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Reviews vs. Testimonials [Differences + What’s Best for Your Business]

July 17th, 2023 No comments

Words — both good and bad — travel far.

So, what your customers experience and then talk about with their peers (online/offline), sets the tone for your sales graph.

Plus, customer feedback is important for businesses to better understand their needs, wants, and desires. This key information can then be used to improve the product or service you’re offering and make it more attractive to future customers.

For example:

You may have seen ads for a new car model with a statement like — The features that set this vehicle apart include: X, Y, and Z. This means that the manufacturer has taken customer feedback into account when designing the car. In fact, they want to know what features are most important to potential customers so they can make sure to include them in their product lineup.

Interestingly, customer satisfaction plays a much bigger role in influencing consumer behavior than price and other factors. That’s why most companies look for repeat business — they know that happy customers lend credibility to their brand and are less likely to defect to competitors.

In this article, we explore the key differences between reviews and testimonials (the two forms of customer feedback) and a few ways you can land more poppy words of customer appreciation.

Reviews vs. Testimonials — What’s The Difference?

Length And Detail

Reviews

Reviews are to-the-point assessments that allow customers to share their opinions and experiences regarding a product or service. They provide a quick summary of the customer’s viewpoint — enabling potential buyers to make informed purchase decisions. Plus, this conciseness allows quick scanning, efficient information gathering, and comparison of multiple products with ease.

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They often focus on specific aspects — like quality, durability, functionality, and customer support efficiency. For example, reviews are critical in eCommerce as they offer insights from previous customers, helping you weigh pros and cons. They can be in the form of star ratings accompanied by comments that highlight strengths or weaknesses.

Testimonials

On the other hand, testimonials are more detailed and personalized accounts of a customer’s experience. They delve deeper into the customer’s narrative — providing a comprehensive and in-depth perspective. 

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They often highlight specific challenges faced and the outcomes or benefits experienced from using a product or service. Service-based businesses utilize testimonials to showcase their expertise and build trust.

Plus, they include additional details like the customer’s name, photo, and sometimes their profession or location — making them more credible. Testimonials can be featured on websites, social media, marketing materials, or shared in video format to effectively communicate positive impact.

Source And Platform

Reviews

When customers want to share their experiences and opinions about products or services, they often turn to third-party review platforms — like Yelp, TripAdvisor, Amazon, Google Reviews. And dedicated review sections on ecommerce websites act as central hubs for customer feedback. If you run an eCommerce or subscription business on Shopify, there are plenty of third-party apps that can help you capture reviews from your customers. 

These third-party review platforms are the goto resources for potential buyers and users who are seeking information and insights before making a purchase. 

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Unlike testimonials, which are carefully selected and displayed by businesses, reviews on these platforms are typically un-curated. They provide a more authentic and balanced representation of the customer’s experience, as they include both positive and negative feedback.

Also, while testimonials are a direct testament to the brand and their offering, reviews can often relate to elements that are external. For example, if your business uses a billing system that is unreliable, or if your physical store is located in a mall that does not offer free parking, these criticisms can often show up against your own product or service. 

The transparency offered by these platforms allows future buyers to assess the reputation and quality of a product or service. They can gain access to a range of perspectives and consider the various positive and negative aspects highlighted by different customers.

Testimonials

Testimonials are feedback directly provided to a company by satisfied customers. They’re usually obtained through specific requests or surveys. And are displayed on the company’s own platforms — such as their website, social media profiles, or marketing materials — to establish trust and credibility with potential customers. A sales-driven organization may also showcase testimonials in their PowerPoint slides to prospective clients. 

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Unlike reviews, testimonials are carefully handpicked by the company, and usually undergo some level of editing to correctly reflect their strengths. Companies seek out testimonials to gather positive feedback that clearly demos the value they provide.

While testimonials may be edited for clarity or grammar, the core message conveyed by the customer remains intact. The editing process aims to present the testimonials in the best possible light without compromising the brand authenticity. Given that testimonials are pivotal in conversion optimization, marketers typically run a lot of A/B tests with respect to the specific words used, presentation, and associated call to action. 

Content And Tone

Reviews

Reviews provide a diverse range of feedback — from positive and negative to neutral perspectives. This offers a well-rounded view of a product, service, or experience. 

Customers share specific details about their experience in reviews, providing an in-depth assessment. They discuss the pros and cons, highlight liked or disliked features, and address any encountered issues. By including such specific information, reviews offer key purchase worthiness insights for other customers or users.

