The impressive growth of artificial intelligence and machine learning technologies in the past few years, have fueled the beliefs that AI might become capable enough to challenge or replace human intelligence altogether. But that is not going to be a reality anytime soon, at least not in the retail sector. Although, we have seen many innovative use cases, AI/ML still falls short when it comes to making decisions where there are many variables and contexts involved. Rather than waiting for AI/ML technologies to reach the stage of perfection, retailers can take advantage of AI and ML technologies to ‘augment’ human capabilities. Identified as “the next wave of disruption in the data and analytics market”, augmented intelligence technology is all ripe to give sweet business results today.

Forrester defines augmented intelligence as:

“The use of AI to improve a human’s ability to do their job combining machine learning technologies for processing and analyzing data at scale; technologies for automating and orchestrating standard processes; and human input, decision making, and action.”

The idea is simple – the concept of augmented intelligence is not to replace humans, but to support human intelligence, meet their shortcomings, speed-up the repetitive processes, and enable them to take quicker and smarter decisions. Let us look at some of the key advantages of augmented intelligence: 

• Augmented analytics is better than either AI or human intelligence alone: Rajen Sheth, Senior Director, Google Cloud AI, rightly says that, "AI is most useful when you get it into the hands of a subject-matter expert." The past decade was all about superior data visualization in retail, but still the task of analyzing that data according to the business-context, exploring relationships, scrutinizing the complex variables and combinations, was far from easy. Today, machine-powered data analytics and prescriptions, combined with contextual business knowledge possessed by humans can open doors for superior decision-making.   

• Augmented analytics optimizes productivity: Till now data consumers were burdened with several repetitive, time-consuming tasks that required little intelligence. With the advent of AI, those tasks can be automated and can be done in almost real-time, thereby increasing human productivity. Augmented analytics can search for and bring to surface vital insights or anomalies in the business processes, analyze all the data in your retail business, study all the in-store surveillance cameras, capture social media information, gather customer insights, correlate it with historical data and churn out insights at a superhuman speed. AI brings the power of speed, scale and efficiency to the hands of every business user.

• Augmented intelligence can deliver more value today: Businesses working on developing full-blown AI solutions know quite well that it is a time-intensive venture, and although there are significant strides, there is still a long time-to-value. On the other hand, augmented intelligence systems can be easily implemented on top of your current retail technology stack. Rather than working hard on doing away with human intelligence altogether, businesses can work on developing automated systems for data preparation, machine-based algorithms, deep learning, advanced analytics and insight discovery to aid decision-making at all the levels of a business.  

• Augmented intelligence can democratize retail analytics in the true sense: Augmented intelligence is getting paired with natural language processing. With the rise of such user-friendly interfaces for conversational analytics, anybody within the organization can take advantage of analytics, even if they don’t have any analytical skills. It not only brings analytics to the lowest level of users, but also makes the high-value data scientists more productive. Analytics always faced adoption challenges in retail, but now it can be used by anybody. For instance, with the use of augmented analytics and NLP, any in-store sales person can simply ask, “What are the top things I can do to improve the sales today?” and get a well-analyzed and complete answer from the system.

The award for “Technology In The Supporting Role” goes to augmented intelligence

Augmented analytics brings out the best of both AI and human intelligence. Deep learning and machine learning based algorithms can generate context-based correlations and insights in real-time and prescribe the best paths to take for specific business outcomes. Humans can then take their contextual knowledge and other variables into consideration and arrive at a decision. AI can then be used to execute those decisions at a superhuman speed. Together they can dwarf all human capabilities.

With the convergence of AI and Analytics the retail business can be transformed into an intelligent, self-aware one that can sense business and user context, auto discover problems or opportunities, auto-generate the insights, recommend the best decision or actions, and even execute them. The analytics system becomes smart, context-aware and automated, which takes away the burden of analytics exploration from users and focuses them directly on the business areas that need attention and action.

