3 AI and data science applications that can help dealing with COVID-19

All industries already feel the impact of the current COVID-19 pandemic on the economy. As many businesses had to shut down and either switch to telework or let go of their entire staff, there is no doubt that it will take a long time for the world to recover from this crisis.

Current prospects on the growth of the global economy, shared by different sources, support the idea of the long and painful recovery of the global economy from the COVID-19 crisis.
Statista, for example, compares the initial GDP growth prognosis for 2020 and the prognosis based on the impact of the novel coronavirus on the GPD growth, estimating the difference of as much as 0.5%.

The last time that global GDP experienced such a decline was back in 2008 when the global economic crisis affected every industry with no exceptions.

In the situation with the current pandemic, we also see that different industries change their growth prognoses.
The IT industry, for instance, the expected spending growth in 2020 doesn’t even exceed the pessimistic scenario related to the coronavirus pandemic, and is even expected to shrink.

It would be foolish to claim that the negative effect of the COVID-19 crisis can be reversed. It is already our reality that many businesses and industries around the world will suffer during the current global economic crisis.
Governments around the world responded to this crisis by helping businesses not go bankrupt with state financial support. However, this support is only expected to have a short-term effect and will hardly mitigate the final effect of the global economic crisis on businesses around the world.

So, in search of solutions to decrease the negative effect of drowning global economics, the world, among all other sources, will likely turn to the help of technology, just as the entire world did when it was forced to work from home.

In this article, we offer our stance on how AI and data scientists, in particular, can help respond to the COVID-19 crisis and help relieve its negative effect.

1. Data science and healthcare system

The biggest negative effect on the global economy can come from failing healthcare systems. It was the reason why governments around the world ordered citizens to stay at home and self-isolate, as, in many cases, the course of the COVID-19 disease can be asymptomatic.

Is increasing investment in the healthcare system a bad thing altogether?

No, if we are talking about healthcare systems at a local level, like a state or a province. “At a local level, increasing investments in the healthcare system increases the demand for related products and equipment in direct ratio,” says Dorian Martin, a researcher at WowGrade.

However, in case local governments run out of money in their emergency budgets, they might have to ask the state government for financial support.

This scenario could become our reality if the number of infected people rapidly increases, with hospitals potentially running out of equipment, beds, and, most critically, staff.

What can data science do to help manage this crisis?

UK’s NHS healthcare data storage

Some countries are already preparing for the scenario described above with the help of data scientists.
For instance, the UK government ordered NHS England to develop a data store that would combine multiple data sources and make them deliver information to one secure cloud storage.
What will this data include?

This cloud storage will help NHS healthcare workers access information on the movement of the critical staff, the availability of hospital beds and equipment.

Apart from that, this data storage will help the government to get a comprehensive and accurate view of the current situation to detect anomalies, and make timely decisions based on real data received from hospitals and NHS partner organizations.

Thus, the UK government and NHS are looking into data science to create a system that will help the country tackle the crisis consistently, and manage the supply and demand for critical hospital equipment needed to fight the pandemic.

2. AI’s part in creating the COVID-19 vaccine

Another critical factor that has an effect on the current global economic crisis is the COVID-19 vaccine. It has already become clear that the world is in the standby mode until scientists develop a vaccine that will return people to their normal lives.

It’s a simple cause-and-effect relationship: both global economy and local economies depend on consistent production, production depends on open and functioning production facilities, which depend on workers, who, in their turn, depend on the vaccine to be able to return to work.

And while we still have over a year before the COVID-19 vaccine becomes available to the wide public, scientists turn to AI to speed up the process.

How can AI help develop the COVID-19 vaccine?

• With the help of AI, scientists can analyze the structure of the virus and how it attaches itself to human cells, i.e., its behavior. This data helps researchers build the foundation for vaccine development.
• AI and data science become part of the vaccine development process, as they help scientists analyze thousands of research papers on the matter to make their approach to the vaccine more precise.

An important part of developing a vaccine is analyzing and understanding the protein of the virus and its genetic sequence. In January 2020, Google DeepMind launched a system that builds the virus’s protein in the 3D mode, AlphaFold. This invention already helped the U.S. scientists study the virus enough to create a trial vaccine and launch clinical trials this week.

However, scientists are looking into the ways, how AI can not only be involved in gathering information, but also in the very process of creating a vaccine.

There have already been cases of drugs successfully created by AI. The British startup Excienta created its first drug with the help of artificial intelligence algorithms. The drug is currently undergoing clinical trials. But it will take this drug only 12 months to be ready, compared to 5 years that it usually takes.

Thus, AI gives the world hope that the long-awaited COVID-19 vaccine will be available to the world faster than it’s currently predicted. Yet, there are still a few problems of artificial intelligence implementation in this process, which are mainly connected to AI being underdeveloped itself.

3. Data science and the fight against misinformation

Another factor, which is mostly related to how people respond to the current crisis, and yet has the most negative effect on the global economy, is panic.

We’ve already seen the effects of the rising panic during the Ebola virus crisis in Africa when local economies suffered from plummeting sectors like tourism and commerce.

In economics, the period between the boom (the rising demand for the product) and the bust (a drop in product availability) is very short. During the current pandemic, we’ve seen quite a few examples of how panic buying led to low supply, which damaged local economies.

How can data scientists tackle the threat of panic?

The answer is already in the question: with data.

One of the reasons why people panic is misinformation. “Our online poll has shown that only 12% of respondents read authoritative COVID-19-related resources, while others mostly relied on word-of-mouth approach,” says Martin Harris, a researcher at Studicus.

Misinformation, unfortunately, happens not only among people but on the government level as well. One of the best examples of it is the U.S. officials promoting a drug against malaria as an effective method to treat COVID-19 patients, when, in fact, the effectiveness of this drug hasn’t been proven yet.

The best solution to treat the virus of panic and misinformation is to accumulate all the information from the authoritative resources on the COVID-19 pandemic to help people observe it not only on the local but on the global level as well.

Data scientists and developers at Boston Children’s Hospital have created such a system, called HealthMap, to help people track COVID-19 pandemic, as well as other disease outbreaks around the world.

Conclusion

While there are already quite a few applications of AI and data science that help us respond to the COVID-19 crisis, this crisis is still in its early stages of development.

As we already can use data science to accumulate important information regarding critical hospital staff and equipment, fight misinformation, and use AI to develop the vaccine, we still might discover new ways of applying AI and data science to help the world respond to the COVID-19 crisis.

Yet, today, we can already say that AI and data science have been of enormous help in fighting the pandemic, giving us hope that we will return to our normal lives as soon as possible.

Author: Estelle Liotard

Source: In Data Labs

3 Reasons to implement a data strategy in your sales processes

Sales managers are resilient folk. For many, adapting to leaps in technology, economic volatility, and radical shifts in buying behavior is the norm. Often they emerge stronger and better equipped to succeed. Not surprisingly then, this Covid era, with any number of unforeseen business challenges has prompted many sales managers to examine themselves and their teams and to commit to up their game. One given in this tumultuous time is a data strategy.

1. Must have a data strategy for sales 

The veil of comfort of a pre-Covid world, where growth is infinite, resources are boundless, and the only perceived limit to success is one’s level of ambition…. for many, that veil has been lifted. And for some it has revealed some blemishes that in more comforting times would be easier to ignore. One such organizational blemish, for many, is the lag in their business to adopt and employ a data strategy that can empower its sales people and improve results.

Let’s face it. Doing what you did yesterday is a good approach if you believe that tomorrow will look similar to today. Not many sales managers share this view of the world anymore. Things are changing, they are changing fast, and many sales organizations that haven’t adopted a data strategy find themselves slow to react and at a disadvantage to their competitors.

2. Data helps sales team understand subtle changes in customer behavior

The contrast in talking to sales organizations with a data solution and those without is striking. Sales organizations committed to data, use buying trends and behaviors of their best customers to educate and inform the rest of their customers as well as increase add on sales and wallet share across their customer base. In a few mouse clicks, a rep can see what upsell and add on opportunities exist and prioritize their calling efforts.

Data driven sales organizations can react to the pulse of their customers, often times pro-actively to head off issues before a customer is fully at-risk. Subtle changes in purchasing behavior can reveal at risk accounts and trends that the salesperson can address pro-actively to retain a customer rather than trying to win them back after they leave.

3. A data strategy is necessary to compete in a shrinking marketplace

Further, as competition for a lesser number of customers in the marketplace heats up, data driven sales organizations have 360 degree view of their customer that allows them to share insights, improve customer experience and add value to every interaction. Customers have come to expect a higher level of communication and experience from their vendors that mirrors what they have experienced online. 

