • The enormous brand awareness that can be attributed to the very high visibility of Baba Ramdev, across a variety of media and issues,
  • Wide and deep market penetration through a large network of outlets and distributors across the country, and
  • Pricing itself below the benchmark competitor in each product area in which it is competing.

Over time, the group has also invested in improving its manufacturing and packaging infrastructure to bring itself on par with well-established competitors.

The group has clearly focussed itself on the mass market, and Patanjali Group’s products become a “go-to” for customers who are more price-sensitive than brand-loyal. This definitely creates pressure on established brands in each of the product segments where the group is now present.

Devangshu Dutta recently participated in a discussion about the phenomenal growth of the Patanjali brand, from yoga lessons to a food and FMCG conglomerate taking well-established multinational and Indian competitors head-on. In a conversation with Zee Business anchor, P. Karunya Rao and FCB-Ulka’s chairman Rohit Ohri, Devangshu shared his thoughts on the factors playing to Patanjali’s advantage. Excerpts from the conversation were telecast on Brandstand on Zee Business:

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Hyperlocals, Aggregators: Developing the Ecosystem

Aggregator models and hyperlocal delivery, in theory, have some significant advantages over existing business models.

Unlike an inventory-based model, aggregation is asset-light, allowing rapid building of critical mass. A start-up can tap into existing infrastructure, as a bridge between existing retailers and the consumer. By tapping into fleeting consumption opportunities, the aggregator can actually drive new demand to the retailer in the short term.

A hyperlocal delivery business can concentrate on understanding the nuances of a customer group in a small geographic area and spend its management and financial resources to develop a viable presence more intensively.

However, both business models are typically constrained for margins, especially in categories such as food and grocery. As volume builds up, it’s feasible for the aggregator to transition at least part if not the entire business to an inventory-based model for improved fulfilment and better margins. By doing so the aggregator would, therefore, transition itself to being the retailer.

Customer acquisition has become very expensive over the last couple of years, with marketplaces and online retailers having driven up advertising costs – on top of that, customer stickiness is very low, which means that the platform has to spend similar amounts of money to re-acquire a large chunk of customers for each transaction.

The aggregator model also needs intensive recruitment of supply-side relationships. A key metric for an aggregator’s success is the number of local merchants it can mobilise quickly. After the initial intensive recruitment the merchants need to be equipped to use the platform optimally and also need to be able to handle the demand generated.

Most importantly, the acquisitions on both sides – merchants and customers – need to move in step as they are mutually-reinforcing. If done well, this can provide a higher stickiness with the consumer, which is a significant success outcome.

For all the attention paid to the entry and expansion of multinational retailers and nationwide ecommerce growth, retail remains predominantly a local activity. The differences among customers based on where they live or are located currently and the immediacy of their needs continue to drive diversity of shopping habits and the unpredictability of demand. Services and information based products may be delivered remotely, but with physical products local retailers do still have a better chance of servicing the consumer.

What has been missing on the part of local vendors is the ability to use web technologies to provide access to their customers at a time and in a way that is convenient for the customers. Also, importantly, their visibility and the ability to attract customer footfall has been negatively affected by ecommerce in the last 2 years. With penetration of mobile internet across a variety of income segments, conditions are today far more conducive for highly localised and aggregation-oriented services. So a hyperlocal platform that focusses on creating better visibility for small businesses, and connecting them with customers who have a need for their products and services, is an opportunity that is begging to be addressed.

It is likely that each locality will end up having two strong players: a market leader and a follower. For a hyperlocal to fit into either role, it is critical to rapidly create viability in each location it targets, and – in order to build overall scale and continued attractiveness for investors – quickly move on to replicate the model in another location, and then another. They can become potential acquisition targets for larger ecommerce companies, which could acquire to not only take out potential competition but also to imbibe the learnings and capabilities needed to deal with demand microcosms.

High stake bets are being placed on this table – and some being lost with business closures – but the game is far from being played out yet.

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Retail Integrated – the Best of Both Worlds

Retailers seem to be fighting a losing battle against the growth of ecommerce, and it is only the nature of the shopping activity, especially for fashion – interactive, social, and immersive as it is – that has kept many retailers relevant and in business.

However, the defensive stance is changing, and now they’re using technology to get the customers back into the store. Forward-thinking retailers are reimagining trial rooms, stores, business processes and entire business models. It’s not a physical versus virtual approach but an approach that integrates both sides. The idea is to create a more immersive experience than pure digital retail can be, using some of the same tools as ecommerce.

It is important to remember that the whole retail environment is a “suggestive” environment. Due to cost and other operational factors most retailers are ill-equipped to provide appropriate levels of excitement, suggestion and support during the browsing and buying process.

For many, the simplest move could be screens serving up their catalogue to customers within the store. For instance, US department store chain Kohl’s has initiated connected fitting rooms that identify products the customer is carrying, and bring up not only those items onscreen, but additional colours and sizes that are available. If the customer wants an alternative, a message goes to a sales associate who can fetch the requested option. Macy’s and Bloomingdales are using tablets in the trial rooms, while Nordstrom, Neiman Marcus and Rebecca Minkoff are attempting to boost their fashion sales using magic mirrors to provide similar enablement. These devices and the processes empower and involve the customer far more, while leaving store staff free for other activities.

A step up, Puma is using “virtual trials” for its apparel products by having a customer take images of herself in specific positions, and then mapping styles on their own images to visualise how they might look. While this needs more work and investment, this is still only a more developed product browser technique from the customer’s point-of-view.

The next level, augmented reality trials and virtual fit, are significantly more sophisticated at creating simulations of a selected garment image draping and falling on the customer’s body even as he or she moves normally. Imaging and texturing of the simulated garments is technically challenging and expensive, repeated for each new style and option. The imaging also needs to mimic the “wearer’s” movements. Nevertheless, retailers such as Polo Ralph Lauren are finding it worth their while to investigate these new technologies, as these reintroduce the much needed “theatre” that are integral to a successful retailer.

For the customer virtualisation expands the number of items “taken” into the trial room, and creates more convenient product discovery. More products can be seen in the same shopping time, and sharing of images and videos with friends and family, engages them in the shopping process as well.

For retailers, the benefits multiply. Inventory can be optimised, and there is reduced handling and shrinkage. Even without sales associates, it is feasible to prompt for alternatives and related products, improving conversion and transaction values, reducing space and costs of physical trial rooms, and increasing the number of customers serviced especially at peak traffic times.

A phenomenal advantage is the data captured that is relevant while the customer is in the store, but which can be linked to future promotions. Valuable intelligence, such as what is being tried and for how long, can help the retailer to quickly gauge demand patterns, and adjust pricing and promotions. Normally retailers only capture sales transactions (post-fact), and miss out the rich information on in-store behaviour that etailers do collect and analyse.

However, massive hurdles to virtualisation remain, including data input accuracy, product accuracy, and the technical capabilities of the tech solution adopted. A bigger concern is whether technology is intuitive and seamless, or whether it gets in the way of the shopping experience. Further, consumers do have privacy concerns about the images and other data collected.

Its important to remind ourselves that, on its own, technology is just a novelty – huge transformation of business processes, organisational capabilities and behaviours must happen as well.

That is perhaps the biggest mountain to climb.

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