Dynamic Pricing Update

The concept of Dynamic Pricing has been around for at least 25 years. I first encountered it in Kevin Kelly's 1998 book, New Rules for the New Economy.

Five years ago, when I was consulting to a large UK supermarket, this concept was starting to be taken seriously. The old system of sending people around the store putting yellow stickers on items that were reaching their sell-by date was seen as labour-intensive and error-prone. There were also some trials with electronic shelf-edge labels to address the related challenge of managing special offers and discounts for a specific stock-keeping unit (SKU). At the time, however, they were not ready to invest in implementing these technologies across the whole business.

The BBC reports that these systems are now widely used in other European countries, and there are further trials in the UK. This is being promoted as a way of reducing food waste. According to Matthias Guffler of EY Germany, around 400,000 tonnes of food is wasted every year, costing German retailers over 2 billion euros. Obviously no system will completely eliminate waste, but even reducing this by 10% would represent a significant saving.

Saving for whom? Clearly some consumers will benefit from a more efficient system of marking down items for quick sale, but there are concerns that other consumers will be disadvantaged by the potential uncertainty and lack of transparency, especially if retailers start using this technology also to increase prices in response to high demand.

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Update October 2024

Dynamic pricing has recently hit the headlines following some extreme examples of surge pricing for concert tickets. See commentary from Hannah Downes of the Consumers' Association.

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Mabel Banfield-Nwachi, Happy hour in reverse: where dynamic pricing may creep further (Guardian18 November 2024)

MaryLou Costa, Why food discount stickers may be a thing of the past (BBC News, 30 November 2023)

Hannah Downes, Oasis tickets: Ticketmaster's 'in demand' pricing could be in breach of consumer law (Which? 10 September 2024) Dynamic pricing: how does it work and is it legal? (Which? 2 October 2024)

Matthias Guffler, Wie der Handel das Problem der Lebensmittelverschwendung lösen kann (EY-Parthenon, 28 March 2023)

Related posts: Dynamic Pricing (April 2006), The Price of Everything (May 2017)

Wikipedia: Dynamic Pricing

Towards the Data-Driven Business

If we want to build a data-driven business, we need to appreciate the various roles that data and intelligence can play in the business - whether improving a single business service, capability or process, or improving the business as a whole. The examples in this post are mainly from retail, but a similar approach can easily be applied to other sectors.


Sense-Making and Decision Support

The traditional role of analytics and business intelligence is helping the business interpret and respond to what is going on.

Once upon a time, business intelligence always operated with some delay. Data had to be loaded from the operational systems into the data warehouse before they could be processed and analysed. I remember working with systems that generated management information based on yesterday's data, or even last month's data. Of course, such systems don't exist any more (!?), because people expect real-time insight, based on streamed data.

Management information systems are supposed to support individual and collective decision-making. People often talk about actionable intelligence, but of course it doesn't create any value for the business until it is actioned. Creating a fancy report or dashboard isn't the real goal, it's just a means to an end.

Analytics can also be used to calculate complicated chains of effects on a what-if basis. For example, if we change the price of this product by this much, what effect is this predicted to have on the demand for other products, what are the possible responses from our competitors, how does the overall change in customer spending affect supply chain logistics, do we need to rearrange the shelf displays, and so on. How sensitive is Y to changes in X, and what is the optimal level of Z?

Analytics can also be used to support large-scale optimization - for example, solving complicated scheduling problems.

 
Automated Action

Increasingly, we are looking at the direct actioning of intelligence, possibly in real-time. The intelligence drives automated decisions within operational business processes, often without a human-in-the-loop, where human supervision and control may be remote or retrospective. A good example of this is dynamic retail pricing, where an algorithm adjusts the prices of goods and services according to some model of supply and demand. In some cases, optimized plans and schedules can be implemented without a human in the loop.

So the data doesn't just flow from the operational systems into the data warehouse, but there is a control flow back into the operational systems. We can call this closed loop intelligence.

