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INTERVIEW: Grocery Retailers Poised to Reap Benefits of AI

January 14, 2019 6:26 am No Comments January 14, 2019

We’re here at NRF 2019!

And Manthan has taken the time to catch up with other NRF and retail experts. We spoke with Randy Crimmins, EVP/Chief Strategy Officer at Relationship (formerly GoThink!) to understand his expectations in omnichannel marketing, retail technology innovations and the AI-driven future of retail grocery store chains.

MANTHAN: What innovation are you most looking forward to seeing at NRF 2019?

RANDY: For me, it’s all about the connected shopper. I’m interested to see what tangible progress companies are making in terms of creating a truly connected, personalized retail customer experience without and within the store.

What advancements are being made to help retailers 1) truly understand their customers holistically and 2) leverage that understanding to relevantly and personally engage with each of them throughout the shopper journey, online and instore?

MANTHAN: What does the ‘store of the future’ or ‘the intelligent store’ mean to you?

RANDY: I go back to the connected shopper. The intelligent store furthers the connected experience bridging the shopper journey and digital engagement from outside to inside the store.

For example, I receive a welcome message via push notification when I walk in the store that alerts me to personalized marketing deals available for that visit. These offers are automatically added to my digital shopping list and highlighted on my digital aisle map in my mobile app so I can easily locate them. When I check out, my account is recognized as being a top customer, which automatically notifies the front end or store manager on her app, who casually stops by to personally thank me for shopping and make sure I found everything I was looking for that day.

The connected shopper experience leverages integrated retailer systems, customer data, the utility of the mobile app, location services and omni-channel engagement—essentially leveraging high-tech for high touch inside the store. That’s the future and it’s here now.

MANTHAN: What would you say are the top three things grocery retailers need to focus on in 2019, to compete profitably?

RANDY: 1. Infrastructure 2. Infrastructure 3. Infrastructure.
Focus on creating the right infrastructure, integration, and processes needed to create highly personalized, relevant connections with your customers online and in-store. Too many retailers are reaching first for the bright shiny objects, without fully understanding the infrastructure needed, or the vision and planning required to deliver the kind of connected customer experience that shoppers are going to demand. Retailers end up with disparate systems and cobbled together processes that don’t integrate, communicate or scale.

Walk around NRF… incredible applications, technology, and solutions everywhere screaming their features. As a retailer, you have to step back, define your vision, create a plan and focus on creating the right data, digital infrastructure and enabling technologies necessary to serve you and your customer’s long term. Digital investment is not a marketing line item—it is as important of an investment as those routinely made for software and hardware in other areas of your business. Change the mindset.

Retailers are challenged to create connected customer experiences that leverage all channels.”

MANTHAN: What are the top omnichannel marketing challenges that retail grocery store chains are still trying to overcome?

RANDY: Retailers are challenged to create connected customer experiences that leverage all channels, and more importantly each shopper’s “channel of choice.” Retailers are also challenged by data integrity and a singular understanding of a customer — “one version of the truth”. Customer account data and integrity is a fundamental and critical component to successful omnichannel engagement.

On the back-side retailers struggle with understanding performance attribution. What/why/how do customers respond to offers, engage with the retailer online and in-store—what device, which campaign, promotion and offer, and taking it further, what was the associated ROI, or incremental gain at a customer level. These are mission critical aspects of being an effective omnichannel marketer, yet the majority of retailers don’t have a clear or complete picture of their efforts or results.

MANTHAN: What recent technological innovation have you seen significantly impact retail marketing?

RANDY: Big Data and machine learning have disruptive potential in retail marketing.
Take for example the process that every grocer does of creating the weekly flyer. The majority of supermarket chains today probably still build their ad based on spreadsheets and historical patterns, just like they have for the past 30 years. It is an incredibly labor-intensive process, probably occupying 30-40% of the merchandising and advertising teams’ time each week. Big Data and AI could transform this process to streamline the workflow, as well as recommend the items that should go into the flyer based on localized customer data, historical purchase behavior, seasonality, trending, inventory levels, even weather-related data. There’s no reason why using AI a retailer could not automatically build the weekly flyer without any manual intervention.

You can also take this “intelligent” flyer concept further online, by taking all the advertised items for that week and presenting them to each shopper in a personally curated form. A truly personalized marketing solutions for every customer, every week.

