Digital Marketing

How Predictive AI Will Change Shopping

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Imagine you’re about to leave the house to pick up your kids. As you grab your keys, you hear a voice from the device on your coffee table: “It looks like you’ll use the last of your milk tomorrow, and yogurt is on sale for $1.19. Would you like to pick up an order from Trader Joe’s, for a total of $5.35?” You say yes, and Alexa confirms. The order will be ready for curbside pickup, on the way home from your kids’ school, in 15 minutes.

This future scenario isn’t so far off. Amazon, Facebook, Google, and Apple are accelerating consumer expectations and what’s technologically possible, from same-day delivery to machine-powered image recognition. You can call an Uber with Siri and book a flight entirely through a Facebook Messenger bot.

Responsive retail has peaked, and we’re about to enter the era of predictive commerce. It’s time for retailers to help people find products in their precise moment of need — and perhaps before they even perceive that need — whether or not they’re logged in or ready to click a “buy” button on a screen. This shift will require designing experiences that merge an understanding of human behavior with large-scale automation and data integration.

Machine Learning Beyond Forecasting

Retail giants have been using machine-learning algorithms to forecast demand and set prices for years. Amazon patented predictive stocking in 2014, and saying that AI, machine learning, and personalization technologies have improved since then is an understatement. Retailers need to think more like tech companies, using AI and machine learning not just to predict how to stock stores and staff shifts but also to dynamically recommend products and set prices that appeal to individual consumers.

Say you’re on a business trip and realize you forgot your phone charger. You’ll pay a premium for a new one delivered to your hotel room before an all-day meeting. An electronics retailer might also predict that you want new headphones. It can offer you a deal on a noise-canceling pair at a price that accounts for current pricing on Amazon, in-store inventory at Best Buy, the current rates for on-demand couriers, and the fact that you’re taking a red-eye flight home tomorrow.

This level of prediction requires detecting subtle patterns from massive data sets that are constantly in flux: consumers’ purchase histories, product preferences, and schedules; competitors’ pricing and inventory; and current and forecasted product demand. This is where AI and machine learning comes in and where companies are investing. Etsy just acquired a company that specializes in machine learning to make its searches more predictive by surfacing nuanced product recommendations that go beyond simple purchase histories or preferences. This is the natural evolution of product recommendations, one that will be the standard for years to come.

Realizing the Potential of Connected Devices and Data

Predictive retail involves inspiring consumers in different contexts — before, during, and after a purchase. Commerce is already becoming less of a deliberate activity than an organic part of how we experience daily life. It’s not just smartphones that make browsing and buying spontaneous; Amazon’s Dash buttons and Alexa-powered Echo device are enabling purchases in the home. You can hit the Tide Dash button in your laundry room when you see that you’re running low on detergent, or ask Alexa to order your mom a bouquet of flowers when you remember that her birthday is next week. This is just the beginning.

The next generation of smart assistants and connected devices will learn from user habits and pick up on behavioral and environmental patterns in order to make these experiences more predictive. Devices like the Echo will access data from everyday interactions to predict specific opportunities for a transaction.

There’s also huge potential for connected devices in retail stores to predict consumer behavior and respond to individual needs. Many stores are already using smartphones to follow customers’ activity and deliver context-specific offers. It’s not a stretch to imagine that the evolution of biometrics, identity technologies, and location sensors will allow retailers to personalize content based on factors such as how you’re feeling, how much time you have to browse, and whether you’re coming from the office or you’ve just finished working out.

Retailers will need to program brick-and-mortar experiences with the same targeting and personalization they offer online. Think about walking past Nordstrom and receiving a notification for an offer on a new pair of sneakers. Your current pair is worn down from running almost 500 miles — all logged by a chip in the sole that sends data to your fitness app. You swipe the notification to select the styles you want to try on, and an in-store map guides you to an associate waiting with your shoes.