(If it takes too much time to process the data and generate the action, the action may no longer be appropriate. A few years ago, one of my clients wanted to use transaction data from the data warehouse to generate emails to customers - but with their existing architecture there would have been a 48 hour delay from the transaction to the email, so we needed to find a way to bypass this.)


Managing Complexity

If you have millions of customers buying hundreds of thousands of products, you need ways of aggregating the data in order to manage the business effectively. Customers can be grouped into segments, products can be grouped into categories, and many organizations use these groupings as a basis for dividing responsibilities between individuals and teams. However, these groupings are typically inflexible and sometimes seem perverse.

For example, in a large supermarket, after failing to find maple syrup next to the honey as I expected, I was told I should find it next to the custard. There may well be a logical reason for this grouping, but this logic was not apparent to me as a customer.

But the fact that maple syrup is in the same product category as custard doesn't just affect the shelf layout, it may also mean that it is automatically included in decisions affecting the custard category and excluded from decisions affecting the honey category. For example, pricing and promotion decisions.

A data-driven business is able to group things dynamically, based on affinity or association, and then allows simple and powerful decisions to be made for this dynamic group, at the right level of aggregation.

Automation can then be used to cascade the action to all affected products, making the necessary price, logistical and other adjustments for each product. This means that a broad plan can be quickly and consistently implemented across thousands of products.


Experimentation and Learning

In a data-driven business, every activity is designed for learning as well as doing. Feedback is used in the cybernetic sense - collecting and interpreting data to control and refine business rules and algorithms.

In a dynamic world, it is necessary to experiment constantly. A supermarket or online business is a permanent laboratory for testing the behaviour of its customers. For example, A/B testing where alternatives are presented to different customers on different occasions to test which one gets the best response. As I mentioned in an earlier post, Netflix declares themselves "addicted" to the methodology of A/B testing.

In a simple controlled experiment, you change one variable and leave everything else the same. But in a complex business world, everything is changing. So you need advanced statistics and machine learning, not only to interpret the data, but also to design experiments that will produce useful data.


Managing Organization

A traditional command-and-control organization likes to keep the intelligence and insight in the head office, close to top management. An intelligent organization on the other hand likes to mobilize the intelligence and insight of all its people, and encourage (some) local flexibility (while maintaining global consistency). With advanced data and intelligence tools, power can be driven to the edge of the organization, allowing for different models of delegation and collaboration. For example, retail management may feel able to give greater autonomy to store managers, but only if the systems provide faster feedback and more effective support. 


Transparency

Related to the previous point, data and intelligence can provide clarity and governance to the business, and to a range of other stakeholders. This has ethical as well as regulatory implications.

Among other things, transparent data and intelligence reveal their provenance and derivation. (This isn't the same thing as explanation, but it probably helps.)

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Obviously most organizations already have many of the pieces of this, but there are typically major challenges with legacy systems and data - especially master data management. Moving onto the cloud, and adopting advanced integration and robotic automation tools may help with some of these challenges, but it is clearly not the whole story.

Some organizations may be lopsided or disconnected in their use of data and intelligence. They may have very sophisticated analytic systems in some areas, while other areas are comparatively neglected. There can be a tendency to over-value the data and insight you've already got, instead of thinking about the data and insight that you ought to have.

Making an organization more data-driven doesn't always entail a large transformation programme, but it does require a clarity of vision and pragmatic joined-up thinking.


Related posts: Rhyme or Reason: The Logic of Netflix (June 2017), Setting off towards the Data-Driven Business (August 2019)


Updated 13 September 2019

The Smell of Data

Retailers have long used fragrances to affect the customer in-store experience. See for example Air/Aroma.

So perhaps we can use smell to alert consumers to dodgy websites? An artist and graphic designer, Leanne Wijnsma, has built what is basically an air-defreshener: a hexagonal resin block with a perfume reservoir inside, which connects over Wi-Fi to your computer. When it notices a possible data leak (like the user connecting to an unsecured Wi-Fi network, or browsing a webpage over an unsecure connection) — puff! It releases the smell of data.