We are doing this today and seeing significant gains in terms of engagement, incremental activity, and ROI. You roll it all up and the total efficiency and effectiveness gain potential are huge, and that’s just one marketing vehicle.

“If any industry is poised to reap the benefits of AI for marketing, it has got to be grocery.”

MANTHAN: AI promises to make data-driven processes more intelligent. Which retail segment do you foresee experiencing the biggest impact?

RANDY: AI and Big Data are beginning to transform retail marketing. Of all the retail verticals, grocery has typically been a laggard from a data sophistication and technology standpoint. The irony is that supermarkets typically have the most data and transactional volume, pioneered the capture of individual data via loyalty programs, and yet they lag other industries in their ability to leverage all this customer information for the betterment of their business.

If any industry is poised to reap the benefits of AI for marketing, it has got to be grocery. There is so much upside. The one example above with the weekly flyer could be connected to the inventory system for automatic ordering/replenishment.

In fact, we are seeing AI being used by forward thinking companies like Manthan to manage inventory, minimize shrink, optimize assortment and promotional performance. Disruption is coming to grocery and it will be led by big data and AI.

MANTHAN: Thank you, Randy!

Interview with Kirk Borne | Big Data Hype? The Worst is Behind Us

January 11, 2019 6:29 am No Comments January 11, 2019

Big Data Analytics & Data Scientist. Global Speaker. Astrophysicist. Space Scientist. And in addition to all those hats, Dr. Kirk Borne is the Principal Data Scientist and Executive Advisor at Booz Allen Hamilton, and a globally acknowledged influencer since 2013.

This week Manthan talked to Kirk to delve into his thoughts on big data analytics solutions for data driven discovery and decision support, and innovating using data science.

MANTHAN: Your personal journey in the field of big data analytics is an exciting one to read . What is the biggest surprise you’ve experienced in this rapidly growing, data driven world?

KIRK: The biggest surprise that I have experienced is how incredibly rapidly the world has woken up to and adopted the power of data and the power of algorithms. I worked in this field for many years and it was hard to get anyone to take it seriously 10-15 years ago. But in the past 5 years, the field has exploded, including startups, new businesses, new lines of business in old organizations, data science advocates in all types of organizations, the number of use cases across all sectors, and the demand for data scientists and data scientist training programs.

The growth of interest, applications, tools, startups, and people working in this field almost seems to be *faster* than exponential.

MANTHAN: You’ve shared an example in our round-up on the impact big dataanalytics has made on society. Could you give us an example of how analysis of big data has helped improve a business situation?

KIRK: A very large financial services company (we’ll call it ABC) was concerned about customer attrition. Whenever a customer took their investments to another business, ABC lost money. So, ABC decided to explore their customer engagement data to see if they could find a precursor signal in the data that indicated when a customer was perhaps likely to take their investments out and move their funds to another business.

ABC invested approximately one million dollars in a “proof of concept” project to search the data and build a model of customer attrition. They did find a signal in the data, which was simply the sudden increase in the frequency of customer logins to their online account in the month prior to withdrawing their investments. So, ABC deployed a friendly customer engagement program in which they reached out to those customers whose monthly login frequency suddenly increased — ABC sent information about a new online investment calculator, investment performance metrics, new investment strategies, updated FAQs, financial management advice, tools to simplify reinvesting in their other financial service products, etc. At the end of the 3-month “proof of concept” test period, ABC estimated that their one million dollar data science investment had saved the company one billion dollars in customer value!

That was an ROI of $1000 for every $1 investment in their program. So, the company decided to invest in a much larger permanent data science team for greater customer engagement and marketing insights.

“The growth of interest, applications, tools, startups, and people working in this field almost seems to be *faster* than exponential.”

MANTHAN: As the Principal Data Scientist at Booz Allen Hamilton, how do you feel perceptions have changed (for good or bad) towards big data analytics solutions?

KIRK: I believe that the worst of the “big data” hype is now behind us. A lot of growth in the data science and big data analytics fields has already taken place in the past few years, despite all of the negative hype. Now, with the hype being diminished, we are settling down to greater growth, investment, innovation, and value creation in the field. So, the perceptions are definitely changed for the good!

MANTHAN: What recent problem have you helped solve by applying a data driven scientific solution?