Embracing Human-Centered Design

The future of predictive retail requires designing new ecosystems for commerce. These systems will be built around the human, rather than around a particular device or around online or offline experience. These systems will need to incorporate human connection and storytelling, spatial design and context, and a lot of data.

Many retailers are getting ahead of this shift by creating innovation labs — teams and spaces dedicated to incubating new ideas and testing digital experiences that connect the online and in-store worlds. Sephora’s Innovation Lab is a great example. The brand introduced a “store mode” for its mobile app, which integrates a user’s online shopping cart and Beauty Insider loyalty card to remind them of the products they’ve saved, the points they’ve earned, and the benefits available to them, such as a free makeover.

Retail chains, brands, and e-commerce companies are also collaborating to bring new ideas to life. Several years ago, Westfield Malls’ lab worked with eBay to build 10-foot-tall interactive screens in its San Francisco shopping center. Shoppers swiped these screens to browse products from brands like Rebecca Minkoff and Sony, which they could purchase directly on mobile.

There’s huge potential to layer predictive capabilities on top of this AI-driven infrastructure. Imagine a store window that connects with your phone to display personalized content. For instance, you might see gifts for your partner’s birthday or swimsuits for your next vacation, customized based on the boards you follow on Pinterest and the brands you follow on Instagram. By connecting data from multiple sources and designing for the user, retailers can create more-relevant experiences that pull you into a store, website, or app. Even more powerful, they can predict what you want before you do.

Considering Privacy, Building Trust

There’s almost always some trade-off between privacy and personalization; this has been true for every generation of technology. Retailers need to move forward with transparency, respect, and security as their priorities. They also need to show value. Google has done this well, not just with personalized search results but also with services such as Google Now, which integrates with your calendar and Google Maps to alert you that traffic to your meeting is heavier than usual and tells you when you should leave the office to arrive on time.

Many of us are inclined to share personal information for experiences that are magical and valuable — and that we can’t get elsewhere. Retailers will need to create experiences that make this magic and value apparent. The revolution is already under way. Tomorrow, people will expect even faster and more-intelligent service than they do today. At a point in the very near future, the expectation will shift from on-demand to predictive commerce. It’s time for retailers to get ahead of that change.

By Amit Sharma is the founder and CEO of Narvar, a post-purchase experience platform, and a former executive at Apple and Walmart.



Cognitive Computing

Machine Learning Applications by Industry 2016

Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data, leading to diverse company-wide strategies succeeding faster and more profitably than before.

Industries Where Machine Learning Is Making An Impact 

The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry.

machine learning industries

Machine Learning Is Revolutionizing Sales and Marketing

Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets. And increasingly data sets are comprised of structured and unstructured data, with the global proliferation of social networks fueling the growth of the latter type of data. Machine learning is proving to be efficient at handling predictive tasks including defining which behaviors have the highest propensity to drive desired sales and marketing outcomes. Businesses eager to compete and win more customers are applying machine learning to sales and marketing challenges first. In the MIT Sloan Management Review article, Sales Gets a Machine-Learning Makeover the Accenture Institute for High Performance shared the results of a recent survey of enterprises with at least $500M in sales that are targeting higher sales growth with machine learning. Key takeaways from their study results include the following:

  • 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimizing propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omnichannel selling strategies today.
  • At least 40% of companies surveyed are already using machine learning to improve sales and marketing performance. Two out of five companies have already implemented machine learning in sales and marketing.
  • 38% credited machine learning for improvements in sales performance metrics. Metrics the study tracked include new leads, upsells, and sales cycle times by a factor of 2 or more while another 41% created improvements by a factor of 5 or more.
  • Several European banks are increasing new product sales by 10% while reducing churn 20%. A recent McKinsey study found that a dozen European banks are replacing statistical modeling techniques with machine learning. The banks are also increasing customer satisfaction scores and customer lifetime value as well.

