Tag Archives for " Retail "

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Advantages of Leveraging User Generated Content for Retailers

Advantages of Leveraging User Generated Content for Retailers

Customer shopping habits have changed rapidly over the past decade. Customers today are looking for retailers that they can connect and engage with. Retailers must also change their marketing methods to interact with customers in an organic manner and provide an authentic brand experience.

One of the methods that is gaining popularity is the use of user-generated content (UGC). UGC is social media content created and shared between customers. Consumers want to be recognized for their input and as a result, retailers can benefit from the online discussion of their products. UGC can allow retailers to engage with their customers before, during and after a purchase. It provides customers with opportunities to promote the brands they like.

UGC in Marketing

UGC goes beyond mere likes or shares on social media. Customers often upload well-captured photographs or videos of the product. This content may even include comments and feedback about their experiences while using the product. Customers also leave reviews or offer recommendations of products they’ve purchased and used. UGC lets existing customers speak to future customers.

Given a choice between believing an advertisement by a retailer or a recommendation by another person, consumers tend to choose the latter. In fact, 93% of the consumers feel that UGC is helpful when making a purchase decisions.

Customers are continually bombarded by advertisements, but the impact is especially powerful when a customer feels a connection with another customer's experience. UGC works best when the message is real and authentic.

For example, cosmetics retailers benefit when their customers share pictures while wearing their products. Make-up artists are very popular on Instagram and they endorse the cosmetics brands they like. Followers of these make-up artists see these recommendations as credible and as a result, they are more likely to buy based on the recommendations. This user generated content showcases how the product works with a practical example, and without a heavy sell by the manufacturer/retailer. The result is that the credibility of the make-up artist is translated to the product and subsequently to the manufacturer/retailer.

UGC in Customer Retention

As a retailer, your job is to attract customers. However, it is equally crucial for you to nurture existing customers. How often are you creating rewards or special offers based on your customers purchase history? Have you considered utilizing UGC to showcase related or compatible products to existing customers?

You've made a big investment in attracting and nurturing your customer base. Don't lose it!

  1. Find UGC for your products where ever it exists and get it in front of new customers. Let your existing customers and fans do some of the hard marketing work for you. 
  2. Use social media to create an online community where users can discuss the products they’ve bought and offer recommendations to each other. Repost the best content on your official channels.
  3. Encourage customers to discover and share multiple uses of a product. Use UGC to showcase the versatility of your products. This retains old customers while drawing in. Retailers can also hold events like sales to boost customer interaction.

Artificial Intelligence in Curation of UGC

UGC is a key element of a successful marketing plan. With the growth of social media, content is being created every second. This can become an invaluable resource. The problem is that most of this content is uncurated as it's published. 

Learn to leverage Artificial Intelligence (AI) to augment your marketing team. You can employ AI to curate UGC and use it for your benefit, by helping your human curation teams to curate faster and with higher accuracy. A side effect is that you can quickly become aware of product issues or bad publicity from a user review, and avoid the business risk associated with that. New ways of employing the content are coming out as the digital channels continue to diversify. You must curate UGC if you want to harness its power.

Trust is key. The conversations initiated by UGC can boost your bottom line. Nurture and reward your best content creators, and you'll see your customer base grow.

Of course with a growing customer base comes increased brand awareness, customer reach, and revenue.

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Social Media: A New Way to Shop Online

Social Media: A New Way to Shop Online

 E-commerce has changed the way people shop, giving retailers and businesses new avenues to interact and engage their customers. Millennials are currently the most valuable target demographic for modern retailers. As they are also the most avid users of social media, a crossover was all but inevitable.  

Retailers, realizing the power of social media, have used these platforms for a while now to engage with their customers online. Social media also lets retailers market their products in a more interactive environment.

Recently, social media platforms like Instagram and Facebook have been investing in retail. This captures wide consumer interest and boosts both revenues as well as followers. As for retailers, they can use social media platforms to better understand consumer behavior and trends.

UGC or user-generated content is one of the most valuable data sources for retailers. Millennials put a lot of trust and faith in peer evaluation of products. Studies show that reviews and recommendations by fellow shoppers, rather than brand messaging, motivate customers to buy products online.