Companies that have not embraced a data strategy for sales find themselves at a tremendous disadvantage. In these rapidly changing times, sales managers of those organizations may be asking themselves how long they can afford to wait before they level the playing field for their team. 

Author: Mark Giddens

Source: Phocas Software

3 Things we have learned about CI during the time of COVID-19

There is no adequate way to express the effect COVID-19 has had on society. It’s changed the way we live and the way we work. Competitive intelligence (CI) might seem like an 'extra' in the time of COVID, but it’s more crucial to your bottom-line now more than ever. 

Here are three lessons we’ve learned about competitive intelligence for businesses in the era of COVID-19.

1: Every single deal matters and good competitive intel equals more revenue

CI is about driving action. It’s not enough to simply push CI to stakeholders and have no action being taken as a result. This causes competitive intelligence to become a nice-to-have at best and a cost-center at worst. When CI is used to drive decision making and action, it becomes critical to revenue generation.

The current scarcity of deals increases the likelihood of a competitor being present in a deal, so you need to ensure that you're setting up your CI to be easily leveraged by your sales team.

Here are a few ways you can use CI to help sales win deals:

• Battlecards: Provide your sales team with competitive battlecards. Making sure battlecards are easily accessible and up-to-date with the most current CI will enable your sales team to knock competitors out of deals quickly.

• Deep dive competitive training: Take time to sit with sales and do a deep dive into one of your chief competitors. Add role-playing into the training so sales can get practice on selling against that specific competitor.
  
  
• Leverage field intel: Your reps spend all day talking to prospects, and in doing so, they gather excellent intel on your competition. Give sales the ability to share great field intel so they can help their fellow reps win more deals.

2: There are more competitive signals being put out there than ever before

While sales have been declining, marketing engagements have increased significantly, specifically marketing email open rates and website visits. Meaning, buyers might not be ready to sign a check quite yet, but they are certainly looking to educate themselves with content and virtual events in the meantime.

This means that your competitors’ marketing teams are likely putting out more content and campaigns than ever before, both on and off their website. Tracking and analyzing these signals is crucial to understanding your competitors’ strategies, and since there is more of an emphasis on engaging and educating prospects, there are now more competitive signals to glean intelligence from.

Here are some competitive signals you should be keeping an eye out for:

• Messaging changes: Track your competitors’ homepages and other website pages for any changes in messaging, it will signal how they are adjusting their strategy during the COVID era. 
  
  
• Employer reviews: Find out what former and current employees of your competitors’ are saying about them. Employee reviews can give you visibility into competitor strategy like what investments (or lack thereof) are being made. Glassdoor now lets you filter reviews by “COVID-19” so you can see how your competitors are handling the crisis internally.
  
  
• Marketing campaigns: Marketing teams are putting out more content than ever to educate and engage buyers. Keep track of your competitors’ social media campaigns, content initiatives, and virtual events to see how they are currently engaging the market.

3: Optimal distribution is key to getting stakeholders to take action on CI

Remote work is the new reality, and with that comes certain challenges. You may feel like competitive intelligence is being ignored if you aren’t interacting with your stakeholders in-person, or that some context is being lost. 

The key to getting others to take action on CI is to deliver it to them in a format that is optimal for their consumption. Stakeholders all have different needs: sales needs to win more deals, executives need guidance on strategy, marketing needs to understand messaging and campaigns, and product needs to understand the competitor roadmap. In addition to having different needs, your stakeholders consume information differently. Tailor your information and communication method for each stakeholder. 

Here are examples of how you can distribute competitive intelligence to different stakeholders:

Embracing the new way of working

No one can predict the future, but we all must adapt to our present reality. There will likely be more changes coming down the road for businesses, and the best you can is do your best to be cognizant of trends and continue to enable your teams and serve your customers.

Author: Lauren Kersanske

Source: Crayon

Agri-Food Industry Barometer COVID-19 implications

Covid-19 is speeding up all kinds of changes in business

A recent survey of more than 200 enterprise business professionals confirms that Covid-19 is hastening both personal and business change across every industry.

In a May 2020 survey conducted by MicroStrategy, 88% of respondents said that Covid-19 has had some or a significant impact on their work, with more than 30% saying the impact has been significant. While 27% said that their employer has a new focus on employee upskilling because of changing priorities related to Covid-19, more than half of respondents (54%) said they now have a personal focus on improving their work-related skills or learning new ones.

Back to business

On the business side, 79% of survey respondents said that the impact of Covid-19 is accelerating their organization’s digital transformation initiatives. Top challenges and priorities that are now being addressed in order to adapt to a “new normal” include:

• Budget changes
• Employee productivity and retention
• Remote work adaptation and collaboration
• Connecting data with new KPIs
• Ensuring trusted data to predict outcomes
• Re-envisioning customer engagement

In relation to customer engagement changes, 91% said that Covid-19 has had some or a significant impact on their organization’s customer experience and customer engagement initiatives, with 38% saying the impact has been significant. And, as a direct result of the pandemic, 68% say their organization’s use of analytics has increased.

“Insight is great. Foresight is gold,” noted Constellation Research VP and Principal Analyst Doug Henschen in a recent webcast detailing Constellation Research’s Post-Pandemic Playbook for Business. Henschen said new leadership models, trust, remote work policies, reskilling investments, plus a focus on communications, mental health support and safety will be key people and process considerations moving forward. For technology, it will be all about continuity, resiliency, cloud to achieve scalability and flexibility, analytics for agility and performance, AI and automation, plus mobility and privacy.

Author: Tricia Morris

Source: Microstrategy

Data science plays key role in COVID-19 research through supercomputers

Supercomputers, AI and high-end analytic tools are each playing a key role in the race to find answers, treatments and a cure for the widespread COVID-19.

In the race to flatten the curve of COVID-19, high-profile tech companies are banking on supercomputers. IBM has teamed up with other firms, universities and federal agencies to launch the COVID-19 High Performance Computing Consortium.

This consortium has brought together massive computing power in order to assist researchers working on COVID-19 treatments and potential cures. In total, the 16 systems in the consortium will offer researchers over 330 petaflops, 775,000 CPU cores and 34,000 GPUs and counting.

COVID-19 High performance computing consortium

The consortium aims to give supercomputer access to scientists, medical researchers and government agencies working on the coronavirus crisis. IBM said its powerful Summit supercomputer has already helped researchers at the Oak Ridge National Laboratory and the University of Tennessee screen 8,000 compounds to find those most likely to bind to the main "spike" protein of the coronavirus, rendering it unable to infect host cells.

"They were able to recommend the 77 promising small-molecule drug compounds that could now be experimentally tested," Dario Gil, director of IBM Research, said in a post. "This is the power of accelerating discovery through computation."

In conjunction with IBM, the White House Office of Science and Technology Policy, the U.S. Department of Energy, the National Science Foundation, NASA, nearly a dozen universities, and several other tech companies and laboratories are all involved.

The work of the consortium offers an unprecedented back end of supercomputer performance that researchers can leverage while using AI to parse through massive databases to get at the precise information they're after, Tim Bajarin, analyst and president of Creative Strategies, said.

Supercomputing powered by sharing big databases

Bajarin said that the world of research is fundamentally done in pockets which creates a lot of insulated, personalized and proprietary big databases.

"It will take incredible cooperation for Big Pharma to share their research data with other companies in an effort to create a cure or a vaccine," Bajarin added.

Gil said IBM is working with consortium partners to evaluate proposals from researchers around the world and will provide access to supercomputing capacity for the projects that can have the most immediate impact.

Many enterprises are coming together to share big data and individual databases with researchers.  

Signals Analytics released a COVID-19 Playbook that offers access to critical market intelligence and trends surrounding potential treatments for COVID-19. The COVID-19 Playbook is available at no cost to researchers looking to monitor vaccines that are in development for the disease and other strains of coronavirus, monitor drugs that are being tested for COVID-19 and as a tool to assess which drugs are being repurposed to help people infected with the virus.

"We've added a very specific COVID-19 offering so researchers don't have to build their own taxonomy or data sources and can use it off the shelf," said Frances Zelazny, chief marketing officer at Signals Analytics.

Eschewing raw computing power for targeted, critical insights

With the rapid spread of the virus and the death count rising, treatment options can't come soon enough. Raw compute power is important, but perhaps equally as crucial is being able to know what to ask and quickly analyze results.

"AI can be a valuable tool for analyzing the behavior and spread of the coronavirus, as well as current research projects and papers that might provide insights into how best to battle COVID-19," Charles King, president of the Pund-IT analysis firm, said.

The COVID-19 consortium includes research requiring complex calculations in& epidemiology and bioinformatics. While the high computing power allows for rapid model testing and large data processing, the predictive analytics have to be proactively applied to health IT.

