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Improving the Odds of Product Launch (NPD) Success with Image Recognition




Image recognition

Recently ParallelDots organized a webinar where we had a detailed discussion on how to Improve the Odds of Product Launch (NPD) Success with Image Recognition. The event was a great success with an audience of over 200 attendees from 5 continents. This blog consolidates the key points discussed during the webinar.

The guest speakers of the webinar were:

  • Robert W. de Bruin, Ex-President and General Manager, Reckitt Benckiser Health
  • May Kwah, Ex-Vice President Marketing, Unilever
  • Neerja Sewak, Ex-Chief Operating Officer, Suntory

The webinar was moderated by Maks Mukundun, ParallelDots CEO for the South East Asia division.


“New product development and launch is the lifeblood of a company”

Robert W. de Bruin.

In the fast-paced world today, the consumer needs and wants are evolving rapidly. It thus becomes a matter of staying relevant for companies to constantly bring innovation, launch new segments, refresh existing brands, and improve consumer engagement with the company. The market leaders act proactively by shaping consumer needs rather than playing a catching up game with their competitors. Thus, by providing a larger portfolio of products to choose from, they not only consolidate their position further as a brand of choice but acquire new customers as well.

Innovation is expensive!
Data on new product launches reveals a bitter truth with 75% of all new product launches in the Consumer Goods Industry failing within a year. Three-quarters of company investments not giving the desired ROI makes product innovations extremely expensive. However as discussed above, with the undeniable importance of NPD for FMCG companies, making this expense becomes a necessary evil.

image recognition


Stakeholder Management is crucial.
Usually, we talk about the 6Ps of NPD: Product, Proposition, Pricing, Pack Size, Promotion, Place. Companies can still have 5 Ps under their control by doing in-depth research. “However, there is one P which is beyond everyone’s control – Place”, says May Kwah. NPD Teams can define a planogram but in reality, FMCG companies don’t own the place where that planogram has to be executed. Retailers have a big role to play in enabling execution and till the time they invest their trust in the product, it won’t find facings on the shelf. Hence, it is important to bring such stakeholders early into the pipeline.

During the development and launch phase, It is critical for Marketing, Sales, and Operations to establish effective collaboration. “Working from silos only brings more inefficiencies into the system. If brand managers don’t coordinate efforts among themselves, salesforce can quickly get overwhelmed with contradicting priorities and may end up de-prioritizing execution for the new product”, says Neerja Sewak. Furthermore, companies really need to ensure that their salesforce has an early buy-in for the new product. Ultimately it is the sales team that ensures execution and is responsible for realizing all the KPIs determined for Launch Success. If they are not confident about the product, on-ground execution can quickly fall apart. 

Launch Assumptions are validated only during the execution phase
Kwah shared that many companies define the planogram several months before launch. By the time the launch date arrives, the on-shelf realities change so much that the planogram no longer seems to be relevant. Similarly, there are so many assumptions and projections that companies make during the process. A lot of these calls are informed by detailed research on existing scenarios. However, it is naive to believe that market scenario, consumer needs, competitor initiatives would remain constant all this while. Instead, the reality is far from it.

“Best products and marketing strategies fail if they are not executed properly”

Neerja Sewak

The real task is in the execution of the product and one can never be complacent about it. Consider a scenario when your competitor launches a new product almost at the same time as yours. Better retail execution from its end might see your product losing facings on the shelf, and ending up becoming a minor shareholder of the shelf. Such factors can really derail all preparations.

Companies that have complex categories with many SKUs create shelving patterns to manage them in-stores. For example, Reckitt Benckiser for its Infant Nutrition category arranges shelves in a manner that guides a new mother through the entire post-natal experience. This only highlights that perfect store plans and shelving plans are critical to the success of the NPD.

“NPD is one shot at success”

Maks Mukundan

A product has a short timeframe to prove its worth on the shelf and in retail execution time is money. When you place the product on the shelf, retailers want your product to move off the shelf. If they don’t see the movement in 3 months they can delist it. Once that happens, for a brand the chance of coming back and reviving itself becomes only bleaker.

