Robyn Fadden

How Review Data Is Changing Retail [2022 Report]

Robyn Fadden — Wed, 15 Jun 2022 13:50:59 +0000

Today, as more and more retail brands face a struggle for survival —shuttering their brick-and-mortar stores and losing revenue in the process—making the shift from in-store to online sales has never been so urgent and so necessary.

Even without calculating for the current pandemic upending retail on an unprecedented scale, double-digit growth YoY in the eCommerce share is set to represent 22% of USD 29 Trillion global sales by 2023—and 57% of global sales growth is expected to be driven by this increase in online purchasing. Almost overnight, brands have become reliant on online sales.

1. Review data is available—and so is the solution to higher sales

Brands that stood out as the forerunners of online sales are rushing alongside late-adopters to embrace a new mode of online marketing and selling. What does that new mode look like? It’s going where consumers are and asking where people are shopping online, how they’re weighing their purchasing decisions, and why they’re buying. This new mode of marketing and selling taps into a wealth of online consumer review data that’s already out there—and growing.

Review data shows brands what consumers are saying in their own words, without lag time and without the biased prompting of surveys that many consumers won’t answer—only 19-26% of buying customers who receive surveys from retail companies respond to them. Consumers’ sudden reliance on online shopping coupled with a decrease in people’s trust in advertising (83% don’t trust advertisements) means brands can’t afford to not be in control of their reviews and those reviews’ value to marketing.

Sophisticated AI customer feedback management solutions put review data front and centre as a marketing channel for community management and consumer insights. Those insights directly and accurately inform marketers so they can craft a customized e-commerce environment tailored to engagement, conversions and high sales. Are retail brands ready for this transition today? The reviews are out there waiting for brands to make a move towards stronger, sales-driving connections with their customers. Equipped with the ability to analyze reviews through an AI solution, brands can leverage each and every customer review today.

2. Where data fits into retail industry challenges

Right now it is imperative for brands to connect with consumers online and engage them in purchasing. In 2018, online reputation management was already a priority for 97% of retail businesses. Now, online marketing and sales is everyone’s priority.

Customers are now being hit with more advertising, email blasts, pop-up deals and other brand marketing in every facet of their online lives. With all that noise, it’s more difficult than ever for marketing to get their message through. And even then, is the customer listening?

The retail industry’s embrace of digital avenues and big data has led to more personalized and customizable marketing strategies that make use of, for example, real-time recommendations for customers as they online shop and human-like chatbots that support customers in decision-making and purchasing.

Yet even with these advances some things remain the same: people still buy on emotion and justify with logic. Emotional decision-making has its own, personal logic and richly diverse context. Today, marketers who understand this are tapping into new data-driven ways to emotionally connect with customers and implementing an unprecedented level of personalization in their marketing strategies.

3. Inside the customer feedback gold mine

As online sales rocket, so do online reviews. Detailed, varied and unsolicited, this customer feedback comes from both satisfied and unsatisfied customers, who openly share their opinions about products and their buying experiences, often explaining exactly why they suited them or didn’t.

In reviews, customers don’t only talk about products, they talk about how these products fit into their lives. In essence, they talk about who they are—from simple likes and dislikes to lifestyle preferences. Looked at individually and in targeted groupings, these reviews provide reasons for purchasing, repurchasing or dissatisfaction while also providing an even more valuable emotional, psychological context for other consumers and for marketers.

Feedback is not a goldmine of information for information’s sake; it’s a goldmine because customer reviews impact buying decisions. A goldmine because it describes habits, routines, lifestyles and preferences. That form of feedback is the kind that makes a significant difference when converting customers and maintaining customer loyalty.

Every piece of feedback is a business opportunity

Customers regard brands and even their related companies as more than manufacturers or service providers: they form value-based and emotional relationships with them. Customer feedback has become an integral part of that relationship, with hundreds of thousands of customers sharing their thoughts in reviews on multiple public websites.

91% of people regularly or occasionally read online reviews, while 84% of those people trust reviews as much as a personal recommendation.
95% of consumers are influenced by online reviews for their purchase.
A +0.1 star rating can increase conversion rates by +25%.
A brand with excellent reviews can experience a +31% spend increase.
More than four negative reviews about a company, brand or product can decrease sales by 70%.

Today, people relate to reviews to connect with the reality of a brand and its products—they’re on the search for other people’s opinions, from how they use products to what they think about the brand itself. They’re also looking to see if brand promises really do come true: 55% of consumers believe the best way retailers can build trust is to “deliver what they promised,” while 67% won’t buy if they find out brands don’t live up to their promises. In the process of reading and writing reviews, consumers are making an emotional, trust-based connection with each other around a public conversation about brands.

Considering the high impact of reviews on conversion rates, strategic marketing campaigns and advertising may be in vain without a comprehensive and accurate way to manage and leverage customer feedback.

4. Why review data should lead marketing strategies today

A 2019 Forrester whitepaper found that the way customers think and feel predicts how they act—this finding empowers brands to understand the “why” behind business outcomes. Since every consumer decision is motivated by emotion, then emotional connection is the most effective way to connect and build trust with customers.

Customers reviews are as effective as word-of-mouth

The fact is, though some customers writing reviews want brands to listen to them, many more are using reviews to speak to their peers about how they think and feel about products. People want to hear from each other about their lived experiences with products—and people trust each other more than they trust brands and their products.

These online reviews should be understood as peer-to-peer conversations and valued for the insight they can provide to marketers. In online reviews, people reveal their realities, from their wants and needs to their everyday struggles.

So it’s no surprise that so many customers consult reviews before making a purchase. Reviews provide a form of peer-to-peer guidance. The information in reviews is perceived by consumers as less biased and more trustworthy than a brand’s advertising.

Currently, 83% of customers don’t trust advertising and most of those customers choose to pay attention to—and trust—other customers’ peer reviews online:

94% of consumers trust social media influencers more than a friend for their purchase.
74% of consumers rely on recommendations shared by influencers for their purchase.
73% of consumers think written reviews are more important than star and number ratings.

On top of that, consumer conversion rates also increase when brands themselves engage with online reviews. A brand that responds to 32% of customer reviews may see 80% higher conversion rates than a similar sized brand replying to 10% of reviews.

All contextual review data can drive conversion rates

All reviews, and especially negative reviews, have the capacity to increase conversion once their data is taken into account. In reviews, customers justify their likes and dislikes of products, adding valuable personal context to their reasoning.

Every review can represent a potential sale—it’s all about context. Even a negative review can be understood as a positive: when one person writes, “This face cream makes my oily skin even oilier!” a consumer who is looking for a product to moisturize their dry skin might just add the face cream to their online shopping cart.

Catching those nuances of contextual detail in reviews is where a feedback management solution makes all the difference to understanding customers and what makes them buy. AI feedback analysis speeds up and focuses this process in a situation such as today’s, where time is of the essence.

Beyond analyzing customers’ online reviews, a fast and effective AI customer feedback solution solves retail brands’ short-term needs for connecting with customers in a time of rapid change to the retail sector, with its channel shift from in-store sales to online purchasing. In the longer term, AI data analysis helps companies readily adapt to future changes in the retail sphere, letting a brand keep conversion rates up while other players try to weather the storm.

5. How public, freely available review data connects brands to customers

The massive surge of digital data in the expanded online retail sector has resulted in a wealth of information about who’s buying what brands are selling and why. While the goldmine of customer feedback is out there, the sheer amount of it can seem overwhelming to even the most seasoned marketing strategist.

The volume of unsolicited feedback is a double-edged sword for brands: both a new opportunity to get to know and connect with customers and a challenge in managing customer expectations and ensuring that their requests and problems are satisfactorily addressed.

However, looked at through the lens of AI data management, that feedback is all opportunity. Sophisticated AI text-analytics provides insights to take advantage of opportunities and resolve challenges via a data-driven marketing strategy. Those thousands of reviews that once seemed inaccessible and overwhelming will launch brands towards greater engagement, increased sales and brand growth.

Under the hood: Why does text analytics matter for deeper customer insights?

Because it powers sophisticated feedback management solutions.

AI text analytics might seem like some kind of abstract, complex technology, but fundamentally it serves as the engine behind Keatext’s powerful customer feedback management solution.

Here are just a few things it allows brands to do:

Uncover the meat and nuanced sentiment behind customer reviews.
Massively expand feedback analysis capacity to thousands of data points per minute.
Continuously gather data about customer sentiment at multiple points throughout a product’s lifecycle, including pre and post-product launch.
Monitor, reply and engage with negative feedback in real time.
Understand and anticipate upcoming trends and pain points before they impact a brand’s reputation and identity.

Data for today’s strategies and the long term

AI data analysis is a key business development tool for staying in immediate and lifelong touch with customers’ needs and behaviours. When applied to publicly available product reviews, data analysis opens the door for brands to tap into customer preferences and purchasing behaviour as they change with global news and trends.

The outcome of applying AI text analytics to these reviews is undeniably powerful: a precise identification of consumers’ needs, wants and motivations. The challenge is to use the available data to sell products and services so a brand can increase revenue and sustain consumer relationships.