Dealing with COVID-19 is about predicting for the immediate, imminent future - from beds necessary in ICUs to social distancing timelines. In the long term, Bajarin would like to see analytic and predictive AI used as soon as possible to head off future pandemics.

"We've known about this for quite a while - COVID-19 is a mutation of SARS. Proper trend analysis of medical results going forward could help head off the next great pandemic," Bajarin said.

Author: David Needle

Source: TechTarget

E-commerce is rising through the roof, but what does this mean for brands and consumers?

With the whole world going virtual last year, it’s no surprise that the ecommerce industry exploded. But as the industry continues to grow, how does this affect brands? Specifically, how they interact with and reach their consumers?

How CIOs are learning from the COVID-19 pandemic to transform towards agile

If the COVID-19 pandemic taught us anything, it’s that you can never be too prepared for change – fast-moving, come-out-of-nowhere change that transforms the way we run our businesses, manage our people, and secure the stability of our enterprises. 

Disruptive events in business are nothing new. As leaders, we have long invested in planning and processes that enable our organizations to withstand many types of disruption, be it social upheaval, unfavorable market conditions, supply chain breakdowns, or environmental disaster.

COVID-19, however, was leagues beyond what even the most risk-aware organizations could fathom, let alone plan ahead for in a specific, actionable way. Now, emerging from the pandemic and moving forward into a work landscape that is perhaps more flexible and adaptable than ever before, the COVID-19 wake-up call must be heeded in a thoughtful, intentional manner that prioritizes digital transformation, so that organizations can be nimble and adaptive to the next major, fast-moving disruption. 

CIOs Are Leading the Digital Transformation Revolution 

Many CIOs are already in go-mode, leveraging the lessons of the pandemic to push for more agile organizations and business and technology scalability. In fact, a March 2021 SAPinsider survey reveals that 62% of executives and leaders have identified process efficiency as a top business priority for 2021. 

With the pandemic forcing business to incorporate unprecedented agility into nearly every facet of operations, it’s not surprising that many leaders now see a highly customized and difficult-to-change ERP system as a liability during times of unexpected change. One CIO who has been leading their organization’s digital transformation project for the past year described the challenges of innovating on an antiquated platform as limiting their ability to do new and innovative things with SAP – and that implementing new functionality, or doing anything new, requires significant effort and testing. Those limitations don’t lend themselves to supporting the level of innovation CIOs are looking for, driving them to build new digital platforms with SAP S/4HANA.

This CIO is certainly not alone in their push for proactive, strategic digital transformation. Right now, CIOs around the world are advocating more forcefully than ever to migrate systems to the cloud and to streamline digital operations by integrating disparate systems and data. The SAPInsider survey backs this up, noting that many CIOs are finding that their pushes toward digital transformation are meeting less resistance these days. With the pandemic proving the irrelevance of location for labor forces and technical infrastructure alike, the realization that the cloud is king has crystallized. The pandemic has ushered in a new era of accord: IT partners, and the technology and innovation they make possible, are not behind-the-scenes vendors, performing tactical functions. Instead, strategic technology partnerships are foundational to most organizations’ livelihoods.

Many leaders are taking note – and taking action. Over half of executives in the SAPinsider study identified SAP S/4HANA as their most strategic investment in the coming year – despite the fact that SAP’s deadline isn’t until 2027 – and many have already committed to the budgets (66%) and headcount (36%) required to make the transformation goal achievable. With proper planning, S/4HANA can be a game-changing break from the complex, disparate systems that built rigidity into the essential fabric of your business. Is the unsorted data you’ve inherited or accumulated serving your goals? Most likely, it’s weighing you down without pulling its weight in terms of delivering trustworthy analytics. Digital transformation on the whole, and S/4HANA more specifically, provide a golden opportunity to assess your current data landscape and develop a proactive plan that will set your organization up for increased resilience and strategic innovation.

If ever there were a moment to advocate for organizational resilience strategies, it’s today. The pandemic may be easing up in the U.S., but if the past year has taught us anything at all, it’s to expect the unexpected. Preparation and agility are not virtues we can afford to forget as the pandemic becomes a more distant memory. 

Author: Steele Arbeeny

Source: Dataversity

How COVID-19 related uncertainties impact predictive analytics

Rapid, sometimes dramatic change has become 'the new normal', but what we know now can help us improve predictive model accuracy.

Businesses have had to grapple with a lot of change caused by the COVID-19 pandemic. One of the obvious side effects was compromised predictive model accuracy. What worked well in 2019 won't work as well or at all in 2020 if the training data is out of sync with what's happening now.

In the beginning

COVID-19's effects are truly novel. While there have been other pandemics and financial crises in recent history, none of them exactly mirror what's happened in 2020. The Spanish Flu pandemic may be the closest parallel, but there's little data available about it compared to the 2008 financial crisis, for example.

Unlike the early days of the COVID-19 pandemic, there's now more information about its effects on organizational and customer behavior. However, at any moment, the current situation could change, such as a second round of shutdowns.

"We need to remind ourselves to be incredibly agile when it comes to building models," said Drew Farris, director of AI research at Booz Allen Hamilton. "I've encountered some environments in the past where they roll out one new model every six months, and that's just not tenable. I think increasing modeling agility through automation is more relevant now than any other time, just simply because the data is changing so quickly."

Continued uncertainty

By now, it's obvious that the pandemic and its effects won't disappear anytime soon, so organizations and data scientists need to be able to adapt as necessary.

"As a data scientist, you need to be  willing to challenge your assumptions, toss out the theories that you had yesterday and formulate new ones, but then also run the experiments with the data to be able to prove or disprove those hypotheses," said Farris. "To the extent that you can use automated tooling to do that, it's very important."

The companies in the best position to adapt to sudden change have been modernizing their tech stacks to become more agile. Nevertheless, they still need a way to identify signals that indicate future trends. Booz Allen Hamilton was recently doing some work involving linear regressions, but it switched to agent-based modeling.

"It's basically setting up a dynamic system where you have individual actors in that system that you're modeling out, and you're using the data about these actors to figure out what steps will happen next," said Farris. "It's really nothing new, but the bottom line is it allows us to look more forward into the future by analyzing system dynamics, as opposed to just sort of measuring the data that we're seeing from past history."

Given the constant state of change, it's important for organizations to be able to respond and adapt to changing circumstances by identifying multiple sources of data that can provide a complete perspective of what's taking place.

"It's gotten to the point, or we're rapidly getting to a point, where it is considerably less expensive to run a plethora of other models to understand different outcomes," said Farris. "I think if there's any takeaway that I have in this particular situation, it is that we have that ability to generate so much scale, do some really oddball stuff like run a model that expects the unexpected. Don't be afraid to introduce complete and total randomness and look for wacky outcomes. Really don't discount them for potentially what they might be showing you or telling you because that ultimately might prepare you for the next crisis."

Scenario modeling helps prepare organizations for change

The future has always been uncertain. However, the global and systemic impacts of the COVID-19 pandemic have resulted in an unprecedented level of uncertainty in the modern business environment.

"We have seen from the business world increased requests [for] and usage of analytics and AI machine learning models and more importantly, simulation models, which can simulate different scenarios", said Anand Rao, global & US artificial intelligence and US data & analytics leader at PwC. "We're also seeing various new techniques being used in AI, so the old techniques and new techniques being combined."

Business leaders have been seeking advice about what they should be doing these last few months because their past experience has not prepared them for recent events.

"Executives basically start to say, I don't know what to do. I don't know where this is going," said Rao. "Is there any way that you guys can come up with anything more than just tossing a coin because any technique is better than my random choice."

The beauty of scenario modeling is it provides opportunities to plan for different possible futures, such as understanding the impacts of future government intervention on supply and demand or how different scenarios might affect business operations, staffing requirements or customer concerns. That way, should one of the scenarios become reality, business leaders know in advance what they should be doing.

Rao also said that data scientists need to develop their own version of agile so they can build and deploy models faster than they have before.

"This is something we should have adopted before the pandemic," said Rao. "Now people are looking more at how to develop models in a much faster cycle because you don't have six to eight weeks."

Author: Lisa Morgan

Source: InformationWeek 

How Greece set an example using online volunteering to battle COVID-19

Assistant Volunteer, a project of Nable Solutions, was born during the HackCoronaGreece online hackathon to better coordinate the efforts of volunteers. Today, Assistant Volunteer’s platform is part of the Greek Ministry of Health’s official response to eradicating the pandemic. 

This year, online hackathons have proven to be a great source of ideation for easily scalable solutions during crises. From a shortage of medical equipment to caring for patients remotely, solutions to better manage the COVID-19 outbreak flourished globally. However, it was still to be discovered whether these solutions could be developed into mature products able to be integrated into the official’s response programs.

