How Trend Forecasters Work


Fashion has its fair share of forecasters (hemlines up or hemlines down?), but every field needs people with the ability to make educated guesses about what the future holds.
Fashion has its fair share of forecasters (hemlines up or hemlines down?), but every field needs people with the ability to make educated guesses about what the future holds.
© Edward Le Poulin/Corbis

They say that in life, there are no sure things, but someone needs to call hot stocks, dispatch police patrols and declare next year's "it" look. That's where trend forecasters come in.

Trend forecasters -- analysts, planners and managers from all walks of life -- project everything from staffing and hiring needs to how best to design, produce, market and sell a product. They emerge in any field, from the scientific to the sartorial, that analyzes time-series data for hints of future trends. But rather than dealing tarot to catch the zeitgeist, they combine instinct, experience, theory and, in some cases, a mountain of math.

When forecasts work, they mean money, both for the companies and their conjecturers. As of 2011, trend forecasting was a $54 billion growth business [source: Barnett]. In 2015, job sites Indeed and Simply Hired listed salaries for jobs with trend forecasting as a requirement that ranged from $32,000 to $156,000 and averaged from $63,000 to $71,000 [sources: Indeed; Simply Hired].

But the spoils of trendspotting are balanced by the risks of making bad calls. In science, failed forecasts can mean lost credibility; in the high-stakes worlds of fashion and finance, backing the wrong horse can mean a multimillion-dollar mistake. Unfortunately, the same instincts that enable some to, as poet William Blake put it, "see a world in a grain of sand" can also make them miss the forest for the trees.

Take two examples from 2006. That year, as interest in '80s fashions resurged, a sharp style scout abroad rightly called the return of skinny jeans after seeing Russian kids roll up their pant legs [source: Zimmerman]. That same year, the Federal Reserve and other economists failed to spot the housing bubble that would precipitate the financial crisis of 2008 [sources: Appelbaum; Nau].

The list goes on. Last century, some authorities predicted that trains were completely off track and that people would never become hung up on telephones. The president of the Michigan Savings Bank called automobiles a fad, and actor Charlie Chaplin said the same thing about movies. In 1939, The New York Times predicted that "TV will never be a serious competitor for radio because people must sit and keep their eyes glued on a screen; the average American family hasn't time for it" [source: Watson]. You wonder what they would have thought of binge-watching.

In the end, no forecast can survive bad data, faulty assumptions or random events. That's why the best trend forecasters don't mistake the map for the territory.

Knowing the Territory

Before maps became widely available, army commanders strove to know every hill, dale, river and forest of the terrain over which they marched. They knew that winning the day depended on this knowledge, applied via their training and experience.

Modern trend forecasters might make their calls in boardrooms instead of on battlefields but, in picking emerging fashions as in piercing the fog of war, theory and observation walk hand in hand. While many degree programs offer courses in statistics, and while fields such as fashion, economics and business management emphasize trend analysis and prediction, a successful trend forecaster also benefits from a broad array of knowledge and experience. Consequently, people break into trend forecasting from a slew of backgrounds. This holds especially true in fashion forecasting, where tracking the publications, films, music, art, culture, politics and economic projections that help inspire the next big color or fabric calls for a diverse array of doyens indeed [sources: Berelovich; Brekke; Pedersen; Pressman].

"Our activity requires a strong creative intuition and, at the same time, a very deep understanding of market realities," says Elodie Jolivet, products marketing and communication manager for PeclersParis, a global trend consulting agency.

"If some trend forecasters of our team are actually fashion designers or graphic designers, some have a finance or sociology background. We are working with economists and anthropologists. We explore marketing studies."

Dawn C L Pedersen, creative director at TRENDZINE Fashion Information Media Network, agrees, and adds that experience helps forecasters put themselves in customers' shoes, be they hiking boots or pumps. Such a connection is essential, because future fashions must pop in a popular way, one that avoids alienating everyday shoppers. Fortunately for fashion folk, technology is today smoothing the transition from catwalk to sidewalk by priming customers to early-adopt avant-garde styles.

"Consumers have become more knowledgeable about trends and are aware of new ideas and concepts via the Internet, where catwalk shows can be watched live, or through social media, where high-profile celebrities, who tend to adopt trends early, pave the way for a fresh new look," she says.

A career in trend forecasting means obsessing over every topic essential or tangential to your field and grasping how they all fit together. This holds true whether you work as part of a large team at a trend-forecasting business or as a company's lone futurist [source: Brekke]. But it's also just the tip of the iceberg, because today's forecasters also must come to grips with the mountains of data now available via big data and social media -- a landslide that is already changing the landscape and rendering yesterday's roadmaps useless.

The Rapidly Evolving Field of Trend Forecasting

Think you could predict the color of the year? U.S. color company Pantone does it all the time. (By the way, the 2015 color of the year is Marsala, or Pantone 18-1438.)
Think you could predict the color of the year? U.S. color company Pantone does it all the time. (By the way, the 2015 color of the year is Marsala, or Pantone 18-1438.)
© Rubtsov, A./Corbis

The successive influences of the Internet, social media and big data have created challenges for established trend forecasting sectors and opportunities for emerging ones.