When the rubber hits the road that’s when it really matters.
Anyone who has been a part of new product development in FMCG companies understands how extensive this exercise is, spanning over many months and involving multiple organization functions to function together in sync. We have already explored that the low success rate of new products makes the entire process very expensive for companies. Thus, a lot rides on making them successful. These factors make these launches extremely high-pressure scenarios for those involved. We have seen how retail execution becomes an important part of NPD launch and ensuring its success. It is in these situations that gaps in execution are highlighted even more prominently and thus, retail execution becomes critical for the NPD execution.

image recognition


Traditional methods for monitoring execution have proved to be redundant and expensive. Collecting data manually is time consuming, inaccurate, and there is a risk of things falling through the gap. By the time the data reaches the management (almost after a month), it is too late and the data is insufficient for the management to intervene and quickly change implementations on-ground.


All the speakers mentioned that ideally, they would need a solution that is less time consuming and easy to use for sales reps and merchandisers to capture data. The data must provide a real-time, visual representation of the shelf to make the reporting more fact-based and to enable quick and effective corrective actions.

In case of new product launch, being able to monitor the movement of the product on the shelf at least for the first 1 month of the launch on key outlets and high-velocity stores may result in highly effective execution. The live data from the shelf may even improve the gaps in planogram and in-store marketing initiatives.

They also agreed that some kind of automation enables in-store measurement to be captured within a few minutes would be ideal. This would ensure that the whole process becomes much faster and the reps are able to cover more stores in one day. It would be further beneficial if quick monitoring can translate to making the data available seamlessly to the decision-makers and ensuring that everyone from the reps to the store owner could be quickly instructed to fix the gaps in the execution strategy.


ParallelDots offers ShelfWatch as an image recognition solution for the FMCG/Retail Industry. The core methodology is as follows. Images are clicked using a handheld device either by the sales reps, the merchandiser or in some cases a third-party auditor. The images are then uploaded to the ShelfWatch cloud server for analysis. Within a few minutes, the sales reps get actionable insights to take the necessary corrective measures. This data also helps the management team measure their execution strategy and gauge how the products are performing on the shelf.

Deploying ShelfWatch is easy and hassle-free. Even for new launches, no extra effort is needed. With low training set-up time, one good quality image of the SKU is all it takes to set up ShelfWatch for product recognition. The training takes less than 48 hours to complete and then ShelfWatch is ready to provide insights from the real-world.

As NPDs are time-sensitive, ShelfWatch’s agile AI training methodology ensures that new SKUs are learned very fast and sales reps are instantly alerted. This is one aspect where Shelfwatch really shines out when compared to other Image Recognition solutions in the market. Most Image Recognition vendors will take 90–120 days setup time during which they collect and manually annotate data. This is an expensive and time-consuming process and does not scale well for new product launches or during peak promotions time.

Shelfwatch’s algorithm is trained in such a manner that it automatically analyzes the images to give out a comprehensive analysis involving KPIs like out-of-stock, share-of-shelf, planogram compliance, etc.


Unless companies have the ability to monitor all elements of success coming to life on the shelf, they risk burning a lot of money on interventions that might not be working up front leaving very little to recoup and readjust down the line.

Robert rightly stated that there is a huge level of difference between average and great execution. Many companies don’t give it the full focus that it deserves. He takes Reckitt Benckiser’s example to explain this further. Traditionally, RB has prided itself on retail execution ability. There were some high-margin, low-velocity brands that were not doing well in the market. RB made strategic acquisitions solely on its ability to execute better. For example, its execution of Dr. Scholl and Durex followed by supreme execution changed the fortunes for these brands.

Today, new age cutting edge technologies like Image Recognition are proving to be game-changers in retail execution providing a powerful tool for FMCG and Retail Industry to improve their top lines. More and more companies are adopting and embracing this change which is now proving to be inevitable.

