All Posts by Divyasri

view of a grocery aisle

Technologies shaping the Supermarket of the Future

Technologies and Trends shaping the Supermarket of the Future

Food businesses will have to change to stay competitive – online, in-store, and at sorting and processing plants too.

The technological boom and the increasing adoption of Industry 4.0 among retailers are creating disruption across all industries. This change is coming to supermarkets which will have an immediate impact on the entire food industry supply chain. Technological innovations – both online and in-store along with the shifting consumer demands will re-shape the supermarket of the future.
Traditional brick-and-mortar supermarket chains are strengthening their own e-commerce capabilities to stay on par with their digitally native competitors. The global grocery e-commerce market is forecasted to expand from an annual value of 43 billion pounds to 135 billion pounds by 2025.

Another aspect that e-commerce players must note is while they are making efforts to establish a strong foothold in the US and European markets, they may face serious challenges because the existing grocery market is saturated and the margins are low. This indicates that the global growth in food e-commerce will be driven by Asia, where there is a willingness to purchase groceries online, along with rapid urbanization, low labor costs, and a newer retail market.

Rising consumer expectations

Widespread food shopping online and fast deliveries to customers’ front doors will only just be the tip of the iceberg in the new world. Computer codes and algorithms will further enable supermarkets to collect data about shopper preferences and habits and use this to personalize their offerings to customers. Recommendation engines further help nudge customers to make purchases similar or related to the products that they have already purchased or been looking for via the “Recommended for you” web pages.

The growing number of people with moderate incomes and lifestyles will become more aware of food safety and more curious about how their foods are sourced and screened. Moreover, food shoppers will develop higher expectations and become critical when buying fresh fruits and vegetables. More will want to know how fresh the produce is and whether or when it is ready to eat.

Consumers will further have the ability to check information about the origins and nutritional value of produce and will be able to see suggestions for recipes and food pairings. This will attract a greater number of customers while making each feel as if they are being provided with individual shopping experiences.

The ad-hoc demand created through the online ‘nudge’ will challenge the traditional food supply chain. Processing lines will need to know precise details about the food – where it is coming from and what is in the storage to meet the demand.

Technology to ensure quality and safety

Grading and inspection equipment – at point-of-origin, prior to shipment to the supermarket, or from the on-line dispatching warehouse – can ensure that the fresh produce has the desired size and ripeness without bruising or mold. In addition, sorting equipment at different stages in the supply chain will be able to provide essential information on sizing, quality and other quality markers.

Traditional supermarkets fight back against the online disruptors – and information about shoppers’ preferences and habits will be an important weapon. Consumer-facing technologies, such as shopping-cart-mounted devices or smartphone apps, will steer shoppers towards the aisles and shelves where they are more likely to make purchases. Sensors in the store’s shelves will keep track of the items customers put in their carts and bill their mobile payment system as they exit the store.

Looking ahead

Another likelihood is that supermarkets will remain the same size but change in concept, becoming destinations for click and mortar shopping. Retailers need to offer consumers a consistent omnichannel experience, stores will connect the physical and digital worlds. Here, consumers can see and feel products they might order online. Here, too, the online product offering could also be accessible via interactive screens.
These changes align with the forecast growth in consumer demand for healthier, high-quality produce, more choice, and greater convenience – a demand which will increase massively as household incomes rise in developing nations, bringing 70 million more people globally every year.

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Tackling Product Matching for E-commerce with Automation

Tackling Product Matching for E-commerce using Automation

There is a vast number of products sold online through various outlets all over the world. Identifying, matching and cross-checking products for purposes such as price comparison becomes a challenge as there are no global unique identifiers.

Here's when product matching becomes extremely important. There are many situations where accurately identifying a product match is essential. For instance, stores may want to compare competitor prices for the same product they may offer. Similarly, customers may use comparison tools to get the best deals. 

Amazon allows different sellers to offer the same products only after ensuring that they are the same before listing the sellers in a single, unique product page.

Numerous products but no method to match them across different stores

Product titles/descriptions do not have a standardized format. Each store, as well as different sellers within a store, might have different titles and descriptions for the same products. Another challenge comes in with respect to attribute listings as different e-tailers follow different formats. The product images of the same product also differ across different e-tailers.