On April 7th-13th, in Berlin and Athens, the global tech community tackled the most pressing problems Greece faced due to COVID-19 outbreak during the HackCoronaGreece online hackathon organized by Data Natives, eHealth Forum and GFOSS with the support of GreeceVsVirus (an initiative by the Greek Ministry of Digital Governance, Ministry of Health, Ministry of Research & Innovation). Just two months later, Assistant Volunteer, matured its solution to the final stages of development and was selected by the Greek Ministry of Health to officially contribute to managing the COVID-19 pandemic in Greece. 

The era of volunteering

COVID-19 paved the way to a new era of volunteerism in response to the crisis. Even though isolated from each other, volunteer movements across the globe found ways to dedicate their time and efforts to help the ones in need and introduce innovative and effective ways of helping humanity.

According to the United Nations, in Europe and Central Asia, the volunteer movement has been officially recognized by some governments for their services provided by volunteers during the COVID-19 pandemic. That’s exactly the case with HackCoronaGreece and the solutions that have been created by diverse communities.

One such solution, Assistant Volunteer, recognizes the problem of coordination – when thousands of people are gathering for a good cause, their efforts deserve outstanding management to maximize positive effects. 

What is Assistant Volunteer?

Assistant Volunteer was developed as part of the HackCoronaGreece hackathon by Nable Solutions, an award-winning startup providing software solutions with a social cause. Assistant Volunteer is an easy-to-use volunteer management software platform for organizations and government agencies. It can be configured to support organizations of all types and sizes to achieve modernization and upgrade of the operations, seamlessly with their workflow. Through the modular architecture design, organisations can coordinate volunteers through the web app and mobile app. 

Any organization can register, create a profile, come up with actions needed, engage with the database of volunteers, track performance & measure impact.

Assistant Volunteer competed with 14 other teams to be selected in the finale of the HackCoronaGreece hackathon and continue the development of their idea. The solution was recognized by the Greek Ministry of Health and selected for assistance in further development. 

Multinational pharma giant MSD Ssupports the project

Another influential supporter of the project is MSD, a pharmaceutical multinational company that contributed with an award for Assistant Volunteer wich is a monetary prize of 7.000 EUR. 

Previously, MSD Greece donated 100,000 euros to the Ministry of Health “to strengthen the national greek health system and to protect its citizens”. 

MSD also donated 800,000 masks to New York and New Jersey. Working with Bill and Melinda Gates Foundation and other healthcare companies, MSD contributes to pushing the development of the vaccine forward, diagnostic tools, and treatments to treat COVID-19 as soon as possible.

The Greek Ministry of Health included Assistant Volunteer in their official efforts to fight the pandemic and facilitated the population of the platform with 10000 volunteer profiles. Now, organizations can take the next steps in coordinating the volunteer movement in Greece and, potentially, beyond.

Author: Evgeniya Panova

Source: Dataconomy

How SAS uses analytics to help with the covid-19 vaccination process

The management of the COVID-19 vaccination program is one of the most complex tasks in modern history.  Even without the added complications of administering the vaccine during a pandemic, the race to vaccinate the populations who need it most all while maintaining the necessary cold-storage protocols, meeting double dose requirements, and still convincing populations of the vaccine safety, is daunting.

The vaccines available today are unlikely to be available in sufficient quantities to vaccinate the entire population in the near term, which creates the need for nimble, data-driven strategies to optimize limited supplies.

Analytics can be used to:

• Identify the location and concentration of priority populations.
• Monitor the relative adequacy of providers capable of vaccinating critical populations.
• Measure changes in need and demand patterns to optimize supply-chain strategies.
• Track community-based transmission and efficacy.

The storage and transportation of the vaccine is a complex logistical exercise, requiring coordination among governments and providers and the safe transport and storage of vaccines from manufacturers to vaccination sites.

Using analytics to shape strategy and execution

Since the pandemic’s beginning, SAS has partnered with customers in using analytics to:

• Monitor the spread of infection.
• Model future outbreaks.
• Uncover relevant scientific literature.
• Share real-time health insights.
• Optimize supply chains and medical resources.

These same analytical strategies can be used for vaccination programs. Why? Because analytics based on trusted data drives the best decisions. Below of some examples of what we mean.

Develop immediate and long-term vaccination strategies

SAS can help you create a data-driven strategy to identify and estimate critical populations that will benefit the most people. Governments have struggled to balance the need to create an orderly, risk-driven prioritization strategy while quickly administering all of the doses they have been allocated. Integrating data to calculate the size of prioritized populations in given geographic areas enables a data-driven vaccine allocation strategy that maximizes throughput and minimizes wasted dosages. Locating and estimating the size of these populations will be critical to developing an effective allocation strategy. This complex task can be fraught with technical challenges; for instance, creating an analytically valid estimation that identifies targeted populations across data sources.

To succeed, governments and health agencies will need to integrate data to identify critical populations, enable populations to be further subset to accommodate unknowns in vaccine supply, and model vaccination impact on priority outcomes. Given the variety of public and private organizations collaborating on this response, the best solution will drive open, transparent communication across diverse agencies.

Visual analytics is paramount because showing priority population data on maps can also speed strategy development. Using proximity clustering and hot-spotting technology, leaders can identify population densities to ensure adequate vaccine supply. Epidemiological models can help ensure continued situational awareness, so that prioritization and allocation approaches don’t become reliant on point-in-time data, but are instead part of a continuous-learning system that is responsive to on-the-ground changes in the pandemic.

Monitor vaccination capacity and adverse events

Identifying and recruiting enough providers to ensure sufficient access to COVID-19 vaccines (especially once supplies increase) will be crucial. SAS has experience working with government health agencies to monitor the adequacy of health care provider networks, a skill set and technology base that can provide agencies with an evidence-driven view of vaccine administration capacity and vaccination goals.

We work with commercial partners worldwide to augment the public health workforce to meet rising demand for vaccines. Related data such as storage capacity and throughput can be calculated and included for a fuller understanding of network adequacy.

As more data is collected regarding adverse events, SAS continues to help with health surveillance and research for many national health regulatory agencies today.

Optimize supply chain strategies

Health and human service agencies are being asked to allocate vaccine supply based on a range of complex, interrelated factors that include populations served and providers’ capability for storing and refrigeration. Optimizing these distribution strategies while facing fluctuating supplies, evolving need and changing provider enrollments will require a strong data and analytic approach.

SAS offers end-to-end supply chain analysis to assist agencies in an efficient, coordinated vaccine distribution response. By capturing inventory, demand, capacity and other related data across the distribution chain, you can create models that determine how agencies can optimize allocation strategies while accounting for the dynamic nature of pandemic outbreaks. The outcome is a set of flexible, adaptable plans for vaccination processing, inventory monitoring and distribution.

Dose administration analytics

Vaccination administrators must report certain data elements in near-real time (through electronic health records or directly via state immunization information systems). This information is a critical tool in creating rapid-response analytics that can guide decision making and future planning. Unfortunately, long-term underinvestment in our public health IT infrastructure has led to significant data quality challenges and weak reporting capabilities, which collectively prevent a data-driven vaccination strategy.

Our data management solutions can assist agencies in creating a trusted, consolidated vaccination record. This includes automating tedious and manual processes such as data preparation, data integration and entity resolution to provide analysts more time for treatment and vaccination efforts. With this reconciled vaccination data, SAS can provide analytics to help agencies:

• Predict evolving resource needs across jurisdictions such as states, regions and countries to optimize allocation strategies.
• Monitor uptake to help ensure alignment with anticipated need, provider requests and vaccine distributions.
• Analyze unexpected gaps in vaccination administration to guide outreach and engagement efforts.
• Anticipate barriers to delivering second doses.
• Gain insights on changes in susceptibility, rate of transmission, status population immunity, etc.

Managing a cold chain for biologics

In the US, the CDC has updated the Vaccine Storage and Handling Toolkit to outline the proper conditions for maintaining an effective COVID-19 vaccine under cold-chain processes. Cold chain is a logistics management process for products that require specific refrigerated temperatures from the point of manufacturing through distribution and storage until the vaccine is administered. But how do you collect data along the chain to ensure product safety? New internet-connected sensors now travel along with the vaccines. Collecting and analyzing data like that allows administrators to monitor, track and optimize distribution strategies in this multi-layered and complex vaccine rollout.

The path forward

As you read this, shipping and logistics companies are recording data on vaccine temperature and location.  Governments are rapidly transforming themselves into organizations capable of allocating, distributing and administering vaccines and their necessary components at massive scale. Retailers (pharmacies) are implementing customer contact programs to help track, administer and verify vaccinations.

The coordination across these various public and private companies is critical for a successful vaccination program. Even though the scale of this operation is historic, the sub-components of the process can be likened to other large, data-driven strategies.

