Valu Valu nows ranks commodities as well, see http://www.valuvalu.com/rankings.html
Since it has been ranked #1 by our model, Sugar has gained 12.7% in only 14 days, and outperformed all other major commodities.
More than a full year without a single post. Traffic down to almost inexistent.
There are only 2 possible explanations:
a) ValuValu is dead-pooled
b) we were busy re-thinking our business and preparing something new..
I’ll let you guess….
It seems Craigslist is a jar of frustration sometimes. The simplicity, the ’100% free’, the ‘local only’ aspects are nice, but more often than not, transactions are far from smooth, and whatever the price you post, there’s always 4 guys asking if you’d be ok to lower your prices by $50…
xConomy Seattle presents a forum about the Future of Search on November, 30th. It made me think about Search trends, and what can we expect in the Future…
First, mobile will become more predominant. Mobile devices as the only truly ubiquitous computing platform, and recent improvements made them, at last, usable as Internet devices (hello, iPhone). But search on mobile will remain very different than on larger computers. Mobile devices are mostly used in interstitial periods. The ‘always connected’, always busy’ mantra of contemporary life makes use draw our smartphone as soon as we’ve got 30 seconds of idle time. Your partner gone to the restroom? Check mail… Waiting at a traffic light? Download news… The main consequence is that mobile devices are only used for short, interruptible periods of time.
Whereas some companies saw Local search as the killer search application for mobile, the main hurdle remains monetization. Imagine an End User looking for the nearest Home Depot. Giving its address and directions for free means no revenue at all; displaying a Lowe’s advertisement instead is unacceptable; asking to pay for the information is just wishful thinking. There’s much less serendipity in search when done from a mobile device, and therefore much less monetization opportunities. Not because of device limitations, but because Users are themselves time and location constrained. While major search engines will continue to invest massively in improving their mobile experience, mobile search per se (i.e. on the go) will never match the money-printing ability of Web search. Unless it brings serendipity in a few seconds. Unless it brings relevant search results even before an End User entered her query…
Even notebook and desktops users want instantaneous and effortless gratification. Internet Users, who spend less time in front of the TV, still appreciate the mindless, motionless comfort of drifting away beholding their screen. Because they have introduced a fundamental new way to consume information, Search Engine are poised to bring the next revolution as well. Continuous, time-unified consumption (e.g.books to TV shows) is progressively exploding into fragmented, self-guided time segments. This is why the Prescient Web will be brought by Search Engines, and not by PointCast revisited.
Automated clustering (grouping alongside most common keywords) never delivered its promise for better results organization. A better approach is simply to organize results based on their source or media. By separating Image, local and Web search results for instance, Search Engines are able to provide a simpler interface to each media. Engines continuously adding new sources (music, social media,…) will further discriminate their origin in order to keep the results intelligible enough for End Users.
But the real change will come from intense personalization. A personalization that goes well beyond the filtering or sorting of results based on previous history, but fully leverages the social web to deliver meaningful, pertinent search results… even before entering any keyword!
People are now connected enough, both technically and socially, to make it possible to ‘map’ all their inbound and outbound influence, their topics of interest, and their information consumption habits. Take all the browsing history, search history, tweets, blog posts, followers, RSS subscriptions, eMail exchanges, and you’ll get a pretty accurate understanding of somebody’s work and hobbies. If of one side, you know what End Users search or consume, and on the others, you monitor the ‘background noise’ in their information sphere, you’ll be able to pinpoint news or data they did not even know they should search for, at the first place.
For instance, I’m into photography. My Search Engine should know that, given the queries I sporadically enter to check out new Canon cameras. It could automatically tell me when a new model’s out, even if I don’t know it could be.
A Search engine could even analyze the transmission path of informations, and significantly reduce the propagation time by short-cutting intermediary nodes and feeding users automatically.
One strength of such Prescient Search is that it doesn’t need to be perfect, for it only supplements the current interface. It’s simply a matter of presenting users, by default, a set of data thought to be interesting for them. Not unlike Amazon.com, a Search Engine home page will display plenty of personalized items, while giving unhindered access to the whole data. It can also be applied one field at a time, and leverage the efforts already made for recommending music or movies for instance. Prescient Search will also be continuously improved, alongside the emergence of the semantic Web, new social networks and the simple fact that computing resources are becoming more affordable.
Needless to say, unlike mobile search, the monetization potential is real. Both on a consumer side, through advertising, and on a business side, where predictive information discovery could revolutionize organizations. Imagine an Enterprise search engine that would surface the problems even before people realize it, or a system that would automatically spotlights emerging trends. How much would it be worth, in the modern information economy?
As you may have noticed, since the last major release, VALUVALU now predicts the prices of consumer goods, 30 days in advance. How can we do that, and what if we could also predict stock prices, and get very rich?
First and foremost, predicting the prices of consumer goods is NOT like predicting stock prices!
Alas, we don’t know of any good method for predicting the stock market! There’s just too much information one should take into account, most of it not publicly available. For instance, one problems is not knowing the ‘investors horizon’: unlike retailers who want to sell-through as soon as possible with the best possible price, investors’s horizon is very disparate, and also change with the stock variations themselves. But most of all, a stock price is driven by the expectation of a company’s future earnings. Some believe the market is efficient, so all future revenues are already displayed in the stock price itself. But then everybody could see it, so theoretically there could be no losers or winners: clearly it’s not working this way! Others think the fundamentals are often overlooked (cf. for instance Value investing). In any case, it’s been decades since very smart (and very dumb) people try to find a winning formula, without much success so far.