Want to see how your own brand is performing on the shelves? Click here to schedule a free demo for ShelfWatch.

Reashikaa Verma is the Content Marketing Manager at ParallelDots. When not blogging about the benefits of the image recognition technology in retail, she divides her time between reading, binge-watching, and petting dogs
Latest posts by Reashikaa Verma (see all)


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7 Awe Inspiring AI Techs That Transformed The Digital World




For many people worldwide, artificial intelligence is slowly making its way into their lives without fussing. From our cars, homes, mobile phones, and our workplace, Artificial Intelligence is everywhere.

Apart from our personal lives, Artificial Intelligence has also made its way into various industries such as automotive, e-commerce, healthcare, and entertainment.

To better understand artificial intelligence’s impact on the digital world, we first need to know what it is. To sum up,

“Artificial intelligence reduces human intervention with the help of algorithms and tools that provide recommendations, predictions, and decisions through real-time data.” 

Now that we know what artificial intelligence is, we can move forward and find out how it transforms the digital world. The use of artificial intelligence is every present and visible in our daily lives.

With the help of a machine and deep learning, artificial intelligence has found its way in computer vision systems, image processing, and voice recognition applications, transforming them in a way never seen before. If you want to know how Artificial Intelligence affects the digital world, this article will help you. Listed below are some of the applications of artificial intelligence in today’s digital world:

Computer Vision Systems

Computer vision analyzes data by using different images that show various objects of interest. It uses deep learning and image processing to recognize patterns and then provides predictions autonomously.

From simple everyday applications such as recognizing human faces to complicated ones such as detecting obstacles when driving autonomous vehicles, computer vision helps AI-enabled technologies and devices to perform their tasks more effectively.

An example of how AI affects computer vision systems is through its use in machine vision systems. A sub-field of computer vision systems, machine vision systems finds their use in automotive applications, such as detecting stop signs, detecting obstacles, etc. Machine vision technology reduces distractions and enables the driver to stay alert while driving.

Creating and Generating Online Content

Who wouldn’t want a machine that writes online content by itself? Although AI cannot write about their opinion for a political blog or its views about new emerging technologies, it certainly can create content for your website that can help attract an audience from every part of the world. It can also help you save money, resources, and a lot of time. You only need to feed it data that it can understand and learn, and it will take care of the rest.

Wordsmith and quill are examples of such programs, which companies such as Forbes and Associated Press use to create new and fresh content for them, leading to numerous visits on their websites. With the use of templates and keywords, these programs generate content readers feel that humans wrote it.

Curating Online Content

AI-based programs not only allow you to create content, but they also help you curate it. It enables visitors to interact with web pages in a better way, only showing them the content they want to see. It helps in providing visitors with more personalized user experience. For example, if you add a product to your shopping cart on Amazon, you will see suggestions relevant to your choice.

“Another example is Netflix. If you watch a movie or a drama serial on it, with the help of AI, Netflix provides you with relevant movie and drama suggestions that piques your interest.” 

From a marketing perspective, imagine showing visitors the content they wish to see. With the help of deep learning and machine learning, you will surely increase your daily clicks.

Email Marketing

Emphasizing user behavior and preferences, companies use AI-based marketing campaigns to make it easier to connect with potential clients. With the help of machine learning, companies can analyze trillions of megabytes of data to find out the time of day to engage with potential clients, what type of content to show them, the email titles and subjects that generate the most clicks, and its frequency.

Wouldn’t you want to know all these so you can save time, money, and effort? Some of the examples of such AI-based email marketing include Persado, Boomtrain, and Phrasee. It will transform how you perceive email marketing and allow you to generate tons of clicks, increasing your online presence.

Digital Advertising

Gone are the days of posting advertisements in the newspaper or the local radio channel. Artificial intelligence has made it easy for companies to find an audience that will be more prone to finding an interest in an advert. A sub-field of digital marketing, digital advertising sees the most benefits when adopting Artificial Intelligence.