Author: Steve Kearney

Source: SAS

Interpreting market data during the COVID-19 pandemic

Business and economic activity fluctuates during the course of the year. Some of this fluctuation is idiosyncratic, but much is tied to the time of year and the change of seasons.

Outdoor construction projects are easier to do in dry, warm weather, so especially in the northern half of the United States, construction spending and housing starts tend to be higher during the second and third calendar quarters than they are during the other two quarters.

The volume of retail sales for clothing and electronics is higher in December because of seasonal gift-giving. Economy-wide, clothing and electronics merchandisers can expect to record about one-sixth of their annual sales in the month of December.

In contrast, grocery stores experience much less monthly variance in the level of sales. Employment, especially for contract and temporary workers, also exhibits seasonal changes.

Identifying “True” Trends for a Business or Economic Sector

When a businessperson is trying to assess the trend of a company’s sales (or when an economist is trying to assess the health of economic activity), he or she will want to filter out these expected seasonal effects to get a better view of the “true” trends. A quick and easy way to abstract from seasonal effects is to compare current activity with the activity in the same period the previous year.

When a CEO discusses financials, he or she will compare sales in the recent months to sales in the same calendar months a year and two years earlier. When the CFO of a public company presents the latest quarterly results to a group of analysts, he or she will compare revenues not just to the previous quarter, but also to the same quarter the previous year.

Common Distortions in the Data to Watch

The “comparable period” approach is not foolproof for abstracting from seasonal effects. For example, the Lunar New Year is a big driver of economic activity in China because it is associated with gift-giving, entertainment, and travel. Measured on the solar calendar, it is a moveable holiday: some years it falls in January, others in February. When comparing economic activity in either of those two months to economic activity a year earlier, one must be aware of when the Lunar New Year occurred in each of those two years.

For economic statistics, a mathematical variant of the “comparable period” approach is employed to “seasonally adjust” the data. A seasonally adjusted statistic for retail sales, motor vehicle production, or gross domestic product is reported at an annual rate that assumes that the seasonal component of activity was at its typical rate during the period.

Thus, if December clothing sales are usually twice as high as in November, and sales in December 2020 are exactly twice as high as those in November 2020, the seasonally adjusted level of sales will be identical for November 2020 and December 2020. If sales in December 2020 are more than twice as high as those in November 2020, then the seasonally adjusted level of sales in December 2020 will exceed that in November 2020.

Seasonal Echoes of the COVID-19 Pandemic

It is pretty ease to see that the COVID-19 pandemic will disrupt the “comparable period” approach to account for seasonal changes. With much economic activity constrained in the second quarter of 2020 because of lockdowns and shelter-in-place orders, second quarter 2021 levels will certainly be higher, but what will we learn from that?

Use of comparable periods from 2019 will be one way to try to abstract from the distortions generated by the pandemic. Eagle-eyed analysts will want to pay attention to what comparable periods companies and journalists are using during this year.

For example, I read a newspaper article last month that discussed growth prospects for electric-powered motor vehicles. To bolster the argument that electric vehicle sales were poised for takeoff, the reporter noted that in China, sales of electric vehicles in January 2021 were six times those of the previous year. The reporter did not mention that sales in January 2020 were depressed by the pandemic, which affected activity in China earlier than in Western Europe and the Western Hemisphere.

A recent blog post from David Lucca and Jonathan Wright of the New York Fed pointed out that the pandemic will likely disrupt the more sophisticated seasonal adjustment mechanisms as well. The large economic disruption of the 2007-2009 Great Recession led to persistent seasonal echoes in seasonally adjusted data in the following years, Lucca and Wright said.

Because seasonal adjustment routines use a weighted average of recent comparable periods to estimate the “normal” seasonal relationship, a large disruption to economic activity, such as with the pandemic or the Great Recession, will introduce spurious seasonal patterns in the historical data. In the case of years soon after the end of the Great Recession, seasonally adjusted data for the first quarter of year typically indicated accelerating economic activity that then seemed to decelerate when the seasonally adjusted data for the second quarter became available.

Making Inferences Will Require Extra Diligence 

While statistical agencies can and have stepped in with manual adjustments to try to mitigate the problems caused when a large, nonseasonal shock appears, the resulting seasonally adjusted series may not be completely “fixed.” Lucca and Wright argue that "There are no easy answers to seasonal adjustment in this environment. The virus changed the economy and seasonal patterns, in some cases temporarily and perhaps permanently in other cases."

When available, analysts should also look at unadjusted data to get a handle on how the economy is progressing, but be aware that inferences about “true” behavior will be more difficult to draw for a number of years.

Author: Thomas Browne

Source: Market Research Blog

Post-COVID market forecasts: examples of industries expected to be in demand

Like many of you, I spend a good part of my day thinking about all the ways the near future will (hopefully) be different from the past year.

I would have traded a roll of toilet paper for a therapeutic massage during the great TP shortage. It will be a relief to pick a restaurant not based solely on whether its food travels well.But what will the future look like? We can look to the past as a first step in answering this question. Some historians directly tie the excess and indulgence of the Roaring Twenties to a collective reaction to the loss and deprivation caused by the Great War and the 1918 flu pandemic. Will that pendulum swing happen again?

I’m no futurist, but I see a short-term demand boom coming for many industries. This list is not all-inclusive but just a few examples.

• Weddings/Celebrations: In addition to those big life events coming up, many may host delayed parties for milestones that occurred during our “lost year.” Will they be low-key events, celebrating face-to-face connections? Or will we go all out since we now know that our future is uncertain?
• Personal Travel/Tourism: Often an annual vacation is the one thing that can keep a person motivated to work for the other 50 weeks of the year. We’ve had time to research our dream vacation, to watch travel shows, and to be influenced by travel influencers. We’ve had dream vacations cancelled. The thought of seeing something beyond our neighborhood is appealing. What will happen with solo and large group trips after the isolation of the pandemic?
• Business Travel: Which never stopped for some—is also likely to flourish. A savvy salesperson won’t let key accounts go without a personal visit for long. Air travel, car rentals, and hotels will soon be back on expense reports.
• Concerts/Movies/Art: Many people say that what they have missed the most are experiences that can’t be duplicated digitally. A live stream of an improv show or a telecast of an opera does not provide the same experience as a live event. Concert halls, arenas, and theaters are already booking up in anticipation of eased restrictions.
• Close-Contact Services: Many medical and grooming services are likely to be booked solid once we fully reopen – hair, nails, medical, massage, spas, and dental – with masks and updated safety protocols.
• Restaurants/Bars: Curbside pickup is a convenience that is likely to be permanent. But we should also expect in-person dining to rebound, as long as restaurants can maintain a perception of safety. Decision Analyst recently asked more than 9,000 U.S. adults to rate considerations when choosing a restaurant to dine at or order from, and cleanliness/safety is now a top consideration in addition to tasty/well-prepared food. With the closing of thousands of restaurants across the country, startups are likely to arise to meet refreshed demand. With this, new restaurant designing and construction, branding, menus and signage, and user-friendly websites will be in demand. Look for innovative service concepts, as well.
• Inventions/Books/Scripts: Our extended “down time” may have allowed for creativity to flow. It is said that the ideas of Isaac Newton and Shakespeare flourished during disease outbreaks in their times. The idea for Mary Shelley’s Frankenstein was born during a dark and cold summer caused by a volcanic eruption (thanks to Drunk History for that fact!). What great works will come out of this pandemic?
• Historians/Analysts/Authors: As we collectively try to figure out what we just went through, there will be plenty of demand to understand it from a 30,000-foot view. We not only endured a pandemic, but we also experienced stressful and unusual national, local, and global events. Politics. Extreme weather. Violence. Tiger King. It’s a lot to unpack and make sense of.

In the market research world, how will all this affect demand forecasting for products and services? Demand for many categories is likely to be different 2 years from now compared to 2 years ago. Changes seen in buying patterns during COVID-19 may not be permanent; they may merely be stopgaps or even desperation purchases. Other consumers may have seen the light—permanently switching brands or entering/exiting categories. Forecasts will need to reset to match reality once things settle. That information is critical in making strategic decisions. Looking at the past may not lead us on the right path anymore.

A boom is always a good thing, right? As we rush to normal, what are the potential downsides?

As a thought experiment, let’s look at tourism. Tourism boards see potential hesitancy as customers decide whether travel is safe for them. Those brave first adventurers post pics of exotic places with no crowds and report thrilled locals ready to revive their economy. If early discounts work, wanderlust is rekindled, which leads to crowds. Human connection is what we asked for, but pushy crowds are never fun. Over-crowded destinations create less-than-desired experiences. Then fewer people want to travel. After staffing up for the boom, a lull in visitors causes overstaffing. Bring on the discounts, and the cycle continues. 