While there are standardized unique identifiers like UPC, MPN, GTIN, etc, they, however, may not be mentioned in the product page in all stores selling them. The attributes themselves may be described differently - for instance 9" and 9 inches. Images may be included but they can differ in perspective, clarity, tone, etc. The brand name may also be referred to in different ways like GE and General Electric.

It is an impossible task for a human to visit different product pages to ensure if they are matching the same products. Although, if the process is to be automated, how can it be ensured that the system makes sense of all the information. This is when AI and machine learning come into the picture. 

Machine Learning for Product Matching

In machine learning solutions for product matching, the solution provider must initially build a database with billions of products. This can be done by collecting information through web crawls and feeds. The system then has to come up with a universal taxonomy. This especially is a unique challenge as different retailers use different classifications for their products, and the same product might be listed in more than one category. For instance, a particular shoe model might be listed under casual shoes as well as dress shoes. The system first must design a standardized taxonomy, irrespective of how a particular store classifies its products.

There are standard classification models such as Google Taxonomy, GS1, and Amazon but a product match solution may devise its own taxonomy. The universal taxonomy is designed by identifying patterns and signals from titles, product descriptions and attributes, and from images.

Once a universal taxonomy is in place, the next step is making particular product matches. Here, there is a need for precise comparisons to ensure a particular product is indeed the same unique product, despite the differences in titles, images, descriptions, etc. First, there is a search for unique identifiers such as UPC or GTIN on the product page. Then, the product titles need to be compared. It needs to be noted that no two product titles are the same across different stores for the same product.

Neural networks play a key role

Neural networks and deep learning techniques are extensively used to identify and learn from similarities, to identify and learn from differences, and produce word-level embedding to create a system of representation for common words. This involves teaching the system to recognize different references to a unique entity such as 'GE' and General Electric or 7" or 7 inches, to come up with one unique representation for each entity.

A product can be identified using its title, description, images and attributes or its specifications list. In many cases, the product title itself will yield a lot of information and the system needs to be trained to differentiate the product name (for instance, brand model) from the attributes.

Product matching - Identify and Sort

The information then needs to be extracted and sorted into the appropriate slots - Phone model, version, memory size, etc. Different techniques might be used to help the system learn to parse and sort the different sets of information. 

The next comparison comes in the form of more information about the product such as the title, description containing additional information and a specs table. These help add more knowledge about the product, and the machine will be better able to identify an exact product match or mismatch in the following comparison.

The standard identifying signals are similar results or positive matches for unique identification numbers (UPC or MPN), classification, brand, title, attributes, and image. For each comparison, the system follows a long procedure of checks or safety valves. The checks pass through a search for the unique identification number, a test for keyword similarities, brand normalization and match (for example, HP is the same as Hewlett Packard), attribute normalization and match ( 9 inches is the same as 9in, 9"), image matching, etc. There is also a check for variation in attributes such as:

For the best product match result, there has to be at least 99% of positive results. It will be considered a mismatch, even if it is a variation of what is essentially the same product. Different product match solutions employ different techniques and training methods, and it is a complicated process. Although, there is an advantage that neural networks and machine learning learn over time, and get better with each use.

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street view of a store front in LA

Transforming the Retail Customer Experience with In-store Analytics

Transforming the Retail Customer Experience with In-store Analytics

While online retailers have the advantage of tracking cookies and web analytics tools to calibrate different aspects of an online shopping experience, brick and mortar retailers aren’t as lucky. They have had to depend on much erratic customer insights.
However,in today’s date, even physical retailers are required to hold up to some very high expectations in shopping experiences.

In fact, in order for physical stores to remain relevant, they have to focus on improving the quality of the experience they deliver. This has led to the creation of entire businesses in retail experience innovation.

One of the ways a brick and mortar retailer can provide a quality experience is through the employment of in-store analytics that provides insights into the behavior of the customers and uses that information to engage with their customers as they shop. The use of in-store analytics has revolutionized how retailers understand their customers and how they communicate with them.

How will in-store analytics build up communication between shoppers and retailers?

