STI Education Services Group (ESG), STI West Negros University (WNU), and iAcademy have received the go signal from the Commission on Higher Education to offer programs specializing in data science and business analytics.

In a disclosure to the exchange on Wednesday, STI Education Systems Holdings, Inc. said STI ESG and STI WNU began offering programs for a Bachelor of Science degree in Retail Technology and Consumer Science and for an Associate degree in Retail Technology for the school year 2020-2021.

Meanwhile, iAcademy’s Bachelor of Science in Computer Science degree now offers specialized tracks for those who wish to major in either Data Science or Cloud Computing.

“We have already seen before that more and more data science-related jobs and careers will be created in the future. It’s actually happening now and it will continue to unfold in the coming years. This is why we wanted our youth to become more prepared and equipped to take on this trend and get ahead in the game,” STI Holdings Chairman Eusebio H. Tanco said in a statement.

STI Holdings President and CEO Monico V. Jacob said recent developments affirm the company’s decision to offer the new courses.Advertisement

“Just recently, the Bangko Sentral came out with data saying that e-wallet transactions have more than tripled due to the COVID-19 pandemic, showing a significant increase in the use of technology. This development just affirms STI Holdings’ decision to offer these new courses, which is a testament to our commitment to advancing the use of data science for our country’s progress,” Mr. Jacob said.

STI ESG is working with Smart Communications, Inc. and Globe Telecom, Inc. to assist its students with the online learning set up.

STI will train students in using data to analyze consumer and market behavior. Students enrolled in the Retail Technology and Consumer Science program will be learning retail marketing, consumer psychology, information technology, and data science to prepare them for careers in business and consumer analytics, digital marketing, and e-commerce specialists.

iAcademy’s Data Science program aims to train students to make data-driven decisions, relying heavily on statistics, data mining, and machine learning. The cloud computing track meanwhile is offered in collaboration with Amazon Web Services, where students will learn to design and maintain local and wide cloud networks. These programs may lead to careers in programming, data analytics, and program management.

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Companies are constantly reinventing themselves, resulting in creative-disruption or creation of new ecosystems. New innovative business models are springing up, inspired by Uber, Ola and Airbnb. The ability to create waves of innovation or ride them is what marks the difference between successful companies and those that are not.

Artificial Intelligence (AI) is one technology that is both the cause as well as the effect of such transformations, and across all industries. However, AI is not just for big companies; even smaller companies create waves or ride them, eventually forming trends. Let’s take one specific industry where the potential of AI is enormous, like in retail, and examine the top five ways how companies can benefit.

Neither AI nor forecasting is new, but applying AI Deep learning for forecasting demand is powerful. According to a McKinsey study, grocery retailers who use AI systems to forecast sales of fruit and vegetables can increase their profit margin (based on total business) by 1 to 2 percentage points. The accuracy of AI-based forecasts for internet sales enabled one trader to reduce his inventory by 20 percent. Combined with the power of AI-based pricing, promotions and campaigns can be personalized resulting in increased sales by as much as 4 to 6% in grocery retail and much more in fashion retail.

It is the ability to crunch in enormous data and scalability in AI that makes it possible to realize leaner inventory and order back office management.

According to a PWC report, “…45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. This is because AI will drive greater product variety with increased personalization, attractiveness and affordability over time”.

The online retail store recommended items based on personal buying patterns and others’ shopping carts, made possible thru AI. Now, AI predicts a user’s needs and scours the internet for the best bargains and deals.

Take another example of a personalized service, pioneered by Stitch Fix – an online but personal styling service company, based in California, USA – heavily dependent on AI for its business. It is all eCommerce, but the model does away with a traditional shopping cart, instead relying on a set of style choices, social data feeds and trends which will is used by AI to predict and decide the results. Flexibility in the model allows users to return, free-of-charge, items which are not liked. An important feature of this process is the results are fed back into the model, thus making it better with time. Increased customer experience leads to increased royalty, social marketing and enhanced revenues.

Loyalty towards stores fare better than loyalty towards brands, so customer experience in stores and online are getting traction. This has given rise to a concept called “experiential stores” where customer experience is given more importance as much as the products themselves, where they come and experience the shopping.

77% OF RETAILERS CONSIDER SOCIAL OR EXPERIENTIAL ENVIRONMENTS FOR CUSTOMERS AN IMPORTANT OR CRITICAL AND STRATEGIC PART OF THEIR IN-STORE APPEAL; 55% OF RETAILERS USE AUGMENTED REALITY FOR THIS PURPOSE.