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The tone of reviews varies based on individual experiences and writing styles. They also reflect the level of customer engagement and satisfaction they experience. Some convey enthusiasm and satisfaction, praising the positive attributes, while others express frustration or disappointment, pointing out the lacks.

Reviews offer customers a free-speech platform to express their genuine opinions and feelings about a product. This emotional element adds depth and authenticity to the feedback which directly translates into true buying advice — which they may not get any other way (well, because company’s sales reps only shine a spotlight on the pros and almost never the cons).

Testimonials

Testimonials at their core exist for promotional purposes, focusing on highlighting the positive aspects of a product or service. They aim to showcase benefits, value, and success stories associated with the customer’s experience, creating a positive perception.

To do so, they emphasize transformation, improvement, or satisfaction, demo-ing how the customer’s situation or life improved due to the product or service in focus.

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Plus, testimonials highlight measurable success metrics, reinforcing the effectiveness and value of the endorsed product or service.

Testimonials have a highly positive tone — often expressing gratitude and admiration for the company’s offerings. This is precisely why the best and most compelling feedback are carefully curated and edited, to build trust and credibility.

How To Capture More Reviews And Testimonials For Your Business?

Before you start collecting more testimonials and reviews from your happy (or unfortunately, dissatisfied) customers, please make sure you’re respectful of their time and preferences. Make it clear that their feedback is valuable and appreciated. 

Let’s take a quick look at a few ways you can increase the likelihood of capturing more reviews and testimonials to promote your business and build trust with potential customers:

  • Reach out to your happy and satisfied customers directly and request their feedback. You can send personalized emails or SMSes asking them to share their thoughts about their experience with your product or service.
  • Implement survey tools (like Jotform) to capture customer feedback and reviews. This can be done through online surveys, email surveys, or even feedback forms on your website. Make sure to ask specific questions that encourage customers to quickly share their experiences and opinions.
  • Offer incentives or rewards to customers who leave reviews or provide testimonials. This can be in the form of discounts, exclusive offers, or entry into a giveaway. Incentives can motivate customers to take the time to drop a review.

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  • Reach out to the satisfied customers who have provided positive feedback and request their collaboration for creating video or textual testimonials. Offer assistance and guidance in the process — such as providing interview questions or helping with video recording/editing, if needed.
  • Simplify the process of leaving reviews or testimonials. Provide clear instructions and direct links to review platforms or testimonial submission forms. The probability of your customers dropping a review is directly proportional to the ease of doing so.
  • Get social with your customers on social media platforms. Encourage them to share their experiences publicly by mentioning or tagging your business in their posts. Monitor social media mentions and reviews to respond promptly and encourage further engagement.
  • Focus on delivering exceptional customer service and a positive overall experience. Ultra-happy customers are more likely to voluntarily leave reviews or provide testimonials without being prompted. An omnichannel customer service that maximizes positive experiences make customers happy.
  • After a purchase or interaction with your business, send follow-up emails or messages to customers. Thank them for their support and encourage them to share their feedback or leave a review. Timing is crucial, so consider sending these requests while the positive experience is still fresh in their minds.

Final Words

The game is all about happy customers. If you keep your customers satisfied, they’re likely to refer you, and it won’t really matter which format you use to land chest-puffing reviews or testimonials.

However, it is worth considering the differences here, because each form of feedback has a unique place in your overall business strategy. Review these formats side by side and see what makes sense for your business and for your customers.

Image by Mohamed Hassan from Pixabay

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Microsoft Replaces its Default Font after 16 Years of Calibri

July 17th, 2023 No comments

Microsoft is making the bold decision to replace its iconic Calibri default font with Aptos, a sans serif typeface based on 20th-century Swiss typography. This change may come as no surprise to those familiar with Microsoft’s hunt for a new font style in recent years. But for anyone who has grown accustomed to Microsoft’s trademark typeface, the decision to switch to Aptos might be an unexpected shock.

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15 Best New Fonts, July 2023

July 17th, 2023 No comments

Choosing a font for your project is one of the most important steps you’ll take in the process; it’s the point at which you finalize the tone of your content. And so, to give you a fresh voice for each new client each month, we write this roundup of the best new fonts we’ve found. Enjoy!