Here are a few instances of how social media platforms are leveraging the retail space:

Facebook Shop and Marketplace

Facebook has a feature called Facebook Shop. Retailers can add the Shop tab to their business page. It lets retailers display their products and sell directly from their company page itself. Considering the huge number of users on Facebook, this offers retailers a wide audience to convert into customers. Retailers can upload a product catalog to their page and customers can browse the products and buy them without having to leave the page. It also allows retailers to manage orders, and mark them as shipped or canceled right on the page itself.

Facebook also introduced Facebook Marketplace, an online market for retailers to display products. This offers free organic distribution for retailers’ products. It curates content and provides product recommendations based on user preferences and relevance. This ensures higher conversion from a user to a consumer. Like Facebook Shop, there is no listing fee involved. It is an online platform for retailers to sell to their customers.

#Instashopping on Instagram

Instagram is one of the most popular social media platforms with more than 1 billion active users; and about 60% or a whopping 600 million people, seek out and discover products on the app. Instagram has introduced shoppable posts which allow customers to go from discovery to buying without having to leave the app. Retailers can add up to 5 product tags per picture on business accounts only. They can only tag products from their Facebook Shop catalog. These tags redirect the customers to a product page that allows them to buy the products.

This seamless and hassle-free shopping experience has a wide appeal for the customers. In June 2018, they added Shoppable Stories which are an added advantage as about 400 million users view Stories every month. As of late 2018, Instagram also added a shopping channel to the Explore page, which is in its testing phase.

Shop and Cop by Snapchat

Snapchat  has introduced ads and product catalogs through its self-service ad buying platform. Recently, they released a dedicated shopping channel called Shop and Cop on the app which will feature exclusive offers and limited time deals through Shopify. These channels will be available in the Discovery section of the Snapchat app. Shop and Cop will feature social influencer posts and content. Shopify capabilities will allow in-app purchases ensuring a smooth shopping experience without the user leaving the app. Snapchat will curate products while Shopify will take care of the buying end. Moreover, Snapchat and Amazon have announced a partnership on a visual search tool. This will allow customers to use the Snapchat camera to search for products on Amazon.



2018 saw a lot of innovations with social media intersecting with e-commerce to give the rise of social commerce.  Retailers stand to make big bucks off of these popular platforms by better understanding shopper purchase behavior and using it to their advantage. They can conduct market research, market their products better and even sell, all in one place. With these innovations, it's only a matter of time before social media takes over the world of retail.

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The Rise of Conversational Commerce

The Rise of Conversational Commerce

Conversational AI is breaking the frontiers of retailer-customer interactions in today’s world. E-commerce used to be about selling products without offering customers a way express needs. However, conversations and personal interactions are essential in e-commerce. It helps retailers understand the customers’ requirements and offer customized products for them.

Today, about 5 billion users across the globe use messaging apps, with a fast-growing adoption rate. Social media and messaging apps are the preferred communication channels for millennials. Retailers can reach their customers by leveraging these messaging apps. Artificial intelligence is one medium that enables conversational commerce to instantly connect with customers. It helps engage and personalize communication to customers, thus driving sales for the business.

Conversational commerce is a two-way discussion between retailers and their customers through chat, messaging apps or voice technology, leading to a fruitful interaction that results in a value-based transaction. It allows retailers to create and nurture a relationship with their customers.

 

Enhancing customer shopping experience

Conversational commerce offers new avenues to connect with customers and improve the user experience.

Suppose a customer is planning to buy a Mother’s Day present, he/she would ideally step into a store and tell an associate about his/her requirement and get recommendations to buy an ideal gift.

Conversational commerce lets retailers take the learnings from the experience and automate the entire process. Retailers can integrate chatbots on their websites or use Virtual Personal Assistants to converse with customers through their online stores. From instantly answering questions to offering personalized choices, automation in commerce boosts interactions.

Additionally, conversational commerce can enable a follow-up experience for customers who abandon their cart. Instead of sending them an email that takes them through a long process to finish shopping, a direct message is a better approach. With a simple message, retailers can inquire whether the customer is willing to purchase the product, ask queries or requires to be reminded later. This allows the customer to take action and complete a purchase, all within the messaging app.