The most popular tools today for understanding customers, such as web analytics tools, focus on quantitative data. On the other hand, customer feedback from online reviews is a mix of quantitative and qualitative data, with the emphasis on unstructured, qualitative data that is submitted with little to no prompting by marketers.

Data gathered through expensive approaches such as surveys provide relatively small datasets to analyze, whereas millions of online customer reviews can be accessed and analyzed quickly thanks to AI’s Natural Language understanding technologies. These online reviews still provide qualitative first-person data on consumers yet can be analyzed as massive data sets to reveal the personal, emotion -based logic of decision-making and purchasing.

Best AI practices in marketing

Embrace customer feedback, especially negative feedback—this data provides valuable insights on customers behaviours and pinpoints what resonates most with them.
Identify points in the customer journey where a company can gather and analyze customer data sets, such as reviews and other feedback.
Combine small data with big data’s broader metrics and insights to boost the success of marketing strategies and link them to a company’s bottom line.

6. Move into the future of retail with AI analysis

With new integrated digital technologies and advances in online purchasing platforms, the retail sector continues to change rapidly. For many retail industry players, this change is serving as an inspiration for finding innovative ways of doing business.

Digital data has opened multiple windows into the complex arena of customer needs and behaviors. As a tool that can take a brand’s retail marketing strategy from being a response to industry trends to being a trendsetter, a comprehensive AI customer feedback solution stands out as the most advanced, future-oriented choice. With data-driven insights, brands can accurately and confidently strategize for higher sales and brand growth.

The post How Review Data Is Changing Retail [2022 Report] appeared first on Keatext.

How to Boost the Power of Consumer Feedback [Guide]

Robyn Fadden — Mon, 07 Mar 2022 14:46:36 +0000

Consumer feedback is and always has been a powerful tool for improving products, service and customer experience. Today, the impact of COVID-19 across the retail and CPG sectors has made consumer feedback more important than ever. At the same time, feedback data has become more accessible, plentiful and packed with information on consumer behaviour—organizations simply need the right tools to uncover valuable insights within the data.

1. Where feedback data fits in

Meeting consumer needs has become more complex with advances in technology, social media engagement and the increased number of consumer channels. At the same time, all that change has resulted in a wealth of data. One of the biggest questions now is how to harness that data to uncover actionable insights into consumer behaviour. New data analysis technologies have made the link between consumer feedback and a company’s bottom line even more clear.

In an era rich with data, businesses in almost every industry are working hard to “unleash the power of data” across every part of their organization. To learn more about what consumers think of products, services and brands, organizations need to implement dynamic instrumentation for listening to customers, learning from them and reflecting consumer priorities within brands and companies themselves. As part of a broader organizational data strategy, that results in the kind of actionable intelligence that helps key decision makers and stakeholders make better, faster decisions.

Trust in the eCommerce era

Word of mouth has been around for centuries: people have always looked to their peers’ opinions and experiences when making a decision about something they’re unfamiliar with. When consumers are trying to figure out what products or services will suit them best and whether to purchase, they show this same innate need for trustworthy information. That hasn’t changed with the growth of digital channels and eCommerce. In fact, the power of word of mouth has become a driving force behind consumer decision making.

In a post-pandemic world, where the in-person shopping experience has drastically changed and the adoption of online shopping has accelerated, consumers rely even more on online peer reviews to make their purchasing decisions.

Instead of picking up the phone to call a colleague or talking over coffee with a trusted friend, people more often turn to the internet—particularly social networks—as their source for trusted opinions, reviews and advice when shopping for any type of item.

These trusted words from online strangers have become an incredibly important part of the buying process:

95% of consumers read online reviews before making a purchase.
76% of consumers trust online reviews as much as recommendations from family and friends, while 91% of consumers ages 18-34 trust online reviews just as much as personal recommendations.
That same research shows that the average consumer reads 10 reviews before they feel they can trust a business.
And those consumers spend 13 minutes and 45 seconds reading reviews before making their decision.

Post-pandemic consumer trends

Needless to say, reviews play a crucial role in driving sales in today’s hyperconnected, multi-channel landscape. In a post-pandemic world, where the in-person shopping experience has drastically changed and the adoption of online shopping has accelerated, consumers rely even more on online peer reviews to make their purchasing decisions.

From April 2020, researchers have measured:

a 129% year-over-year growth in U.S. & Canadian e-commerce orders, and
a 146% growth in all online retail orders.

Engagement with reviews has climbed in line with increased online sales volumes, with shoppers heavily relying on review content to make purchasing decisions, especially when it comes to products they haven’t previously had to buy before, online or otherwise.

When consumers are less inclined to go into a store to physically experience, inspect and research a product, they turn to the next best thing: online reviews. As brands plan for the future, it’s important to pay attention to these new trends that are set to outlive COVID-19.

Leveraging quality consumer feedback to drive sales

As people increasingly rely on ratings and reviews to justify their purchasing decisions, companies should be giving consumer feedback the same weight as their customers do. How a company drives and leverages consumer feedback can be completely customized, including how the company engages with and relates to customers, what marketing and service channels encourage customer feedback, and how easy it is for customers to give feedback.

To get the most from feedback data:

choose the right method of collection,
choose a syndication platform,
study the findings.

Collect feedback data using:

Post-interaction emails
Product sampling
Contests and sweepstakes
Online reviews
Social media interactions

Optimize current channels, including product surveys, ratings and reviews, social engagement and product testing by asking the following questions:

Are current marketing channels efficient and approachable from a consumer feedback perspective?
Can you deploy product surveys to get quick feedback?
Do you have a place that syndicates and analyzes the survey data you collect?
Can consumers share their reviews and your product with friends and family on their social channels?

2. Where feedback begins: Trying, testing, sampling

The value of consumer feedback for engaging consumers, understanding customer behaviour, and driving sales during a pandemic is clear. And while gathering and analyzing a diversity of high-quality feedback has become much faster and easier thanks to new technologies, what about getting products into customers’ hands these days?

Prior to the pandemic, companies had several avenues for consumers to trial or purchase samples of their products: in-store and online sampling, experiential marketing plays, store promotions and more. With brand representatives and customers alike spending much more time inside their homes, getting products to consumers for testing—and in turn, receiving authentic feedback—has become a bigger challenge.

Fostering relationships from a distance

Instead of pulling back on marketing spend, brands need to reach consumers wherever they’re at, whether that’s at home online, in transit or on their lunch break at the office. Creating and maintaining genuine relationships with consumers while fostering brand advocacy requires diversifying the marketing mix and opening more direct lines of communication.

One of the ways that brands can reach and engage their targeted consumers from a distance and in a memorable way is by simply putting their products into their hands. By leveraging digital product sampling, brands can target consumers online and deliver product samples directly to doorsteps, allowing customers to physically experience the brand from the comfort (and safety) of their own homes. Personalized sample-matching and delivery company Sampler has found that 1 in 4 consumers who get a free sample leave a review of the product.

Increase the likelihood of receiving reviews by optimizing all feedback channels to:

Allow for consumers to try a product,
Easily leave reviews,
Share feedback with friends.

Free samples = major feedback

By leveraging digital product sampling, brands can target consumers online and deliver product samples directly to doorsteps, allowing customers to physically experience the brand from the comfort (and safety) of their own homes.

Companies can also integrate product sampling into current marketing channels by adding a sampling CTA to existing ads or influencer marketing campaigns. These are just two ways to populate ratings and reviews quickly for new items or to refresh the reviews on established SKUs.

For example, o.b. decided to address a millennial target audience by focusing on tampon users who were new to o.b.’s non-applicator product. The brand launched social media ads and influencer video segments in tandem to promote their free product offer. They used digital product sampling to gather product feedback and sentiments from trial users on their experience.

Some key results from the o.b. strategy:

64% of net new consumers had never tried o.b. previous to trial.
82% said they were likely or very likely to recommend to a friend.
55% of Samplers converted to the brand’s CRM (allowing them to continue the conversation even after the program).
45% participated in a follow-up survey.
39% have converted to a purchase since trial.

The big takeaway? Consumers who watch influencer videos are 1.5 times more likely to convert to rating and review:

45% of participants left a review.
85% said they would likely recommend o.b. to a friend.

By adding a product sampling call to action to their influencer program, o.b. was able to empower their consumers to become micro-influencers themselves.For brands gathering early product feedback, it’s important to understand that consumers prefer to have an impact on product development and have an avenue for leaving their feedback on the sample they’ve tried.

Even in the current “stay-at-home” landscape, brands can reach and delight consumers with something as simple as a free product sample delivered right to their doorstep. The result is more engagement with customers coupled with more ratings and reviews that can both influence future buyers and be analyzed as data for marketing, CX strategy, R&D and more.

3. Making consumer feedback actionable with AI data analysis

The increase in consumer feedback on multiple channels has brands asking how they can leverage this data to create more effective customer experience and marketing campaigns and to ultimately boost sales. Able to tackle large amounts of feedback data, AI-powered data analysis helps organizations better understand and strategically act upon consumer reviews in meaningful, relationship-building ways.