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Google’s Bard Raises the Stakes with Exciting New Update

July 14th, 2023 No comments

Amidst the battle for AI chatbot supremacy, the team behind Google’s Bard has released a new update detailing some monumental changes. Top of the list is Bard’s long-anticipated entry into Europe. With the AI language model now supporting 40 languages – including Portuguese, Arabic, Chinese, Hindi and Spanish – Bard seems set to capture the market and rival ChatGPT during the remainder of 2023.

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5 Tips for a Successful Typographic Logo

July 14th, 2023 No comments

Every designer knows that a strong brand identity is essential for a company that wants to embed itself in the minds of its customers. And when it comes to impact, there’s nothing stronger than a typographic logo.

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How Will AI Help You Better Understand Your Customers

July 14th, 2023 No comments

Society is growing to rely on Artificial Intelligence more and more. Over the years AI has become embedded in everyday life, such as our smartphones and devices that control smart technology in our homes. One of the major components of AI is its ability to continuously learn so that it is constantly getting better and becoming more efficient. There are so many capabilities and it’s hard to imagine the limits of this technology. One such capability is to help businesses better understand their customers.

With customer expectations higher than they have ever been, it is important for companies to adapt and continue to be aware of those expectations. One report shows that about 80% of customers would prefer not to do business with a brand after just one bad experience. This shows that the bar is extremely high for companies to get it right from the beginning. That’s where AI can help.

There are plenty of people who have already come to embrace the idea of AI-enhanced CX (AI/CX). It has been found that 70% of executives believe their industry is ready to adopt AI/CX and three out of four predict AI will have a vital role in the future of their organizations.

When used in a contact center, artificial Intelligence can also save a business time and money, which is always a plus. However, being able to tap into the knowledge behind what drives customer engagement is key to success. The great thing is that AI will do the work for you and you get all the benefits. So, this is what AI can do for you.

Improving Customer Service

Having a responsive and helpful customer service department is essential to any business that wants to be successful.  90% of consumers say “immediate” response time is very important when they have a question. Customers need to be able to have direct access to businesses to file complaints or resolve any issues they may have. So having different channels where people can express those things is necessary for companies to retain customers. Multiple kinds of AI-powered software have been developed to enhance the customer experience.

Chatbots are one way that artificial intelligence can improve customer service. They are very relevant in today’s world, as 67% of people expect to use messaging apps to talk to businesses. They can easily answer frequently asked questions or simpler questions that do not require intervention from a customer service representative. They’re also available 24/7 so people have access at all times, without the need for shift workers. 

Receiving feedback from customers is one of the best ways for companies to know how they are doing and what they need to improve. AI is a tool that can exceed what the standard survey provides. For example, AI can facilitate online focus groups in real-time and opens up a channel for communication, where you can have a conversation with people and participants don’t have to give one-sided responses. 

Predicting Behavior

Being able to predict customer behavior has a major impact on the way companies conduct their business. In predicting behavior, brands can offer a more personalized experience and drive sales. Artificial intelligence can capture and analyze customer data to a much higher extent than ever before.

Programs, like Google Analytics, Facebook Ad Insights, Adwords, and CRM data are designed to track customers across platforms and generate reports with information about people’s online habits. They will track things such as which websites people visit and specifically what products they are looking at. They can get very detailed to even include how long someone stays on a page and how often they return.

By knowing ahead of time how people shop and what products/services they are looking for, brands can plan accordingly. They are able to design their websites in a way that draws people’s attention to exactly what they want and makes them more user-friendly. They can also put their time and effort into funding the design of products that they know people want. 

Targeted Marketing

Marketing has drastically changed over the years. Traditional marketing no longer makes the cut and now people listen more to ‘influencers’ and spend so much of their time on social media, where they are seeing ads bombarding them in a whole new way.

It’s no coincidence when someone logs onto Instagram and sees ads for products they specifically like. That is because these ads are tailored for and targeted at each individual. AI is to thank for that. Algorithms are built into these platforms that run based on AI technologies and machine learning to make recommendations on content for users. It can almost be eerie in the ads that users see, especially if someone has just been searching for that very product being advertised.

Having AI keep up with customer data allows brands to know exactly how to customize their marketing tactics for each person. This keeps the customer drawn in and clicking that add to cart button. It also helps prevent churn and retains the best customers by detecting when they are starting to lose interest and can launch new campaigns to keep their attention.

