If we look more closely at the AI and analytics deployment landscape, we see that these projects tend to suffer from siloed development processes, where teams work without a single vision. This leads to disconnected solutions that may crack a unique problem but do not add anything of value to larger business goals. The result? The value derived ends up being short-lived.

Building point solutions is no longer the echoing mantra despite the buzz around it. Rather, today, the focus is on building integrated solutions to business problems while ensuring lasting impact.

Data science practitioners need to evolve from providing specialized point solutions to developing comprehensive platforms and products, which enhance efficiency, scalability, and usability for businesses. To achieve this, a product-thinking mindset may be a game changer in how teams operate, develop and implement AI projects.

Having been an active part of the advanced analytics industry, we at Tiger Analytics anticipated this direction by developing strong capabilities in building robust platforms and productized solutions.

Bringing in Product Thinking to Contrast Traditional AI Deployment

When deploying AI, the typical focus is on solving immediate problems or delivering quick solutions. Product thinking takes a different approach, looking beyond the short term and focusing on creating value that lasts. Here’s what sets it apart:

• Holistic integration: Ensures AI works smoothly with current systems and future plans.
• Scalability and adaptability: Builds solutions that grow and change as needed.
• Continuous improvement: Keeps refining and improving based on ongoing feedback.
• Strategic vision: Aligns AI projects with the long-term goals of the company.

Ultimately, AI projects turn into integral components of an all-encompassing strategy

Why Should AI and Data Science Experts Embrace Product Thinking?

At its core, product thinking focuses on the user and what they want to accomplish. Adopting this user-centric approach ensures that AI projects are not just technically sound, but also relevant and impactful in terms of greater user satisfaction and higher adoption rates. A product-thinking mindset also helps data experts align solutions more closely to business objectives and bridge the project value gap.

• It encourages continuous improvement, adaptability, scalability and creative problem solving
• It ensures relevance in a rapidly changing landscape
• It shifts the focus to long-term product quality and maintainability

So, organizations must embed product thinking into their foundation so that they remain aligned with market shifts and are always ready to tackle new challenges lurking around the corner.

At Tiger Analytics, we began experimenting with various use cases, developing AI solutions with a product-thinking mindset.

Using A Product-Thinking Mindset To Enhance Planogram Compliance And Store Digitization

One of the most compelling examples of product thinking in action is our collaboration with a leading American multinational in the consumer goods sector. The client was looking to transform its approach to planogram compliance and store digitization.

Identifying the challenges

Their existing process relied heavily on manual store visits, where representatives would physically inspect store shelves to ensure they aligned with planograms. This process was error-prone, labor-intensive, and expensive.

Hence, the first phase was to establish the objective of building a scalable solution for automating the compliance audit and digitizing store operations through a mobile application.

Developing the solution

In the next phase, we developed a mobile application that could function without constant internet connectivity. The architecture was modular, enabling global features with market-specific customization.

This cross-platform solution leveraged various frameworks, standardizing the process around ONNX for model optimization. It included developing capability modules, such as tools for video analytics, smart annotation, and image stitching.

Our team also adopted an iterative development approach, continuously improving the solution based on feedback. Data challenges, like the lack of historical data for new SKUs, were addressed through few-shot learning and heuristic-based decision rules for rack identification. The result was a high-performance model that could be deployed on mobile devices.

Unlocking the impact of product thinking

Previously, store representatives had to physically visit each location, manually inspecting the shelf layouts. Audit reports were also generated by hand, which only added to the inaccuracies and costs.

With the implementation of Tiger Analytics’ automated solution, the process was streamlined.
Now, representatives can take a picture of the shelves using a mobile app designed for real-time processing. The app immediately checks the images against the planogram, automatically generating key performance indicators that guide them to execute rack adjustments.

Once the adjustments are made, the data is synced to the cloud for instant report generation and real-time monitoring.

Driving measurable business outcomes

This shift from a manual to a digital process reduced the time required for audits from 30-40 minutes to five to six minutes— an 85% improvement. Creating an end-to-end workflow, from strategy to compliance, reporting, and analytics, automated this process across 10 different markets globally.

The accuracy of the audits too improved, rising to 90% from the previous 60%.

On the technical front, the size of the machine learning model was reduced from 25MB when deployed on the cloud to just 5MB on edge devices. Image stitching, a crucial component, improved significantly, reducing the completion time from four minutes to five seconds.