Manthan is at the forefront of this development

Realizing the disruptive power of these technologies, Manthan Systems has been focusing heavily on AI and machine learning techniques to automate data management, algorithmic processing, and insight generation. Our AI-powered retail analytics platform offers a conversational analytics interface which allows users to talk to the system in natural language. Users can not only run descriptive analytics use cases, but also complex predictive and prescriptive ones. For these reasons, Manthan’s innovative retail analytics solutions have found special mention in categories including, “AI in Retail” and “Algorithmic Retailing”, in Gartner’s Hype Cycle for Retail Technologies, 2018.

Manthan’s augmented analytics solutions leverage the power of AI to offer superior business outcomes. Meet us at NRF 2019, Retail’s Big Show to take a peek into the future of retail.

The year 2019 would be all about algorithmic retailing as it encompasses the combined power of advanced analytics and artificial intelligence to transform retailing as we know it. Retailers have been already using various advanced analytics technologies till now, but with usable AI coming into the picture along with machine learning algorithms, smart data discovery, context-aware computing, and deep learning technologies – the process of retail decision-making as well as the mindset of retailers are in for a drastic change. One of the areas that are predicted to gain prominence in 2019 is algorithmic merchandising.

According to Gartner’s Hype Cycle for Retail Technologies, 2018, “algorithmic optimization improves and automates the decision-making required to support seven merchandising processes — assortment, space, replenishment and allocation, price, promotion, markdown, and size and pack — to adjust assortments by store, cluster or channel; determine buy quantities; optimize space allocation and planograms; and maximize product allocation and availability.” Predicted to be highly beneficial for retail, algorithmic merchandising will enable retailers to attain higher sales and margins. The technology seamlessly supports complex analytics that customer-centricity requires, enabling smarter decisions at any level of the retail organization.

Let’s dwell on how algorithmic merchandising can have a huge impact during the different stages of seasonal or cyclical retail businesses.

The Pre-Season Stage

During this crucial planning, stage retailers perform a lot of demand forecasting which consequently drive the allocation, production, and sales plans. During this phase analytics systems are used to forecast how products will fare in the market. Retailers have never completely relied on or trusted the data-driven demand forecasting model as they know that there are multiple other variables that affect demand. Even if analytical tools are used, a lot of gut and experience-based micro-decisions are made to generate demand forecasts. For instance, in fashion retail, new products are designed every season based on several factors like style, fabric, color, cuts, patterns, fit, finish, and texture that eventually influence shopping decisions.

In such a scenario AI and machine-powered analytics systems offer greater control over the use of these variables in modeling demand and therefore promise higher accuracy on the demand forecast. AI-augmented analytics takes into consideration the business context, real-time data, external influencers like weather, promotions, social media reviews, the performance of similar products, and a lot more to forecast demand. Accurate demand forecasting in-turn determines how well the inventory gets managed, how the pricing decisions get made, what kind of customer-engagement strategies get implemented, and much more.

The In-Season Stage

During this phase, the retailer’s focus is to execute according to the sales plan and generate maximum profits from the inventory. Inability to closely monitor how products are selling across the stores and controlling their inventory movement to match the plan typically results in out-of-stock situations or excess stocks that have to be heavily marked-down and cleared-off at the end of the season. With thousands of products and styles moving across hundreds of stores, businesses today rely on algorithmic processes. These can detect patterns and bring to light the under-performing products that need a temporary price reduction tactic early on in the season to maximize their revenue during the rest of the season.

These algorithmic anomalies also uncover other opportunities in the business, like changing store merchandising or running behaviorally targeted campaigns to lift sales. In this phase, retailers can leverage algorithmic retailing for inventory optimization, price recommendation, assortment tuning, new product sales optimization, customer-centric offer recommendations, personalized promotions, and a lot more. All these results in optimized sales and profits.

The End of Season Stage

After the season is over, products that have not sold well despite in-season optimization efforts have to be marked down in a specific promotional/clearance window. AI and machine learning techniques are useful in processing answers to questions like – “What percentage of markdown price is ideal for each product to clear-off its inventory?” Or “Which products have most chances of a sale in which store locations?”