Instead of spending a huge amount of time traditionally gathering consumer feedback and analyzing customer behaviour, brands can access readily available online feedback and use new AI-powered analysis tools. Keatext’s platform uses artificial intelligence and natural language processing to extract sentiment and insights from consumer conversations.

Able to tackle large amounts of feedback data, AI-powered data analysis helps organizations better understand and strategically act upon consumer reviews in meaningful, relationship-building ways.

That is, Keatext’s AI analyzes text-based feedback from any source of product surveys and reviews, such as Amazon, Sephora, Influenster or a brand’s own site, and quickly processes huge volumes of feedback. Brands can use these insights to concentrate on what actions to take to increase sales or improve customer experience.

Trust in advertising vs. trust in reviews

As the pandemic increases people’s time spent online, consumers are now being hit with more advertising, email blasts, pop-up deals and other branded marketing in every facet of their online lives. With all that noise, it’s more difficult for marketing to get their message through.

According to a 2019 report, 83% of consumers don’t trust advertisements while 76% do trust reviews. Reviews have the potential to help organizations build their brand, advocate base, and customer base, and directly increase sales. At the same time, bad reviews can hurt a brand.

According to Forbes, 94% of consumers avoid a company that has bad reviews. Even just a handful of negative reviews can decrease sales by up to 70%. Yet engagement can make a big difference: 45% of consumers say that they’re more likely to visit a company that responds to its negative reviews. Reviewers are also 70% more likely to change their review if brands respond within one hour, 40% if brands respond within 24 hours, and not at all after that.

The benefit of responding to customer reviews goes beyond immediate changes in customer behaviour. Connecting with customers builds longer-term trust in a brand. With 25% of customers saying they would pay more for products and services from brands they trust, the value of engagement is, in fact, measurable.

Striking gold in consumer feedback

While a goldmine of consumer reviews is out there online, the sheer amount of data within all those reviews can be overwhelming and impossible to manage in a timely and costly manner. Not only is the volume of feedback information exploding, especially in light of how the pandemic has reshaped online purchasing behaviour, but the sources of customer reviews have multiplied.

While many organizations are embracing a strategy around online review production, they often haven’t ventured too far into the next phase of feedback management: gathering and analyzing review data. Understanding why customers are saying what they’re saying results in actionable insights that can be used to shape strategy and increase revenue.

Managing reviews through engagement

Online review data collection should be as exhaustive as possible, from all eCommerce sites and any other sources of consumer feedback that a brand has access to. Analyzing the voice of the customer results in insights that go beyond sentiment to reveal the reasoning behind customer opinions and behaviour. Insights also let brands build personalized experiences, identify trends and pass on customer expectations to the rest of the organization.

Data analysis of feedback lets brands understand what customers think and feel, opening the door to authentic engagement with customers and the trust building that comes with that.

Data analysis of feedback lets brands understand what customers think and feel, opening the door to authentic engagement with customers and the trust building that comes with that. Consistent online engagement with feedback can turn a negative review from a detractor into a positive review from a returning customer. It can also create a connection with advocates, helping to build the brand and ensure its resilience.

The rapidly-produced insights from data analysis allow companies to:

Always respond, and respond quickly,
Be authentic and personal,
Establish a connection
Always show empathy.

This feedback management process is even more important when customer expectations or distribution channels are changing rapidly, as is the case right now. Companies that start including review management in their digital strategy today will ensure that they’re equipped to leverage an ever-increasing amount of online feedback.

A data-rich feedback management process can help companies drive their brand image in tandem with consumer trends and be ready for Gen Z, the first digital generation of consumers eager to reshape the future of brand connection.

In unprecedented times like today, nobody can predict what customers will need or prefer, yet listening to customers has never been easier. Analyzing review feedback and responding in personalized ways to customers results in conversation, connection and an evolving customer-brand relationship.

The post How to Boost the Power of Consumer Feedback [Guide] appeared first on Keatext.

How to Choose AI for Customer Experience [Guide]

Robyn Fadden — Tue, 22 Feb 2022 19:12:33 +0000

Artificial intelligence is officially in business. Expanding on traditional CX metrics, such as surveys and Net Promoter Scores, sophisticated artificial intelligence (AI) tools dig into feedback data from hundreds of thousands of customers, across multiple channels, providing CX teams with meaningful, actionable insights into customer behaviour.

The capabilities and scope of AI have increased multifold in the past five years. While machine learning and natural language processing have been on the rise for decades, their big successes primarily occurred in the realms of academia, R&D and industry operations. Today, AI platforms cross sectors and industries, applicable to every department in a company and affordable to small and medium-sized businesses. AI tools can learn, be trained, process algorithms, gather and analyze data, even make predictions.

According to recent Forrester research, companies that leverage advanced insights-driven business capabilities are 2.8x more likely to report double-digit year-over-year growth than firms that have only started to employ AI tools. That same research shows that more than 60% of global data and analytics decision makers planned to increase their data management and analytics budgets from 2020 to 2021. Meanwhile, AI technologies carry the potential to have a huge impact on CX as a field and on the companies who leverage AI for their CX initiatives and strategies.

“AI gives you the ability to mine for pieces of gold in data for the really insightful patterns within your customer behaviour,” explains Certified Customer Experience Professional and current President & Chair of CXPA Toronto Lawrence Levinson. “That forces you to look at yourself from your customers’ perspective, in ways you might not have before.”

Though AI has entered the mainstream across industries, only 34% of consumers say they think they’ve interacted with AI technology—yet when asked about the technologies they use in their daily lives, it turns out that 84% had recently used at least one AI-powered service or device.

AI tools in everyday use today include:

Chatbots and related customer-service and sales automation
Augmented and predictive recommendation engines, search engines, and virtual assistants
Data gathering, sentiment analysis and related insights
Other machine learning platforms, including customer need prediction and empathy-oriented AI.

These tools identify patterns and trends in how customers interact with a company—some aggregate and track data, while others delve into analyzing qualitative data, namely information supplied by customers in their own words.

With more companies across industries adopting AI tools into their customer-facing strategies, it’s no surprise that both customer expectations and customer behaviour have changed accordingly. Today, CX is completely intertwined with AI by necessity. Thankfully, CX professionals now have more AI tools than ever to enhance and deliver effective strategies. This guide to AI and CX outlines how to bring AI into every aspect of customer experience and use these tools to design and deliver empathetic and meaningful experiences across the customer journey.

1. Defining AI through a CX lens

By aggregating different forms of customer data from several channels—such as long-form answers from survey programs or verbatim reviews on social media—organizations have the opportunity to see recurring emotional patterns in their customer journeys. “This data can show the friction points for a business, whether it’s already a customer-focused organization or not,” says Levinson.

With the right AI tools, companies can use data to reveal patterns and insights that CX teams might not have thought about before. This is especially valuable for understanding the significant emotional component of the customer journey, including how people feel about a brand and why, as well as when in the journey their emotional engagement changes. Emotional feedback is central to solving the experience equation and generating the detailed insights needed for informed decision-making.

3 crucial uses of AI for CX:

Providing insights: Numerous AI-powered platforms are focused on gathering, aggregating and analyzing data to help CX teams identify customer insights that can shape CX strategy, inform sales, customer service and other departments, and help companies unsilo their data to make holistic business decisions.
Enhancing interactions. When companies understand their customers better through data, they can create engaging brand experiences that resonate with them from service queries to making purchases.
Automating processes. From workflow to customer service and sales interactions, AI automation saves companies time and money that can be reinvested into creating more effective CX strategy with greater ROI.

New AI tools for CX can:

be implemented throughout the customer journey,
enhance CX that contributes to company growth,
solve critical problems around customer acquisition, retention and loyalty.

2. Charting a personalized customer journey with AI

With more customers engaging with brands through multiple channels online and offline, the customer journey map has become more useful, more elaborate and nonlinear. In other words, much more akin to how people behave and as unique as every customer. While the digital customer journey now feels more relatable and human, the solution to mapping it and making sense of every interaction depends on machine learning that allows CX professionals to personalize the journey and meet customer expectations. With 80% of customers more likely to purchase a product if brands offer personalized experiences, the link between CX, AI and business outcomes is clear.

A customer experience map using AI tools for every touchpoint, channel and device

Today’s customer journey takes many twists and turns, from online research to in-store interactions with customer service or from a chatbot query to an e-commerce purchase. Some customers might browse product catalogues on their phones, but only buy when they’re on their desktops, while others feel most at home using major retail marketplaces for the majority of their purchases. Reaching customers where they’re at and giving them choices for engagement relies on using data intelligently. Here are several steps along the customer journey where AI tools create personalized engagement with brands and retailers.

Discovery and inquiry

Search queries and predictive analytics to identify interests, develop awareness and help with product and service recommendations.
Website activity tracking to direct customers to relevant sites.
Proactive messaging to showcase products, sales or other incentives.

