Mass customization: big data and innovation tech

Mass customization: big data and innovation tech

Goods can no be produced and sold without considering customer needs and recognizing the heterogeneity of those needs” Wedel and Kamakura, 2001

Economies of scale in manufacturing and distribution brought down the price of mass-produced goods so much that most consumers were often willing to forgot their individuality and settle for standardized goods. However, with the advent of social media services, such as Twitter, Facebook and Pinterest have made it easier for people to have a window to show and share moments of life and to brand themselves. In turn consumers started to consider products as another form of expressing their uniqueness.

To fulfil consumers’ needs, companies throughout the world have embraced mass customization to provide unique value in an efficient way.

How we got to Mass Customization

  1. The concept of mass customization began coming back into view when companies discovered market segmentation in the 50s and niche marketing in the 80s.
  2. The rise of mass customization in the 1990s gained space with the notion of segments of one: every consumer is his or her own market segment with specific requirements that must be fulfilled. Consumers got more informed and empowered, and businesses had to meet their demands to stay competitive and relevant. Mass marketing become less effective since customers expected personalized, real-time communications so businesses started to employ individualized marketing efforts to remain connected.
  3. Mass Customization term has become well-known since Joseph Pine published his book “Market of One- Creating Customer Unique Value through Mass Customization” where he defined Mass Customization as “producing, developing, marketing and delivering affordable low cost and high quality of goods and services that give customer nearly what they want.


Companies that seize the opportunity of profitable mass customization:

  • build loyalty
  • increase revenue and gain a competitive advantage and engagement vs. competitors
  • use their consumer base as an engine of advocacy to potential buyers

Big data for mass customization

As consumers customize products, they are volunteering extensive data about their preferences that brands can use to inform future product development. Data analytics have the ability to track customer episodes, predict certain customer behaviors and perceptions, and prescribe how a company should engage with those behaviors to deliver more value to customers.

– Crowdsourcing

It is the process of getting ideas from a crowd of people, usually online. It has emerged as one popular method for mass customization

PepsiCo: FritoLay “Do Us a Flavor” campaign, has been built around seeking consumer input: consumers suggested new product flavors, and the winning flavors were developed and launched.

– DARWIN (Decision Algorithm Rating What Ingredient’s Next)

Graze is an online healthy snack supplier that relies on an artificial-intelligence algorithm called DARWIN to customize snack boxes based on the preferences subscribers enter on the site. “It’s possible to get 4.9 million different combinations of snacks in a Graze box,” says Jones.


– Smart algorithms for dynamic pricing

Some companies are managing on-demand capacity by using smart algorithms and better data-processing capacity to enable dynamic pricing.

Walmart: dynamic pricing is done by monitoring pricing at your competitors, in real-time and it means dynamically changing prices to optimize revenue. Walmart changes its prices roughly 50,000 times a month.

– Recommendation Engines

Recommendation engines it is  what advise product choices based on previous selections and it is now moving into the customization space by helping customers configure products.

Chocri: customizes and ships chocolate bars, helping consumers configure their own bars from four base chocolates and 100 different toppings. Recommen­dations are based on popular choices users of the site have made and are edited by the company.


Innovation technologies for mass customization

3 D Scanning and Modeling

3D scanners let you gather data from real-world objects that can be analyzed and collected and can be used to construct 3-D digital models.  VTT Technical Research Centre of Finland Ltd aims to develop advanced food manufacturing technologies by combining expertise in food, material science and 3D printing technology. Researchers have the long-term vision of developing high-tech vending machines that provide customised purchases.

Supply chain

Key issues

  • Production process organization: it is necessary to avoid early proliferation of consumers orders
  • Supply chain structure for mass customization: it is crucial to manage physical and information flows with consumers, customers and suppliers in order to improve not only efficiency but also consumers and customer satisfaction.

Research’s key takeaway

SCM World, in cooperation with the non-profit association MESA international, recently completed a survey of 174 supply chain and operations executives to understand the future of manufacturing . According to the research,  manufacturing is now entering a new phase of customization-oriented production that is less concerned with productivity and efficiency and more focused on agility and responsiveness. Fast increasing rates of investment in advanced robotics, additive manufacturing and advanced digital simulation of manufacturing processes all lend themselves to shorter production runs and more unit-level customization.

What is the focus for Brand Development

Mass customization offers the opportunity to perceive and capture latent market niches and subsequently to develop technical capabilities to meet the diverse needs of target customers. For Brand Development it is necessary to:

  • Challenge the Mass-Market Mind-Set : in fact for mass producers, the focus of the marketing group is not about spotting differences but it’s about identifying and exploiting needs that are similar.
  • Embrace Big Data as a new way of doing business.It is essential to incorporate advanced analytics and insights as key elements of all critical decisions.

Beatrice Rossano

Can Food Data really match Tech & Food for your Health?

Can Food Data really match Tech & Food for your Health?

Food Data: Technology and food are matching for the sake of your health

The invasion of Food Data is being spread onto our tables and in our plates via the internet of things, with the stated aim to reconcile food and health while providing conveniency and comfort. Large groups and startups are positioned in the connected devices and wearables markets by offering solutions that are the continuity of our daily used objects.

Pairing with connected object

hapifork food dataThe integration of algorithms in tableware (HAPIfork, SmartPlate ect.), cookware (Situ Balance Scale , pressure cooker Nutricook Connect) or accessories ( gourd Moikit Seed, BitBite headset for counting calories, detector Nima gluten), allows an ever finer customization, and the ability to monitor and control your eating habits. Neo Smart Jar jar connected to store and track food consumption. HapiFork, the connected fork to analyze diet instantly identifies the ingredients and measure their nutritional balance! Interesting as wellness market is growing

The man-machine duo will always surpass the machine!

To date, the best known cases of application is in the area of health with the Memorial Sloan Kettering Cancer Center (MSKCC) where Watson plays the assistant for oncologists. Having “learned” a number of diagnoses, Watson helps doctors determine the right treatment to the specific requirements of a given patient. Every interaction with the medical staff  makes Watson wiser, and more relevant. (Related with our pharmacy 2.0 post)

Algorithms to define the “right” ingredients

To offer you the best culinary combinations, “Chief Watson” application also learned how ingredients are used in different styles of cuisine as well as the learnings of food chemistry and human taste preferences thanks to beta testers. But be careful not to fall into the easy shortcuts often read here and there: Watson is not and will never be a real dietitian! It is just a help, an assistant.

If the machine is superior to man, the human couple + machine always greater than any machine.

This is the logic of Watson: interact and develop synergies with the human to be both better than a human or a single machine, as powerful as it is!