Algorithmic markdown analytics continuously optimizes markdown price for the highest return on inventory in each store cluster and store location. It analyses which products need to be discounted, what should be the amount of the discount, the price elasticity, competition from other retailers, ongoing promotions, other marketing techniques, shelf placements, and so on. AI-augmented algorithms can build several decision-trees at the same time on a variety of sub-groups and then combine them all to present a predictive solution. They can also interface with pricing systems to automatically (or through a workflow) implement recommended price changes across the store network, thereby, making it easier to implement the pricing decisions.

Manthan’s Algorithmic Merchandising Solutions

Realizing the disruptive power of these technologies, Manthan has been focusing heavily on AI and machine learning techniques to automate data management, algorithm processing, and insight generation in order to improve analytics consumption across the retail organization. We apply these technologies in retail merchandising applications to automatically sense the merchandising user’s decision context, machine-generate insights, make AI-augmented recommendations, and execute the decisions.

Our AI-powered analytics platform also offers a conversational analytics interface which allows users to talk to the system in the natural language. Users can not only run descriptive analytics uses cases, but also complex predictive and prescriptive ones. For these reasons, Manthan’s innovative retail analytics solutions have found special mention in categories including, “AI in Retail”, “Algorithmic Retailing” and “Algorithmic Merchandise Optimization”, in Gartner’s Hype Cycle for Retail Technologies, 2018.

Manthan’s customer-centric Merchandising Analytics Solution leverages the power of AI to offer superior business outcomes. Meet us at NRF NYC 2019 (National Retail Federation), Retail’s Big Show and Expo to take a peek into the future of retail.

For over a decade, retailers have been trying to democratize analytics across their business units, departments and people, but it always seemed like a distant dream. With the use of artificial intelligence, it has become possible to smartly blend analytics into the very fabric of your retail business. Retailers can leverage AI-augmented retail data analytics that is context-aware, has all the insights needed for prescribing the right actions, and can even implement your decisions.

As data explodes, the human ability to manually explore data, to find out issues or opportunities, and to device tactics to address them, is becoming a thing of the past. Today’s systems have the capability to sense what is happening within your store locations as well as departments of the entire retail business, develop focused insights about any operation, predict the upcoming challenges or opportunities, prescribe the best way to move forward, and then execute the decision. Such an analytics model, that presents an augmented AI experience to retail staff at any level as well as to the customers, is what is needed in the near future.

It’s time to make a transition to an algorithmic business

Now that most retailers have transformed themselves digitally, the next big step is to transition into an algorithmic business; one that uses AI to make the analytics system more aware and intelligent, and therefore auto-processes many of the tasks humans do. Machine learning based systems can learn what a user typically analyzes, what patterns in data they typically solve, and what business processes or goals they usually drive.

Gartner’s Hype Cycle for Retail Technologies 2018 predicts that, “In the next couple of years significant benefits will emerge as retailers absorb the impact of AI as part of an algorithmic retailing strategy. Algorithmic retailing takes a broader view of use of mathematical algorithms, deep learning, smart data discovery and other advanced analytic capabilities to make major contributions to the effectiveness of the retailers’ decision-making processes.”

With augmented AI solution, users can specify the expected outcomes. For instance, if they would like to improve sales of a specific product line while protecting margins by at least X%. Earlier, they would typically use the enterprise BI and Analytics tools, set data filters as they seem fit, use the dashboards to drill down additional details in order to get the specific insights to support their decision – and doing all this required a certain level of expertise.

But today, the AI-based system can take in your final goal as an input, then automatically processes data, recognize opportunities among the various tactics businesses use to improve sales by modeling the outcome against variables like product, price, availability, and customer preferences to recommend the best action to execute. What’s more; once you decide upon a suitable action, the solution would implement that decision for you. With many business contexts already modeled into the analytics system, it can function as a perpetual prescriptive engine for every process in retail and CPG – and for every individual in the retail organization.

How can it help retailers?