In order to answer that question it is important to know how In-store analytics works.
For example, when a furniture store to offers free Wi-Fi, it may seem a bit strange.
Actually when the Wi-Fi is enabled on one’s phone, the device sends out a connection request every few seconds on every Wi-Fi channel available. It updates the list of the available networks after listening for a fraction of a second for a response to come back,

Interestingly, when a device probes the Wi-Fi spectrum, it broadcasts its unique MAC address to any device that’s listening. So, as one walks around in that furniture store, every Wi-Fi probe then acts as a beacon for the location. With multiple Wi-Fi access points available inside a single store, it becomes possible to considerably precisely locate each address. As far as the owner of the device is concerned, this happens passively without having to actually join a Wi-Fi network.



Although nothing about a device’s owner is being shared, the retailer can build a picture of what individuals do as they walk around a store. Such as, the number of customers who went to the first floor, the time people tend to spend in a particular region, the waiting period of customers before they come back to the shop.

This aids in understanding the broader shopping habits and interceding with informed in-store customer communication. Instead of having communication with customer transpire at the convenience of the retailer, it can happen at the customer’s convenience. 

Sending an SMS to inform of a sale as an effective marketing tactic. Sending emails every month or even good old direct post may increase customer movement towards a local store. However, a more customer-centric communication of a timely WhatsApp message offering assistance when the furniture store operator gets to know that the customer has spent over 20 minutes in the dining table department.

MAC address tracking to deliver a more personalised Customer Experience


Anonymously tracking a MAC address results in a more personalized customer communication and in understanding individual behavior in the retail experience. As the data increases, the MAC addressing question ceases to just be a randomly generated number and instead represents the behaviors of a real person. At this stage, there’s nothing to identify the individual who owns the phone but it’s possible to build a picture of who they are.

Whether gathered in multiple locations or over a longer time period in just one location, as the data builds it becomes useful in crafting more personalized communication, which can help increase sales and enhance the customer experience.

Relying on anonymized data can deliver only so much, though. And that brings us back to why the furniture store offers free Wi-Fi. As soon as someone signs up for that Wi-Fi, the store can associate the MAC address with whatever data they capture in the sign-up process. At the very least, that’s likely to be a name, email address and cellphone number. Again, that person never has to use the Wi-Fi: as long as they keep the same device, their MAC address and identity are linked.

Other retailers might not rely just on free Wi-Fi. They might have a loyalty or coupon-based mobile app that requires users to provide some personal data. Depending on the phone’s operating system, that app might be able to access the MAC address itself and make the connection for the retailer. Either way, retailers can incentivize shoppers to make their MAC address personally identifiable. And when that happens, communication can truly become personalized.

Respecting the shopper’s personal data

Either through inertia or without realizing it, most people publicly surfing the web are constantly being tracked. Sure, there are some loud voices of complaint but the vast majority of people accept it or don’t care.

As the company behind smart recycling bin advertisements in London and Nordstrom in the US discovered, people are less keen to have their physical location tracked. Even if it’s only an anonymized MAC address, such tracking could feel intrusive.

A value exchange for a richer retail experience


The answer, perhaps, is to take a tip from the loyalty schemes of large retailers: provide a genuine benefit to customers in exchange for gathering valuable data on their habits. Just as loyalty schemes such as Air Miles and Tesco Clubcard offer coupons, cashback, and exclusive store events, retailers can build similar value into retail location tracking and analysis. Rather than silently track customers, they can volitionally opt in to a mobile-phone enabled rewards program when they enter the store-a loyalty scheme for the 21st century.

Location tracking has the potential to transform how retailers communicate with their customers. It will provide the insight to know precisely when to engage and when to leave someone alone. However, it will work only if customers can see a tangible benefit to giving up some of their privacy.

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AI is Redefining Experience in Customer Support Centres

AI is Redefining Experience in Customer Support Centres

Businesses need to understand the complexities of individual transactions and customer behavior over multiple touch points and channels, now more than ever. With AI in the fore, and technological integrations becoming increasingly popular and customer support centres or contact centres have the opportunity to stand as industry leaders and reimagine every aspect of their business.

