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Are You Guided By Fashion Intelligence Noise (Or) Signal?

Two-thirds of fashion made in the world do not meet demand as per forecast. This is too significant a gap to ignore. This is happening year after year. The impact is serious economic and environmental wastage. This is not a black swan event, but an elephant in the room. This edit is an attempt to get to its cause.

It is the Fashion Intelligence Noise.

The fashion industry has no dearth of data. This is true, particularly with ever-increasing online interactions.

Data Is Not Always Good

There is a myth that the more the data better it is. In fact, the saying “Garbage In Garbage Out” is true from the data context.

There is always a significant portion of noise in the data and less signal. If this is not removed, data is not useful. Do not get misguided by the presentation of data.

We get excited when we see good looking presentations. The infographic below is a good representation of how to tell a story with data.

Data Story Telling | Courtesy Forbes

The story is distorted when the noise exists in the data. The real story comes when the data signal drives the story.

data noise distorts reality
Data Noise Distorts Reality

Fashion Intelligence Noise

Considering the low fashion strike rates, we have 2/3rd noise in any observed data (quantitative and qualitative). The fashion forecast information and so called data-led insights available are distorted by the data in itself. Let me explain this.

Imagine you have a sample of data which in itself is not representative of the true consumer demand, anything you derive from that will not give any meaningful insight. This is one of the reasons, we are not seeing an exponential impact on fashion effectiveness.

The observed or collected information in fashion today is driven by supply. Fashion supply is hugely disconnected with true demand. No amount of facelift will solve the fundamental problem of the noise in the base data.

Misguided By Fashion Intelligence Noise

A real test for the fashion noise is to review your own data without sales numbers. Will such a review reveal the true demand of customers of your range? In fact, we published an article earlier (read here) on how we can be misguided in identifying true demand from our own data. The data collected from the public domain is like that. Unless one figure out the true demand, there is just noise.

A quick review of publicly available fashion intelligence visualizations can provide us some hint in this direction.

Distorted fashion intelligence
Distorted Fashion Intelligence (Trends & Proportions)
Fashion Intelligence Noise
Noise Of Supply In Fashion Intelligence

The New Future Of Fashion Intelligence | Less Is More

This noise of data in fashion industry was worrying us considering the economic impact of ill-informed decisions.

This led us to invent a completely different approach to data collection, processing, and remove the noise from the signal. We created Demand ScienceTM a method to derive true demand for fashion products from the data. It is an award-winning AI engine that dynamically demand-senses the fashion market globally and comes out with winning products, categories, attributes, etc.

This demand science enables you to take,

  1. Quick decisions (5x faster), Ability to spot Niche & Popular winning trends
  2. Low cognitive load on decision-makers (Less Is More)
  3. High probability of winning with exponential gains (spot winning trends not supply trends)

The proof is always in the impact on the ground. Check these customer voices of transformation on success rates tripling (300%). Stylumia Customer Voice 1Stylumia Customer Voice 2Stylumia Customer Voice 3

We would like to leave you with the following thoughts,

  1. Don’t you think fashion intelligence needs to be questioned more than ever?
  2. Would you like to see a true demand signal than supply noise to take key merchandise/assortment decisions?
  3. Would you like to know the “unknown unknowns”?

Choose intelligence wisely if you want to get your chance of winning better than a coin toss.

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