After a customer has purchased an item, retailers can notify about the shipment of the order, allowing customers to easily track their package. Once the order is delivered, retailers can connect with the customers to rate the overall experience, submit reviews or share pictures, with the click of a button.

 

Driving online sales

“Conversations are the driving force behind Conversions”

Customers want an easy and simple platform to purchase their desired products. Having a direct line of communication with their customers helps retailers make sure of sales. Conversational commerce is the way to engage customers at the point of sale to increase the rate of conversion. It opens the door for retailers to have a deeper interaction with customers during the crucial period between winning or losing a sale.

Conversational commerce reduces sales & support costs, overcoming the challenges of mobile browsing. It is a direct, personalized, dialog-driven approach to establishing long-term relationships, collecting data and increasing sales.

 

The purpose of conversational commerce is to provide personalization and convenience throughout a customer’s journey, from sales to service. It helps create a positive experience for the customer and earn loyalty. Moreover, it represents a customized online presence that lets customers request information they need rather than depend on browsing aimlessly through an online store.

Conversational commerce is a big deal as it represents a paradigm shift in the way retailers interact with their customers. It will have a huge impact on the entire customer shopping experience.

 

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Retail Trends Prediction for 2019

Retail Trends Prediction for 2019

From online shopping expanding to social media to customer-controlled delivery and returns, 2018 saw an exciting time in retail which drove a massive shakeup in the traditional retail landscape. Besides instant gratification, today’s connected customers expect newer experiences and hyper-personalization in retail. As we enter 2019, retailers are increasingly faced with the challenge to push boundaries and meet the evergrowing expectations of their customers.

Here are 5 retail trends that will gain momentum in 2019 -

Customer Datasets

Data plays a crucial role in the growth of any business. Accurate data will serve as a vital tool to enable retailers to understand customer behavior and reward their loyalty. This data will further enable retailers to deliver personalized shopping experiences to each customer. Retailers can target product offers more effectively due to access they have to customer datasets, AI and Machine Learning. Closer customer engagement will provide insights that can be valuable for retailers who want to grow their businesses. Forming stronger connections will be beneficial for both the customers and retailers.

Cognitive Technology

Cognitive Technology has led to automation across different parts of the retail sector, and this is a trend that will grow exponentially. Retailers will leverage cognitive computing to provide better customer service technology that can analyze enormous amounts of data. Retailers who integrate cognitive computing in their customer service can offer faster service. Setting computers and robots that can actually understand natural language and respond to simple questions will free up employees to attend to more complex queries. Robots can work alongside humans in many areas of retail increasing efficiency and productivity.

Voice-activated Shopping

As of 2018, Amazon and Google have together sold about 27 million voice-activated devices in the USA. 29% of these customers are already using the devices to shop online, and this number is slowly increasing day after day. Customers also expressed confidence in the recommendations provided by these digital assistants. These devices have a large following that can’t fully embrace their shopping abilities due to the lack of visuals. Companies are combating this by adding screens to their voice assistant devices. For example, Amazon Echo Show allows its users to view the products on the screen which increases confidence in their purchase.

Shopping anytime, anywhere

Consumers today are on the move and are going to expect services that could be available in transit. Social commerce will gain momentum by providing users/customers more choices for making purchases on the go. Retailers will be able to do targeted marketing and offer new ways to make online shopping more convenient, social and instantaneous. Customers will be able to shop from their vehicles while commuting. New innovative social commerce solutions will also emerge throughout 2019.

Augmented Reality

Augmented reality is quite popular with many apps that offer users a chance to blend the digital with the real world. These interactive experiences are not just fun but also offer retailers an opportunity to use them in shops. 

For example, if customers want to buy furniture, it could be quite difficult to picture the store items in their own house setting. If they pick wrong, they could end up ruining the aesthetics and ambiance of a room. Augmented reality apps can place 3D models of furniture right inside the customer’s home.