Data mining now has the ability to look at every single customer and personalise the brand’s interaction with each of them. Harnessing the massive rise in unstructured data through AI will play a crucial role in helping reshape contact centres into customer experience centres, helping them provide insights into customer needs which would drive increased efficiency and drive profitability, greater customer satisfaction and create more valued and meaningful work.

A seamless, individual customer experience

Digital convenience is a huge motivator for consumers. Several companies such as Apple, Google, Facebook and Amazon have set the bar for integrated customer experience that provides individual customer service across multiple channels. Consumers today expect to move seamlessly through the different channels seamlessly.

While the customer expectations are high, their brand/company loyalty is not as much - while customers will cross channels if they cannot complete a task on their first channel of choice, they only want to engage through the channels they want to use. This is one of the integral reasons why retail businesses must understand the intricacies of individual transactions, as well as the context of customer behavior over multiple channels.

Businesses that are cognisant of their customers’ issues, moulding their experiences and creating meaningful engagement creates value for customer and company. Leveraging AI, businesses can receive immediate feedback - systematically and quantitatively, from every interaction without creating any points of friction or customer effort at an individual customer level or aggregated to the level of your choice. It links all channels to create an individual yet seamless customer experience.

Multiple channels fuel customer contact

Customers are increasingly demanding choice and control and even expect brands and retailers to anticipate their needs without invading their privacy. While digital touch points are becoming the interaction channel of choice for customers, there is still a significant amount of customer support centres that do not use data analysis tools, despite analytics being voted the top factor to change the shape of the industry in the next five years.

Furthermore, customers have reported that the phone as a channel is the most frustrating contact option, an industry study found that its dominance has not declined as quickly as expected. In 2017, almost half of the customer support executives have utilised phone and digital channels. Moreover, it is predicted that more than 50% of organisations would manage a multichannel customer support centre in the immediate future.

Augmenting Intelligence 

While AI can help augment human behavior, there is still a very real bias for humans to want to talk to other humans. Customer support is still an important competitive point of difference for business, with success gauged on customer experience outcomes. A key challenge is maintaining integration levels across all channels while providing consistent service.

Today, customer support centres are experiencing an offloading of transactional activities into alternate channels. Calls are more complex and add more value for the customer as well as the business. 

This means AI will take the the existing analysis techniques of those calls to the next level. It will have the ability to map word and concept level relationships within conversations and then deduce business specific intelligence and insights. Speech analytics will be able to measure everything from the reason the person called to their mood at any stage of the call or contact.

AI can link key words and phrases and carry out semantic matching (which matches phrases on their similarity of meaning). This will enable customer support centres to improve the customer experience, monitor contact centre quality, reduce operational costs and gain critical business insights. Critically, it will do this seamlessly from the conversation, not through set questions or a survey. Today’s data, informs tomorrow’s decisions.

The road ahead

There is no denying that contact centres are entering a period of intense disruption. The rise of cloud-based infrastructure will see new forces enter the market and force existing operators to become more flexible.

For large established businesses, offering a frictionless multi-channel offering will not be something new but something expected by customers. So much so, customers won’t think about dealing with different channels within a company but simply with the company. Accurate, consistent and personalised interactions with customers will be essential.

AI software will be instrumental in helping contact centres reimagine their role from contact to resolution. It will free staff to work on meaningful, more complex and intuitive scenarios with customers as AI performs transactional and predictable tasks. The elevation of work in a contact centre has the potential to create a more stable workforce with improved corporate culture.

Ultimately, people still want to interact with other people. A contact centre is a fine example of that. Utilising AI will allow contact centres to focus less on mundane, transactional activities and more on its interactions with its customers. It will see far more opportunity for meaningful human interaction beneficial to customer and company.

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Speech Analytics vs Voice Analytics: What is the difference?

Speech Analytics vs Voice Analytics

Businesses today have access to more consumer data than ever before, especially through their customer support and service centers. The essence lies in understanding the optimal way of extraction and utilization of that data. Speech analytics and voice analytics are two approaches to call analytics that can be used for the same function. Even though they may both analyze phone conversations between customer support representatives and customers in order to reveal customer insights, their mechanisms are quite distinct. Discerning these differences is crucial to determine which solution of call analytics is ideal. Moreover, both the methods are used by customer support and service centers to gather information about the market performance of products. However, speech analytics and voice analytics are very different tools. The operating principles of analyzing used are very diverse.