Why Machine Learning Adoption Is Accelerating

Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimize outcomes. Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimizing decisions and predicting outcomes.
The economics of cloud computing, cloud storage, the proliferation of sensors driving Internet of Things (IoT) connected devices growth, pervasive use of mobile devices that consume gigabytes of data in minutes are a few of the several factors accelerating machine learning adoption. Add to these the many challenges of creating context in search engines and the complicated problems companies face in optimizing operations while predicting most likely outcomes, and the perfect conditions exist for machine learning to proliferate.

The following are the key factors enabling machine learning growth today:

  • Exponential data growth with unstructured data being over 80% of the data an enterprise relies on to make decisions daily. Demand forecasts, CRM and ERP transaction data, transportation costs, barcode and inventory management data, historical pricing, service and support costs and accounting standard costing are just a few of the many sources of structured data enterprises make decisions with today. The exponential growth of unstructured data that includes social media, e-mail records, call logs, customer service and support records, Internet of Things sensing data, competitor and partner pricing and supply chain tracking data frequently has predictive patterns enterprises are completely missing out on today. Enterprises looking to become competitive leaders are going after the insights in these unstructured data sources and turning them into a competitive advantage with machine learning.
  • The Internet of Things (IoT) networks, embedded systems and devices are generating real-time data that is ideal for further optimizing supply chain networks and increasing demand forecast predictive As IoT platforms, systems, applications and sensors permeate value chains of businesses globally, there is an exponential growth of data generated. The availability and intrinsic value of these large-scale datasets are an impetus further driving machine learning adoption.
  • Generating massive data sets through synthetic means including extrapolation and projection of existing historical data to create realistic simulated data. From weather forecasting to optimizing a supply chain network using advanced simulation techniques that generate terabytes of data, the ability to fine-tune forecasts and attain greater optimizing is also driving machine learning adoption. Simulated data sets of product launch and selling strategies is a nascent application today and one that shows promise in developing propensity models that predict purchase levels.
  • The economics of digital storage and cloud computing are combining to put infrastructure costs into freefall, making machine learning more affordable for all businesses. Online storage and public cloud instances can be purchased literally in minutes online with a credit card. Migrating legacy data off of databases where their accessibility is limited compared to cloud platforms is becoming more commonplace as greatest trust in secure cloud storage increases. For many small businesses who lack IT departments, the Cloud provides a scalable, secure platform for managing their data across diverse geographic locations.

By Louis Columbus, Director, Global Cloud Product Management at Ingram Cloud. Previous senior analyst at AMR Research (now Gartner).

Cognitive Computing

A Review of Machine Learning Algorithms and Applications

With the explosion of data generation, getting optimal solutions to data driven problems is increasingly becoming a challenge, if not impossible.The importance of machine learning algorithms, which can handle this burst of data and assist in intelligent decision making, is thus realised among data scientists. Within this category of machine learning algorithms, a special focus area is bio-inspired algorithms. This review article provides the readers some inputs on the advances in the domain of bio inspired algorithms and their potential applications across domains.

machinelearningIt is increasingly being recognised that applications of intelligent bio-inspired algorithms are necessary for addressing highly complex problems to provide working solutions in time, especially with dynamic problem definitions, fluctuations in constraints, incomplete or imperfect information and limited computation capacity. More and more such intelligent algorithms are thus being explored for solving different complex problems. While some studies are exploring the application of these algorithms in a novel context, other studies are incrementally improving the algorithm itself. However, the fast growth in the domain makes researchers unaware of the progresses across different approaches and hence awareness across algorithms is increasingly reducing, due to which the literature on bio-inspired computing is skewed towards few algorithms only (like neural networks, genetic algorithms, particle swarm and ant colony optimization). To address this concern, we identify the popularly used algorithms within the domain of bio-inspired algorithms and discuss their principles, developments and scope of application.

metaheuristicsSpecifically, we have discussed the neural networks, genetic algorithm, particle swarm, ant colony optimization, artificial bee colony, bacterial foraging, cuckoo search, firefly, leaping frog, bat algorithm, flower pollination and artificial plant optimization algorithm. Further objectives which could be addressed by these twelve algorithms have also be identified and discussed. This review would pave the path for future studies to choose algorithms based on fitment. We have also identified other bio-inspired algorithms, where there are a lot of scope in theory development and applications, due to the absence of significant literature.