James Vincent, What does a data leak smell like? This little device lets you find out (Verge, 31 Aug 2017)

That's all very well, but it only sniffs out the most obvious risks. If you want to smell the actual data leak, you'd need a device that released a data leak fragrance when (or perhaps I should say whenever) your employer or favourite online retailer is hacked. Or maybe a device that sniffed around a corporate website looking for vulnerabilities ...

I'm sure my regular readers don't need me to spell out the flaws in that idea.

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Related posts

Pax Technica - On Risk and Security (November 2017)
UK Retail Data Breaches (December 2017)

UK Retail Data Breaches

Some people talk as if data protection and security must be fixed before May 2018 because of GDPR. Wrong. Data protection and security must be fixed now.

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Morrisons (2014)

The High Court has just found Morrisons to be liable for a leak of employee data by a disaffected employee in 2014. (The perpetrator got eight years in jail.) 

http://www.theregister.co.uk/2017/12/01/morrisons_data_leak_ruling/
http://www.bbc.co.uk/news/uk-england-42193502

Sports Direct (2016)

A hacker obtained employee details in September 2016, but Sports Direct failed to communicate the breach to the affected employees.

https://www.theregister.co.uk/2017/02/08/sports_direct_fails_to_inform_staff_over_hack_and_data_breach/

CEX (2017)

Second-hand gadget and video games retailer Cex has said up to two million customers have had their data stolen in an online breach

http://www.bbc.co.uk/news/technology-41095162
https://uk.webuy.com/guidance/

Zomato (2017)

Up to 17 million users affected by data breach at restaurant search platform Zomato

https://www.infosecurity-magazine.com/news/zomato-breach-exposes-17-million/
https://www.zomato.com/blog/security-notice

Tesco Bank (2016)

Cyber thieves steal £2.5m

https://www.theguardian.com/business/2016/nov/08/tesco-bank-cyber-thieves-25m
https://www.theregister.co.uk/2016/11/10/tesco_bank_breach_analysis/
https://www.itproportal.com/features/lessons-from-the-tesco-bank-hack/

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Related posts

The Smell of Data (December 2017)

The Price of Everything

The relationship between the retailer and the customer can be beset by calculation on both sides. The retailer is trying to extract enough data about the customer to calculate the next best action, while the customer is trying to extract the best deal.

There is nothing new about customers comparing products and prices between neighbouring shops, and merchants selling similar goods can often be found in close proximity in order to attract more customers. (This is especially true for specialist and occasional purchases: in large cities, whole streets or districts may be associated with specific types of shop. London has Denmark Street for musical instruments, Hatton Garden for jewellery, Saville Row for made-to-measure suits, and so on.)

And as Tim Harford points out, exploitative algorithms are using tricks as old as haggling at the bazaar.

But nowadays the villain, apparently, is eCommerce. As a significant share of the retail business migrates from the high street to the Internet, many retailers are concerned about so-called showrooming. It may seem unfair that a customer can spend loads of time in the high street, wasting the time of the shop assistants and shop-soiling the goods, before purchasing the same goods online at a better price. To add insult to injury, some people not only practice showrooming, but then blog about how guilty it makes them feel.

There is a common belief that the Internet can generally undercut the High Street, and there are several reasons why this belief seems to make sense.

• Internet businesses compete on price rather than service, so the prices must be good.
• An internet store can provide economies of scale - serving the whole country or region from a single warehouse, instead of needing an outlet in each town.
• An internet store can offer a much larger range of goods without increasing the cost of inventory - the so-called Long Tail phenomenon
• An internet store typically has lower overheads - cheaper premises and fewer staff
• An internet business may be run as a start-up, with less dead wood. So it is more agile and less bureaucratic. 

But there are some questionable assumptions here, as well as some counterbalancing concerns.

• The economic and logistical costs of delivery and return can be significant, especially for low-ticket items. With clothing in particular, customers may order the same item in three different sizes, and then return the ones that don't fit.
• Investors previously poured money into internet businesses, and the early strategic focus was on growth rather than profit. As internet business become more mature, investors will be looking to see some decent returns on their investment, and margins will be pushed up.
• And then there is differential pricing ...