Call Analytics

Call Analytics refers to the collection, measurement, analysis, and reporting of data collected over phone calls. Retailers and brands can use the insights gained from call analysis to optimize call handling and marketing campaigns. Call analytics also allow for viewing and analysis of both the macro and micro phone traffic patterns and sort the collected data into informative call reports. There are two different methods of call analysis, i.e speech analysis, and voice analysis.

Speech Analytics

Speech analytics analyzes the spoken content of a phone conversation by analyzing what is spoken between support representatives and the customers, and the context of the conversation. It does so by using phonetic indexing or converting speech to text for the organization of the content. Speech analytics makes it feasible to search and locate the speech of a representative or a customer and their response to each other. The context of the conversation is divulged by the method of isolating specific words and phrases in proximity to one another.For example, if a customer calls a business to ask about the shipment of an order and when it is expected to arrive, then the execution of a search for the words “order” and “shipment” of the customer with close proximity to a search of the customer care representative using the word “shipment,” crucial information such as the reason for the customer’s call as well as whether the customer received a satisfactory response may be determined.Important factors such as keywords and syllables based on a frame of reference searches set up by the business play a vital role in speech analytics. Unveiling the most common phrases and words used by customers during such a conversation enables speech analytics to give businesses better insights into the latest trends. This, in turn, prepares the business to be able to create informed marketing strategies and make decisions that provide the customers with the best experience possible.

Voice Analytics

In contrast to speech analytics, which focuses on the words and phrases used in an interaction between the representatives and the customers, voice analytics targets the intonation of how it was spoken. Voice analytics work by analyzing the audio patterns for vocal elements such as the tone, pitch, tempo, rhythm, and syllable stress,  to gauge emotional quotient. This provides businesses with a deeper knowledge of the mood of a customer.For example, if a customer uses the word “amazing”, voice analytics can be used for the detection of cues, such as anger or sarcasm, which utterly changes the meaning of the word. It is crucial to understand the demeanor of a customer in order to be able to provide them with a satisfactory experience.

After the assimilation of raw vocal data, it is run against an emotional voice database, comparing factors generated by the voice analytics system with known factors associated with emotions such as anger, happiness, fear, and sadness, to correctly identify and classify the emotional state of the customer. In essence, voice analytics captures the emotional aspect of speech in a conversation.

Speech analytics is essential in cases where specific keywords and phrases show a strong indication of potential sales opportunities as well as situations such as cancellation of orders. Speech analytics determines the needs of a customer by the use of keyword detection whereas voice analytics, saves time and labor by guessing the meaning of words and phrases used in a conversation.

Furthermore, by analyzing a customer’s response and emotional state, voice analytics can help predict future behaviors. This is used in second call targeting by focusing on customers likely to make similar purchases. Thus, differentiating between what has been said with respect to how it was said can provide with different kinds of information, that may be used to improve the quality of operations in various ways.


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Conversational AI – The next Step in E-commerce Evolution

Conversational AI - The next Step in E-commerce Evolution

There is no doubt that AI is a popular buzzword in the retail landscape and retailers are slowly recognizing its potential and are increasingly adopting at least one form of AI into their customer journeys or internal processes. By 2019, 40 percent of retailers will have developed a customer experience architecture supported by an AI. Retailers that choose not to incorporate an AI-backed solution into their business strategies will face consequences that can severely affect their bottom line.

Conversational AI can fundamentally transform the way consumers communicate and transact with brands. While this is true across all industries, retailers, in particular, can reap multiple benefits, depending upon their adoption of new technologies. To help retailers understand the importance of implementing Conversational commerce into their retail strategy, here are some aspects where it makes a real difference:

Meeting the customer where they are

Messaging is one of the popular means to interact with one another and that’s how they prefer to interact with brands, too. Conversational AI allows retailers to tap into the most immediate form of communication i.e. messaging and reach consumers in a very convenient manner at a higher scale which was not possible before.

Moreover, with Amazon Alexa, Google Home and now ubiquitous technology in the home and office, as well as with the growing familiarity towards similar technologies, people are shopping with voice-based assistants in greater numbers.