KIRK: I cannot discuss client-specific problems and solutions, but one case that we are developing for a future potential client is around a fairly well known and common problem in the field of talent analytics: using data and modeling techniques to predict employee performance, job satisfaction, and possible attrition.

We considered an ensemble of different predictive modeling techniques that gave greater insights into the problem than a single model approach, with most of our focus is on the employee attrition problem (which is much like the customer attrition problem mentioned above): what are the key signals in the data that might indicate when such an outcome is likely to occur?

“I believe that the worst of the “big data” hype is now behind us.”

MANTHAN: What do you imagine the future will be like when we have greater computing capabilities to fully harness the potential of our big data?

KIRK: There are 3 essential contributors to big data analytics solutions and data science success, and why big data science is so popular these days, are these:

larger and more comprehensive data sets.
more efficient and effective algorithms.
faster more powerful computing capabilities.

As computing capabilities increase, then similarly will our abilities increase to explore even greater dimensions and combinations of dimensions of much larger data sets.

The insights to be gained will continue to grow in proportion to the computing power that we can apply to the problem. The combinatorial explosion of different combinations of diverse data sets to be explored will become even more enormous as the Internet of Things grows in diversity and ubiquity. The discovery potential from all of these new acquired data will be lost unless we also acquire greater computing capabilities.

So, I expect that the 3 essential contributors to big data science success will continue for a long time to come!

MANTHAN: Thank you, Kirk!

Predictive Analytics – A Necessity for Retailers: Interview with Mindtree

January 11, 2019 6:25 am No Comments January 11, 2019

In anticipation of NRF 2019 next week, Manthan caught up with the retail experts from Mindtree; Vinaysheel Palat Global Head of Consulting for Retail, CPG and Manufacturing and Ronojoy Guha, a specialist in predictive analytics platform for Merchandising, Stores and Supply Chain logistics; to talk about technology, predictive analytics platform and the future of retail.

MANTHAN: What does the ‘store of future’ or ‘intelligent store’ mean to you?

VIN & RONO: Future stores would become experience centers rather than just market places, a destination providing multiple services to customers, like dining, dry cleaning, and shipping. A one-stop shop for all needs.

The store sizes are likely to shrink as they look to bring in granular clustering based on the demography of the location. E.g., currently grocery stores use much space on bulk goods, these would be shrunk and taken to the backroom and fulfilled on orders. While their backroom stores would become distribution centers for their online orders, bringing in true omni channel, not just from a consumer standpoint, but from a supply-chain as well.

Technology play will be much higher, there would be more cashless and card-less transactions, curb-side pickup, etc. We will see more differentiation in store formats catering to local demography. Customer segmentation would be used to tweak experience.

Grocery store experience centers would be different compared from downtown to suburbs. The store itself would have a personality and how it will have a conversation with its shoppers would differ. E.g., Suburbs with young families would perhaps have centers where recipes with organic food are cooked. A fast-moving store in downtown would focus on information.

MANTHAN: What would you say are the top three things retail stores need to focus on in 2019, to compete profitably?

VIN & RONO: The most significant would be personalization at an individual consumer level. Consumer behavior would have to be analyzed to offer differential pricing, personalized and real-time promotions, and better service. Personalization would not be restricted to consumer but would extend to the store and products as well. Depending on the store format and the products being offered, retailers would look at dynamic assortment, driven through an app in the store.

Pricing is a relatively ignored area in retail today and has huge potential to increase profitability for the store.

The other areas would be traditional levers of Cost and Risk management. Labor is the biggest operating cost component for a store (25-30%), can technology reduce this? How can inventory and shrinkage be reduced? Retailers will have to think about these questions. E.g., retailers spend significant money each year servicing claims from customers who meet with minor accidents in stores, how can these be avoided or reduced. Energy consumption within the store is another cost driver that would be rationalized.

Retailers must also revamp their processes to embrace omni channel in its true sense, be it making replenishments in smaller batches in a day, better managing staff with rotation of shifts, and such, to make their store future ready and have a meaningful conversation with their customers.

MANTHAN: The phrase ‘Data is the new oil’ has been popular for the past few years. Have retailers realized this value? What is stopping them from maximizing the value of data?

VIN & RONO: Like oil, data is invaluable, not just for retail, but any industry of today. It is going to revamp the retail sector in coming years and stop the physical store from extinction and becoming the dinosaurs of our generation.