One of the key differences between traditional stores and online stores is in pricing. Although high street retailers often drop prices to clear stock - for example, supermarkets have elaborate relabelling systems to mark-down groceries before their sell-by date - they do not yet have sophisticated mechanisms for dynamic pricing. Whereas an online retailer can change the prices as often as it wishes, and therefore charge you whatever it thinks you will pay. According to Jerry Useem,

The price of the headphones Google recommends may depend on how budget-conscious your web history shows you to be.

I heard Ariel Ezrachi talking about this phenomenon at the PowerSwitch conference in Cambridge a few weeks ago. (I have not yet read his new book.)

There is an assumption is that the internet is a blessing when it comes to competition. Endless choice. Ability to reduce costs to close to zero. etc ... What you see online has very little to do with the ideas we have of market power, market dynamics, etc. everything is artificial. It looks like a regular market, with apples or fish. But because it’s all monitored, it’s not like that at all. What you see online is not a reflection of the market. You see the Truman Show — a reality designed just for you, a controlled ecosystem. (via Laura James's liveblog)

In his play Lady Windermere's Fan, Wilde offered the following contrast between the cynic and the sentimentalist.

Lord Darlington: What cynics you fellows are!
Cecil Graham: What is a cynic?
Lord Darlington: A man who knows the price of everything and the value of nothing.
Cecil Graham: And a sentimentalist, my dear Darlington, is a man who sees an absurd value in everything, and doesn’t know the market price of any single thing.

According to one of the participants at the PowerSwitch conference, some eCommerce sites quote higher prices for Apple users, based on the idea that they are less price-sensitive and can afford to pay more. In other words, the cynical Internet regards Apple users as sentimentalists.

If there is an alternative to this calculative thinking, it comes down to reestablishing trust. Perhaps then retailers and consumers alike can avoid an artificial choice between cynicism and sentimentalism.

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Update (2020) added a link to a new paper by Frederik Borgesius, which looks at some of the legal as well as ethical implications of differential pricing.

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Emma Brockes, I found something I like in a store. Is it wrong to buy it online for less? (Guardian, 3 May 2017)

Frederik Zuiderveen Borgesius, Price Discrimination, Algorithmic Decision-making, and European Non-discrimination Law (European Business Law Review, 2019/20)

Ariel Ezrachi and Maurice Stucke, Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy (Harvard University Press, 2016) - more links via publisher's page

Tim Harford, Exploitative algorithms are using tricks as old as haggling at the bazaar (Financial Times, 5 October 2018)

Laura James, Power Switch - Conference Report (31 March 2017). Further links including video via Power Switch Conference (March 2017)

Joshua Kopstein, Is Amazon Price-Gouging You? (Vocativ, 4 May 2017) via @charlesarthur

Jerry Useem, How Online Shopping Makes Suckers of Us All (Atlantic, May 2017)

Price-bots can collude against consumers (Economist, 6 May 2017)

The Dilemma of Showrooming, (Daniels Fund Ethics Initiative, University of New Mexico)

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Related posts: Online pricing practices to be regulated? (October 2009), Predictive Showrooming (December 2012), Showrooming and Multi-Sided Markets (December 2012), Showrooming in the Knowledge Economy (December 2012), The Price of Fish (January 2013), Power Switch Conference (March 2017), The Idea of Showrooming (July 2017), Shoshana Zuboff on Surveillance Capitalism (February 2019)


Updated 11 July 2020

Inspector Sands to Platform Nine and Three Quarters

Last week was not a good one for the platform business. Uber continues to receive bad publicity on multiple fronts, as noted in my post on Uber's Defeat Device and Denial of Service (March 2017). And on Tuesday, a fat-fingered system admin at AWS managed to take out a significant chunk of the largest platform on the planet, seriously degrading online retail in the Northern Virginia (US-EAST-1) Region. According to one estimate, performance at over half of the top internet retailers was hit by 20 percent or more, and some websites were completely down.