Increased Customer Interaction with Conversational AI

Technology is evolving at a rapid pace, and both web and apps which were once quite the rage among retailers are now tools that are causing friction between the customer and retailer. Conversational AI has the ability to add a new layer of interactivity to online shopping.

It further enables a richer, more complex customer engagement, featuring personalized shopping assistants and concierge bots answering questions, recommending items, and handling individual transactions. This helps to personalize the digital experience at each touch point of the customer journey.

Conversational Design is the New Personalized Web Design

Like the human language, conversational commerce is flat. This allows brands to engage in real relationship-based commerce not usually achievable through websites and apps. While it has the ability to handle a broad set of commands, without AI, it lacks the capacity to understand complex inquiries.

 The integration of AI breaks down these barriers and retailers can turn towards messaging solutions such as chatbots and program them to echo the brand voice as well as provide a more personalized and positive experience unique to each customer.


Conversational AI has the ability to change the way all brands conduct business. It connects them with their customers more organically and creates personalized experiences tailor-made for every individual. This will serve as the first universal interface, increasing the efficiency among retailers and brands as well as maximizing profits.

 

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Conversational AI – Getting Started

Conversational AI: Getting Started

With the increasing list of benefits and a growing demand for voice interfaces, the retail space is abuzz with Conversational AI as a key component for any digital transformation strategy. From improving customer service and boosting online sales, to new ways of differentiation using voice interfaces, Conversational AI is becoming increasingly popular among businesses. As the demand for this technology builds, the question shifts towards how to get started. The technology available is vast, yet only a few platforms meet the stringent demands of enterprises.

By 2019, a large number of retailers with online stores will have voice search, as well as voice navigation, enabled onto their sites. Today, speed and convenience are the two most important aspects retailers keep in mind while creating a customer journey. Here, the Conversational AI interface delivers the most important aspect over any channel, personalized content, and human-like conversation - a friction-less experience.

Delivering a frictionless experience

Conversational AI has the ability to understand the consumer's wants over a simple voice recognition software. For instance, a customer can pointedly ask for an item such as - "I am looking for a red ball gown like the one that Halle Berry wore for the 2014 Oscars" and yield the results rather than just entering a search query for "a red ball gown". This allows the consumer to have a natural conversation like that with a human rather than a machine.

This, in a nutshell, is the difference between conversational AI and the voice recognition that most vendors offer. Conversational AI has the ability to understand the user, deliver personalized content using information received from the conversation, preferences, to deliver the response the user expects. If the virtual assistant has the ability to diagnose a problem, it should be intelligent enough to arrange the solution for the same.

Increasing Customer Engagement

Chatbots are the first thing that comes to mind when mentioning Conversational AI, but it is only the beginning of the journey - most retailers are moving further away from this step and using conversational AI to reach more consumers across multiple touchpoints.

This helps them improve customer engagement right from the initial interaction through the entire journey, providing a connection in the final product. This plays a crucial role when building customer loyalty in the already competitive landscape. Retailers that implement a digital assistant which can actually interact with the user through the entire customer journey right from the initial touchpoint to after sales care, enabling them to move closer towards delivering a frictionless experience to their customers.

Looking ahead

As the demand and expectations of the consumers grow, the impact of conversational AI will affect the retailer. Conversational AI is the next technology, from enhancing customer shopping journey and driving online sales, it is definitely a technology to watch out for.

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Voice-enabled chatbots vs Messenger bots: What you need to know

 Voice enabled chatbots vs Messenger bots: What you need to know

There are two distinct ways in which a conversational interface works: text conversations and voice. Consumers interact with chatbots using both interfaces on a daily basis, with each having it’s own set of advantages and disadvantages. The big difference between a messenger chatbot and a voice chatbot or a voice assistant is the way people interact with them. 

The major differences

A text based messenger exists in one or more messaging platforms, including features of SMS and web chat messengers. This enables users to interact on a device screen via text or button presses. However, in the case of voice bots, users interact with the bot using their voice in natural language. The voice bot then responds leveraging pre-recorded messages, text-to-speech responses or a mixture of both. 