But just like oil, data can be inflammable too, if not utilized effectively. We see data to be regulated increasingly in the future, it has already started in EU with introduction of GDPR. But in future, data would be marketed, sold, and utilized in pre-set conditions.

Retailers will have to be judicious on how they collect and use data. How the data is analyzed and benefitted from would be most important, rather than racing to collect more and more of it. Governance standards would have to be established with how data is standardized. Many companies today are not doing a good job of this.

“Retail sector today is highly under-leveraged in predictive analytics. Retailers should invest in this in 2019.”

MANTHAN: Can you share your thoughts on predictive analytics and it’s expected impact on retailers?

VIN & RONO: Predictive customer analytics has had a giant evolution in a very short time and it now is a necessity, a given. Retailers need to utilize this in physical stories to maximize the potential. It should be used for defining pricing, inventory, merchandising, and even number of stores needed.

Predictive customer analytics in future should be used beyond this, it should help influence consumer behavior. So influence external entities rather than just internal. Consumers should become influencers of your brand.

The retail sector today is highly under-leveraged in this area. It has tremendous un-utilized opportunity to gain from predictive analytics. Retailers should invest in this in 2019.

Retailers need to take the organization along to make any predictive customer analytics program successful. Technology is one aspect, but business should understand the value and be an equal partner.

MANTHAN: AI promises to make data-driven business processes more intelligent. How do you see this impacting retailers?

VIN & RONO: AI has infinite potential to disrupt retail. This should be looked at in two broad spectrums.

At the customer facing end, we have seen apps from home improvement retailers where you can take a picture of your room and the app shows you assortment of products enabled through deep learning. Or take a picture of a pair of shoes and suggestions roll up on your screen based on your preferences.

On the backend, AI can impact on how pricing algorithms are defined to make almost real-time price suggestions for individual customers. It can help take decisions on inventory movement, or control energy consumption in the stores, or guide store associates.

MANTHAN: What innovation are you looking forward to seeing at NRF 2019?

RONO: I am looking forward to seeing how AI or Robotics would impact the store of the future.

VIN: I am really interested in learning how this high influx of technical innovations that are replacing store associates (think Amazon Go) replace the human touch, empathy, or need for dialogue that a shopper visiting a physical store looks for.

Thank you Vin & Rono!

BIG Expectations: 15 Retail Experts tell us what they expect from NRF 2019

January 4, 2019 1:28 pm No Comments September 24, 2024

The NRF Retail Big Show 2019 is fast approaching, and we are excited!

This year at NRF 2019, Manthan will be showcasing The Store That Knows, an AI-powered, omni channel marketing entity that offers analytics and insights 24×7, giving retailers smart recommendations while implementing real-time decisions.

As always, we talked to several retail experts to find out what they are looking forward to seeing at NRF this year, and here’s what they had to say:

Manthan’s Question:

What big retail idea do you hope to see at NRF RETAIL’S BIG SHOW 2019?

Matthew Shay (@NRFNews)

President and CEO, National Retail Federation

Retailers are constantly evaluating and implementing new technology that improves the customer experience, both online and in store. There is so much opportunity for retailers to integrate voice intelligence, and we plan to see examples of this come to life at NRF 2019: Retail’s Big Show.

Bob Phibbs (@theretaildoctor)

www.RetailDoc.com

I’m looking forward to hearing new ways retailers are dealing with 70% cart abandonment online. Just sending more coupons within a few hours doesn’t seem to be working. That and innovative ways the human touch is returning as benchmarks of customer service.

Joe Skorupa (@joeskorupa)

Editorial Director, RIS News

A big idea I would like to see presented at the NRF Big Show 2019 is the consumer genome. Personalized marketing based on a retailer’s available data has had only mediocre success. What is needed is the creation of a consumer genome that enables retailers to sequence, analyze and interpret the habits, preferences and behaviors of shoppers. Humans are complex and retailers who once thought they were drowning now must realize they not have enough data or do not have enough of the right data.

Doug Stephens (@RetailProphet)

retailprophet.com

What I’d like to see at NRF 2019 is a dialogue around the power of physical stores as a media channel and the ways in which pioneering companies like Nike, B8TA, Dyson and others are designing and measuring their physical spaces and customer experiences in new and dynamic ways.