“For example, Google Ads and Facebook Ads use Artificial Intelligence and machine learning to find people that will most likely have an interest in your Digital Adverts.” 

With the help of AI, both these platforms analyze user information such as demographics and interests to detect users that suit a company’s advertisements.

Website Design

If you think that a great website cannot exist without the help of a coder or a programmer, then we have news for you. Nowadays, various AI-aided website design programs exist that can easily design a website with the help of images, call to actions, and text provided by the user.

And all that without any need of a programmer or a website designer.It allows companies to save money and makes their website look like someone with a college degree designed it.

Artificial Intelligence Chatbots

Nowadays, brands usually communicate with their potential clients through Facebook messenger, WhatsApp, and other online communication platforms. As everybody already uses these platforms, it provides companies with a quick way to send the word out about their brand. Such a medium of communication leads to a requirement for faster responses. That is only possible through AI aided chatbots.

Chatbots are also available 24/7, which is not possible for a human being. For example, a big brand like Sephora uses chatbots to provide visitors with recommendations and make-up advice, depending on their interests, and without human intervention.

Final words
As you can see, artificial intelligence in the digital world provides numerous benefits, whether in marketing, advertising, or providing a great user experience to a customer. Also, to clarify, Artificial Intelligence is not here to replace human beings, but it helps them perform their task more effectively and efficiently.

However, for something like this to happen, they must give Artificial Intelligence a chance. Otherwise, they risk facing the inevitable.

Also Read Artificial Intelligence Myths and Facts


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AI and Machine Learning Technologies Are On the Rise Globally, with Governments Launching Initiatives to Support Adoption: Report




Kate MacDonald, New Zealand Government Fellow at the World Economic Forum, and Lofred Madzou, Project Lead, AI and Machine Learning at the World Economic Forum have published a report that explains how AI can benefit everyone.

According to MacDonald and Madzou, artificial intelligence can improve the daily lives of just about everyone, however, we still need to address issues such as accuracy of AI applications, the degree of human control, transparency, bias and various privacy issues. The use of AI also needs to be “carefully and ethically managed,” MacDonald and Madzou recommend.

As mentioned in a blog post by MacDonald and Madzou:

“One way to [ensure ethical practice in AI] is to set up a national ‘Centre for Excellence’ to champion the ethical use of AI and help roll out training and awareness raising. A number of countries already have centres of excellence – those which don’t, should.”

The blog further notes:

“AI can be used to enhance the accuracy and efficiency of decision-making and to improve lives through new apps and services. It can be used to solve some of the thorny policy problems of climate change, infrastructure and healthcare. It is no surprise that governments are therefore looking at ways to build AI expertise and understanding, both within the public sector but also within the wider community.”

As noted by MacDonald and Madzou, the UK has established many “Office for AI” centers, which aim to support the responsible adoption of AI technologies for the benefit of everyone. These UK based centers ensure that AI is safe through proper governance, strong ethical foundations and “understanding of key issues such as the future of work.”

The work environment is changing rapidly, especially since the COVID-19 outbreak. Many people are now working remotely and Fintech companies have managed to raise a lot of capital to launch special services for professionals who may reside in a different jurisdiction than their employer. This can make it challenging for HR departments to take care of taxes, compliance, and other routine work procedures. That’s why companies have developed remote working solutions to support companies during these challenging times.

Many firms might now require advanced cybersecurity solutions that also depend on various AI and machine learning algorithms.

The blog post notes:

“AI Singapore is bringing together all Singapore-based research institutions and the AI ecosystem start-ups and companies to ‘catalyze, synergize and boost’ Singapore’s capability to power its digital economy. Its objective is to use AI to address major challenges currently affecting society and industry.”

As covered recently, AI and machine learning (ML) algorithms are increasingly being used to identify fraudulent transactions.