Product Thinking For Data Scientists – The Tiger Way

Establishing a culture of product thinking ensures we deliver services and solutions that are tailored to the dynamic needs of today’s market. Here’s how we are building this culture of product thinking at Tiger Analytics:

• Integrating product thinking into every project
• Crafting efficient and scalable solutions that solve real-world problems
• Ensuring our cross-functional teams collaborate seamlessly, so that each solution is not only data-driven but also built on a comprehensive ecosystem that is efficient and scalable

We believe that embedding product thinking into every project aids us in creating smarter and more responsive solutions and ultimately, refined product development journeys.

Impact of Product Thinking-Led AI: The Time Is Now

Adopting a product-thinking mindset in AI projects can transform how we approach the development process. The focus has shifted from AI projects for short-term pain points to creating solutions that get to the root of the real-world problems and drive tangible business impact. Considering the speed at which AI is becoming an integral force of daily operations, a product-thinking mindset empowers AI projects to thrive – not just survive.

For instance, instead of only optimizing a predictive model’s accuracy, the goal can be expanded to consider how the model can improve user experience, such as recommending products based on customer behavior. This mindset also encourages cross-functional collaboration with larger teams like product management and engineering, leading to better-aligned solutions

Then, instead of only crunching the numbers, AI solutions can continuously deliver insights that matter, help predict user behaviors, fine-tune features, and more. As AI systems learn and improve using the data they process, this iterative cycle of data collection, analysis, and application will strengthen a continuous feedback loop.

Thus by focusing on building a resilient data analytics framework based on AI advancements, tomorrow’s products will be increasingly responsive, predictive, and to business owners’ and data science practitioners’ collective relief, more successful.

The post The Data Scientist’s Guide to Product Thinking and Why it Matters appeared first on Tiger Analytics.

Key Opinion Leaders: Pharma-Influencers or more?

KOLs are usually researchers in a particular therapeutic area, they may be editors or contributors to key journals, may hold offices in professional associations and are frequent presenters at conferences. Traditionally, KOLs helped disseminate information through publications, speaker programs conducted by pharmaceutical companies, conferences organized by the government or professional associations, seminars etc. However, in the current digital era, they also share their opinions through blog posts, digital news articles, social media, or webinars.

Due to their strong pedigree and professional stature, KOLs exert a strong influence over other Health Care Professionals (HCPs). Their assessment of various treatment options influences the prescribing patterns of HCPs and in many cases tends to influence patient behavior as well.

Figure 1: Role of KOLs at various stages of the Drug Lifecycle

As KOLs rise in prominence over the course of their career, their circle of influence expands to cover larger audiences of peers and patients. For the pharmaceutical industry, KOLs can therefore have an impact on the entire drug lifecycle, right from identifying unmet needs, to drug development, product launch, and market performance.

KOL Mapping, Identification and Profiling

Pharmaceutical companies generally have Medical Science Liaison (MSL) teams within the Medical Affairs group to build and nurture relationships with KOLs. Traditionally, KOLs are identified based on their academic background, professional affiliations, research, publications, and participation in conferences and events, all of which are lagging indicators while identifying KOLs i.e., these approaches help identify KOLs after they have risen in prominence and established their influence in professional communities.

There is a growing need to identify KOLs early and start building relationships with emerging KOLs early in their career.

At Tiger Analytics, we’ve used advanced analytics and a data-driven approach to help identify future KOLs by analyzing the research and publication activity of early-stage professionals. This helps pharmaceutical companies find Heath Care Professionals (HCPs) with whom they can start building relationships at a nascent stage in their careers so that these could potentially be leveraged when they become KOLs in the industry.

Our predictive algorithm uses PubMed data to identify KOLs. PubMed which comprises more than 33 million citations for biomedical literature from Medline, life science journals, and online books, also provides a historical snapshot of all research and publications for over fifty years.  Using this data, our algorithm maps out pathways that the current KOLs traversed to get to their current professional standing. 

In addition to this, we’ve built on the identification approach to then predict who among the current early-stage professionals is displaying the right ‘signals’ to potentially become future Key Opinion Leaders. 

This ability to predict future KOLs will help pharmaceutical companies to start engaging with potential KOLs very early in their career so that they can leverage their relationships to develop and launch successful products in the future – earning them the first-mover advantage over their competitors and peers. 

The post Unlocking the Power of KOLs in the Pharmaceutical Industry: From Expert Advisors to Influential Game Changers appeared first on Tiger Analytics.

Data challenges faced by automotive dealers

There are many reasons why auto dealers still struggle to collect and use data. The biggest one is the presence of legacy systems that bring entangled processes with disparate data touchpoints. This makes it difficult to consolidate information and extract clean, structured data – especially when there are multiple repositories. More importantly, they are unable to derive and harness actionable insights to improve their decision-making capabilities, instead of merely relying on gut instincts.