In today’s hyper-complex retail environment, retailers come across a wide range of crossroads every day, where they need to choose which path is perfect for their business. The data and information are all there, but what is still lacking is an ability to understand business context, identify anomalies that need attention, correlate all that data, predict outcomes, and prescribe the best decision or action.

Today’s AI-augmented retail data analytics solutions are autonomous, omnichannel entities that can help retailers execute repetitive, data intensive tasks at speed and scale. It can help retailers with resolving business challenges and taking decisions pertaining to all areas of retail, including: new product forecasting, inventory management, in-store engagement, real-time personalization, assortment optimization, promotions, campaign management, store operations, price markdowns, loyalty management, and a lot more.

This AI-based retail data analytics solution knows what your customers want, knows your manpower needs and inventory levels, can sense winning product combinations, categories, and promotions, and keeps getting smarter over time as more and more decisions are implemented.

How does it work?

There are three vital pillars needed for retailers to build an AI augmented environment that would democratize analytics across their business. These include: machine-driven data management and algorithmic insight generation, AI-augmented analytics process automation, and simplified user-interaction through conversational analytics.

The system must have an AI powered platform that ingests and unifies data and prepares it for analysis based on the context. It should offer a friendly user interface that humanizes interaction through natural language – so the users, just need to talk to it and the intuitive analytical system would start churning recommendations immediately. Once a question gets asked, it understands the context, processes the data and uses advanced algorithms to machine-analyze and prescribe the best action for a business outcome by considering decision contexts and simulating potential impact.

Welcome to the future – A Store That Knows

The stage is all set for AI to make a transformation in the way retailers operate. Algorithmic retailing will enable retailers to optimize sales for specific segments and categories while providing unrivaled operational efficiencies by automating repetitive and complex data-driven tasks. AI could be implemented for optimizing the entire retail business so that it operates at peak efficiency levels.

Gartner’s Hype Cycle for Retail Technologies 2018 estimates that, “up to 50% of retailers have adopted some form of algorithmic optimization application, and expect algorithmic retailing will grow from its current penetration relatively quickly.”

A Store That Knows, is a concept devised by Manthan Systems, and will soon become a widespread reality in retail. Manthan is at the leading edge of this evolution and is today enabling the underlying technology capabilities for several retailers. Manthan is pioneering innovations in analytics by using the power of AI to enhance decision-making across various dimensions in the technology stack, including data management, insight generation and analytics consumption.

Gartner’s Hype Cycle for Retail Technologies 2018 is out, with trends for technology leaders. This year, the Hype Cycle has identified democratized AI as a key trend – products and solutions that “blur the lines between human and machine”.

We are pleased that Manthan has found mention in 5 categories in the 2018 Hype Cycle.

1. AI in Retail (Rating: Transformational)
2. Algorithmic Retailing (Transformational)
3. Cognitive Expert Advisors (High)
4. Customer-Centric Merchandising and Marketing (High)
5. Algorithmic Merchandise Optimization (High)

These ratings are a testament of invention and innovation in Artificial Intelligence, Advanced Analytics, and Cloud at Manthan. And a vision, to create context-aware, AI-powered analytics products that bring the true power of AI to every role in your business.

Manthan’s efforts have been focussed on bringing analytics-driven decision-making to the real user. To design sophisticated analytics products in a manner that everyone can use them. Which we believe, is critical to the businesses dealing with the real-time, connected customer.

We architected our products by re-interpreting 4 critical areas – Analytics Consumption, Algorithmic Processing, Solution Engineering and Data Management.

1. Re-interpreting Analytics Consumption

Our primary goal, as Gartner puts it, is to “blur the lines between human and machine”. This is what led to the creation of Maya – the world’s first AI-powered conversational agent for business analytics.

With this, analytics becomes easy to consume. In a simple, conversational format that remembers context and processes information in real time, based on the user’s intent and his flow of analysis. Maya is integrated with mobile, desktop and personal assistant devices and can be invoked anytime. It offers both general and role-based models for analytics consumers.

Maya makes use of machine learning, deep learning, advanced analytics, cloud computing, natural language processing (NLP) and generation (NLG), intent analysis and context-aware computing. But all you need to do is ask.