Imagine the scale of such data inter-relationships at play; this is huge, even a mid-sized retail with hundreds of thousands of data points. But we do not need to look beyond AI to create the insights required for providing the rich but personal experiences at these experiential stores. AI also bridges the gap between online and physical stores, bringing in personalization at scale for delivering the much-needed customer engagement in its interactions. Amazon-Go is a case in point – conceptually it is akin to a driverless-car in retail. In effect, it combines the best of both worlds – physical shopping and online, since the biggest pain point in physical shopping is waiting in line to pay your bill.  On this concept (of Amazon-Go), according to Wharton marketing professor Peter Fader: “To the extent that it revolutionizes retail, the idea here is knowing who is buying without relying on loyalty programs. But in addition to knowing who is looking at what, who is picking an item off the shelf and in what sequence — that idea of really seeing everything could have dramatic implications.” It could change the way stores are laid out, he notes, and it could change where a concierge person comes in. “I think that the data part of it could be the big breakthrough, but at this point it’s still icing on the cake.”

One of the most common shortcomings in AI-based business applications, at least in the emergent phases, is its lack of transparency and complexity to use. However, due to advances like Natural Language Generation – NLG , it is possible to develop better interfaces.  Natural Language Generation simply turns data into plain English which translates well for end consumers. This helps build user interfaces in mobiles and desktops to deliver personalized and easy-to-use information, useful for quick and informed decision-making, and reducing costs in running them.

The rise of chatbots has also given way to enriched conversational interfaces. Imagine generating insights when all “intents” work together for a company! The data can be used to design new products, unearth customer service issues and a lot more.

Furthermore, according to a report from KPMG, “…by 2020 an estimated 80 percent of business-to-customer conversations will be conducted by machines. That will have enormous implications for all organizations both in terms of business processes and also future staffing needs”.

AI takes a lot of skill and this is one reason why it is expensive to develop and complex to build. The AI stack also looks a lot different than the traditional software stack. Large or tech-driven companies can handle this, but how do we reach the small and mid-sized outfits? The answer is AI on the cloud. Hosting AI applications as a set of services on the cloud can largely mitigate both the issues. Products and services available in the SaaS model make them modular and affordable as users pay only for what and how much they use. However in some cases there could be a small upfront fee but it is quite affordable on long range plans.To conclude, even small and mid-sized retailers can ride the AI wave and benefit from the trends that are happening in business & technology.

The article has been penned down by Kishore Rajgopal,CEO & Founder of NextOrbit

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Retail-specific artificial intelligence startups are boasting billions in funding, cutting deals for tech innovations that range from robotics to carefully-crafted communications — and it’s only just getting started.

Driverless grocery delivery options and Amazon Go’s cashier-less stores present very visible examples of AI in the wild, but there’s far more in store — literally.

Reimagining Operations

Only 26% of today’s active AI use cases in retail encompass operational technology, but those cases pack a serious punch.

Data-driven intelligence can help retailers make smarter moves at every step, from procurement to on-shelf pricing. Optimized supply chain planning, theft detection, seamless pick-and-packing, and trend prediction can all be shouldered by clever AI algorithms and savvy robotics.

For example, the team at Bossa Nova builds real-time robots to perform basic inventory management: shelf scanning, data mapping, and product monitoring. The company’s already cut a major deal with Walmart for quick, incredibly accurate shelf stocking assistance, and are further developing their technology to meet needs beyond standard grocery and big box stores.

By saving associates’ time and guesswork for efficient stocking — all while collecting valuable data in purchasing trends — the friendly robots offer perfectly seamless integration into the modern retail space with big value benefits.

Communicating with Customers

The perks of AI for warehousing, inventory, and supply chain applications present themselves clearly, both in efficiency and sheer ROI. But the key to effectively engaging an AI strategy for retail rests in plain sight: optimizing interaction with the customer.

In an already saturated marketplace, engaging the customer through quality experiences and genuine communication can make the difference — and the sale — even above and beyond the all-powerful price point.

Subway Restaurants has already made international news this year with a targeted communication campaign, locking in customer loyalty through innovative one-to-one mobile marketing tied directly to the in-store experience. The company launched a partnership with Mobivity Holdings Corp. using Google’s Rich Communications Services to speak directly to customers — and place tailored promotions right within reach.

