The Sigmoid Curve or Second Curve, is a useful tools in understanding the natural life cycle of a product, an organization or even a relationship. The secret to constant growth is to start a new S Curve before the first one ends and the right place to start the second curve is at a point A when there is the time, energy and resources to get the new curve through its initial stages before the first curve plateaus and declines.

Why do we not recognize the obvious principle of beginning the new curve at point A? Because at point A all the messages received by the individual or organization indicate that everything is fine... there is no need to change. The second curve, whether it is a new product, a new strategy, or a new program, is going to be different from the old. It has to be and so are the people leading the new curve. Those who lead the second curve are often not the people who led the first curve. For a time, new ideas and new people have to co-exist with the old until the second curve is established and the first begins to wane.


Markdown Prediction (Wall Street Journal Article)

Several retailers such as J.C. Penney Co., L.L. Bean Inc., Liz Claiborne Inc. and Gymboree Corp., is trying to perfect the science of the markdown. They have been experimenting with sophisticated new software programs to test principles similar to "yield management," which airlines mastered years ago to eke out the maximum profit from every seat. Like a seat on a particular flight, an item such as a bikini is in demand for a limited time; as the end of the season approaches, its value to customers plummets.

A big challenge: trying to outfox customers who have been more willing to wait and wait for a bargain. Using number- crunching consultants, armed with mathematical models pioneered by think-tank researchers and astrophysicists, the stores analyze historical sales data to pinpoint just how long to hold out before they need to cut a price-and by just how much.

Their progress marks a new step in a growing trend toward highly flexible prices-for everything from mortgages to eBay merchandise. Instead of taking a one-price-fits-all approach, buyers and sellers are increasingly meeting in customized marketplaces transformed by technology. With exploding competition from discounters and specialty stores, markdowns are soaring, making them a decisive issue in retailing. Marked-down goods, which accounted for just 8% of department-store sales three decades ago, have climbed to around 20%, according to the National Retail Federation.

Retailers hate markdowns. Discount an item too late, and stores are stuck with truckloads of inventory. Too early, and they lose profits as people snap up items thrown on the bargain table prematurely. Last month, Gap Inc. said its profit margins on June sales fell well below its internal forecast after it was forced to take deeper-than-expected markdowns on a mountain of merchandise, from Gap T-shirts to Old Navy shorts. And last week, Neiman Marcus Group Inc. cited steeper-than-planned markdowns for the second time in as many months in estimating a loss for its fiscal fourth quarter, ended July 28.

Much of the attention on markdowns in recent years has been regulatory, as state attorneys general charged numerous retailers with deceiving consumers by raising prices and then offering a discount off the inflated price. In some cases, investigators couldn't find any evidence that the goods had ever been sold at the so-called "original" price. In 2003, Kmart Corp. responded to a complaint from the Jewelry Advertising Review Program, a coalition of local Better Business Bureaus, which contended that Kmart was claiming to sell its jewelry at discounts from "original prices" that weren't frequently offered. Without admitting wrongdoing, Kmart said it had changed its jewelry pricing to sell its jewelry at regular prices for at least 183 days each year.

Behind the surprising gains: pricing analysis similar to that developed by the airlines, which can calculate with great precision just how many seats to hold open at premium fares for last-minute passengers and how many to sell ahead of time at lower prices. By analyzing several years' worth of sales data from similar items, Spotlight's retail software estimates a "seasonal demand curve" for each new product.

Sometimes resembling a jagged peak, other times a smooth wave, the curve predicts how many units would sell each week at various prices. For merchandise with short-term appeal-the bikini, for example-sales typically climb for several weeks, spike, then trail down until the "outdate," or the date a retailer wants to sell out of the item. The software also uses sales history to predict how sensitive customer demand will be to price changes, what economists call "price elasticity."

Retailers were drowning in sales data. To determine which items were stagnating on shelves, store buyers had to sift through stacks of weekly reports with overall sales of each product. The reports also listed inventory levels and how many weeks remained until the outdate. With thousands of different products selling in more than 100 stores, overwhelmed buyers had to plan most markdowns before each season began. They revised their plans twice a month, marking down an item at the same time across all stores.

That chain-wide approach, common to many retailers, often sacrifices goods that could have sold at full price in some stores, and ends up leaving too much merchandise unsold in others. Another common practice is to chip away at price tags, with lots of small discounts. Research concluded that a combination of two markdowns will never be as profitable as a single markdown. Arriving early enough to tempt customers, the first markdown gives the greatest boost to profits, and extra price cuts simply add profit-eroding labor costs.

Before using a software that utilize Second Curve to forecast markdown, one retailer had tried shifting its markdown dates. At one point, it carried some leftover merchandise into the next year, a practice it has abandoned. For the most stubborn clearance merchandise, it even offered an extra 25% off. "That created a lot of traffic, but it was terribly hurtful to the gross margin performance," says the retailer's chief executive.


S-Curve Technology Jump

Stories of technological obsolescence forced by a competitor can strike fear in the hearts of industry executives. Wise management at any company at any company must adopt a strategy of meeting change head on to ensure the firm's continued success. To anticipate technological progress, one tool often used by company strategist is the technology S-Curve. The concept of S-Curve tracks a progress of a base technology as a function of the R&D Effort or, if R&D is constant, of time. The concept was so named because the plot of technological progress most often takes the approximate shape of an S. In the beginning, progress for any new technology is slow. Then, as a critical mass of engineering expertise in the technology builds up, progress can be rapid, even exponential. After a while, however, the technology matures and progress slow.

To make the S-Curve method useful for decision-making in a commercial product environment several issues must also be considered. One is that the technology parameter used for the y-axis in an S-Curve analysis must be relevant from a Business/Financial/Market Perspective. That is, it must reflect the fact that the technology is less costly, more attractive to potential buyers, or in some way more profitable. For instance, when plotted on the same axes, the S-Curve for the incandescent lamp and for the fluorescent lamp product a technological discontinuity that results in a superior S-Curve. In terms of lighting efficiency, the fluorescent lamp provides greater value than the incandescent lamp. In such case, a company may gain competitive advantage by adopting the new technology - in other words - "by jumping to the new S-Curve". The rationale for making that leap is strengthened by the possibility that the new technology will ultimately replace the old one.

Another case is the 14-inch diameter magnetic disks that in the late 1970s dominated the hard drive technology. Magnetic drives with 130-mm disks, when introduced at the beginning of the '80s, were inferior to the 360-mm drives in areal density, for instance, and were not taken seriously by the makers of large-diameter disk drives. All the same, the areal density of 130-mm drives increased at a faster rate and came to exceed the density of 360-mm. The 130-mm drives overtook the 360-mm models as the dominant technology and the companies that had invested in them prospered. Had the makers of large-diameter drives periodically plotted the two S-Curve, by 1984 they would have seen the 130-mm drives were progressing at a faster rate and were thus worth switching to.

If the S-Curve methodology is such an effective technology and a central tool for corporate planning, why do many companies fail to make jumps to new S-Curves? The reason companies have refrained from jumping to new S-Curves, or in fact continue to have difficulty in making that jump, have much to do with how they have approached three areas;

• Business risk
• Identification of competitors
• Customer satisfaction