What have we learned from this? Yahoo Finance tells us not to worry.

"The good news: Amazon has addressed the issue, and is working to ensure nothing similar happens again. ... Let’s just hope ... that Amazon doesn’t experience any further issues in the near future."

Other commentators are not so optimistic. For Computer Weekly, this incident

"highlights the risk of running critical systems in the public cloud. Even the most sophisticated cloud IT infrastructure is not infallible."

So perhaps one lesson is not to trust platforms. Or at least not to practice wilful blindness when your chosen platform or cloud provider represents a single point of failure.

One of the myths of cloud, according to Aidan Finn,

"is that you get disaster recovery by default from your cloud vendor (such as Microsoft and Amazon). Everything in the cloud is a utility, and every utility has a price. If you want it, you need to pay for it and deploy it, and this includes a scenario in which a data center burns down and you need to recover. If you didn’t design in and deploy a disaster recovery solution, you’re as cooked as the servers in the smoky data center."

Interestingly, Amazon itself was relatively unaffected by Tuesday's problem. This may have been because they split their deployment across multiple geographical zones. However, as Brian Guy points out, there are significant costs involved in multi-region deployment, as well as data protection issues. He also notes that this question is not (yet) addressed by Amazon's architectural guidelines for AWS users, known as the Well-Architected Framework.

Amazon recently added another pillar to the Well-Architected Framework, namely operational excellence. This includes such practices as performing operations with code: in other words, automating operations as much as possible. Did someone say Fat Finger?

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Abel Avram, The AWS Well-Architected Framework Adds Operational Excellence (InfoQ, 25 Nov 2016)

Julie Bort, The massive AWS outage hurt 54 of the top 100 internet retailers — but not Amazon (Business Insider, 1 March 2017)

Aidan Finn, How to Avoid an AWS-Style Outage in Azure (Petri, 6 March 2017)

Brian Guy, Analysis: Rethinking cloud architecture after the outage of Amazon Web Services (GeekWire, 5 March 2017)

Daniel Howley, Why you should still trust Amazon Web Services even though it took down the internet (Yahoo Finance, 6 March 2017)

Chris Mellor, Tuesday's AWS S3-izure exposes Amazon-sized internet bottleneck (The Register, 1 March 2017)

Shaun Nichols, Amazon S3-izure cause: Half the web vanished because an AWS bod fat-fingered a command (The Register, 2 March 2017)

Cliff Saran, AWS outage shows vulnerability of cloud disaster recovery (Computer Weekly, 6 March 2017)

On Customer Insight

On Customer Insight from Richard Veryard

Showrooming and Multi-sided Markets

As a retail phenomenon, #showrooming exposes a conflict of interest between online and traditional retailers. Many shoppers will examine a product in a traditional store, and then buy it from an online retailer or discount warehouse. The first retailer incurs costs - including cashflow, wear and tear on the product, as well as unproductive use of staff time and knowledge - while the second retailer takes the revenue.

To complete the story, there may be another class of customer, who is happy to buy the ex-demonstration product from the first retailer at a discounted price. Thus there are five distinct roles in this game: the product supplier, the first and second retailer, the first and second customer. (In addition, if the customers are using their mobile phones in the stores, we should add the players in the mobile ecosystem.)

The earliest manifestation of this I can remember was buying records. You could listen to an LP in the record store, and then get a pristine copy (without the shop assistant's fingerprints) by mail order from a company appropriately called "Virgin".

Many retailers believe they lose out from this phenomenon, and some have attempted to prevent it. (Ever wondered why you don't get a good cellphone signal inside a large store?) Earlier this year, both Target and Wal-Mart decided to stop stocking Amazon devices, although continuing to stock Apple devices. More recently, Wal-Mart has changed its position, and now claims to embrace showrooming.

By singling out Amazon, Target and Wal-Mart were making it clear that it is Amazon's role as a retailer that they regard as a competitive threat. Although Apple also sells its devices online, it is presumably not regarded as an equivalent threat. In which case, banning Amazon products looks like a gesture of despair rather than an effective tactic.