The digital furniture is also resizable and observable in any light, from any angle. Customers can even save room designs for the future. There is a huge level of customer engagement that can be achieved through these apps. They will allow customers to buy the best products and consequently come back to buy more in the future.

These trends may prove useful by providing retailers with some foresight. The landscape of the retail industry is evolving at a fast pace, and with Gen Z driving seismic shifts in shopping behavior, 2019 can be a very interesting year in retail.

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fork truck loading a pallet into a tractor trailer on a loading dock

Leveraging AI to Improve the Supply Chain Efficiency for Grocery Retailers

Leveraging AI to Improve the Supply Chain Efficiency for Grocery Retailers

Food companies are increasingly prioritizing supply chain transparency and efficiency. IBM expanded its food supply chain network, IBM Food Trust, with Carrefour rolling out the solution to all of its brands worldwide by 2022 and Topco Associates, Wakefern, and suppliers Beefchain, Dennick Fruit Source, Scoular and Smithfield joining the blockchain traceability program.

Half of U.S. grocery retailers are turning to artificial intelligence to improve supply chain efficiency. Nearly two thirds of the 50 retailers surveyed, most of which were grocery executives and managers, struggle with a disconnect between systems, and 48% rate their forecasting technology as average to very poor. While they would prefer that each supply chain component work together, few retailers have established a unified process.

The challenge for grocery retailers is that they lack connected systems, with consumers indicating they have separate demand planning, replenishment, allocation and order management systems for store and e-commerce orders. Combined with the fact that a small portion of consumers indicating they don’t manage each of their modules on the same platform, disparate demand replenishment systems appear to be a significant burden to efficiency.

Retailers are being pressured to push past barriers and produce more accurate demand forecasts. The pace of innovation is a significant issue, with 43% of grocery retailers saying their technology can’t keep up with business demands. Forty-two percent describe less-than-optimal synchronization between their inventory and channels, and nearly as many worry about fulfilment complexities, stocking inefficiencies and high product lead time.

When they do invest in needed technology, grocery stores are most inclined to spend on supply chain systems that increase stock availability and decrease stock holding, as 44% invest in new technology because their existing systems are unable to sustain new growth.

In an effort to keep reasonable service levels, food retailers often tend to overstock, but then over course-correct and understock instead. While 43% say they’re challenged by lack of real-time visibility of overall supply chain inventory, six in 10 say they are actively taking steps to address this hurdle and increase inventory visibility.

AI and machine learning hold a lot of potential to improve supply chain efficiency, and forward-looking retailers are already investing in these technologies. Grocery retailers say AI’s greatest potential to improve supply chain management relates to quality and speed of planning insights, while nearly 50% identified demand management as one of the top three areas for AI in the next five years.

One in three food retailers incorporate AI capabilities into their supply chain management processes, and one in four are working toward that goal. Artificial intelligence has the possibility to provide faster, more reliable demand insights, quality management capabilities and real-time updates along the way, the study noted.

Tree Branches

How can I use AI to Categorize Product Data

Is there a best way to leverage AI to categorize product data?

Have you ever tried searching for a product on your favorite online shopping site, only to be disappointed when you couldn’t find the product that you’re looking for? Most product site search engines leverage accurate product categorization attributes to help narrow the search results for a user.

In this article we’re going to look at the impact that proper categorization has on search and how it’s now possible to automate product categorization with a machine learning model.

What is Categorization?

Categorization starts with a well designed product category taxonomy. The product taxonomy defines how each product type is related. The first couple levels of a product taxonomy contain broad category labels. For a grocery taxonomy, the top levels might be organized by departments within the store. It’s a logical representation of the way that a shopper would look for a given product in the physical store. A taxonomy is often referred to as a “Product tree”, with each product category referred to as a “branch” and each individual item referred to as a “leaf” on that branch.

Grocery taxonomy example:

  1. Meat & Seafood

    1. Fresh Meat

      1. Ribs

      2. Smoked Ham

      3. Specialty Meat

      4. Kosher Meat

      5. ...

    2. Fresh Seafood

    3. Packaged Meat

    4. Packaged Seafood

  2. Produce

  3. Deli

  4. Bakery

  5. Adult Beverages

  6. Beverages

  7. Floral

  8. ...