Author: Jennifer Murphy

Source: Decision Analyst

The rise of Online Communities as the centre of market research

Once believed to be a minor player, online communities have become a central pillar of an effective research strategy now that consumers, governments, and companies rely on virtual interactions.

Online Research Communities have been growing in importance as a central pillar of an effective research strategy for many years due to the benefits they unlock: centralized research management, ongoing engagement with important constituencies, flexibility of methodology deployment, and ROI to name just a few. However, prior to 2020, they were not necessarily strategically mission-critical.

Like so much in our world, the impact of Covid-19 has changed that.

As the crisis unfolded in Q1/Q2, we saw several dramatic changes that impacted research organizations rapidly:

1. Communication became almost entirely digitally centric, with face-to-face interactions curtailed virtually to the point of complete cessation for large portions of the population.
2. Consumer behaviors, values, and planning began to shift in response to new realities. Maslow’s Hierarchy was validated once again as many people were focused on safety and security as primary motivators in a way unseen outside of wartime.
3. Brands, governments, NGOs, and all companies who serve them were under immense pressure to urgently engage and understand these changes in both immediate and long-term contexts.
4. Budget pressures increased for many buyers of research, with some under extreme duress, while their information needs were only increasing.
5. Social and emotional impacts for many people were extreme, resulting in a newfound willingness, even need, to connect with others including researchers.

Those are just the most obvious trends, but they clearly pointed to the Online Community as a potential solution, and we saw that reflected in the business performance of the category. Recollective and virtually every company that offers solutions in the Community/Digital Qualitative space saw a massive and incredibly rapid shift to their platforms in response to new market dynamics. This shift has also spurred a new surge of innovation as supplier companies rise to the occasion to meet the evolving needs of users.

Online Communities are here to stay

For those of us that have been advocates of communities and virtual qualitative for many years, this shift made perfect sense, as previously outlined herein. However, the question before us now is whether this was a short-term reaction or a long-term strategic shift? Certainly, it started as the former, but I believe it is now the latter. Covid-19 has been the impetus for a “tipping point”, and there is no going back now. The key stakeholder groups have adapted to what truly is the “new normal” in the research world:

• Buyers of research have been convinced that they can successfully duplicate the information needs of qualitative research in an online environment while saving the expense and liability issues associated with face-to-face research. Unless the research requires some level of sensory input (touch, taste, smell) or has an experiential component (car clinics, shop alongs, etc..) virtual qual is here to stay and will be the new majority use method.
• Consumers are now comfortable engaging via video for almost all aspects of information sharing and allowing others into their lives via video. The ubiquity of cameras in a myriad of devices combined with continual enhancements to internet bandwidth makes the barrier to usage minimal in most of the developed world, and with the scaling of internet satellite systems, soon the whole world.
• Users of research now know they can get information needs met quickly via digital channels and can cost-effectively build long-term engagement channels with constituent groups relatively easily. They are seeing the ROI of insights, especially proprietary communities and panels, and will continue to support them.

With all these factors in mind, I think it is clear now that we will continue to see the large-scale adoption of online communities as one of the central pillars of research operations, and the development of further innovations to increase cost and speed efficiencies while empowering greater quality and impact of insights. I wish it hadn’t taken a global pandemic and all the negative aspects of this situation for so many to get here, but Necessity is the Mother of Invention and I am grateful companies like Recollective and their peers were here to help make the transition as easy as possible. The world has changed, but Online Research Communities and all the great benefits they provide are here to stay.

Author: Leonard Murphy

Source: Greenbook

Three developments the customer journey is going through since 2020

If you're working off a strategy that doesn't address data privacy concerns, rapid digitization, and your online presence, you're behind the pack.

The customer journey has drastically changed since 2020. New challenges and restrictions have made both companies and customers seek out ways to adapt. This has informed much of the shifts we’ve seen in 2021 thus far, and continues to create a new customer journey with unprecedented speed.

The annual PWC Global Consumer Insights Survey revealed how the past year saw a sudden boost in the pace of consumer changes in behavior and habits. In one of its core insights, more individuals are open to sharing their data if it helps bring about improvements. This is up from the previous year, and very indicative of the new direction that the customer journey is taking.

Consumer accessibility has shifted

The customer purchase journey has now become arguably more convenient, but it also means that access to resources has changed. Even the way companies interact with their target market has seen a palpable change. For instance, part of getting core analytics is being able to get members of selected demographics to share their data, provide key insight, and inform marketability. As individuals become more aware of the impact and security that goes with sharing information, there is a need for smarter market research. Consumers have responded better to surveys via encrypted platforms and have become warier about the reliability of companies they share their information with. With shifting public sentiment and a desire for convenience, consumers seek out reviewable platforms that also provide shorter surveys to cater to their attention.

Currently, over 48% of transactions are done online and over 58% say they will continue to shop online even when restrictions ease. Now, it seems that consumer sentiment simply demands a solid online presence. Be it for shopping goods or reaching out to customer support, customers want to establish that connection quickly and easily from remote bases.

Turnaround time and expectations have shortened due to digitization

The past year saw a sudden acceleration in the adoption of technology. Digitization has suddenly jumped ahead from former projections, and in turn, consumers and workers alike have faced quicker expectations. As companies attempt to be more agile in meeting online consumer needs, they are faced with a public that expects faster turnaround, secure platforms, and efficient processes throughout their purchase journey.

This applies all the way from the UI of a business’s landing page or the user-friendliness of an app, all the way to their payment options and checkout process. This also translates over into the marketing side of things, as consumers respond more to digital advertising than traditional forms. In turn this has led to a huge demand for learned and qualified professionals in the field. In order to keep up with this thriving industry, higher education institutions have increased their offerings of online degrees in marketing, which are now designed to focus more on digital trends, like interactive marketing. Digital marketing skills are consistently listed as in-demand skills from various job analysts in Forbes, HubSpot, and even the U.S. Bureau of Labor Statistics. Given that marketing is a crucial part of the customer’s journey, businesses need to strategize how it fits into their customer’s experience.

It seems the biggest concerns have been fuss-free processes and data privacy, so companies have had to get a better grasp of consumers’ preferences based on the various interactions they have with brands, minus any pain points that mar online data gathering. As marketers have relied heavily on automation, smarter keywords, video research, and apps in the past year, we saw results in traffic as platforms like Zoom boomed with 1.7 million users at its peak.

New limitations on customer interactions have sparked a need for business adaptability

Many of the changes mentioned have also come with new limitations. Priorities have been reevaluated and consumers just don’t make the time for companies that seemingly don’t have their best interests at heart anymore. The new consumer is heavily informed by the connection (or lack thereof) that they have with a brand. Some 96% of consumers even say that they are more likely to support a business that values their experiences. This means that in lieu of in-person customer experience executions, brands are relying on their online personas instead. Creating an online platform is not just about creating a resource for information, but also for community.

Constraints on location and accessible executions also mean that brand perception has largely been based on digital presence, online feedback, and viral news over the past year. Because of this, consumers have become more likely to cater to brands that have provided a good story or have a significant amount of genuine feedback to back them up.

Seeing that most experts predict that more changes in digitalization, CX, and market research will come hand-in-hand over the next few years, proactive planning and action will be pillars in future-proofing our industries.

Summary

After the unprecedented challenges of 2020, the field of market research was forced to adopt new procedures and drop old habits. The most apparent changes in this bustling sector are, namely: rapid digitization, new and socially intelligent means to gather data, and the shift in consumer interactions. As the past year witnessed countless consumer attitude fluctuations, market research is expected to become a thriving and dynamic field that all businesses should not neglect.

Author: Romy Jacobs

Source: GreenBook blog

Using advanced analytics to forecast demand in the life sciences market during COVID-19

The COVID-19 pandemic has revealed the vulnerability of pharmaceutical supply chains. Pharma companies are focusing on risk management to improve the resilience of their networks. Most of the measures they will take, including on-shoring, over capacities and redundancies, will lead to higher costs. To decrease inventory levels across these new supply chains and control costs, pharma companies should also focus on improving their demand planning.

Demand planning challenges

The life sciences industry faces a variety of unique challenges in demand planning during the COVID-19 pandemic. Life sciences organizations are struggling to meet supply, from raw materials through to the pharmacy, as a result of COVID-19 related disruptions. The crisis is changing the demand for over-the-counter (OTC) medications and therapeutic devices and how people shop for them.

Meanwhile, demand for supplies for clinical research is a moving target. Some trials are delayed due to distancing measures. And new research is popping up to explore the efficacy of existing drugs on the treatment of COVID-19.