Exploration and comparison

Search comparisons and recommendations to give customers more relevant product and services information, options for comparison and other recommendations. This ensures higher conversion rates, customer satisfaction and upselling, as well as lower return rates.
Life-like images, video and virtual reality options to let customers see how products and services fit in their lives and spaces.

Evaluation and purchasing

Data gathering and analysis to learn more about customers and provide more personalized experiences, including products, upgrades and other recommendations.
Chatbots and other virtual assistants to quickly answer the most common customer questions on products and services, ensuring higher customer satisfaction.

Retention and support

Qualitative data analysis and natural language processing to respond accurately to customer questions empathetically.
Data gathering and analysis tools to respond to customers’ website and app activity and identify customer problems.

Feedback and advocacy

Insights-oriented sentiment and data analysis to understand customer behaviour and respond accordingly.
Feedback solutions to engage customers in conversation with brands and with other customers, determine the real why behind customer problems and deliver individualized responses, whether automated or with service and sales agents.

3. AI platforms tailor-made for CX

How a personalized AI search tool gets CX results

New AI platforms are making sure that every website search, ecommerce search, chatbot query or voice assistant question returns a relevant answer and furthers customer engagement. As a part of CX, the internal search function relies on quality data and intelligent tools to harness that data. AI-powered search company Coveo is responsible for a relevance platform that is changing the way businesses integrate data into customer experience and bring product and content together in the right space in the right place in the customer journey.

Coveo’s machine learning models are capable of making data more meaningful to customers and to companies who invest in data-driven CX. That means using data to give people the answers they need, alongside relevant content recommendations, wherever they are in their digital journey with a brand. The result is a more personalized, satisfying and successful retail experience.

Coveo has focused on being a layer of technology that enables any enterprise to not worry about the structure and form of all of their content. Rather, Coveo gathers that information together from its enterprise search background, connects it and unifies that data into a common index. As with customer service-related searches, the search box of an e-commerce site or a retail company’s online store is a way for customers to interact in their own words with a company—the difference is that this AI-powered search covers all product details and related relevant products, and leads directly from brand interaction to increased sales.

“Our view is that you have all these people, all this content and modes, so why not use machine learning to give you the best chance of getting the most relevant content to that individual wherever they are on that journey.” – Marc Floisand, Coveo’s SVP Product & Industry Marketing

Fasken rebuilds their online customer experience

PROBLEM:

Global law firm Fasken was struggling with a mess of unconnected data silos outside of their Sitecore instance. They were stuck with a limited site search that was unable to contextualize their 40,000+ documents depending on the intent of their visitors.

SOLUTION:

They implemented Coveo directly into their existing Sitecore UI, using Coveo’s out of the box connectors to expand their search index outside of just their Sitecore content, to reach across the organization. Then, leveraging Coveo’s artificial intelligence alongside Sitecore XDB, they were able to contextualize their content based on a user’s intent while on the site, giving each of them a tailored experience within their content search.

RESULTS:

Fasken saw an increase in search utilization of 32%, as well as an increase in the quality of content that they were providing. By personalizing each user’s journey, they increased both their time spent on site (up 22%) and their seminar registrations (up 12%) –the latter of which was a KPI they were highly focused on.

Conversational AI enables people to communicate with companies through websites, apps and mobile devices simply by writing or talking as they naturally would—no phone trees to navigate or forms to fill out. Montreal-based AI company Heyday puts conversational AI into action in the retail sphere, where the customer journey has taken more than a few twists and turns recently.

How an AI chatbot makes customer engagement flow

Heyday’s platform focuses on not only recognizing human language and automating common interactions with ease, but understanding what customers want, wherever they are in the CX journey and through whatever channel they’re using, be it Facebook Messenger, website chat, WhatsApp, or email. Typically seen as a website chat, Heyday’s AI layer is on the front end of a brand’s website, there to discern the customer’s intent as soon as the chat is activated.

Based on past experiences, comparative audiences and the mining of deep data, the AI chatbot is able to process the natural language of up to 80% of FAQs, while the remaining 20% are sent to the appropriate human agents. In terms of customer experience, that also means customers can decide how they want to talk to the brand—whether over messaging, email, SMS on their phone or other modes.

Heyday’s e-commerce and CRM integrations with Shopify, Lightspeed, Magento and Salesforce make it an attractive solution for both personalized engagement at scale, the monetization of customer service, and an opportunity to replicate the in-store experience via online chat. With Heyday’s AI product search feature, users can hint at what they’re looking for, and the chatbot can evaluate needs, and deliver the right product to them directly in-chat: just like being pointed toward the right product by an expert sales associate in a store.

The added bonus of Heyday’s AI layer is data capture from chats. That data can be brought into a company’s data strategy for understanding customer behaviour, creating a better customer journey, boosting sales and maintaining meaningful customer relationships.

“Heyday is the bridge between the e-commerce technology and the customer’s voice. Every connection, every touchpoint, is important.” – Brad Wing, VP Strategy and Partnerships at Heyday

Popeye’s Supplements makes gains in online sales through customer service automation

PROBLEM:

At the outset of the COVID-19 pandemic, Popeye’s Supplements had to find a way to replicate the in-store experience online for its highly specialized product offers.Popeye’s was experiencing a significant uptick in online orders for the first time ever, as customers felt safer shopping online. This led to higher demands for online customer care and assistance.

SOLUTION:

Heyday proposed implementing an AI chatbot to offer immediate assistance to customers, and alleviated strain on the customer support team. Afterward, Heyday implemented an AI product search functionality to accelerate ecommerce sales and shepherd customers on their purchasing journey to replicate in-store conversations via a 24/7 chatbot.

RESULTS:

Popeye’s has saved at least 50% in customer service costs with Heydays. Popeye’s has automated 20% incoming requests with our FAQ automation feature. Heyday’s chatbot finds the ideal product for ecommerce shoppers 60% of the time, without any human intervention from a Popeye’s sales associate.

Companies who have been gathering survey and other customer data over the years, compiling online reviews of products or services, logging every customer service interaction, are sitting on an untapped data goldmine of authentic feedback. It’s qualitative meaningful data, gathered from thousands of people, that’s too complex for even a small team to analyze. Keatext’s text analytics AI platform mines this unstructured data to reveal the motivations and reasoning behind customer behaviour.

How text analytics uncovers insights in customer feedback

To understand the full story of customer engagement at an individual and emotional level, companies need AI text analytics tools that can understand customers in their own words and surface insights that tell compelling stories, from customer satisfaction and frustrations to the consequences of a negative interaction. Being able to map all those moments through data can bring CX to life via personalization.

Keatext’s AI text analytics for customer experience allows CX teams to structure text feedback into normalized data that is suitable for conducting analytics: they can find variations of occurrences, show trends and create new insights, such as predicting the likelihood of a specific customer to churn. When CX professionals are able to combine “big data” with “big stories,” they join customers’ lived realities with a brand’s strategies for creating emotional connections—they can see what works and what doesn’t, measure effectiveness and prove ROI.

“By using vivid data gathered from text analytics to understand customer behaviour, CX professionals are able to tell stories rooted in empathy—that engages not only potential customers but makes a compelling argument to company executives for AI-powered CX.” – Narjès Boufaden, Keatext Founder and CEO

Driving product improvements – and a culture change – at Bombardier Recreational Products

PROBLEM:

Eager to better understand customers, BRP saw the potential value of the insights contained in their feedback. But they faced one massive roadblock: they had over 10 years worth of survey responses and call centre transcripts, unstructured text in both French and English.

SOLUTION:

BRP found in Keatext an agile solution that could be up and running in an instant without extensive training. And it was advanced, not just frilly word counts – Keatext gave BRP tangible data to influence internal stakeholders to make customer-centric decisions.

RESULTS:

The result? A corporate shift. Keatext helped BRP find not only a wealth of insights that allowed them to drive product improvements, but greater success as a customer-centric organization that prioritized solutions based on customer needs and wants.

4. Choosing the right AI tools for your organization

AI tools for customer experience give companies the ability to understand customer behaviour right down to individual preferences for product options and channels of engagement. These are the tools helping retailers and brands get ahead of trends and come out on top in their sector. But with so many new AI-powered solutions out there, companies need to determine their data needs and scope before diving into the wide world of AI.

As the digital customer journey evolves, creating an AI strategy to meet it needs to be a quick and flexible process.

Look at your most important CX questions and research the technological solutions available, asking:

How will this AI solution align CX strategy with both specific tasks and organizational goals?
What are the most pressing CX problems we need to solve using data?
How are we currently listening to customers and what are they telling us?

As with onboarding any new technology, AI platforms must fit in with current strategies and systems. Many new AI tools are designed to act as an integrative layer within existing CX or customer service, helping to index and make more effective use of existing and newly gathered data.

Choose AI tools that:

Are insights-oriented and align with current strategies to solve your team’s or company’s most pressing problems,
Integrate easily into company systems and CX channels, don’t require an entire IT team to oversee, and are flexible as company objectives change,
Put customer behaviour, needs and emotional connection to your brand first, enhancing their experiences across touchpoints and channels,
Can also be used to measure CX strategy success and its relation to ROI.