Thinking of this as a multi-sided market prompts us to look at the direct and indirect flows of value between the players. It is as if the first retailer is providing an unpaid "service" to the second retailer, and the first customer is providing an unpaid "service" to the second customer. At present these are not genuine services, but it is possible to conceive of an ecosystem in which the product supplier or second retailer paid some form of commission to the first retailer. For all I know, that may already happen in some sectors.

Wal-Mart hopes to control showrooming by encouraging its customers to use its own mobile app, which attempts to steer customers towards its own online store. I wonder how many customers will accept this control, and how many will take the trouble to resist it.

Some large High Street retailers seem to have given up the idea of stocking goods: if you like something on display, you can order it. This has long been true for large furniture items such as beds, but is becoming more common for smaller items, as Simon Heffer complains.

Meanwhile, showrooming can work both ways. Last week I ordered a book from my local bookshop, having previously looked it up on Amazon. It was 5pm Friday when I placed the order, and they phoned me at 11am on Saturday to tell me it had arrived. (If I'd ordered it from Amazon, paying extra for 48 hour delivery, when would it have arrived? Monday, Tuesday?) So that's showrooming in reverse.

Finally, instead of selling individual products, the showroom itself can become the experience. @KBlazeCarlson sees IKEA as a prime example, and quotes Alan Penn, professor of Architectural and Urban Computing at UCL, describing the IKEA experience as "psychologically disruptive". "Part of their strategy is to take you past everything," he says. "They get you to buy stuff you really hadn’t intended on. And that, I think, is quite a trick."

Chris Petersen adds, "Instead of product centric merchandising, IKEA’s showroom is perhaps the ultimate place merchandising, where the consumer solution is focused on the most personalized dimension – the consumer’s own lifestyle and living space." Whether IKEA can replicate this experience online in the virtual world, as suggested in Patrick Nelson's piece, is another matter.

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Kathryn Blaze Carlson, Enter the maze: Ikea, Costco, other retailers know how to get you to buy more (National Post, June 2012)

Dani Deahl, Amazon granted a patent that prevents in-store shoppers from online price checking (The Verge, 15 June 2017)

Simon Heffer, My futile hunt for a lamp in John Lewis reveals why the High Street is doomed (Daily Mail 15 January 2013)

Brett Molina, Is 'showrooming' behind Target move to drop Kindle? (USA Today, May 2012)

Patrick Nelson, Brick-and-Mortar's Showrooming Scourge (E-Commerce Times, Nov 2012) via First Insight

Sarah Perez, Amazon, now a physical retailer too, is granted an anti-showrooming patent (TechCrunch, 16 June 2017)

Chris Petersen, To beat showrooming … change the showroom! (IMS results count, June 2012)

Marcus Wohlsen, Walmart.com CEO: We Embrace Showrooming (Wired, Nov 2012)

Amazon's Showrooming Effect And Quick Growth Threaten Wal-Mart (Forbes, Sept 2012)

Related posts: Showrooming in the Knowledge Economy (December 2012), Predictive Showrooming (December 2012)

Updated 16 June 2017

Generalization as Business Strategy

#entarch @BBCRadio4 Just listening to a business story on the radio: the music retailer HMV is now going to be selling clothes from its flagship Oxford Street store. Of course this kind of thing is nothing new - retailers have always tried to diversify, and there are clothes shops in Oxford Street that are trying to sell CDs, while large retail chains that started as grocery stores now sell books, clothing, computers, mobile phones and furniture, as well as financial services.

For many enterprise architects, this kind of diversification strategy seems like a "no-brainer". If you have a robust retail process, together with the assets and infrastructure to support the process, then surely it makes sense to make as much use of the process as you can.

But successful diversification is by no means a "no-brainer" (how I despise that term and its implications), but requires careful consideration and planning. This kind of planning should be meat and drink for enterprise architects, but not if they imagine that the decision follows automatically from some abstract process model.

So what models and techniques would be relevant to support an intelligent diversification strategy? And how comfortable are enterprise architects with using these models and techniques to support business decision-making?