In addition, the sudden growth of the BEV/PHEV market has proven to complicate matters – with increasing pressure on regulatory compliance.

But the reality is that future-ready data management is a must-have strategy – not just to thrive but even to survive today’s automotive market. The OEMs are applying market pressure on one side of the spectrum – expecting more cost-effective vehicle pricing models to establish footprints in smaller or hyper-competitive markets. On the other side, modern customers are making it abundantly clear that they will no longer tolerate broken, inefficient, or repetitive experiences. And if you have brands operating in different parts of the world, data management can be a nightmarishly time-consuming and complex journey.

Future-proofing the data management strategy

Now, it’s easier said than done for the automotive players to go all-in on adopting a company-wide data mindset. It is pertinent to create an incremental data-driven approach to digital transformation that looks to modernize in phases. Walking away from legacy systems with entangled databases means that you must be assured of hassle-free deployment and scalability. It can greatly help to prioritize which markets/OEMs/geographies you want to target first, with data science by your side.

Hence, the initial step is to assess the current gaps and challenges to have a clear picture of what needs to be fixed on priority and where to go from thereon. Another key step in the early phase should be to bring in the right skill sets to build a future-proofed infrastructure and start streamlining the overall flow of data.

It is also important to establish a CoE model to globalize data management from day zero. In the process, a scalable data pipeline should be built to consolidate information from all touchpoints across all markets and geographies. This is a practical way to ensure that you have an integrated source of truth that churns out actionable insights based on clean data.

You also need to create a roadmap so that key use cases can be detected with specific markets identified for initial deployment. But first, you must be aware of the measurable benefits that can be unlocked by tapping into the power of data.

• Better lead scoring: Identify the leads most likely to purchase a vehicle and ensure targeted messaging.
• Smarter churn prediction: Identify aftersales customers with high churn propensity and send tactical offers.
• Accurate demand forecasting: Reduce inventory days, avoid out-of-stock items, and minimize promotional costs.
• After-sales engagement: Engage customers even after the initial servicing warranty is over regarding repairs, upgrades, etc. as well an effective parts pricing strategy.
• Sales promo assessment: Analyze historical sales data, seasonality/trends, competitors, etc., to recommend the best-fit promo.
• Personalized customer engagement: Customize interactions with customers based on data-rich actionable intelligence instead of unreliable human instincts.

How we helped Inchcape disrupt the automotive industry

When Tiger Analytics began the journey with Inchcape, a leading global automotive distributor, we knew that it was going to disrupt how the industry tapped into data. Fast-forward to a year later, we were thrilled to recently take home Microsoft’s ‘Partner of the Year 2021’ award in the Data & AI category. What started as a small-scale project grew into one of the largest APAC-based AI and Advanced Analytics projects. We believe that this project has been a milestone moment for the automotive industry at large. If you’re interested in finding out how our approach raised the bar in a market notorious for low data adoption, please read our full case study.

The post Data-Driven Disruption? How Analytics is Shifting Gears in the Auto Market appeared first on Tiger Analytics.

Importance of commercial waste management

The commercial waste management lifecycle comprises collection, transportation, and disposal. Ensuring that all the waste materials are properly handled throughout the process is a matter of compliance. After all, multiple environmental regulations dictate how waste management protocols should be implemented and monitored. Instituting the right waste management guidelines also helps companies fulfill their ethical and legal responsibility of maintaining proper health and safety standards at the workplace.

For instance, all the waste materials generated in commercial buildings are stored in bins placed at strategic locations. If companies do not utilize them effectively, it will lead to bin overflows causing severe financial, reputational, and legal repercussions.

Impact of data analytics on commercial waste management

Data analytics eliminates compliance issues that stem from overflowing bins by bridging any operational gaps. In addition, it provides the precise know-how for creating intelligent waste management workflows. With high-quality video cameras integrated into the chassis of their waste collection trucks, image-based analytics can be captured and shared through a cloud-hosted platform for real-time visual detection. From these, data insights can be extracted to evaluate when the trash bins are getting filled and schedule the collection to minimize transportation, fuel, and labor expenses. They can also determine the right collection frequency, streamline collection routes, and optimize vehicle loads.

By monitoring real-time data from the video cameras, the flow of waste in each bin can be managed promptly to avoid compliance-related repercussions. The trucks also receive real-time data on the location of empty bins, which helps them chart optimal routes and be more fuel-conscious.