5. Make AI a part of your customer experience strategy

“The future economy will belong to those who can digest and aggregate and tell stories about data. It’s where we’re headed already.” – Lawrence Levinson, President & Chair of CXPA Toronto

New AI tools are made to be applied to such an evolving customer journey, regardless of the cause or the magnitude of the change. They help companies understand trends and gather information on individual decision-making. AI technologies that harness vast quantities of recent customer feedback can give companies the flexibility to respond quicker to shifts in customer behaviour, whether they’re emotional responses to services or different channels of engagement.

CX teams today have a goldmine of data at their fingertips. To put massive amounts of customer data to use, they need AI tools that gather customer information from the first click of a search, analyze what customers are really saying in their feedback, respond more helpfully, and provide insights into customer behaviour. AI tools are already changing the way CX professionals engage with customers, tell customer stories and shape strategy. With the ability to track their own effectiveness, the AI tools of 2022 and beyond have a measurable impact on a company’s customer-facing image as well as its quarterly revenue.

The post How to Choose AI for Customer Experience [Guide] appeared first on Keatext.

How to Analyze Zendesk Support Tickets: 3 Easy Steps

Robyn Fadden — Wed, 24 Mar 2021 14:47:37 +0000

Brands can leverage their support tickets with Keatext's AI-powered Zendesk analysis integration for powerful customer sentiment insights.

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What text analysis of top Canadian bank reviews can tell us

Robyn Fadden — Wed, 18 Nov 2020 15:48:13 +0000

When it comes to banking, Canadians tend to be pretty satisfied customers: in 2020, this top Canadian bank received a 794 out of 1,000 rating from survey respondents, while Canada’s other top banks received an average ranking of 788 points. Digging deeper into the ratings and reviews, the reasoning for those rankings becomes clear, revealing both what’s working for these banks and what isn’t.

With rapid improvements to digital banking technology, for instance, customer expectations of their banking experience have risen. In light of these evolutions in client banking trends, we decided to look at what might in fact be a dying trend: clients’ experiences at branches in Montreal and Toronto. Balancing that data, we included reviews from clients across Canada as well.

According to a 2019 report by the Canadian Bankers Association, the majority of Canadians (76%) use digital channels for their banking. However, that same report also states that in-branch banking, “while declining in popularity, remains a valued method of conducting a wide variety of banking transactions.” When a client goes to a branch in person, it’s often due to a preference for an in-person experience or a specific need that can’t be dealt with online. As in the retail sector, omnichannel customer experience remains valuable in banking.

To discover what clients had to say about their experience, we used Keatext to analyze:

1,193 client reviews
1,739 comments analyzed within the reviews
From Google Reviews (910 reviews based at Toronto and Montreal branches) and ConsumerAffairs.com (282 reviews with no specific geolocation)
Between 2005 and 2018, with online reviews growing over time and peaking in 2018 with 626 reviews, as well as three distinct peaks in reviews at the end of February in 2016, 2017 and 2018

We discovered that:

Comment sentiment across the two channels skewed largely negative, with only 455 praises but 1,189 problems
Client reviews also included 61 suggestions and 33 questions for this bank
Top topics from clients were: customer service, staff, branch, account, manager, money, knowledge, debit card, phone call, and information

In all of these reviews, customers wrote commentary that is both colloquial and candid: valuable qualities of customer feedback that aren’t often found in surveys and other methodologies within voice-of-customer programs. Analyzing customers’ verbatim comments as qualitative data leads to an actionable level of insights that can be used for creating new CX strategies, customer service initiatives, and more.

What this bank’s ratings and reviews reveal about customer experience

Our analysis team chose these two data sources—Google Reviews and ConsumerAffairs.com—because they contained the greatest number of publicly available, verbatim client reviews. On first glance at the average ratings provided on both channels—2.8 out of 5 on Google Reviews and 1.09 out of 5 on ConsumerReports.com—it would seem that most customers have a negative opinion of this bank. More importantly, however, are the nuanced reasons why customers gave these ratings.

To add context to this analysis, the Keatext team identified some of the benchmarks listed on the American Customer Satisfaction Index (ACSI) for banks and specifically searched online reviews to see what clients were saying about this bank in relation to these benchmarks. By bringing the following benchmarks into account, we were able to add crucial experiential context to clients’ negative sentiments. In other words, this AI analysis reveals the individuality and often emotionally-based reasoning within people’s opinions.

Benchmarks of satisfaction that mattered most for clients

Financial transaction speed and staff courtesy

These two benchmarks are listed separately in the American Customer Satisfaction Index but are strongly correlated in Keatext’s dataset.

The topic “customer service” garnered 190 praises and 261 problematic comments, with associated opinions that included: happy, upset, inconsistent, worst, helpful, easy, disrespectful, short, no respect and quality. The topic “staff” had 164 praises and 127 problematic comments, with associated opinions that included: helpful, happy, disrespectful, unaccommodating, upset, like, inconsistent, easy, available and right.

Some clients complimented this bank on the quality of its service, saying that staff were courteous, polite and knowledgeable. However, these compliments are often related to complaints, such as disappointment with the speed of the service.

Some clients complimented this bank on the quality of its service, saying that staff were courteous, polite and knowledgeable. However, these compliments are often related to complaints, such as disappointment with the speed of the service. “At this branch the service was slow, but the staff very friendly and this is often the most important for the customers,” wrote one client, reflecting a number of similar comments.

Clients also take issue with employees trying too hard to up-sell and lacking professionalism (as one client pointed out: “Great service. A little bit salesy, which I didn’t appreciate when all I wanted was to deposit a check.”), while some clients wrote more extreme negative commentary on transaction speed and poor employee attitude, reflecting their feelings that the bank does not care about its clients: “Good service, relatively courteous, but not very fast and lacks a little professionalism / neutrality. For example, an agent who puts forward political opinions without solicitation and encourages acts of violence is not really what I expect from a customer service.” Clients also feel there is a lack of communication between employees, leading to confusion and inferior client experience: “Extremely poor communication. No one knows what’s going on.”

Interest rate competitiveness

In their comments, some clients advised others to shop around for better interest rates on RSPs. There was also mention of difficulty getting out of RSPs. The same advice was given in reference to mortgages regarding high rates and mortgages being hard to get out of. Some clients felt there was a lack of notice before interest rates were raised. Lower interest rates in hardship programs or payment plans are perceived by some clients as neither readily available nor easily extendable. Clients complained of an inability to negotiate lower interest rates and that money is withdrawn from their account by the bank without notice.

One client wrote: “Without my knowledge and no prior warning, they took money from my checking account where the money from family was transferred. Outraged, I called to negotiate a payment plan and they said there was absolutely no way to lower the interest rate back to the original amount or to agree on a payment plan.”

Call center

Clients complained of rude service and the inability to speak to a manager upon request. Dealing with problems while abroad through the call center proved challenging. Issue was also taken with being recorded. One customer wrote that “the employees at the call center was not aware of the procedure to cancel the recording and the client could not be serviced,” while another said a call center representative was “rude and wouldn’t even get me a supervisor. I will cancel all my business with this bank as they don’t know anything about customer care, does not even deserve one star.”

Website

Clients also criticized the look and feel of the this bank’s website and wrote of their concern over reliability of remote access due to their experiences of the website going down. The security of online transactions remains a concern for some.

Clients also criticized the look and feel of the this bank’s website and wrote of their concern over reliability of remote access due to their experiences of the website going down. The security of online transactions remains a concern for some. The hackathon organized by this bank was also criticized. One client raised concern about the remote US deposit app, stating that it “allows you deposit a check for more than $5000 then tells you 4 days later that it is not allowed and you have to mail it for deposit… their website is not working properly.”

ATM number and location

We were also able to garner insights peripherally related to ATMs. Analysis found that lack of after-hours access to ATMs was an issue for some: “Came to this branch on the weekend to use the ATM. It was after hours and typically you have to enter your card to enter the foyer to use the ATM’s. Definitely disappointed as the door would not open.” Other clients had issues with the cleanliness of the area housing the ATMs was addressed, singling out the Tour Jean Talon branch in Montreal and the branch located at 101 Dundas St. W, Toronto.

A number of complaints surfaced indicating a lack of understanding of how long it takes for cheques to clear once deposited in an ATM. And a number of clients complained about not being able to access funds after depositing cheques in ATM: “Worst branch ever… went in to get my money from a cheque I put in over the weekend, and then told that the process of the bank is that they hold the money that is deposited in atm till cleared… Long story short my money is being held HOSTAGE by the bank and they openly admit they don’t advise their clients of this rule, till it is an inconvenience!”

AI data analysis of this bank’s ratings and reviews lead to actionable insights

Analysis of this data sample of candid client feedback represents just one example of findings that can be surfaced using Keatext’s AI text analysis tool. This level of data analysis is capable of revealing crucial insights into client needs and behaviours—insights that are useful in crafting CX strategy, on-trend customer satisfaction and loyalty programs and other client-facing initiatives that measurably impact company operations and revenue generation.