The custom communication has already garnered Subway over 10 times a return on their investment, taking data-driven details and AI capabilities to task in providing a premium customer connection. Loyalty — once defined by a card subscription or in-store coupon — has gotten smart and gone mobile, and it’s getting people in the door.

Personalized Purchasing

Subway’s mindful messaging illustrates just one angle of AI for personalized purchase experience: retailers are employing chatbots, voice shopping, and even custom apps to meet shoppers on their own terms.

Beauty powerhouse L’Oreal joins trendsetters like Sephora and Estee Lauder in embracing AR apps to give shoppers a real-time product testing experience: the company acquired startup Modiface in March of 2018. Modiface specializes in crafting incredibly realistic AR try-on apps, revolutionizing the customer shopping experience for hair, make-up, and much more.

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Amazon is working with top media holding companies and brands to make its data more available for use in their media planning, according to people familiar with the plans.

The e-commerce giant has been huddling with the agency world—companies like Omnicom, WPP, Dentsu Aegis and others—about how they can partner on the future of advertising on the platform, especially when it comes to applying data to targeting ads and measuring how those ads perform.

“Amazon, for the first time ever, is starting to realize that monetizing the data they have and making it available for purchase, not personally identifiable information, could open a revenue steam that wasn’t there before,” said one agency executive who is familiar with the talks Amazon is having with agencies.

Amazon is developing data and analytics tools for brands, backed by machine learning and its ubiquitous web services. Amazon already is running experiments with different agencies and brands, some that look at targeting ads and some that measure attribution, showing which ads lead to business results. There is a flurry of ongoing trials, but advertisers say they expect it will eventually lead to a coherent concrete data service operated by Amazon.

Agencies and brands will be able to build customized ad bidders and reporting tools, buy ad inventory across the web, and import consumer data to learn more about them, and ultimately build ad targeting models that are more exact. “The Amazon cloud already has a bunch of utility built into it,” said a second agency executive who has worked closely with the technology.

The possibilities are greater than anything Amazon has offered so far through its first-generation self-serve ad platform or basic audience matching, advertisers say.

Amazon’s secret weapon in its advertising push against the Google and Facebook duopoly is Amazon Web Services, the cloud computing platform used by companies as varied as Kellogg’s, Comcast and Major League Baseball. Amazon Web Services has become one of the most significant technology layers undergirding industries around the world, and it is a key component to how Amazon will meld data and marketing, according to multiple advertisers.

To that end, Amazon’s ad team has been promoting what’s known as a “clean room” for complex data and analytics research, according to these advertisers, who spoke on condition of anonymity because they were not allowed to discuss details of their dealings with the company. “Clean room” is a generic name for a data-sharing platform that adheres to strict guidelines around privacy and tries to prevent any information from leaking.

That could also lead to more sophisticated data and analytics tools similar to Google’s Ads Data Hub, which is a platform for advanced research into consumer behavior and brands’ businesses.

“What Amazon is building will enable brands to craft a full-journey, attributable marketing experience,” said Chris Apostle, the evp and head of performance at Havas Media, who actively leads the relationship between the agency and Amazon, but said he can’t share further details on all of the retailer’s data ambitions as they are still evolving. However, he has heard the term “clean room” and knows what direction that will take Amazon’s advertisers.

“The digital data ‘clean room’ will provide [insights] into behavior across consumers’ purchase paths,” Apostle said. “This is very different than anything advertisers have been able to do with Amazon until now.”

Amazon wants to use data as a lure for big advertising spenders to commit to investing in its platform, where advertising is the fastest-growing segment of the business. Amazon is expected to hit $4.6 billion in ad revenue in the U.S. this year, making it the third-largest digital advertising platform behind Facebook and Google.

Basic stacks

Amazon already offers ad targeting technology, the kind that most digital ad rivals provide, like being able to their match customer e-mail lists to people shopping on Amazon to target ads to them. Amazon also has basic demographic and shopping data that let advertisers target consumers based on characteristics like age, location, gender and purchase history.

Amazon ad formats include search ads, which can be targeted to the queries people type into the search box. It also has display and video ads, which are not as easy to target because they don’t come with an immediate signal like search intent. Google solved this problem on YouTube, for instance, by targeting video ads based on a viewer’s search history.

Amazon is building a marketing ecosystem that could rival Google, though. It also has video, through Fire TV and Twitch, the streaming service similar to YouTube. Amazon also owns IMDB, which shows video ads, and also has publisher services that deal with websites just like Google’s publisher ad network, where it can target ads outside the websites it directly owns.