As reported in August 2020, the Hong Kong Institute for Monetary and Financial Research (HKIMR), the research segment of the Hong Kong Academy of Finance (AoF), had published a report on AI and banking. Entitled “Artificial Intelligence in Banking: The Changing Landscape in Compliance and Supervision,” the report seeks to provide insights on the long-term development strategy and direction of Hong Kong’s financial industry.

In Hong Kong, the use of AI in the banking industry is said to be expanding including “front-line businesses, risk management, and back-office operations.” The tech is poised to tackle tasks like credit assessments and fraud detection. As well, banks are using AI to better serve their customers.

Policymakers are also exploring the use of AI in improving compliance (Regtech) and supervisory operations (Suptech), something that is anticipated to be mutually beneficial to banks and regulators as it can lower the burden on the financial institution while streamlining the regulator process.

The blog by MacDonald and Madzou also mentions that India has established a Centre of Excellence in AI to enhance the delivery of AI government e-services. The blog noted that the Centre will serve as “a platform for innovation and act as a gateway to test and develop solutions and build capacity across government departments.”

The blog post added that Canada is notably the world’s first country to introduce a National AI Strategy, and to also establish various centers of excellence in AI research and innovation at local universities. The blog further states that “this investment in academics and researchers has built on Canada’s reputation as a leading AI research hub.”

MacDonald and Madzou also mentioned that Malta has launched the Malta Digital Innovation Authority, which serves as a regulatory body that handles governmental policies that focus on positioning Malta as a centre of excellence and innovation in digital technologies. The island country’s Innovation Authority is responsible for establishing and enforcing relevant standards while taking appropriate measures to ensure consumer protection.


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Is GPT-3 the “Adam” of Natural Language?




OpenAI has long asserted that immense computational horsepower in conjunction with reinforcement learning is a necessary step on the road to AGI, or AI that can learn any task a human can [14].

The fathers of AI 2.0, such as Yoshua Bengio and Yann LeCun, argue that AGI is not possible to create from current AI technology. They think we need self-supervised learning (actually GPT-2 and GPT-3 are self-supervised) and advanced neurobiology-based advancements [15].

However, the fathers of AI 1.0, the grandfather’s of AI, such as Marvin Minsky and Andrew McCarthy, argued that an abundance of knowledge (data) and a “society” of common-sense reasoning specialists was the road to AGI [16].

GPT-3 is the existence proof that scaling up the amount of text (data), scaling up the parameters (model size), and scaling up the training computes results in better accuracy (scary performance) on a specialist for few-shot NLP tasks.

Do the model’s architecture, size of the model, and the amount of training computes realize a common-sense reasoning specialist? Do data and common sense reasoning get us to AGI?

Speculation About a Possible Future of Artificial Intelligence

So like, the biggest mistake that I see artificial intelligence researchers making is assuming that they’re intelligent. Yeah they’re not, compared to AI. — Elon Musk [12].

Sixty to sixty-five years ago, one of the first computers filled a room. Sixty years later, a computer core, about the size of my head, has scaled up about 1 billion times (maybe more) the first computer.

Suppose the first viable quantum computer fills an entire room. Will it be 60 years before a quantum computer core, the size of my head, scaled up about 1 billion times the first quantum computer?


Imagine a quantum computer, with an AGI (Artificial General Intelligence) model of a scale of 1 billion times the parameters of GPT-3 or about 3 million times the parameters of the human brain.

“I’ve predicted that in 2029, we will pass the Turing test,” stated Ray Kurzweil [11].

Note: GPT-3 is real close to GPT-3 passing the Turing test [13].

GPT-3 is quite impressive in some areas, and still clearly subhuman in others. — Kevin Lackey, Just, 2020.

Do you think we will have a Hawking-Musk nightmare or a Havens-Kurzweil dream [11,12]?

We may have both, or we may have neither.

I put money on our tool-making. I doubt we will change, or should we change this behavior.

I feel that Elon Musk, with the NuralLink project, is making a bet on our tool-making about the future potential of AI [17].


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