Read the complete paper here

Citation:  Kar, Arpan Kumar. “Bio inspired computing–A review of algorithms and scope of applications.” Expert Systems with Applications 59 (2016) : 20-32.


Digital Operations: Robotic Process Automation

“Looking to the future, the next big step will be for the very concept of the “device” to fade away. Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you through your day. We will move from mobile first to an AI first world.” 

— Sundar Pichai, CEO Google

BotReadinessRobotic process automation (RPA) is becoming a mainstream topic at leading corporations as C-Suite execs look at new automated strategies to do more with less.

Process automation is taking center stage again. Outsourcing, offshoring strategies are delivering diminishing returns so a new frontier enabled by a virtualized workforce of software robots is emerging.

I have seen a massive uptick of interest in digitizing work – automate key processes and increase efficiency – via robotic process automation. Large corporations like Citibank are openly discussing this trend with vendors as they race to cut operating costs further.

Digital robots ∼ Apple Siri, Microsoft Cortana, IBM Watson, Google DeepMind, Facebook Chat Bots, drones and driverless cars ∼ are now mainstream. What most people are not aware of is the rapidly advancing area of enterprise robots to create a “virtual FTE  workforce” and transform business processes by enabling automation of manual, rules based, back office administrative processes.

This emerging re-engineering of key back-office and front-office operations is called Robotic Process Automation (RPA).  Machine Learning (ML), guided ML, NLP and graph processing are becoming foundations for the next wave of advanced bot use cases. Speech recognition, image processing, translation have gone from demo technology to everyday use in part because of machine learning.

RPA – What?

According to Blue Prism, “Robotic automation refers to a style of automation where a machine, or computer, mimics a human’s action in completing rules based tasks.”

RPA is essentially the novel application of analytics, machine learning and rules based software to capture and interpret existing data input streams for processing a transaction, manipulating data, triggering responses and communicating with other enterprise applications (ERP, HRMS, SCM, SFA, CRM etc.).

RPA is not a question of “if” anymore but a question of “when.”  This is truly the next frontier of business process automation and enterprise cognitive computing. Immediate impact is being seen around self-service processes, customer facing processes, call center interactions, finance and accounting processes.

Industrial robots are remaking factory and warehouse automation by creating higher production rates and improved quality.  RPA, simple robots and complex learning robots, are revolutionizing the way we think about and administer business processes (e.g. customer service), workflow processes (e.g., order to cash), IT support processes (e.g., auditing and monitoring), and back-office work (e.g., data entry).

I strongly believe that as machine learning becomes mainstream, RPA is going to impact process outsourcers (e.g., call center agents) and labor intensive white collar jobs (e.g., compliance monitoring) in a big way over the next decade. Any company that uses labor on a large scale for general knowledge process work, where workers are performing high-volume, highly transactional process functions, will save money and time with robotic process automation software.

RPA picture

Business Impact of RPA – Where?

RPA is already being applied to a wide range of industries to improve speed, quality and consistency of service delivery of digital work.

Virtual FTE robots can:

  • Learn from natural language interactions in order to solve customer problems and respond easily to a wide range of queries
  • Automate data and rules intensive activities like HR, procurement, invoicing, billing. Now it is possible to create complex cross-enterprise apps (xapps or composite apps) like order-to-cash automation.
  • Orchestrate other application software apps through the existing APIs or user interface

Workflow and Process automation

Clerical labor is replaced by software.

Best projects for robot automation are bulk repetitive rules based procedures. Process automation can expedite back-office tasks in finance, procurement, supply chain management, accounting, customer service and human resources, including data entry, purchase order issuing, creation of online access credentials, or business processes that require access to multiple existing systems.