A voice enabled chatbot can be called upon in many devices such as mobile phones, computers, smart speakers (such Google Home or Alexa), wearables (Apple AirPods) or other IOT devices. These chatbots enable users to accomplish tasks efficiently hands-free. The advantage of using a voice chatbot is its ability to exist on multiple messaging platforms, that can be synchronised across different devices. Some messenger bots are also available via smart speakers - which function like platforms themselves - enabling them to perform dual actions as a voice chatbot as well as text-based bots. This can be seen with Fitness Tips via Google Home.

Key Similarities

Given their differences, the choice of interface depends largely on the purpose and context for a chatbot’s use. While both these platforms enable users to accomplish tasks or find the information they need via natural language, text-based bots can double as a voice enabled chatbot where a user can dictate using their phone’s text-to-speech feature or the bot may be available as a skill integrated into a voice chatbot. While both types of bots depend on NLP to make sense of user input and provide a response, each type has its own set of challenges unique to the interface.  Some of them include text messenger bots understanding shorthand and typos - common to mobile users and voice chatbots understanding different accents across the world. 

One major differentiator between a chatbot and a virtual assistant is its accessibility. A messenger chatbot could be a better choice for consumers who prefer to chat via their mobile phones to get information about different products. Unless voice chatbots exist on a consumer’s phone or computer, interacting with it requires getting a new smart speaker device. However, it is the perfect choice for consumers who prefer to multitask and do not want to use their hands while accomplish a task. For example: It works well for consumers who are looking for the different ingredients for a recipe and can add the items onto the shopping list via voice while doing other tasks. 

Conclusion

Text messaging is especially popular as a mode of communication among Millennials, making messenger chatbots a more natural fit for communication with consumers. The ubiquitous nature of mobile devices further help lower the barrier of entry to the consumers while using a messenger bot. While text messenger bots are popular, voice enabled chatbots are not far behind, gaining popularity in the past year.

Ultimately, the choice of the interface lies with the retailer and the products it offers. Another aspect retailers may consider is the point of contact for the bot and the consumer. Retailers must understand their target audience and tailor make an assistant that can best help them execute the tasks they need to do. 

 

In the next article, we will explore more about how retailers can choose between Voice enabled chatbots and Messenger bots to enhance their customer experience.

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shopper with sunglasses and shopping bags

AI Is The Best Present For Retailers This Holiday Shopping Season

AI Is The Best Present For Retailers This Holiday Shopping Season

Brands are undergoing massive digital transformations of their own in order to keep pace with the growing demands and expectations. The customer of today is constantly connected to whichever device is closest at hand, and are using mobile devices as a means to solve real-world problems. Some of the queries naturally pivot towards local results such as - ‘where to get party costumes’, or ‘best Korean restaurants near me’.

For retailers, however, consumers are willing to wait for delivery if the price or quality of a particular product is better online. E-commerce provides the convenience of home delivery, free shipping options, and the ease of using voice-activated speaking assistants to accomplish shopping tasks due to which consumers are increasingly moving towards shopping online. Early AI adopters are already reaping the benefits such as gaining better understanding and visibility into their customers and increased productivity.

With the holiday shopping season beginning this week, here are some AI-powered applications retailers can leverage to create memorable customer shopping experiences.

Implementing AI-powered Chatbots to pave the way for meaningful shopping experiences

Initially, chatbots were programmed to execute a specific of queries and responses, but with enhancements in technology and AI, these bots are enabled to “learn” the site’s content and consumer preferences. This further helps them to bring back relevant answers and supplying potential customers with the right information.

This service element can be taken a step further with AI-powered shopping assistants. This was seen with Macy’s On Call - an AI shopping assistant leveraging Natural Language Processing designed to aid consumers with information in 10 of Macy’s retail stores around the country, as they navigated through each one. This is a viable direction for retailers to look into in the future. 

Optimizing the site content for voice search queries

As voice search grows, retailers are tailoring content enabling them to better answer queries and also demonstrate to search engines that their answers are the best choice to showcase as results. Voice searches are more likely to be from mobile consumers who are only looking for simple answers and don’t want to engage in lengthy interactions.