Take fashion and design. Back in the '60s and early '70s, fashion research basically meant hanging out in European clubs and cafes. Stylebooks were literally printed volumes published with an 18-month lead time to give designers and buyers time to work [source: Blair]. As the sector expanded, so did its methods, which soon involved market analysis, political and economic trends, and fads in music, food and design [source: Zimmerman]. But by 2008, forecasting firms began to distinguish themselves by using the Web to offer cheaper, more targeted sub-services and short-term trend tracking [source: Barnett].

Meanwhile, new data and analysis methods spurred other fields, including law enforcement and health care, to capitalize on trend forecasting methods. Predictive policing uses techniques like hotspot analysis, crime mapping and heat maps -- color-coded grids showing crime intensity by time or area -- to deploy resources more effectively [source: Perry et al.]. Health officials, historically hesitant to adopt trend forecasting -- due to a science-based culture and limits imposed by privacy rules and a segmented provider marketplace – are also beginning to see its value, particularly as electronic health records systems gain prominence and as mergers and acquisitions increase [source: Blumenthal].

"One of the major new initiatives in health care is evidence-based medicine," says Larry Blumenthal, chief financial officer of Good Night Pediatrics, which provides nighttime medical care for children. "This approach uses available historical data to more accurately identify trends, develop more effective and standardized clinical pathways and protocols, and adopt those protocols on a broader scale."

But some critics question whether we have a handle on where all this data is taking us. Take business and economics, for example. The traditional models of economic forecasting used factors such as demographics, government deficits and debt, interest rates, oil prices and employment data to gauge the economy's direction. Businesses then merged these forecasts, along with data related to their own fields, to guide their budgets, hiring and salaries. Enter big data, which channels oceans of up-to-the-minute information to analysts based on the notion that more data is better. And in many cases it comes in handy, as when businesses use it to track how customers and employees use goods and services, or to trace the spread of opinions and tastes via social media, or to build models of customer preferences, all of which open doors to new trend analysis methods [sources: Lohr; Rosenbush and Totty].

But critics have grown increasingly uncomfortable with big data's reach and access. More importantly for trend forecasters, they argue that it produces algorithms that are too secret, too unfair and too poorly understood [sources: Auerbach; Pasquale].

The Devil in the Details

Trend forecasters tease out patterns via a mixed toolbox of techniques, but their predictions are only as good as their data, assumptions and skills. You can run a trend analysis in Microsoft Excel but, without a guiding rationale, what you end up with is likely worse than useless -- it's probably misleading to boot.

If you want your forecasts to say something meaningful, you can't just "plug and chug." You must gather good data, make sure it says what you think it says and check that it doesn't contain hidden relationships that will torque your results. You might need to adjust it, too, for inflation or seasonal variations. And, most importantly, you must know and respect your core assumptions -- including whether you can reliably project present patterns into the future.

Three of the most commonly used forecasting methods are called linear trend (aka simple linear regression), multiple regression and autoregressive integrated moving average (aka ARIMA). Here's the gist of each:

  • Linear trends fit a line to scattered data. They make for fairly vague trend gauges, and analysts typically turn to them when stuck with scarce or unreliable data [source: Nau].
  • Multiple regression provides a handy way to deal with variables when more than one influence is in play, as is the case with interest rates and other economic indicators. Such models only work properly when you know and have data for all of the relevant forces [source: Nau].
  • ARIMA allows forecasters to deal with events that are not independent from one another, and excels at smoothing out noise, outliers and random fluctuations. Seasonally adjusted unemployment figures are a good example of trends typically analyzed using ARIMA [sources: Meko; Vogt].

However many fancy charts and graphs you have, and however solid your theoretical framework, predicting the future remains an uncertain prospect. Trends are blunt tools. They trace out average inclinations, often through a field of data that scatter wildly or that contain other patterns when viewed at larger or smaller scales. It's one thing to stare at data, Magic Eye-poster style, and perceive a pattern; it's quite another to then assume that pattern will repeat in some predictable way, without random events tossing a wrench in the works.

Good trend forecasters wear many hats. They are part philosopher, part historian, part enthusiast, part scientist and part artist. They are big-picture thinkers who don't skimp on details, team players who rely on their singular hunches and knacks. If that sounds overwhelming, we don't blame you. But if it sounds like the challenge you've been looking for, then we foresee an exciting career for you in trend forecasting.

Author's Note: How Trend Forecasters Work

Trend forecasting is a bit like the Force. It surrounds us and penetrates us. It binds us together. It controls our actions, but also obeys our commands. Fashion forecasters channel current trends, or exert so much influence on the industry that they help push their predictions to fruition. Economic forecasters influence stock prices, but stock market behavior feeds back into the economy. Markets rise and fall based on investor confidence, which is not nearly as rational as we would prefer.

And then there's big data, with its big algorithms, ginned up with little clarity and less transparency. It, too, threatens to define the very aspects of our lives that it purports to predict, but with much direr consequences. That's a trend we might want to get out ahead of.

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