2. Re-interpreting Algorithmic Processing

Today’s digital business is generating millions of data points, across sources, every day. Taking traditional hierarchical approaches to analyze data is just not physically viable. Your solution should be able to conduct auto-discoveries, root-cause analyses, and auto-recommend best outcomes, based on simulations.

Manthan’s analytics platform algorithmically processes anomalies, outliers, and exceptions to recommend actions that can achieve clear, smart goals.

3. Re-interpreting Solution Engineering

As analytics processing becomes complex, analytics experience needs to become intuitive. Solutions need to be designed for the real decision makers and should embed real business contexts.

Manthan’s solutions are designed to bring together advanced analytics and algorithmic capabilities for specific use cases across retail. Our solutions also come with the ability to scale and to incorporate new use cases.

4. Re-interpreting Data Management

The digital business generates much more data than the traditional one, with new data sources emerging all the time. While some of this data drives repeatable use cases built around standard business processes, you also cannot lose sight of new use cases that elevate the customer experience.

Manthan offers a full-featured data management platform that can deliver production-grade enterprise analytics in a governed model. But at the same time, it also drives rapid experimentation and innovation with an architecture that can ingest and mash new data sources at scale in a data lake architecture. This supports on-demand data-processing, giving businesses real-time decision-making abilities.

We have what you need.

We have re-interpreted analytics delivery with AI. And an elastic cloud infrastructure and server-less computing capabilities provide the necessary performance and agility you need from a new age data a platform that can deliver real-time decision-making.

Tomorrow’s technology does not require a screen in front of you. Or you in front of a screen. It will walk with you, whispering real-time recommendations in your ear, based on intense, granular analysis.

That’s truly democratized AI. And that’s what we have for you, today.

“It’s 4 PM: your customers are just beginning to think about what’s for dinner. 81% of American consumers are unsure about what’s for dinner,” says Grocerant researcher Steven Johnson. This is driving the evolution of the Grocerant Store experience, blurring the lines between restaurants and grocery shopping.

Why is this important from a grocer’s point of view? Research shows that there is a strong correlation between brand perception and how grocers like Whole Foods address the essential need for food by various products and services (therefore creating new experiences). Providing consistent experience around eating healthy, eating fresh, using organic ingredients etc. extends a brand’s connect with a consumer segment. By widening the definition of the need category you are serving, more opportunities to improve customer experience opens up.

But all of this really starts with deep insights from your data and an ability to transform them into customer experiences. You need an understanding of customer behavior that is much deeper than you probably have right now, to develop innovative customer-centric strategies.  You can use analytics to understand how to shape and develop customer needs, attitudes and preferences to food, health, life situations, and influence behavior and motivation to shape new and rewarding experiences. And with micro-targeting and personalized engagement capabilities you can shape customer decisions at any stage in their journey.

NPD’s foodservice market research found that in-store dining and take-out prepared foods from grocers has grown 30% over the past eight years, accounting for $10 billion of consumer spending in 2015. Millennials are a growing segment of ultra-conscious grocerant shoppers, and having your finger on the pulse of this segment means you really need to unlock more insights on what drives their choices.

Fashion and apparel brands have now taken a bite out of this trend. Retailers like Tommy Bahama, Nordstrom, Macy’s, Brooks Brothers all have or are planning restaurants selling freshly prepared food in-store.

When you put customer experiences at the center of your strategy, be prepared to experiment, innovate and disrupt your operating model, and get the right customer analytics and engagement technologies to enable your strategies. Getting it right could create significant and lasting value for your brand.

If you are looking for ideas to improve your store experience, Manthan can help you. Manthan’s Retail Analytics solutions are built exclusively for retail and tailored to fit unique retail decision-making needs. With the experience of serving 100s of customers across 22 countries, across Fashion & Apparel, Food & Grocery, Specialty and Mass Merchandise, you are bound to get what you are looking for.

For a quick overview download this data sheet:

If you are interested in a 15 min. discovery call with Manthan, do write to us at online.enquiries@manthan.com