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Bruce Temkin: “The future of CX depends on actionable insights”

Robyn Fadden — Thu, 29 Oct 2020 14:19:19 +0000

For CX professionals, understanding customers has evolved from mapping customer journeys and analyzing survey results to taking full advantage of customer data to pinpoint every interaction along the way and pull meaningful insights from it. This progression—and its increased speed and relevancy during the COVID-19 pandemic—is what CX expert Bruce Temkin focused on in his recent talk, CX Now: Current Realities & Future Opportunities, hosted by CXPA Toronto in celebration of CX Day.

“In the past 10 years, CX has increased the volume of customer insights that an organization can take action on. Today, we need to make insights way more actionable and find more of them,” said Temkin, cofounder of CXPA, creator of the XM Institute and Head of Qualtrics XM.

Even beyond searching out and identifying more meaningful ways to engage with customers, CX professionals armed with massive amounts of data-based insights have a role to play in every area of an organization, from employee satisfaction to stakeholder decision making, across industries. Recent Forrester marketing research shows that companies that leverage advanced insights-driven business capabilities are 2.8x more likely to report double-digit year-over-year growth than firms that are still at the beginner stage.

Throughout his talk, Temkin shared his experience in advancing the field and community of CX and his vision for its ongoing evolution.

How history illuminates the future of digital customer experience

In 2010, CX was just starting to be established as a discipline. It was also the year Temkin left Forrester to start his Experience Management (XM) company Temkin Group, and work with fellow CX professionals to establish the Customer Experience Professionals Associations (CXPA), an organization that focused on raising the level of thinking and capabilities across the community of CX professionals.

With shared best practices, a common language and competency models came a higher level of consistency in CX that defined the field and scope of CX that exists today, where customer journey mapping, “voice of the customer” and the customer feedback loop have become essentials.

In 2020, CX professionals are in clear agreement on what they do—and, given that CX functions with a broader cultural and social context where change is constant and quick, CX professionals are always in the process of learning more. The capabilities of CX to provide organizations with insights into customer behaviour is at a high point. Understanding the audience you’re speaking to involves understanding what’s going on around them.

CX’s capability to also improve employee experience and drive business growth is why Temkin created the CX-related term and practice of Experience Management (XM).

The capabilities of CX also go beyond understanding customers and can be applied in multiple ways within organizations. CX’s capability to also improve employee experience and drive business growth is why Temkin created the CX-related term and practice of Experience Management (XM), which sees opportunities for improvement in all interactions within and with an organization. In the next 10 years, the capabilities of CX are set to grow as the discipline increases its capabilities and becomes an essential part of organizational processes.

Digital customer experience in the next 10 years: Propagating actionable insights

“The future of CX success comes from CX’s ability to help organizations continuously learn what all the people in their ecosystem—customers, employees, partners—are thinking and feeling, then propagating those insights to the right people who can do something about it,” says Temkin.

What holds organizations back is their ability to take action on insights, says Temkin: “Organizations will differentiate themselves over the next 10 years not only from the insights that CX generates, but their ability to rapidly adapt across the organization based on the growth of meaningful insights.”

To adapt to customer trends, it’s time to go beyond static listening to customers, such as the same listening posts, surveys and similar sample groups that lead to repetitive reporting. To learn more about customer behaviour and what they think of products, services and brands, says Temkin, CX needs more dynamic instrumentation for listening to customers, learning and reflecting the priorities of organizations. That results in the kind of actionable intelligence that helps key decision makers and stakeholders make better, faster decisions.

To learn more about customer behaviour and what they think of products, services and brands, says Temkin, CX needs more dynamic instrumentation for listening to customers, learning and reflecting the priorities of organizations.

Creating actionable intelligence and embedding insights

CX professionals share a common goal: to help organizations make better decisions in a better way. To reach that goal in today’s climate, Temkin names actionable intelligence as the new focus, no matter what metrics are being used.

To encourage actionable intelligence, he suggests CX professionals move towards adaptive processes within organizations, where insights are fed directly into operational processes, from new product development to customer service, CRM scripts to marketing campaigns. Not only does this show how effective the learnings of CX are for decision makers, but that CX can help organizations rapidly adapt to findings and trends.

To that end, CX professionals need dynamic instrumentation to learn in different ways and capture more valuable insights. Since dynamic instrumentation programs rely on high-quality recent data, those insights need to become more automated, says Temkin. New AI and machine learning tools that efficiently gather and analyze an abundance of customer data from multiple sources are able to directly link quality customer data to actionable insights, improving the effectiveness of CX strategy and its value to decision makers. Embedded into organizational processes, CX-based data-driven insights enable those same processes to become more agile, bolstering organizations to do more, mature faster and drive change.

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What Canadians think of the new COVID Alert app

Robyn Fadden — Thu, 15 Oct 2020 14:11:02 +0000

Can mobile phones help people avoid getting COVID-19? That’s the concept behind the Government of Canada’s free and voluntary COVID Alert app. Designed to send out randomly generated codes via Bluetooth to other app users, the app lets Canadians know if they’ve been in contact with someone who tested positive for COVID-19. While concerns about the app’s privacy and accuracy dominate the media, Keatext decided to dig into the data to see what app users are saying about their experience with COVID Alert.

Since launching, COVID Alert has been downloaded more than 4.3 million times, with that number on the rise. Using Keatext’s AI text analytics technology, Keatext’s team analyzed COVID Alert’s publicly available unsolicited reviews from the Google Play Store and Apple’s App Store, where customers offer their opinions candidly in their own words, usually with the expectation that their feedback will be heard. User data could very well be the key to more people downloading the app and using it — we already know that the more people who use the app, the better it will work to keep Canadians safe and healthy.

Keatext analyzed:

2,505 comments from 2,496 commenters
1,896 comments from the Google Play Store
609 comments from the Apple’s App Store
Posted between July 31 and October 5, 2020

The objective of Keatext’s analysis was to learn more about how people are using the app, what their concerns were before and after downloading it and what they think about various aspects of the app, from ease of installation to privacy concerns.

What the data shows about COVID Alert

Keatext’s first high-level level analysis of the data shows the following distribution of comments, with each comment categorized according to the emotion attached to it:

1,787 problems (45.19%)
1,570 praises (39.71%)
390 suggestions (9.86%)
207 questions (5.24%)

The average numeric rating of the app is 3.6 out of 5, with a slightly lower rating from Apple’s App Store commenters (3.5) than the Google Play store commenters (3.6).

The comments submissions over time show a peak during the month of August, from which the submissions decreased gradually until October.

With this high-level information in mind, Keatext dove deeper into six main areas that COVID Alert users were concerned with – privacy, ease of use, Bluetooth connectivity, battery life, support for phones, notifications – to reveal several insights about users’ experiences of the app.

Privacy and Canadian health

As with any app that connects people through their data, issues of privacy arise. Yet in the case of the COVID Alert app, users expressed far less negativity (19%) than positivity (80%) around the issue of privacy.

Users expressed far less negativity (19%) than positivity (80%) around the issue of privacy.

For many, comments about public health went hand in hand with comments about privacy, such as in this comment: “I was initially concerned about privacy, but this app provides a balance between preserving privacy and factual information to help Canadians help each other.”

For most users, once they downloaded the app and understood that it uses only Bluetooth technology to connect people, with no tracking of location or identity, their concerns about privacy dissolved. As one person comments: “Many people worry about privacy but I think this app really protects your privacy and has one purpose, to protect and serve the Canadian community health and welfare.”

Meanwhile, some users would like more information shared, such as dates and times when contact with COVID-19 occurs: “[Date and time] is not a privacy issue as no ID of positive contact and helps to rule out where one might have been exposed. We need more useful and therefore impactful information!”

Ease of use

For an app that is meant to be used by all Canadians, regardless of their level of tech know-how, ease of use is paramount. Alongside that, people expect a clear explanation of how the app works, especially valuable for users concerned about privacy.

Looking at the comments, 227 comments (9%) praised the app for being easy to use, without any complicated steps for installation or for use, as this comment illustrates: “App does a great job at introducing the issues around privacy and explaining them. Wish more did such a thorough job at explaining why they are using privacy impacted features.” Meanwhile, another comment points out that ease of use is often tied to ease of mind: “Super easy to understand and no complicated steps! I feel more calm now that people will be notified.”

Bluetooth connectivity

The technology the COVID Alert app uses is well-known to most users: Bluetooth’s common usage has been around since the ‘90s. The world of technology and connectivity has changed vastly since then and along with it people’s expectations, so it’s no surprise that Keatext uncovered 130 comments about Bluetooth, a main concern among users.

The majority of these comments were negative (61%): some related to battery life (“Downside: on all but the newest, most expensive phones, Bluetooth is a battery killer”), others related to the conflict that the app creates with other Bluetooth devices (“Its Bluetooth usage seems to conflict with/interrupt the Bluetooth connectivity between my Continuous Glucose Monitor (aka CGM) and my phone, causing that connection to drop 10-15 times daily”).