Retailers must create a strategy that maps the content through the customer journey with intent, context, and formatting for voice search. Furthermore, retailers must keep in mind to take a personalized approach, using a more personalized tone, mirroring back specific questions and limiting the answers to a sentence or two in order to better optimize for voice searches.


Combating abandoned carts with AI-powered shopping experiences

Shopping cart abandonment is an industry-wide problem. It is estimated that a whopper of 70%
online shopping carts are abandoned before customers complete their purchases.

A great application in this space can be seen with North Face which personalizes the customer experience by creating a psychoanalytic profile of customer data in near real-time. North Face’s AI assistant asks questions to understand how customers plan to use their apparel and then provides ranked product suggestions which save both effort and time for customers from browsing different websites to get the right product for their requirement.

AI can help make informed recommendations that reduce the uncertainty consumers feel while shopping for a specific product, thereby increasing their confidence and the likelihood of them proceeding through checkout. 

Stepping forward with AI in the future

With AI slowly becoming the new norm in the world of e-commerce and retail, several numbers of brands and retailers are increasingly adopting technological tools into their business processes and strategies. The concern of consumers over the safety and security of shopping online - which was a major concern for brands and retailers up until a few years ago, have given way to demands for more seamless, intuitive and personalized shopping experiences.

Retailers that have not adopted AI into their businesses, and are trying to understand ways to tap into the opportunities provided by AI, the holiday season is the best time to demonstrate these applications to help gather data to pave the way for the future.

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Kicking off Black Friday and Holiday Shopping with Artificial Intelligence

Kicking off Black Friday and Holiday Shopping with Artificial Intelligence

US retailers are making final preparations for Black Friday in both their physical and digital stores to support the expectation of high volume shoppers. 2018 holiday sales are estimated to climb between 4.3 and 4.8 percent over 2017 to between $717.45 and $720.89 billion – all due to the rising health of the economy, low employment records, and increasing wage margins.


While the economy has been improving over the past year, technology has also been making progress – both online and offline. This is especially seen with making more personalized recommendations through the use of AI and machine learning.

AI-Driven Personalization takes priority

With retailers increasingly leaning towards AI and utilizing AI-driven platforms, they are choosing more sophisticated platforms to make more personalized recommendations for their customers, ultimately increasing revenues for retailers and brands.
Some studies even concluded that brands that invest in creating personalized experiences leveraging advanced digital technologies and proprietary data for customers see a bump in their revenue by 6% to 10% – two times faster than those brands that don’t.


For the holiday season, and the upcoming Black Friday shopping, AI can be a wonderful tool used to automate the process of helping online and offline shoppers find what they want to shop for. Shoppers often have trouble finding a memorable gift for friends and family, but do not have a clear starting point – this may need browsing extensively through different e-commerce websites and searching through several aisles in different stores to find the right gift.

AI simplifies this process by giving retailers and brands the ability to ask their customers questions about their gift recipients and offering personalized recommendations based on individual tastes and preferences.
The use of AI-driven personalization for e-commerce channels has increased over the past few years, but according to experts, the future of AI is limitless – especially in the physical store. Furthermore, the future physical retail is believed to be a mix of the speed and convenience offered by AI with a human touch.


Customers want to be engaged through human interaction rather than special effects using light and sound, so retailers can do well to create community events and use data to offer personalized in-store experiences.

In-store Personalization to Support Retail Employees

As more and more technology is being integrated into the store environment, retailers need to move towards an autonomous management reducing the dependency on manual management by store staff. Recent studies even predicted retailers providing in-store recommendations to shoppers through AI engines to be the most mainstream in-store technology in the coming years.

Though AI is often pegged as a technology of the future, it’s a concept that is slowly taking shape and is not too far into the future. AI capabilities enable retailers to pursue customer personalization in real time – which will soon become a top priority becoming important for shoppers. The capability to display prices and promotions, which are subject to change, also coincides with the concept of a more personalized consumer-friendly store.

Conclusion

As we approach Black Friday – the official kick-off for the holiday shopping season, it will be interesting to see in which ways retailers and brands will leverage AI into their shopping strategies this holiday season. While personalized offers and promotions to enhance shopper loyalty will definitely be in the mix in the months of November and December, retailers can also take advantage of the data they receive to encourage repeat business throughout 2019.

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