These are questions I've looked at before on this blog (see the generic v specific category) and will certainly look at again. Meanwhile, I'd welcome your ideas.

Two Second Advantage

There are two reasons I like TIBCO's new slogan "The Two-Second Advantage". (I have some reservations as well, but let's start with the good bits.)

advantage means something to business

The first reason is that it actually means something to the business, unlike the widely misunderstood technical term “real-time”. One company that is currently boasting “real-time” performance is SAP, which claims that its in-memory databases will result in analytics that are “really in real-time”. Larry Dignam quotes Hasso Plattner as follows. “In analytics, there’s theoretically no limitation on what you can analyze and at what level of detail. (In-memory databases) mean reports on a daily basis, hourly basis.” [ZDnet] @davidsprott calls this In-memory madness. Even if an hourly or daily report counted as “real-time” (which it doesn't), this kind of technical wizardry doesn't make any sense to the business.

On a Linked-In discussion thread recently, I've seen vendors excusing their misuse of the term "real-time" (to describe software that isn't strictly, or sometimes even remotely, real-time) by claiming that the meaning of technical terms evolve over time. Oh yeah, very convenient. But we shouldn't allow vendors to fudge perfectly good technical terms for their own marketing purposes, any more than we should tolerate car manufacturers self-interestedly redefining the word "friction".

(In The 2 second advantage...the 2 culture disadvantage? Vinnie Mirchandani praises TIBCO executives for being able to talk business, and contrasts them with the IT analysts in the expo hall, with a special dig at Gartner.)

Unfortunately, TIBCO is not content with the "two second advantage" slogan, and we find TIBCO CEO Vivek Ranadivé over-egging the pudding by introducing some additional notions including Enterprise 3.0. Transcript of HCL keynote by TIBCO CEO Vivek Ranadive (April 2010). For @neilwd "Enterprise 3.0 ... is the sound of a company trying too hard" (TIBCO, Enterprise 3.0 and the two-second advantage, May 2010. See also Tibco’s Hits and Misses (Ovum, May 2010).

advantage is relative

The second reason I like the phrase "Two Second Advantage" is that it focuses our attention on the business advantage - not of raw speed but of getting there first. If you are a speculator who judges that some asset is overvalued or undervalued, the way to make money from this judgement is to buy or sell and then wait for other speculators to arrive at the same judgement. Being just ahead of the pack is actually more profitable than being a long way ahead, because you don't have to wait so long.

And although simple decisions can be taken quickly, complex decisions need time for understanding. (See my presentation on Mastering Time.)With complex decision-making, it's about spending just enough time to process just enough information to make a good enough judgement.

Ranadivé also talks about two trends - the increasing volume of data and the diminishing half-life. (In physics, the concept of "half-life" suggests a long tail of residual value - just as a radioactive sample will always remain somewhat radioactive, so the value of data never reaches zero.)

But as @neilwd points out, these trends don't necessarily refer to the same kinds of data, especially if we measure data volumes in terms of physical storage, since these volumes are dominated by email attachments and rich media. Maybe we need to find a way of measuring data volumes in terms of information content ("a difference that makes a difference"): as the cost of data transmission and storage continues to get smaller, it is not the number of megabytes but the number of separate items (giving managers the experience of being overloaded with information) that really matters.

Even if we limit ourselves to traditional data, the relationship between data volumes and response speed is not as simple as all that. Let's look at a specific example.

If a retail store gives a hand-held scanning device to the customer and/or places electronic tags on all the goods, it can collect a much higher volume of data about the customer's behaviour - not merely the items that the customer takes to the check-out but also the items that the customer returns to the shelf. As technology becomes cheaper, this enables a huge increase in the volume and granularity of the available data, collected while the customer is still shopping, and therefore the retailer actually has more time to use the data before the customer leaves the store.

For example, you might infer from the customer's browsing behaviour that she is looking for her favourite brand of pasta sauce. The shelf is empty, but there's a new box just being unloaded from a truck at the back of the store. Find a way of getting a jar to the customer before she reaches the checkout, and there's your two second advantage.