Consumer goods market is different for several, other fundamental reasons. For one, there’s almost no ‘accident’. In high-tech, for instance, products got some reviews, are more or less appreciated, then will progressively become obsolete. It’s very unlikely the price of a digital camera will significantly go up, 2 years after launch. Seasonality is also a strong trait. If historically skis were always discounted heavily the last week of March, it’s probable it will happen again. Best of all, we can apply that probability on a new model, even if without any prior history on that product in particular.
To summarize, observing the history of consumer goods prices allows to understand the underlying trends in supply/demand, and therefore predict future prices. That’s simply not really the case with company stocks. And believe me, I like Technical Analysis!
1) Like any wars, Price Wars often start for the wrong reasons, and cannot be truly won. Avoid price wars! If you need to revise your positioning, try to ‘add value’ and benefits instead (e.g. free shipping, return policy, generous loyalty program), which may cost you less and are easier to suppress later.
2) Anticipate Demand/Supply trends to change your prices before your competitors. For instance, if there’s more demand than supply for a product, do not wait for stocks to be depleted, and increase your prices now. Not only will you generate more revenue, but mostly you’ll build inventory to sell at a much higher price in the future. Likewise, it’s more profitable to discount early (but not too much).
3) Identify and keep an eye on the Price Leader (if there’s one). You should match the price variations of this Price Leader before the rest of the market, especially when he raises his prices.
4) Unlike Consumers, Competitors are rational… most of the time. It means they’re more likely to be objective, and more prone to act Strategically. This is why lowering your price may have a devastating, long-term effect as in can trigger a price War.
5) When you increase prices, you need your competitors to know, so they feel more inclined to raise theirs as well, and the whole industry makes more money. When you decrease your prices, that’s a short-term advantage, and you need the Customers to know, but (if possible) not your competitors.
Please welcome the new version of VALUVALU.com. A long, long time ago (like, 10 years!), people thought the Internet would bring perfect Price transparency. All prices would become available on the Web; therefore both Consumers and Businesses would know, instantly, the true market prices. But eventually, Prices became even more confusing.
On one side, businesses now tend to price more dynamically. The idea of a ‘fixed Price’ that remained constant over time is passé. Retailers increasingly rely on specials, promotions and various discounts to maintain the most efficient price levels. And the rate of price changes has increased dramatically in the last 20 years.
On the other side, Price comparison engines do not give a complete, accurate view of all prices. Their business model being getting a sales commission when sending a customer to an affiliate reseller, they can not, want not and could not list all retailers or give ‘neutral’ advices to potential customers. As a matter of fact, economically speaking, retailers willing to give away affiliation fees can not be the cheapest ones!
We started VALU VALU with the motto of ‘Bringing Scientific Pricing to Everybody’. After testing and fine-tuning our Pricing Engine on video games classifieds, we’re very excited to announce today the next Major release of VALU VALU: a tool to monitor prices for ANY standard manufactured product!
What’s the true market price? Where can I find the really cheapest sellers? How much would it cost 30 days from now? When was the last significant discount? How do I position myself vis-à-vis my competitors? All those questions now have an answer: valuvalu.com
Which of the following prices would be the best, from the seller’s perspective: $29.95, $29.99, $30.00 or $30.05?
According to the principle of ‘Price Elasticity’, the highest the price, the lower the sales. Therefore $29.95 is the price driving the largest volume.
According to very basic psychology, Customers make a divide between the ‘less than $30’ and the ‘more than $30’, putting $29.95 and $29.99 on one side, and $30.00 and $30.05 on the other. People being attracted toward cheaper prices (except if it’s a Veblen good) to maximize their surplus, $29.95 and $29.99 will drive significantly more volume.
My take: the answer relies within the market, and it’s $29.99; but not for the reason you may imagine!
Let us pre-suppose the market is efficient. Imagine millions of people sell millions of products at the price they want. Run this ‘experiment’ for a little while, and you could observe the most efficient price. The good news is, thanks to the Internet, such information is available. You ‘simply’ have to count the number of occurrences for each possible price.
Even more important, if some price levels are way more frequent than others, Buyers will tend to mentally settle on those prices. By showing what Buyers have in mind, one reduces their effort to ‘visualize’ the price. Reduced effort equals happier customers equals more sales. Therefore you’d better put your prices on those ‘magnets’.
Here are a few ‘best sellers’ in the $10 to $50 range:
$10
$14.95, $14.99 or $15.00 (all equivalent)
$19.99
$25.00
$29.99
$35.00
$39.99
$45.00
$49.95 or $49.99 (equivalent)
Oddly enough, $17.99 is more popular that $18.00, but $38.00 is more popular than $37.99.
By far, the most common price level in the whole range is $19.99. As for the ‘loosing price’, it’s $49.65.
Next time you price, remember the best price from the Seller’s point of view is often the price the Buyer had in mind… Except if you really want her to remark your price!
A caveat: One would have to poll the number by product categories. Nothing proves the ‘psychological’ price levels of food or electronics are the same.