What Amazon is missing is better technology and data to make sense of its sprawling marketing platform and unite its disparate formats. Advertisers say the design of its self-serve ad management platform is outdated and clunky. That’s partly the reason why the company has worked so closely with the agency world and third-party marketing tech platforms like Kenshoo in the past year, to help test and design new ways of buying ads on Amazon.

Amazon’s ad business was streamlined this year, too. For years, Amazon’s ad offerings were a jumble of services with different acronyms and run by separate teams. There used to be Amazon Marketing Services known as AMS, and Amazon Media Group known as AMG, and Amazon Advertising Platform known as AAP. Now, all those fall under Amazon Advertising.

Amazon declined to comment for this story, and the exact details of any planned data products are still in the works. Advertisers said they were still in discussions with Amazon about how they could help develop these next-generation data services, and anything concrete would likely come next year.

Data deals

What is certain is that sophisticated data partnerships are already forming. For instance, agencies have been using Amazon Web Services to analyze data from Facebook ad campaigns, according to a person familiar with the offering. That means advertisers are able to import data out of Facebook and dissect it in Amazon’s environment, which requires a partnership between the two web rivals.

Amazon declined to comment on the arrangement. However, advertisers said that the whole point of the “clean room” technology is so that no data from Facebook leak into Amazon.

“It could get tricky for a lot of brands,” said Andy LaFond, executive media director at R/GA Chicago, who is not involved in Amazon’s plans, but is familiar with the platform. “Data is Amazon’s power for sure and brands have to be really careful about what data they’re willing to share with Amazon.”

Amazon is already known for being able to give brands solid intelligence about ads on its properties and tell them when those ads lead to sales on Amazon. But the company is expanding its ambitions and wants to be able to help advertisers serve ads anywhere online and measure the impact, even if the final sale doesn’t take place on Amazon.

Earlier this year, Amazon developed a pixel, a common technology that marketers use to measure ad performance. In this case, Amazon wanted to show marketers that its ads are more efficient than its rivals, and brands could attribute the results of marketing campaigns to their spending on Amazon.

Amazon’s data ambitions include giving brands the ability to target their exact consumers, identifying what they bought down to the last nail, according to advertising executives. Also, instead of just targeting people by keyword when they’re searching Amazon, the same type of targeting could be applied to display and video ads across the web, the advertising executives said.

“This is a faster, more direct way to get to data,” said a digital agency advertising executive who works closely with Amazon. “From the search side it’s easy, but from the display side it’s a little harder to target that exact consumer who bought that exact product.”

Amazon data products could also provide unique insights about consumers who buy certain products, like figuring out what else they might buy, even when the link is not so obvious.

“Amazon is dealing with the most valuable data asset in the world, even more than Google’s search data,” said an executive at a top marketing technology and analytics firm. “If I want to know what someone will buy, then I have to know what they already bought in the past.”

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Manufacturers and retailers are inundated with information from an ever-increasing number of disparate sources, but the real challenge that they face is how to ensure their big data becomes smart data.

Only if retailers and brands can gather, integrate, analyse and create actionable insight from their point of sale (PoS), customer loyalty and e-commerce data will they win shoppers’ hearts and minds and gain a competitive advantage.

In fact, the ability to turn big data into smart data has become a game changer for organisations that have chosen to embrace such a positive strategy. They know that being able to study the minutiae of individual consumer preferences and customer choices can have a real impact on their bottom line.

A data strategy has to be layered to be really effective. The first level should be the big data, the second tier the smart data and the third layer the insight that can be actioned from it.

Gaining access to an increasing amount of data does not necessarily lead to better decision-making. The key is to know what business questions to ask that your data might be able to answer. You also need the knowledge, and technology, to make sense of what can be mountains of data captured and stored in different parts of the organisation, and often in several different locations. Having sales and marketing data in silos, for example, makes it difficult to create accurate short- or long-term forecasts.

What is clear is that sales data analysis and data science do provide retailers with new insights into areas such as product availability, repeat purchases of promoted and new items, range assortment and shopper missions.

There is so much untapped potential from smart data, but organisations need to be brutally honest about whether their data is working hard enough for them. It is likely there is currently not enough investment in combining big data and analytics to strengthen the impact and ROI of their pricing, promotional and media strategies.

The reality is that a competitive advantage is being missed because a retailer will not have an accurate picture of what is the right price point for a particular product. They may also be unable to create and deliver the most effective promotional campaigns.