Ultimately, leveraging sophisticated data analytics helps build a leaner and greener waste management system. In addition, it can improve operational efficiency while taking an uncompromising stance on environmental and health sustainability.

Tiger Analytics’ waste management modeling use case for a large manufacturer

Overflowing bins are a severe impediment in the waste management process as they increase the time required to process the waste. Waste collection trucks will have to spend more time than budgeted for ensuring that they handle overflowing bins effectively – without any spillage in and around the premises. It is also difficult for them to complete their trips on time. When dealing with commercial bins, the situation is even more complicated. The size and contents of the commercial bins vary based on the unique waste disposal requirements of businesses.

Recently, Tiger Analytics worked with a leading waste management company to harness advanced data analytics to improve compliance concerning commercial waste management.

Previously, the client had to record videos of the waste pick up process and send them for manual review. The videos were used to identify the commercial establishments that did not follow the prescribed norms on how much waste could be stored in a bin. However, their video review process was inefficient and tedious.

When the pick up takes place, the manual reviewer is expected to watch hours of video clips and images captured by each truck to determine violators. Thus, there was an uncompromising need for accuracy since overflowing bins led to compliance violations and potential penalties.

Tiger Analytics developed a solution that leveraged video analytics to help determine whether a particular bin in an image was overflowing or not. Using cutting-edge deep learning algorithms, the solution enabled a high level of accuracy and eliminated all activities related to the manual video review and the associated costs.

Tiger Analytics’ solution was based on a new data classification algorithm that increased the efficiency of the waste collection trucks. Based on the sensor data collected from the chassis, we empowered the client to predict the collection time when the truck was five seconds away from being in the vicinity of a bin. Furthermore, with advanced monitoring analytics, we reduced the duration of the review process from 10 hours to 1.5 hours, which boosted workforce efficiency too.

As a result, the client could effortlessly de-risk their waste management approach and prevent overflow in commercial bins. Some of the business benefits of our solution were:

• More operational efficiency by streamlining how pickups are scheduled
• Smarter asset management through increased fuel efficiency and reduced vehicle running costs
• Improved workforce productivity – with accelerated critical processes like reviewing videos to confirm the pickup
• Quick risk mitigation of any overflow negligence that leads to compliance violations

Conclusion

New avenues of leveraging advanced analytics continue to pave the way for eco-conscious and sustainable business practices. Especially in a highly regulated sector like commercial waste management, it provides the much-needed accuracy, convenience, and speed to strengthen day-to-day operations and prevent compliance issues.

Day by day, commercial waste management is growing into a more significant catalyst for societal progress. As mentioned earlier, more companies are becoming mindful of their impact on the environment. In addition, the extent of infrastructure development has taken its toll – thereby exponentially increasing the need to optimize waste disposal and collection methods. Either way, data provides a valuable understanding of how it should be done. 

This article was first published in Analytics India Magazine.

The post Waste No More: Making a Difference with Tiger Analytics’ Data-Driven Solution for a Greener Tomorrow appeared first on Tiger Analytics.

Here are the various ways through which banks can leverage advanced analytics to function smoothly and mitigate risks during these uncertain times.

Managing Credit Quality

The current pandemic could rejig any bank’s loan book substantially. While banks expect some delinquencies from SME and consumer lending, there is going to be even more pressure to toughen the approval standards for new loans. Banks could see a cushion in their capital coming from the Fed’s interest rate cut and a massive stimulus package. Next, the credit risk teams have to do an exceptionally fine job of making these additional loans. Their capabilities could now be augmented by advanced analytics to help in the better determination of the creditworthiness of any individual or business. Using advanced algorithms, banks can proactively identify the potential delinquencies early in their lifecycle and take corrective actions. In these pressing times, there is even more need for banks to come up with a centralized analytics wing that could use data from across the organization and aid the RMs by creating a 360° profile of their customers.

Stress Testing for New Scenarios

The 2010 Dodd-Frank Act reforms and the CCAR after the 2008 sub-prime mortgage crisis have led to periodic monitoring of capital adequacy at US banking institutions. The bank regulatory bodies came up with stress tests designed to determine the ability of a bank or financial institution to deal with adverse economic scenarios. Some of the scenarios suggested by regulators include:

• What happens if the unemployment rate rises to x% in a specific year?
• What happens if oil prices rise by z%?
• What happens if there is a war outbreak?

As a fallout of this pandemic, banks should now also consider scenarios on worldwide pandemics such as COVID-19 or disruptions caused by climate change. The recent turn of events shows that the possibility of these scenarios can no longer be negated.