Andrew Busby (@andrewbusby)

Retail Analyst & Keynote Speaker

I’m hoping to see the store of the future. Not a box selling stuff but an immersive space, inviting me to spend time there through a combination of intrigue, excitement, theatre, inspiration topped off with a light sprinkling of magic. Yes, I’m hoping to see the store of the future.

Nicole Leinbach Reyhle (@RetailMinded)

Retail Minded, Founder& Publisher, Independent Retailer Conference, Co-Founder

At the upcoming NRF Retail’s BIG Show, I expect to see a lot of action within the payments category. There are a variety of innovative, exciting solutions that are available to merchants to help streamline but also strengthen security when it comes to accepting customer payments. As consumers demand more precise and effective shopping experiences, this specific space is one that retailers should not overlook. I anticipate at the NRF Retail Big Show there will be a variety of conversations surrounding payments and ways to best support consumers across the generations and across their preferences when it comes to both buying online and in-stores.

Greg Buzek (@gregbuzek)

President – IHL Group, Advisory Board – Retail Orphan Initiative

I expect to see major solutions around customer engagement and customer experience. Retailers have been spending quite bit on unifying their channels in the past few years, but with 55% of US Households having Amazon Prime, they must give reasons for consumers to actually visit stores. So areas of inventory accuracy, speed of service and positive, engaging experience.

Tony D’Onofrio (@tonycdonofrio)

CEO of TD Insights LLC

NRF19 arrives at a critical time for retail – a positive USA holiday season, Amazon at an inflection point, and the end of an over-hyped retail apocalypse. The future of retail includes increased personalized branding and immersive customer experiences through technology. Which companies have stepped-up to this challenge in 2019?

Caroline Baldwin (@cl_baldwin)

Editor – Essential Retail

One topic I hope to see take centre stage at this year’s NRF is sustainability. Be it on the conference agenda or technologies showcased at the Expo. Retailers are crying out for solutions to help them to improve their businesses to become environmentally friendly, as shoppers have finally woken up in 2017 to the realisation that we need to be more mindful in our consumption of goods if we want our planet to last for generations to come.

Steven P. Dennis (@StevenPDennis)

President & Founder, SageBerry Consulting, LLC

At the NRF Big Show I’m looking to see what retailers that are trapped in the boring middle are doing to become more truly customer relevant and remarkable.

Diane J. Brisebois (@LoveRetail)

President & CEO, Retail Council of Canada

At NRF’s Big Show I will be looking for innovative e-commerce and brick-and-mortar solutions in marketing and personalization – how retailers communicate, via different platforms, with customers in a way that enables loyalty, while managing privacy guidelines, increasing the coolness factor versus the creepiness sometimes associated with personalized messaging – ultimately leveraging analytics solutions to truly deepen the bond between brand and customer.

Debbie Hauss (@dhauss)

Editor-in-Chief, Retail TouchPoints

I am hoping to see great ideas to help frontline brand ambassadors – retail store associates – become more motivated, empowered advocates. It’s not a new concept, but it is increasingly and vitally important for retailers seeking to succeed as omni channel marketing retailers. Some technology/solutions I’ll watch for include: empowered scheduling, new mobile initiatives and unified commerce strategies.

Melissa Gonzalez (@MelsStyles)

Award winning retail strategist

For NRF 2019, I am looking forward to seeing more applications of augmented reality outside of beauty. It’s been truly transformative to the traditional experience of trying on makeup looks and new products so interested to see how other categories can truly benefit from it. And as the usage grows, how we can glean actionable data from customer interactions.

Cate Trotter (@insidertrends)

Head of Trends at Insider Trends

Less customer-facing tech and more conversation-supporting tech. There’s too much focus on shiny gizmos that are used to patch poor store experiences. I’m hoping to see tech that quietly sits behind the scenes, allowing for more interesting (and more informed) conversations with customers. Done right, it doesn’t just build rapport – it works like magic!

Cathy Hotka (@cathyhotka)

Cathy Hotka & Associates, LLC

The biggest opportunity for retail going forward is leveraging customer and transaction data for meaningful marketing. I’m looking forward to talking with innovative technology companies that can make sense of this data and help create personalized outreach that builds new business.