For a new product to be put into the online product catalog, it first needs to be categorized appropriately into the correct level of the product taxonomy. This is easy enough for a human to complete the product categorization, however, when you have thousands and thousands of products, this can be a tedious process.

Why is Categorization Important?

The science of search has evolved over the last two decades. Trying to determine the searchers intent from one or two words is not a simple process. We’re not going to dive into that in this article. However, in the specific use case of product search for an ecommerce website, most shoppers will generally include the object of their intent as part of the search input. In most cases this data can be used to quickly narrow the results set based on the product taxonomy. After all, the consumer isn’t looking for organic lettuce in the seafood section, nor would they be looking for seafood in the produce section. So one method to quickly close the search breadth is to narrow the search to specific sub-branch of the product taxonomy.

One downside to improper categorization is that improperly categorized products can become “lost”. When a product is mis-categorized on an improper branch of the taxonomy, the search engine may either (1) not find the product or (2) relegate the mis-categorized product to the bottom of the search results.

Don’t believe me? Try this: go to your favorite ecommerce provider, search for something, and then go to the last page of the search results. What do find there? Don’t let this happen to your product catalog.

In addition, the product category for a given catalog item can help define the product schema that should be employed to display the product information for the consumer on the product data page. The schema can also help define the meaning of generic product attributes, depending on the product type.

What is ATOM?

ATOM is the product categorization service from IceCream Labs. We developed ATOM as an API service which can be accessed automatically from your product information manager. ATOM takes a product title or description as an input and outputs the recommended product category for the item. ATOM is powered by a machine learning model that has been trained on millions of product records. It’s constantly learning as it processes new data.

With ATOM, you can properly categorize or validate a new product item before accepting it into your production product catalog.

To learn more about ATOM, or see a demo, contact our sales team: sales@icecreamlabs.com

PHOTO CREDIT: Photo by Min An from Pexels

three trays filled with salad ingredients

Personalization: The important role it plays for Grocery Retailers

Personalization: Why is it important  for Grocery Retailers


In today’s hyper-local and hyper-personalized consumer demands, delivering a tailor-made and individualistic message becomes extremely important.

They can be put off by irrelevant messages and the likelihood of them seeking products elsewhere increases. They want to buy from innovative companies who create better experiences tailored to their preferences and previous behavior.

While grocery has often been a leader in data and personalization, the focus was not entirely on creating a genuine and valuable customer experience.

To keep up with the ever-changing customer expectations and to stay a step ahead, food companies need to facilitate a consumer’s needs before they arise, and the retailers that capture on this trend, are more likely to succeed in the future.

Personalized recommendations is not a new concept. Spotify creates playlists based on songs that a user has previously enjoyed and Amazon’s recommendations based on previous purchases.

Personalized recommendations are not news. YouTube is recommending which songs we should listen to next, Spotify is creating playlists based on songs we enjoyed in the past, what day of the week it is or time of day, Amazon is letting us know which books we might like based on what’s in our cart, but we feel frustrated if the recommendations feel impersonal.

In a society with a unique sense of self, search with the term “for me” is growing exponentially and food companies are looking for ways to create food recommendations that will not let the consumer down.

Grocery retailers have recognized the need for creating personalized shopping experiences as well, but are still struggling to implement every step of a connected and delightful consumer journey.

Leveraging both the data provided by the consumer and past purchase behaviors can help grocery retailers deliver more personalized and meaningful shopping experiences, thus increasing customer loyalty and basket size.

In the blog post, we will explore more about why consumers expect Personalisation from grocery retailers. 

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Leveraging AI and Machine Learning for Product Matching

Leveraging AI and Machine Learning for Product Matching

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.

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.


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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, for example:

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.

<Phone model images>Samsung Galaxy Note 8 (US Version) Factory Unlocked Phone 64GB – Midnight Black (Certified Refurbished)Samsung Galaxy Note 8 is the phone model, and the title provides additional information like the memory size, US version, Factory Unlocked Refurbished, etc. 

Identifying and sorting product matches 

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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pepper the robot

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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