61% of comments about Bluetooth were negative. Some of them highlighted the conflict that this app creates with other Bluetooth devices.

Contrary to this, 29% of comments about Bluetooth were positive, particularly around the issue of privacy, as this comment shows: “Awesome app for society, and using Bluetooth not only helps with privacy it also allows for data to be stored in the app for long term use.”

Some of the comments (4%) included useful suggestions for further app updates: “Easy to use. Hope everyone downloads it. Message for the developers: Send a push notification whenever Bluetooth is disabled advising users that the app will not properly operate if Bluetooth is disabled!”

Effect on battery life

While the COVID Alert app doesn’t take much power to run on its own, if Bluetooth is constantly left on, it will use power. Of the 116 comments on battery use, most tended to skew negatively (63%), illustrating an area that needs improvement either with the app itself or instructions on how to use it.

Most of the comments on battery use got straight to the point (“Huge battery drainer.”), while other comments expressed a need for more clarity, with questions such as: “If my spouse and I are normally together, and we both download the app, it must constantly send codes for the two devices. Will this impact battery?”

Support for all phones

Another area of the negativity that Keatext’s analysis reveals concerned the app not working on all phones, especially on older phones such as the iPhone 6. Of the 100 comments about phones, many skewed negative (76%), while also revealing that users wanted to use the app but could not due to technology limitations: “This app should be usable no matter what, and Wifi and outdated phones should not be working against people wanting to be safe and know. I truly love this idea and I hope you make it usable for everyone.”

Notifications

The Covid Alert app lets users turn on notifications if they wish. Users who wanted that option expressed some frustration, with some not able to enable notifications at all, while others found the exposure report disconcerting and didn’t like getting notifications that weren’t exposure related.

From the 101 comments about notifications, 74% were negative, reporting problems such as notifications enablement issues or alerts simply not working on their particular phone.

From the 101 comments about notifications, 74% were negative, reporting problems such as notification enablement issues or alerts simply not working on their particular phone. Users also had questions and concerns about notifications and how they relate to testing: some commenters said they were alerted that they had been exposed to COVID-19, got tested and received negative result, yet continued to receive exposure notifications. Some comments focused on the app’s weekly push notifications being too distressing and alarmist, especially because they featured the “freaky” red COVID icon and weren’t proximity warnings, while others suggested that the title of the “Exposure Report” be changed to something less disturbing.

From user comments to actionable insights

Every app review tells a piece of the story about user experience: added up, they can paint a bigger picture of user behaviours and needs. Yet too often those comments aren’t analyzed for the valuable insights they can provide to decision-makers. Through AI text analysis of COVID Alert app feedback – which shows that users are primarily concerned with privacy, ease of use, Bluetooth connectivity, battery life, support for phones, and notifications – Keatext aims to add further insight on the available data that could lead to more Canadians using the app.

Positive or negative, every review provides more data that can be used to augment the COVID Alert app itself or aspects surrounding the app, such as help guides and public relations and marketing materials. With AI analysis, app developers are able to look at the true scope of their app’s impact and identity points of concern and points of positivity.

Unlike a simple star system, AI analysis of user comments provides the kinds of insights that can make a big difference on the success of the app itself as well as on app adoption across a broad cross-section of users – in the case of COVID Alert, a user base doesn’t get much broader or more diverse than all Canadians.

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Insights with impact: How AI technology improves CX

Robyn Fadden — Fri, 11 Sep 2020 16:27:23 +0000

Every day, in companies the world over, CX teams search for insights into customer behaviour. Some still laboriously scan through hundreds of survey responses and NPS scores, while others strategize around insights from tens of thousands of customers, from multiple feedback channels. The biggest difference between these CX teams isn’t experience, intelligence or even data quantity – it’s insights-oriented AI technology.

Recent Forrester marketing research shows that companies that leverage advanced insights-driven business capabilities are 2.8x more likely to report double-digit year-over-year growth than firms that are still at the beginner stage. With numbers like that, it’s no surprise that “more than 60% of global data and analytics decision makers plan to increase their data management and analytics budgets from 2020 to 2021.”

“AI gives you the ability to mine for the really insightful patterns within your customer behaviour. That forces you to look at yourself from your customers’ perspective, in ways you might not have before.”

The Customer Experience Professionals Association has also been tracking AI technology’s impact on the business world, and noted in 2018 that because of its “looming pervasive reach into customers’ lives, the technology also carries the potential to have a huge impact on CX,” as a field itself and on the companies who leverage AI for their CX initiatives and strategies.

Certified Customer Experience Professional and current President & Chair of CXPA Toronto Lawrence Levinson has seen first-hand how relevant AI has become for CX in his more than 20 years of driving top-line growth and ROI. “It’s amazing how sophisticated AI has become not only in terms of speed but in terms of accuracy when aggregating data for recurring patterns or trends,” he observes.

“AI gives you the ability to mine for pieces of gold in data for the really insightful patterns within your customer behaviour,” Levinson adds. “That forces you to look at yourself from your customers’ perspective, in ways you might not have before.”

Ironing out points of friction

For CX professionals, reviewing voice-of-customer data is a daily task – and it’s not an easy feat. Teams spend incredible amounts of time manually processing data from listening posts, double-blind audits, NPS, CSAT, customer effort score and other survey programs, along with customer reviews and social media mentions. “It’s not only time-consuming, but it sets the individual up to miss insights that could be quite impactful to the business,” remarks Levinson. “When AI works, it works really well at isolating data clusters for teams to analyze and determine what actions are required.”

By aggregating qualitative data – such as long-form answers from survey programs or verbatim reviews on social media – organizations have the opportunity to see recurring emotional patterns in their customer journeys. “This data can show the friction points for a business, whether it’s already a customer-focused organization or not,” says Levinson.

By aggregating qualitative data, organizations have the opportunity to see recurring emotional patterns in their customer journeys.

He gives an example from his own experiences of parking and grocery shopping in two very different big cities. “In several businesses, I discovered that both customers and employees are equally frustrated with parking lots,” he says. “When we talk about competitors we often think we’re competing on things like quality of services, deadlines or the other typical criteria we’re selling against, when in fact when you really dive deep into the data to identify the emotional component of why people would rather work with one company vs. a competitor, you see something as seemingly simple as ‘one parking lot is full and the other isn’t.’ These personal friction points really do make a difference.”

Data can reveal insights that CX teams might not have thought about before, especially when it comes to emotion. “If 95% of purchasing decisions are subconscious and based on emotion, then how people feel about how they work with you is the missing component, the goldmine in customer experience,” says Levinson. “The patterns in how people are feeling is what AI and machine learning can bring to light.”

He encourages companies to keep an open mind and explore what’s possible today in AI technology relative to what was possible five years ago. “I myself was turned off by early-stage, exciting sounding AI technologies that didn’t deliver on their promise, yet it’s been a big five years in that space,” he says. “The future economy will belong to those who can digest and aggregate and tell stories about data. It’s where we’re headed.”

Using data to understand behaviour changes in COVID times

The urgency to have the right AI tools in place has been even more evident since the beginning of the COVID-19 pandemic. “COVID completely changed the journey,” says Levinson. “So many people used to make fun of me for using a click-and-collect grocery shopping program – now everyone’s doing click and collect. Back in late March of this year, customers were frustrated with all things insincere. Companies that used their customer data to add a sincere and customer-focused spin on their surveys or ads did well.”

“The future economy will belong to those who can digest and aggregate and tell stories about data. It’s where we’re headed already.”

New AI tools are made to be applied to such a changing customer journey, regardless of the cause or the magnitude of the change. They help companies understand trends – such as the massive shift in grocery shopping habits during the pandemic – or just gather more information on individual decisions to change phones or data plans, for instance. AI technologies that harness vast quantities of recent customer feedback can give companies the flexibility to respond quicker to shifts in customer behaviour, whether they’re emotional responses to services or different channels of engagement.

Companies can have a goldmine of data at their fingertips, yet if they’re not using AI to analyze what customers are really saying and share those stories with their company, they can’t fully engage with and respond to customers. “The future economy will belong to those who can digest and aggregate and tell stories about data,” says Levinson. “It’s where we’re headed already.”

The post Insights with impact: How AI technology improves CX appeared first on Keatext.

Amanda Robinson: A passion for data meets a goldmine of information

Robyn Fadden — Fri, 14 Aug 2020 19:34:14 +0000

When Amanda Robinson started her Master of Library and Information Studies degree over a decade ago, she pictured dusty books not data. To her surprise, the field was rapidly expanding to encompass the wider world’s growing digital directions, from a massive increase in channels for information to tracking customer experience in the digital realm with customer data analysis—and the heaps of information that came with them.