Banks’ risk management teams can quickly build multiple scenarios like climate change, pandemics, natural or man-made disasters into their calculations and predict its effect on banks’ capital and margins. As easy as it may sound, getting the right data for predicting such alternative futures is a daunting task. Banks need to normalize the data to retrain or redevelop, and have a defined process for model overlays to address the limitations of scorecards and forecasts used in such decisions. Banks who successfully transition to data-led governance and a strong data culture are better positioned to correctly incorporate such scenarios.

Changing Operations Mix at Banks

With many local and nationwide shutdowns, banks experienced a surge of calls and tickets at their contact centers. The preference of digital channels (like mobile payment, digital wallet, online banking, point-of-sale) over traditional ones crammed some channels leaving others with unused capacity. There were sudden cash withdrawals from ATMs with the withdrawal size being unusually large. Nevertheless, customers expected a seamless experience across all digital channels.

In such a scenario, banks could face a dearth of customer care representatives, CRM software licenses, or low availability of computing and networking resources. An AI engine can be used to predict and redistribute peak demand levels during an exigency. Sophisticated machine learning and deep learning techniques can be used to predict the channel occupancy levels based on variables like network downtime, marketing activities, new product launches, etc. Digitally savvy banks could also explore AI-led chatbots to help decrease the traffic of tickets and calls at a contact center.

Banks could simulate such spikes and come up with flexible and scalable solutions to staffing and resource optimization. The key, especially during uncertain times, would be to establish a robust data pipeline to consume real-time data.

Adaptable Marketing Promotional Mix

A pandemic can lead to a significant change in the promotional mix strategy. Media consumption habits could change dramatically. Due to cancellations of sports events and concerts, marketing managers need to reallocate their marketing budgets to a different TV channel or even a different medium. It makes less sense for banks to put a lot of dollars on search marketing or social media marketing during pandemic times. It would be much wiser to withhold that budget until the situation becomes normal again. It is very difficult to get the mindshare of customers when the primary thing customers are interested in is content that is breaking the news at that instant.

One of the few ways in which banks can promote themselves could be through their social messaging. Purposeful lending by banks to pandemic-hit businesses and individuals could spread a good word about the bank in the community.

Marketing attribution models with Bayesian models and VAR (vector autoregression) techniques working at the backend can help in forecasting the right channel mix. Scenarios for pandemics, war, natural disasters, global recessions built on top of these models can take into account the sudden movement of independent variables and tailor the marketing mix accordingly.

Increased Cyber Security due to Work from Home

Banks have always been very apprehensive about the Silicon Valley way of making people work from remote locations. This pandemic and subsequent lockdowns have been the largest sandbox for banks, a little forced one albeit, to analyze if they can pull all operations with a major workforce connecting remote. But this poses some challenges. Bloomberg has warned the banks to expect an increase in cyber-attacks during and post the Coronavirus outbreak. As business-critical data leaves the banks’ physical network, there have been increased reports of employee-led frauds in banks. Cases of phishing and social hacking are higher when an employee is working from unsecured networks. All these threats are not meant for banks to necessarily retreat to their older ways of working. AI and analytics could alert such transactions even when the miscreant is only starting to scheme things. Such systems could flag alerts by looking at anomalous behavior of users, unusual query logs, unauthenticated accesses, and flagged devices, etc. Scoring algorithms can bucket fraudulent activities with severity and priority indicators. Infosec teams can choose to intervene in the flagged cases as desired.

Finally, each bank should have their takeaways from the current crisis to build an even more financially and operationally resilient organization. Data is at the center of all this. Leading institutions are falling back on data as the fuel to help navigate through this crisis. Tiger Analytics has created a COVID-19 response playbook to assist financial institutions to tackle the current situation. As the quote goes, “Never let a crisis go to waste”. Banks that can make a quick and decisive transformation reflecting the new environment will survive and thrive.

Sources

1. https://www.wsj.com/articles/europes-banks-urged-to-cut-dividends-to-shore-up-capital-11585665244
2. https://www.americanbanker.com/news/banks-stick-with-dividend-plans-despite-potential-for-blowback
3. https://www.nytimes.com/2020/03/14/business/coronavirus-cash-shortage-bank.html
4. https://www.bloomberg.com/news/articles/2020-03-06/banks-told-to-prepare-for-cybercrime-jump-in-coronavirus-fallout
5. https://euobserver.com/coronavirus/147869
6. https://journals.sagepub.com/doi/full/10.1177/1847979018808673

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