3 Things Every Retail Marketer Needs (and where your current marketing tools may be letting you down)

January 3, 2019 10:58 am No Comments September 24, 2024

Retail marketing today is shifting from understanding what the customer wants and providing them with it, towards being a trusted advisor who adds value to their life. Imagine how pleasantly surprised a shopper would be if she came across that perfect stole she wasn’t even actively looking for.

But to enable such experiences for customers, retailers need an army of staff who are studying customer behaviour, preferences and activity all the time. Alternatively, this can be done seamlessly with data science. Artificial Intelligence and advanced analytics are pushing the boundaries of what is possible, equipping retailers to become truly customer-centric.

The More Things Change…

The last two decades have clearly demonstrated that the fundamentals of marketing remain the same-businesses that are able to consistently delight customers are the ones that succeed. What’s changed are the mechanisms used to achieve this. Pre-digitization, communications were one-way through mass media and only touchpoint was at the physical store.

Since shoppers now consume information differently and through multiple channels, marketers are changing as well. When it comes to campaign management, these 3 things that are a must for marketers:

Seamlessly Connecting Channels for a Consistent Experience: Managing customer interactions across channels comes across as a basic requirement, but to do this, marketers need to make sure channels and devices converge to offer a meaningful experience. This requires not just a customer data platform, but also a campaign management solution that is truly omnichannel.
Predicting Customer Behaviour and Activating Personalization: With segmentation, marketers can understand customers and their preferences. What differentiates a leader is their ability to predict behaviors, and activate those insights such that customers experience one-to-one personalization, like they did with their friendly neighbourhood store owner. AI based personalization is not limited to just basic variables such as gender and age, but uses deeper insights such as the individual’s lifestyle, the image they want to portray, how much they value exclusivity versus discounts, their life-cycle stage, and more.
Communicating with Customers in Real-Time: For retailers, being able to act on customer micro-moments is critical to positively impact conversion. This is where in-depth understanding of customer journeys come into play. If a shopper was browsing for a product, but did not purchase, it is easier to nudge them towards purchase with a timely notification or offering. Mobile in-app and push notifications are becoming increasingly important for engagement – it is real-time, easy to consume, and does not encounter the friction of channels such as e-mail.

The Right Campaign Management Solution Does the Heavy Lifting

The value proposition of data-driven marketing has changed. Earlier, data was used for reporting, creating dashboards and keeping tabs of spend and revenues. Today, data is used to generate prescriptions and recommendations, so that you get the best returns from marketing while maximizing customer lifetime value.

This is the new era where only what needs attention gets highlighted, and best actions are suggested. The heavy lifting of data aggregation, insight generation and simulation take place behind the scenes.

A truly advanced campaign management solution can listen to customer behaviour and execute personalization in real-time.

Mobile app based customer targeting: The right solution should enable marketers to easily target customers who have downloaded a mobile app but not registered. Or even customers who have registered, but not purchased or haven’t interacted with the app in a few weeks. This can be highly impactful with the addition of rich media notifications and in-app personalization of banners and coupon wallets.

Location proximity-based marketing: Knowing when your customers are near can offer a tremendous advantage. Being able to send promotions to customers, based on their past purchase history, when they are nearby or inside your store, can draw them to make more purchases. For example, a customer in the golf shoes aisle might be interested in a bundling promotion you are running for golf tees.

Intelligent journey builder: Drip campaigns and sequential promotions can extend journeys using customer’s response on a channel or can be based on predictive micro-segmentation. Communications that are customised (aspects of when to send, what offer to send, and what channel to send on) to live user activity have a higher conversion rate. For example, events such as mobile app launch, cart updates, or even inactivity can be used to tailor the next communication, and move customers towards a purchase.

Test and Experiment within journeys: It’s hard to know upfront what channel, what time and what combination of copy, offer and creative treatment will have the maximum impact. With A/B testing, marketers can easily assess the performance of each component and arrive at the best arrangements. Similarly, test and control is a great way to measure the effectiveness of a new tactic.

A New Era of Delivering Experiences at Scale

Technology is evolving to make interactions contextually relevant to customers. By working with online and offline data (including POS, mobile, text messaging, e-commerce and email), artificial intelligence can manage it all, at scale, in a quick timeframe for tens of millions of customers.

And retail marketers who do this well will be able to form deeper relationships, enhance customer engagement and loyalty and retain their best customers to impact growth.