For high-selling OTC medications and seasonal treatments like flu vaccines, fully automated traditional time series forecasting works well. However, for very specialized treatments and sudden occurrences like COVID-19, machine learning models have proven to be much more accurate to forecast demand.

Advanced analytics

Demand sensing, forecasting and planning are critical to understanding the many variables at play, especially during a pandemic. These are complex problems, requiring advanced analytics to best understand how demand is changing on the ground and to predict future changes as a result of the pandemic. For example, we can take data for demand spikes and lulls in regions first affected by the pandemic and use it to make predictions in areas that have yet to experience their peak. Similarly, we can use data from regions that are reopening to forecast changes to demand in other regions as they reopen. Organizations can then use this insight about demand to inform manufacturing and supply chain decisions as they work to mitigate disruptions.

Author: Alexander Daehne

Source: SAS

Using Artificial Intelligence to see future virus threats coming

Researchers use machine learning algorithms in novel approach to finding future zoonotic virus threats.

Most of the emerging infectious diseases that threaten humans – including coronaviruses – are zoonotic, meaning they originate in another animal species. And as population sizes soar and urbanisation expands, encounters with creatures harbouring potentially dangerous diseases are becoming ever more likely.

Identifying these viruses early, then, is becoming vitally important. A new study out today in PLOS Biology from a team of researchers at the University of Glasgow, UK, has identified a novel way to do this kind of viral detective work, using machine learning to predict the likelihood of a virus jumping to humans.

According to the researchers, a major stumbling block for understanding zoonotic disease has been that scientists tend to prioritise well-known zoonotic virus families based on their common features. This means that there is potentially myriad viruses unrelated to known zoonotic diseases that have not been discovered, or are not well known, which may hold zoonotic potential – the ability to make the species leap.

In order to circumvent this problem, the team developed a machine learning algorithm that could infer the zoonotic potential of a virus from its genome sequence alone, by identifying characteristics that link it to humans, rather than looking at taxonomic relationships between the virus being studied and existing zoonotic viruses.

The team found that viral genomes may have generalisable features that enable them to infect humans, but which are not necessarily taxonomically closely related to other human-infecting viruses. They say this approach may present a novel opportunity for viral sleuthing.

“By highlighting viruses with the greatest potential to become zoonotic, genome-based ranking allows further ecological and virological characterisation to be targeted more effectively,” the authors write.

“These findings add a crucial piece to the already surprising amount of information that we can extract from the genetic sequence of viruses using AI techniques,” says co-author Simon Babayan.

“A genomic sequence is typically the first, and often only, information we have on newly discovered viruses, and the more information we can extract from it, the sooner we might identify the virus’s origins and the zoonotic risk it may pose.

“As more viruses are characterised, the more effective our machine learning models will become at identifying the rare viruses that ought to be closely monitored and prioritised for pre-emptive vaccine development.”

Author: Amalyah Hart

Source: Cosmos

What to expect from the post-pandemic restaurant industry? 3 foodservice trends

The COVID-19 pandemic took a heavy toll on the restaurant industry. The National Restaurant Association called 2020 the most challenging year for the industry in its history. More than 110,000 eating and drinking establishments closed last year, and 2.5 million restaurant industry jobs were wiped out. And that is in the U.S. alone!

However, the devastating downturn experienced in 2020 and 2021 is anticipated to be short-term. Global foodservice revenues are projected to rise 3.7% per year to reach $4.4 trillion by 2024, according to forecasts from The Freedonia Group, a leading market research firm and division of MarketResearch.com.

1. Global Foodservice Market Primed for Growth

Freedonia identified a range of factors that will boost long-term restaurant market growth:

• Continued adoption of advanced technologies to streamline operations, enhance customer experiences, and increase sales
• Rising popularity of online food delivery and the expansion of delivery networks, including cloud kitchens (also known as ghost kitchens or virtual kitchens)
• Healthy and sustainable eating trends driving higher-value sales in mature markets
• Strong investment in the global expansion of multinational chains
• Growing incomes in developing markets widening the local consumer base for restaurants

2. A Burst of Digital Adoption and Tech Innovation 

Due to COVID, old analog systems had to be quickly transformed as restaurants pivoted to online, contactless ordering. The intense challenges of the pandemic and the cutthroat competition within the foodservice industry will continue to accelerate innovation and force restaurants to rely more on digital technology. 

A common way to leverage technology is through the use of proprietary mobile apps that help brands to offer personalized incentives and foster repeat business. Foodservice brands are also expected to invest in automated and robotic technologies such as self-service kiosks, cashierless technologies, and restaurant robots to improve efficiency. 

Burger King and McDonald’s employ a range of technologies including data analytics, geofencing, and mobile apps to improve customer experiences, reduce labor costs, and optimize upsells.

3. Ethical Considerations and Sustainability Remain Key

Technology is not the only way restaurants can boost revenues. Offering higher-quality menu items and ethically sourced ingredients is another popular strategy.

Particularly in mature markets, consumers are increasingly looking for foodservice options that minimize environmental impacts and align with their healthy eating lifestyles. The rising influence of generation Z and millennial consumers is also expected to reinforce the importance of corporate social responsibility and sustainability.

As a result, more and more brands are focusing on:

• Healthy, high-quality menu items
• Diet-friendly options such as keto, vegan, or vegetarian
• Responsibly sourced and certified ingredients, including non-GMO and plant-based
• Sustainable business practices to reduce waste

Chipotle, Panera Bread, and Starbucks are all examples of this trend; their menus emphasize high ingredient quality through ethical sourcing such as cage-free, pasture-raised, and antibiotic-free livestock. Subway also centers its branding on health and wellness with the “Eat Fresh” tagline.

Author: Sarah Schmidt

Source: Market Research Blog

Which products and services will prosper in the New Normal?

In times of crisis some markets are doing well, others don’t.

The question is what will prosper in the New Normal?

‍Already in 1975 Igor Ansoff wrote (in California Management Review) the for Market Intel companies evergreen article ‘Manage Strategic Surprise by Response to Weak Signals’. In the article he discriminated between two strategic options companies have to respond to strategic surprises. (1) Develop capabilities for effective crises management meaning fast and efficient after-the-fact responsiveness to sudden discontinuities. (2) Treat the problem before-the-fact and thereby minimize the probability of strategic surprise.

Although both options deserve full management attention, the second one (at least for Market Intel companies) is the most fascinating one. The crucial question here is: can we prepare for a strategic surprise before the discontinuity actually happens by detecting early warnings or weak signals? The basic assumption here is that if we are able to detect weak signals and interpret them accordingly, we can defend ourselves or maybe even profit from the consequences of a disruption.

But let’s be honest. This is not easy.

Ansoff distinguishes five states of knowledge which differ to the amount of uncertainty which comes with weak signals. The earlier the signal, the more uncertain the situation a company has to deal with. If we look at the Wuhan situation in January this year, weak signals were certainly detected but due to may possible intervening variables they were not interpreted in the right way (we can conclude so afterwards) and left companies with a lot of uncertainty.

Now we are in Covid-19 crisis the consequences of ‘social distancing’ are almost too obvious to mention. I will do so anyway. In food, Out-of-Home sales almost dropped to zero, while Retail sales prospered in the first weeks of the crisis. Except for those categories which come with typical encounter moments (giving presents like chocolates, flowers etc.) almost all food categories profited from anxiety in society which stimulated people to hoarding. While crises last longer the hoarding effect disappeared and it became more obvious which categories profit and which loose. For example, potatoes industry is suffering a lot these days (-70% turnover). However, the high value-added segment of coated fresh potatoes wedges distributed by retail flourishes, because of the ambition to compensate in-home for the missing of Out-of-Home quality moments. For the same reason delivery services thrives as do the companies that supply them with websites, (e-)bikes and digital solutions to communicate on distances.

‍So, we can conclude there is a typical crisis market which is an awful threat to some companies but a beautiful opportunity for others.

‍Companies could have anticipated better on the weak signals the Wuhan situation evoked if they would have interpreted them with use of different scenarios. Most of the companies, though, were convicted to Ansoff’s first scenario: Crisismanagement. Some did a better job (were more creative and responsive) than others.

However, now the situation emerges in a less unsecure situation (another state of knowledge in Ansoff terms). We have detected, or even ‘know’ the weak signals but have to interpret what they will mean for the New Normal. What possible situations can emerge if we come out of this crisis? What are the scenario’s and what responsive options does companies have?

Dutch Bakery market

Let’s take the Dutch Bakery industry as an example for the current situation and the scenario’s that possibly emerge. If we would have detected the weak signals early enough and interpreted them the right way an industrial supplier of breads could have been prepared to prosperous retail and bad Out-of-Home (OOH) times and adjust its product portfolio accordingly. Since typical Eastern activities would suffer under the conditions of social distancing the selling of Eastern breads and products could expected to be much lower. Since people work at home and kids don’t go to school the necessity of take away bread packages diminished. The question, however, is what scenario will emerge when the crisis disappears.