Asking the right questions

Data provides an understanding of shopper behaviour, so a manufacturer or retailer must ask the right questions to obtain a clear picture of how changes in behaviour might affect their own business now and in the future. For example, what data does it need today and how might this data be used to deliver the ROI it requires? What data has already been collated and is ready to analyse, and what important data has yet to be gathered?

With so much data available every organisation needs to know exactly what business issue it is trying to solve.

For a retailer, is the aim to improve overall sales and margins only?

With the growth in the omnishopper, smart data can reveal which channels particular shopper segments tend to use and when. This becomes increasingly important as consumers’ expectations continue to rise, and the choices provided by multiple channels can help shoppers to change their behaviours.

Mobile has become more important to the whole shopping experience and smart data analysis is helping retailers and brands create a seamless experience for consumers. Indeed, being able to turn big data into smart data can help to find ways to remove the friction points and barriers that make an offline or online retail experience frustrating.

This can boost loyalty, which is created not so much by low prices these days, but by other factors such as customer service and transparency.

Making smart thinking the norm

As mentioned, there are obvious business benefits from mixing data with analytics to help adapt to changing shopper behaviour. This is certainly true when it comes to digital and the amount of data now being collected from high-speed applications and from ads viewed on mobile devices, for instance.

Retailers and manufacturers need to be able to analyse and assess people’s digital footprint to discover whether the sales achieved by a particular campaign justified the marketing spend. Being able to identify what has been purchased at a point of sale after an advertising campaign is the best option for a brand to define its ROI.

Technology has a role to play in helping organisations extract and analyse data more effectively and assist with new product development and range optimisation. For example, retailers are realising the benefits of automatically loading, integrating and augmenting data from their stores to create an up-to-date single view of their customers.

The process involves analysing everything from point of sale, CRM and loyalty card data, as well as inventory and consumer panel information. When these data sets are coupled with analytics for price, promotions and assortment analysis, retailers get a better understanding of how they and their suppliers can best target their own shoppers.

Imagine the business opportunities from using trip mission segmentation where the analysis tells you why customers are visiting stores and reveals the clusters of different shopping missions that need to be fulfilled across the entire store portfolio.

Better data analysis certainly means more effective targeting when spending money on promotions. Smart data will reveal what is working best in which store in which region or country and with which consumer segment.

In some areas households might be more sensitive to promotions than in other places and smart data allows brands and retailers to be sensitive to these differences and react accordingly.

If a campaign is going to be cost effective, a manufacturer also needs to be able to create and deliver the most effective promotional and media campaigns. They only achieve this with a better analysis of customers’ individual preferences and buying habits.

Collaboration between retailer and manufacturer

This means manufacturers must work harder to fight their corner and if their own data is not as detailed as that held by the store owners they should consider collaborating with research companies that can help them to plug any of their data gaps.  The findings can then be shared with stores to demonstrate a product’s value to a particular category.

Manufacturers can discover what is unique about a particular product from the shopper’s point of view. Every product must have some level of ‘uniqueness’ to convince people to buy it over and above another brand.

Hot sauces is a good example because the consumer is faced with so much choice today. What makes shoppers chose one variety of hot sauce over another? What product characteristics are the most relevant for the different shopper segments?

A retailer might already stock three Indian flavour sauces, for example, and is unlikely to want to sell another one. However, by using predictive analytics, manufacturers can analyse the key attributes, such as size, packaging, flavour, brand name and price, and combine key competitor analysis to pinpoint what is more likely to sell in different geographies and stores. They can then work out how incremental a product will be before taking it to market.

  Changing technology requires smart approach to data

Technology is developing at an incredible speed and smart data has become a powerful weapon to ensure technology is used as effectively as possible to connect with consumers.

Technology is not only helping to gather data at the point of sale and through in-store activity such as sampling, it is also assisting retailers in planning their stores of the future.

Smart data fuels innovation because it reveals to marketers what works and what does not.

Manufacturers can dive deep into the key attributes of their products, and demonstrate through analysis the value generated for the specific product and the category. The value proposition can ultimately help to persuade retailers to keep a product on the shelves.

Smart data might reveal issues with packaging, merchandising, pack size or problems around category management, price or a promotion that can be solved with an innovative solution. It can also reveal gaps in the market that could be exploited for competitive advantag

In today’s world data is knowledge and knowledge is power, and having access to data and being able to analyse it quickly to make commercial gains is absolutely crucial. Unfortunately, many organisations have only scratched the surface of what is possible.

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