In Montreal’s start-up community, Robinson found a cache of real-world applications for her skills and knowledge. Over the past 10 years, she became an experienced customer data analysis and text analytics pro, working at text analytics companies to analyze vast quantities of previously untapped data and integrate text analytics technologies into some of their biggest client projects. Currently Keatext’s head information scientist, Robinson guides clients in every industry on how to apply AI-powered text analytics tools within their own organizations, whether their goals are to quickly solve customers’ problems, improve products and services, increase employee satisfaction or support more effective business growth strategies.

Q: Why focus your career in information sciences on text analytics?

Text analytics especially is a great fit for me because it’s all about organization, about gathering the data, breaking it up, and putting it back together in so many different ways.

AR: It’s funny because who falls in love with metadata? It turns out, me. In my graduate work, I gravitated to tech-oriented courses, relational database design and metadata. To me, it felt like an incredible puzzle I could put together; and even though I didn’t have a handle on the technology at the time, I loved the concepts behind it. It’s work that is great for people who like organization and list-making. Text analytics especially is a great fit for me because it’s all about organization, about gathering the data, breaking it up, and putting it back together in so many different ways.

From my perspective, I see AI-powered text analytics as a tool that isn’t industry specific. It can be applied to everything from retail marketing and sales to cosmetics industry R&D to employee satisfaction in a major transportation company. It’s all data that we can break up and look at in different ways.

Q: You entered Montreal’s tech start-up community soon after graduating. What was it that appealed to you about working in start-ups?

AR: I saw how vibrant and innovative the start-up community was and continues to be, and that I could apply my specialization in text analytics at these companies without having to be a developer or know multiple programming languages. As an information scientist in start-ups, I function more like a free electron, zooming around different teams, from sales to dev, and helping them meet their own targets as well as the company’s goals.

To the dev team at Keatext, I’m their “superuser” because I’ve used text analysis tools for years. I work with the dev team to determine new features we should add —we’re answering the question, what would someone need to see to get the most out of the tool? In client onboarding, I’m the first person a client has significant contact with outside the sales team, when clients start to use the tools themselves. I take a look at their data and show them how the tool is best suited to their particular needs—it’s tailored, results-oriented training. I’ve worked for enough years as a data analyst using these types of tools that it’s immediately evident for me how clients can apply our tool to their particular data sets.

I’ve helped clients design a dashboard that lets them quickly identify product issues highlighted in online reviews from various channels, so they can more effectively focus their online response to those issues.

I’ve helped clients design a dashboard that lets them quickly identify product issues highlighted in online reviews from various channels, so they can more effectively focus their online response to those issues. Or we can hone in on certain details in the data that affect a company’s bigger picture: I worked with a large recreational product company to analyze repair shop tickets from their locations and dealers to identify recurring problems as well as positive feedback, so head office could definitively say where major issues were happening with certain models, while sales could see why certain product were more popular at certain locations.

Q: Sometimes the amount of information and communication we’re dealing with these days, both as individuals and as businesses, seems insurmountable or at the very least extremely difficult to organize in any timely way. Why do you see text analytics as a path towards not only understanding the data organizations gather but making better use of it?

AR: If your organization has been gathering survey data over the years from customers and employees, compiling online reviews of products or services, logging every customer service interaction, you’ve got qualitative data just waiting to be analyzed. So most companies are already sitting on an untapped data goldmine of authentic feedback. It’s qualitative data, gathered from hundreds if not thousands of people, that’s too difficult for any one person or even a small team to analyze.

The fact is, qualitative data is where the juicy part of the analysis is. Quantitative data can show us scores or rankings, but what you really want to know is why people gave that score.

The fact is, qualitative data is where the juicy part of the analysis is. Quantitative data can show us scores or rankings, but what you really want to know is why people gave that score. If you don’t analyze the text, and many organizations don’t—it just gets left alone or filed away—you’re missing a massive part of the puzzle, you’re missing the meat of the data, the very human why of it. Now we have AI text analytics tools to help us understand that data quickly, determine patterns in what people are saying, and help organizations use those insights from the data to solve certain strategic and operational problems or guide changes to services or products.

Q: Where do you think most organizations are at right now with understanding the value of text analytics not only to their customer data analysis but to their bottom line?

AR: Text analytics in my opinion is a major part of business intelligence. I think companies are having an awakening where this is concerned. Most companies will have a web presence and loads of data already at their fingertips—online reviews full of unsolicited feedback, surveys with open-ended answers, help desk tickets, email correspondence, anything that has gathered data over the years.

Right now they’re starting to see the potential of all that information. Rather than going out to get new information, they’re seeing they have the payload right there and they want to find a way to unlock what it’s saying. Text analytics does that for them: the right software tools reveal patterns and insights in the data, so organizations can see what’s working for people and what isn’t. From there they can take a deeper look inside the most timely or crucial issues for particular teams or the company as a whole, and work on implementing solutions backed by the data.

The post Amanda Robinson: A passion for data meets a goldmine of information appeared first on Keatext.

You’ve Got Customer Journey Insights, What’s Next?

Robyn Fadden — Wed, 29 Apr 2020 18:31:03 +0000

A product launch that generates influencer buzz and proves your company is a trend-maker. Brewing controversy about your brand on social media. Varying online reviews on your brand’s site and popular e-commerce sites. Tackling all that customer feedback might seem overwhelming, but from the perspective of AI analysis, it boils down to a rich source of data. That data’s greatest value and competitive edge comes from the action you take on it.

When analyzed through AI text analytics tools that provide detailed insights, customer feedback data tells you more about your customers than ever before, getting to the heart of why they feel the way they do about your products, services, and ultimately, your brand. It not only lets you find out what are people saying about you from day one and throughout the customer journey, but provides insights for following through on that feedback—with positive results.

Data’s greatest value and competitive edge comes from the action you take on it.

In that sense, customer data needs to be looked at strategically. McKinsey research shows that organizations that leverage customer behavioural insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin. To get the most out of your feedback, you need tools that inspire well-timed, calculated action, based on insights from the data sources that matter most to you and your team, whether you’re a leader in customer experience, sales or product development.

As a customer feedback solution, Keatext makes AI text analytics operational for your organization. This is how we do it.

How AI-analyzed customer feedback drives action

An advanced tool for linking customer feedback and related metrics with ROI across departments, AI text analytics is the engine behind Keatext’s sophisticated feedback management solution. This technology uncovers the sentiment and intent behind a variety of customer feedback, from thousands of unstructured customer reviews gathered from online retail and other consumer sites, to social media platforms.

The way customers think and feel predicts how they act.

Once you’ve analyzed that blend of qualitative and quantitative data, the next step is making use of that feedback. How customer data provides actionable insights on customer behaviour and decision-making is at the core of Forrester’s 2019 report How Customers Think, Feel, And Act: The Paradigm Of Business Outcomes, which shows that “the way customers think and feel predicts how they act” and that “small data is better at conveying how customers think and feel.” The report’s recommendations details how brands can use insights from AI-analyzed data, including to:

Add context. Insights are by their nature contextual, telling hundreds of customers’ stories through their own words and linking those stories together to show both the bigger picture and the details at once. Those insights often reveal how different customers relate to a brand on an emotional level.
Develop a holistic customer view. It’s all too easy to get lost in transactional and interactional data to explain how consumers act, when the truth is that how customers act always changes with context and time. If you want to engage customers more effectively, solve problems add value, look at data that is quantitative and qualitative and comes from multiple sources, revealing how customers really think and feel—then incorporate those insights into your broader strategies.
Break down company silos. Collected, analyzed and acted upon deliberately, the same data can connect different departments. AI feedback analysis provides insights that are tailored to certain leaders and strategies yet anchored in the same customer feedback. With the same access to data, departments can share their perspectives and collaborate separate actions they plan to take.

Follow the customer journey through data

If you’re already feeling inundated with data analytics and are struggling to get the most value out of both the technology and your data, you need an action-oriented solution. For CX and other customer-facing leaders, Keatext’s analysis and insights validate the development and personalization of customer relationships throughout the customer journey.

A product’s reviews can increase its conversion rate by more than 270%.

Attracting and retaining customers means being thoughtful, attentive and strategic about every customer touchpoint and experience. A complete AI feedback solution lets you continuously gather data about emotion throughout the customer journey and even a product’s lifecycle, including pre and post-product launch.

Next, Keatext lets you monitor, reply and engage with all feedback, including negative feedback, instantly—especially important when a product’s reviews can increase its conversion rate by more than 270%. Quick engagement with customers keeps ratings high. You can also track changes in metrics over time and measure results of VoC-initiated activities and their impact on customer loyalty.

Ultimately, in an increasingly competitive landscape, you’ll be able to use these insights to monitor the performance of your products from the perspective of customer experience and sales, allowing you to understand and anticipate new trends and pain points before they impact your brand’s reputation and identity.

This level of solutions-oriented data analysis all in one platform ensures that the right insights are being addressed by each of your organization’s teams so they can meet and integrate goals for customer experience and brand awareness, sales and product development, budget and ROI and beyond—all the while staying ahead of industry trends.

The post You’ve Got Customer Journey Insights, What’s Next? appeared first on Keatext.