Augmented Retail Analytics – Supporting Human Intelligence With Superhuman AI Capabilities

January 2, 2019 11:18 am No Comments September 24, 2024

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.

Customer Centric Algorithmic Merchandising, The Next Game Changer In Retail

December 14, 2018 11:16 am No Comments December 14, 2018

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.

Automation and Augmentation of Retail Data and Analytics | NRF 2019

December 12, 2018 7:06 am No Comments September 24, 2024

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.

Analytics & Insights: Interview with rue21’s Chief Analytics Officer, Mark Chrystal

December 6, 2018 7:21 am No Comments December 6, 2018

rue21 has been making news for its investment in analytics in order to transform itself into a more customer-centric business.

We caught up with Dr. Mark Chrystal, Chief Analytics Officer at rue21 to understand more about how he perceives the role of analytics in retail today, his upcoming talk at NRF’s Big Show and the future of retail.

MANTHAN: In your role as the Chief Analytics Officer, what would you say is the biggest challenge facing rue21 in 2019?

MARK: The biggest challenge I face is the ability to explain what is happening in the industry and more importantly, with our current, lapsed and potential customers. My job is to help the business navigate the environment and provide insights that help chart a course to success. This is particularly challenging in the current retail environment and for a company that is in the midst of a turnaround.

“We are now seeing analytics embedded across each functional unit as means of explaining what is happening, where it is happening, and how best to respond.”

MANTHAN: In your 20 years of retail experience, what have you noticed about the changing retail industry’s attitude towards analytics?

MARK: When I started in retail, analytics was being thought of as secondary to the success of a retail business. Analytics groups, if they did exist, were often in their own silos away from the day-to-day running of the business. At that time, most of the CEOs and head merchants across retail were trained based on having direct face-to-face interaction with their customers, and therefore thought about the business through a much more qualitative micro-level lens.

With the advent of eCommerce and social media and social influencers, the environment is far more diverse and complex than it was twenty years ago. We are now seeing analytics embedded across each functional unit as means of explaining what is happening, where it is happening, and how best to respond.

“Retailers need to employ real-time analytics to help them identify emerging themes, issues and opportunities.”

MANTHAN: AI promises to make data-driven business processes more intelligent. What are the top use cases you think might have big impact in retail today?

MARK: The top use cases for AI today, are in the automation of rote tasks, and in the identification of patterns and opportunities that are not as readily discernable via other analytical methodologies or business processes.

MANTHAN: We understand you’ll be speaking at NRF. What is the product paradigm shift going to be about?

MARK: I will be speaking at NRF about the shift within retail towards data-driven decision-making and organizational culture. The presentation will focus mostly on how merchandising functions need to, and are, making this shift.

“Retailers need to create organizational cultures that are capable of interpreting real-time insights and taking action on those insights.”

MANTHAN: As enterprise and customer data continue to grow and customer journeys evolve, how can retailers keep up with sensing, analyzing and responding to opportunities potentially unfolding every day?

MARK: I believe retailers need to employ real-time analytics to help them identify emerging themes, issues and opportunities with their customers and competitors. This means having models tuned to real-time analysis, alerts and insights across the retail footprint. This also means that retailers need to create organizational cultures that are capable of interpreting real-time insights and most importantly, taking action on those insights.

Most retail organizations have not evolved to this point yet and are still grappling with the change from the old merchant model to the model that modern customers clearly demand. The best retailers understand this, have made those changes, or created those types of cultures at inception and they are reaping the rewards.

MANTHAN: Thank you, Mark!

rue21 has selected Manthan, a leading provider of cloud analytics and artificial intelligence solutions, to help advance its analytic capabilities. The retailer will be rolling out Manthan’s Customer Data Platform, Customer Analytics and Enterprise Retail Analytics solutions to gain insights within the business and better connect with consumers.

Visit us at NRF 2019, Booth 4719 for more information on how Manthan can help you use analytics to become a future ready retailer.

[Infographic] Why will you attend NRF 2019?

December 4, 2018 6:08 am No Comments September 24, 2024

This year at the NRF Retail Big Show 2019, Manthan will be showcasing The Store That Knows

This AI-powered, omnichannel entity offers analytics and insights 24×7, giving you smart recommendations while implementing your decisions. All in natural language.

And if that’s reason enough, take a look at the following infographic to see who else is likely to be at the NRF Big Show.

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