The Dutch Bakery market in 2019 showed beautiful figures. And this trend seemed to continue in the first months of 2020. Both volume and value went up and artisanal bakeries were growing above par. The Covid-19 crisis, however, seems to have inverted the beautiful but vulnerable trend (see Figure 1). However, the question remains which scenarios can be depicted in The New Normal?  

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Figure 1.

‍Scenario 1: Assumes that when the virus in the future completely disappears everything will be instantly back to normal. OOH activities will explode because everybody wants to celebrate the ending of a crises. Mass events will prosper. In this situation specialty high value breads, mainly sold in OOH environments, will grow to a maximum extend and the value of that part of the market increases accordingly. Retail will remain constant and OOH grows, making a growth scenario in Value Added categories plausible. The Dutch market after a hausse will come to stabilization in a normal of €2.3 bn/yr with more chances for sustainable categories addressing health benefits 

‍Scenario 2: Assumes that when the virus slows down, many things will be back to normal. However, anxiety in society for the virus will remain. This will result in a ‘voluntary’ continuation of social distancing infecting mainly OOH but the Artisanal segment as well. Mass events will not take place for a longer period of time.OOH will grow again in this scenario but at a low pace and land at 50-70% of before crises value (to a max of €445mln. a year). In total bread market in this scenario will land at €1.85bn/yr to 2.1 bn/yr.

‍Scenario 3: Assumes that the virus slows down at a very slow pace. A lot of things remain for a long time as in crises lock down situations. OOH suffers for a longer time and turnover will be minimal compared to before crises times (30%). Retail might profit from this situation because of primary need of people. This might also lead to an increase of volume in pre backed category because of the need of people to simulate OOH celebration moments in home. Bread market will be around €1.7bn/yr.

‍Scenario 4: Virus does not disappear. Mutation is probable. It affects social distancing in a substantial manner. Events are not organized for about 1,5 yr. Social distancing rules stay manifest. High value breads in OOH suffer a lot (-90%), Artisanal shops suffer as well due to three consumers per shop policy and their popularity amongst elder people (-10%). Retail in volume stays stable but in value decreases due to high supply vs lower demand balance. Pre-backed categories profit from the situation because special moments are celebrated in home. Bread market could decrease in this situation to €1.5 bn/yr for a longer time.

‍Although the above scenario’s are simplified abstractions of reality, they illustrate that ‘The New Normal’ could have significant effects on the value of the bread market and it's different segments. Suppliers of bread and bread ingredients should think about the possible scenario’s. Despite the uncertainty of their emergence the scenarios can help companies to increase their ‘before the fact treatment capabilities’. This provides them with a competitive advantage. Market intelligence companies offer help by their ability to detect and capture driving signals as well as by their interpretation and giving of meaning.

Looking at the Covid-19 crisis an interesting discussion could be whether European governments, keeping the Wuhan situation midst January 2020 in mind, perform better in crisis management (option 1) or before the fact treatment (option 2). For businesses, from an information point of view, it’s more interesting whether we can learn from the current crisis, how it emerged and how we can prepare ourselves from the perspective of an unknown changing future.

Author: Egbert Philips

Source: Hammer, market intelligence

Why data management is key to retailers in times of the pandemic

Jamie Kiser, COO and CCO at Talend, explains why retailers, striving to ensure they’re not missing out on future opportunities, must leverage one thing: data. By utilizing customer intelligence and better data management, retailers can collect supply chain data in real-time, make better orders to suppliers based on customer intelligence.

While major industries from tech to the public sector felt COVID’s pain points, not many felt them as acutely as retail. Retailers must now contend with everything from unreliable supply chains to a limit on the number of customers in-store at any given time, as consumer behavior shifted with social distancing guidelines and new needs.

For example, e-commerce grew by 44% in 2020. As we begin recovering from the pandemic, retailers increasingly push to deliver their customers an in-store shopping experience as seamless as it is online. However, these new digital strategies rely on precise inventory management, which remains a pain point for many brick-and-mortar stores.

For retailers to ensure they’re not missing out on future opportunities, they need to leverage one thing: data. By utilizing customer intelligence and better data management, retailers can collect supply chain data in real-time, make better orders to suppliers based on customer intelligence. Data will help retailers fully integrate their supply chain, customer, and in-store data to ensure they’re creating an in-store experience that’s competitive to shopping online and other new shopping behaviors.

Eyes on the supply (chain)

The pandemic has revealed the fragility of the supply chain. With unprecedented unpredictability in what products stores will and will not have access to and when retailers need to integrate real-time data management into their online operations. Investing in supply chain data strategies enables retailers to adapt and adjust to sudden breakdowns from their suppliers.

Like Wayfair and Dick’s Sporting Goods, some companies leverage real-time inventory data to make their supply chain transparent to their customers, so they are always up to date on what is and isn’t on the shelves. Investing in data management tools to collect supply chain data in real-time empowers retailers to create better customer experiences and saves stores the estimated billions in lost sales to customers discovering their desired item is out of stock.

However, supply chain erosion is not the only supply problem retailers will have to overcome. Some issues facing supply and inventory come from consumers, like last March’s run on toilet paper or the PS5 selling out before they even made it to shelves. Seemingly instantaneous changes in customer behavior can instantly impact what items retailers are prioritizing in orders from suppliers. But without understanding customer behavior, retailers can overcorrect and wind up with dead inventory on their hands.

Customer analytics influence orders

To avoid collecting dead inventory and ensure orders to suppliers are accurate to their customers’ desires, retailers need to integrate customer intelligence with their supply chain information systems. Combining this information empowers retailers to place orders from suppliers based on precise predictive models of customer behavior. This way, retailers will keep up with rapid consumer behavior changes and keep supply chains up to speed with the latest trends in brick and mortar shopping.

For example, buy online, pick up in-store (BOPIS) shopping experiences have been a growing trend among retailers and consumers the past few years, and this trend shows no sign of slowing down. One survey found BOPIS sales grew by 208% in 2020. But BOPIS is not the only trend growing during the pandemic. It has also accelerated research online, purchase offline (ROPO). Finding success in both BOPIS and ROPO is entirely contingent on understanding what’s on the shelf, what suppliers bring in, and which items are unpopular and creating dead inventory. By collecting specific customer intelligence, such as the products customers are researching, retailers can build predictive models for when online research turns into an in-store sale.

Leveraging customer intelligence not only helps brick and mortar retailers keep shelves stocked with the products their customers wish to purchase but can also be integrated with supply chain data to optimize operations. Investment in data management and integration can positively impact retailers’ profits by allowing them to make purchasing decisions from suppliers based on supplier circumstances and customer demand. Pooling data from both suppliers and customers into a single source of truth gives retailers the ability to operate under intelligent predictive business models. It also will prevent direct profit loss to competitors. Research shows an estimated $36.3 billion is lost to brick-and-mortar competition annually to customers purchasing elsewhere upon discovering their desired item is out of stock.

An integrated approach to supply chain management

Integrating real-time supply chain data with customer intelligence can prevent customer walkouts and increase profits by mitigating the risks to the supply chain created by the pandemic. However, when this external data combines with internal data — like sales, restocking times, demand surges, and more – brick and mortar retailers can position their supply chain to be in sync with the real-time shopping occurring online and in-store. Better business intelligence and supply chain data management empower retailers to offer customers a competitive experience to find online. Doing this requires a robust data management system and a business-wide data strategy that integrates data across all verticals.

For example, European clothing retailer Tape à l’oeil, replaced an aging ERP system with a new SAP and Snowflake-based infrastructure to better capture digital traffic data as they made operations digital due to the pandemic. This addition to their existing platform allows Tape à l’oeil to capture customer feedback through surveys to capture satisfaction with a new collection. Now digital campaign results are easily retrieved from Facebook and Instagram to cross-reference them in Snowflake and share comprehensive reports with management. Making data the heart of their business strategy.

This new data strategy has allowed Tape à l’oeil to find success during these tumultuous times by integrating customer data into predictive models to help them act faster and mitigate risks in their supply chain. Tape à l’oei’s CIO said that leveraging data has allowed them to improve operations overall and give them the “agility” to react to disruptions in the supply chain swiftly.

The brick and mortar way forward

A year into the pandemic and the retail industry remains in an ever precarious state. However, consumer trends show there are still growth opportunities for the brick-and-mortar stores prepared to meet customer demand.

Making data management an integral part of retail operations will help companies meet the supply chain challenges presented by COVID-19 and empower them to keep their business growing.

Author: Jamie Kiser

Source: Talend