From Espresso to Instant

Starbucks is changing its business model. Or as CEO Howard Schultz tells the Huffington Post, Staying Real in an Instant. Starbucks will be selling shots of instant coffee, for under a dollar a cup. The UK price is said to be around 60p.

Some people may have thought that "espresso" was the Italian word word for speed. (It isn't - it means "pressed".) So what could be faster than express coffee? Instant coffee!

Of course the word "instant" isn't about getting the coffee more quickly either, it is about doing away with all that fancy machinery, in whose use Starbucks makes such a charade of training its baristas. (A year ago, Schultz ordered all US stores to close for a three-hour training session "as part of an effort to improve coffee quality and revive the chain's flagging fortunes" [Guardian, 26 Feb 2008].)

Shultz now claims to be responding to the increasing mobility of consumers. "Imagine a cup of Starbucks VIA Ready Brew on a mountaintop" he says, as if willing us to imagine millions of Starbucks customers on some remote and implausible trek.

But clearly his real interest is selling mass market coffee. He hopes that the Starbucks instant coffee will be not only better-tasting but also "paradigm-changing" (whatever that means), and hopes "to turn on a whole new set of coffee drinkers to the Starbucks brand". But the obvious risk is that the old set will be turned off. He acknowledges that this move is a gamble (he calls it "a considered bet"), and expects "to learn a lot ... over the coming weeks". You bet.

In what sense does this count as a new business model? Starbucks already sells ground coffee and coffee beans in supermarkets across the USA. Many rival coffee purveyors have already shifted to the Gillette model, in which the coffee machines are sold cheap or practically given away, and you make your money selling overpriced pods of coffee.

The challenge faced by Starbucks is not choosing one business model, but attempting to combine two or three different (and possibly incompatible) business models at the same time. Such composition faces questions of cross-subsidy, brand dilution or erosion. Are there any reliable rules or patterns governing the interoperability (compatibility and composition) of business models?

See also

• Starbucks to Combat Brand Erosion with Loyalty Program (Marketing Vox, March 2008)
• Starbucks: How Growth Destroyed Brand Value (Business Week, July 2008)

Update

• Why Starbucks should become a retail bank (John Gapper, FT, May 2009)
  

Dynamic Pricing

In his marketing blog, Seth Godin asks about dynamic pricing.

Why doesn't fresh fish cost more than the same fish a day later? bowling a few cents less when it's not so crowded? movie tickets more on the day a movie debuts? why don't computers with a three-year obsolence cycle have predictable pricing that starts high and gets near cheap just before the new upgrades?

I first came upon this idea in Kevin Kelly's book New Rules for the New Economy (available online).

A head of lettuce today ... does not contain any financial information beyond a price sticker. Once applied, that price is fixed, too. It doesn’t change unless a human changes it. The economic consequences of lettuce sales elsewhere, or a change in the general global economy do not affect the head of lettuce itself. Instead, lettuce-related information flows through wholly separate channels—news programs or business newsletters—that are divorced from the lettuce itself. The lettuce is economically inert.

The realm of the animated is different. It’s vastly interconnected. In this coming world a head of lettuce carries its own identity and price, displayed perhaps on an LED slab nearby, or on a disposable chip attached to its stem. The price changes as the lettuce ages, as lettuce down the street is discounted, as the weather in California changes, as the dollar surges in relation to the Mexican peso. Traders back in supermarket headquarters manage the "yield" of lettuce prices using the same algorithms that airlines use to maximize their profits from airline seats. (An unsold seat on a 747 is as perishable as an unsold head of lettuce.)

[Source: Chapter 5]

Kelly describes this as animating the lettuce, following the principle of "Feed the Web First". (Presumably Webb's Wonder 2.0.)

This kind of animation calls for new and more complex kinds of system interconnection, and new and more complex kinds of commercial arrangement. Shifting the enterprise further into the real-time.

See also Dynamic Pricing Update (November 2023)