Technologies like BPM software – a technology that mimics the steps of a rules-based, non-subjective process without compromising the existing IT architecture – are able to consistently carry out prescribed functions and easily scale up or down to meet demand.

Automated agents and assistant

Large call centers are going to get restructured. The people answering simple queries will be replaced by 2020 with software bots.

As in voice recognition software, IVR or automated online assistants, developments in how machines process natural language, retrieve information and search mean that RPA can provide answers to self-service customers without human intervention. I can see demand reducing systematically for armies of low-cost labor offshore that do simple tasks like status checking…. query multiple systems and respond;  data entry…input into multiple systems and error check.

Voice driven self service bots are going to transform call centers. Siri and FB Chatbots are the precursors of what’s coming.  Translation is another example. Recently combined translation with computer vision and doing it all on the phone, where you can take a picture of a sign that say “Exit” and have it translated into another language.

The bot engines rely on NLP and machine learning. It means that you can feed the bot sample conversations so that it can handle many different variations of the same questions. The potential is quite big as developers could improve their bots over time. So for instance, you could open up a conversation with a Movie bot and casually ask questions about movie showtimes, ratings and more. It will be like talking with a human agent.

Monitoring support and management

‘Human only’ processes will shift as machine learning and data-driven decision making evolve further.

Activity, fraud and risk monitoring is going thru some changes. Automated processes in the remote management of IT logs, audit trails, security, and other risk related areas can consistently monitored, flagged and exception handled faster.   In IT function specifically, RPA can improve service desk operations and the monitoring of network devices.

KPMG, for instance, is leveraging IBM Watson in improving Audit, Tax processes. One current initiative is focused on employing supervised cognitive capabilities to analyze much larger volumes of structured and unstructured data related to a company’s financial information, as auditors “teach” the technology how to fine-tune assessments over time. This enables audit teams to have faster access to increasingly precise measurements that help them analyze anomalies and assess whether additional steps are necessary.

This example highlights how cognitive technology is further advancing improvements to sampling processes, in which auditors review subsets of data to analyze thousands or millions of actions to draw conclusions. Cognitive technology helps allow for the possibility of a larger percentage of the data to be analyzed, providing KPMG professionals the potential to obtain enhanced insights into a client’s financial and business operations. At the same time, cognitive-enabled processes allow auditors to focus on higher value activities, including offering additional insights around risks and other related findings.

Many of professional services rely heavily on judgment-driven processes. Adding RPA and cognitive technology’s massive data analysis and innovative learning capabilities to these activities has the potential to advance traditional views on how talent, time, capital and other resources are deployed by professional services organizations.

How is a Software Robot Trained?

  • A robot is trained through a flow chart of the procedure. This flow chart is managed and audited to document how well the robot follows the procedure.
  • Management information (e.g., log files)  is gathered automatically as the robot operates. All processes generate statistical profiles as a by-product of doing the action. This allows tuning and development of a process in light of real data.
  • Modern robots systems come with failover and recovery inbuilt as core capabilities. It means that if changes take place, or downstream failures occur a “smart” response can be trained into the overall system.
  • Software robot platforms have full audit and security authorization meaning that all changes and all access is recorded and regulated. Back-up process steps are managed, roll-back and recovery, as well process change-highlighting, are all automatically captured by the robot platform.

[source: Blue Prism]


The robots are coming to digitize work! Enhanced scalability, greater accuracy, digital integration with APIs, improved compliance and reduced cycle times to deploy – as these improve… RPA adoption will take off.

Analytics enabled Robotic process automation (RPA) will drive improvements in accuracy and cycle time and increased productivity in transaction processing (e.g., healthcare claims processing) while it elevates the nature of work by removing people from dull, repetitive tasks.

RPA is in early days.  So, sometimes the hype can get ahead of the reality.  But this is an area where I am going to be digging deeper in subsequent blog posts.

Source: disruptivedigital