Artificial intelligence is only going to continue improving and has so much potential in businesses. Brands are in a unique position, compared to those in the past, in the way they can use technology to tap directly into customer insights. By having a better understanding of customers’ expectations and habits, a company can completely transform itself to cater to its customers. The customer experience is everything. And AI is a powerful tool that can revolutionize the way customers interact with a brand. So, to get started with integrating AI to create a better customer experience, check out your options with LiveVox.

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Apple’s New OLED iPad Pro Models Could Arrive as Early as Spring 2024

July 13th, 2023 No comments

Rumors are circulating that the newest iPad pro model could feature an OLED screen. These new displays would be a significant upgrade on the standard LCD Displays used on all but one iPad model (strangely, the 12.9-inch iPad Pro uses a Mini-LED screen, instead).

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The Engineering Behind Recommendation Systems

July 13th, 2023 No comments

When you want to buy something, you seldom directly opt for an unknown brand or a new shopping portal you’ve only heard of. This is because you are driven to trust the brands or e-commerce portals, or a particular offline store based on your previous experience. You feel connected because somehow you find items that align with your interests and preferences. 

In offline stores, one can say it largely depends on the relationship building of the owners and the shoppers. When it comes to online shopping, there is no face-to-face meeting. Yet, your favorite brands know what you like and recommend products you will likely purchase sooner or later. 

This is no magic. Say hello to recommendation systems, one of the most frequently used machine learning techniques. 

What are recommendation systems?

Recommendation systems are advanced data filtering systems powered by AI, recommending the most relevant item to a particular customer or user. It uses behavioral data and machine learning algorithms to predict the most relevant content or services for customers.

For instance, when you watch a web series or a movie on Netflix, you will see recommendations for shows and movies of similar genres. Netflix uses recommendation systems to analyze your watching preferences and browsing activities to suggest relevant content. 

Doing this helps reduce users’ time to browse through thousands of contents to find a new show or movie to watch.

Recommendation Systems: The Science Behind It

A recommendation system is an algorithm that uses big data to suggest relevant products and services to users. Since big data fuels recommendations, the inputs required for model training are crucial. 

It can work on various types of data depending on your business goals. For example, it can be based on user demographic data, past behavior, purchase habits, interactions with your product or website, and even search history. Recommendation systems analyze the data to identify patterns and then make suggestions for products most likely to appeal to the users.

For instance, on Amazon, you will discover related products on the homepage, upon adding items to your cart, reviewing a particular product, or completing a purchase. There is always a recommended list of items that align with your past purchase and overall browsing patterns. 

Source: amazon.in

Recommendation systems can offer personalized experiences to customers by providing relevant recommendations on various topics. They also help businesses increase customer engagement and loyalty by recommending related items and services.

Why use Recommendation Systems?

An essential component of recommendation engine algorithms is filtering based on different aspects. The recommender function gathers information about the user and predicts the most relevant product. For example, they predict user ratings and preferences based on recurring actions.

Recommendation system models are crucial in helping us have a hassle-free and seamless user experience. It also helps expose more inventory or content that we might not discover otherwise amidst the enormous volumes of data. There are many uses for recommendation engines:

  • Personalized content: Improves the on-site experience by creating dynamic recommendations for a diverse audience.
  • Better search experience: Categorizes products based on their features.
  • Increased sales: Recommends products to customers based on their purchase history, allowing them to make more informed decisions. 
  • Re-engagement strategies: Helps bring users back by suggesting relevant items or services they may be interested in.
  • Cross-selling and up-selling opportunities: Identifies complementary items that customers may have yet to consider or be aware of. 
  • Collecting valuable customer data: Generates useful insights into user behavior and preferences. 
  • Optimizing marketing campaigns: Analyzes customer data to refine targeted promotions and campaigns to drive higher conversion rates.

Product recommendations on online shopping apps, social media news feeds, and Google ads are examples of recommendation systems in our day-to-day life. 

Engineering Behind Recommendation Systems

Recommender Systems (RSs) are advanced software tools and techniques that recommend products to a user that they find useful. The recommendations involve different decision-making processes, like things to buy, music to listen to, or online news to read.

The four clearly defined, logical steps of a recommendation system are:

1. Data Collection

The first and most crucial stage in developing a recommendation engine is obtaining the appropriate data for each user. There are two kinds of data:

  • Explicit data is information gathered from user inputs like product ratings, reviews, likes, and dislikes.
  • Implicit data comprises details obtained from user behaviors such as web searches, clicks, cart actions, search logs, and order histories

Since each user’s data profile will change over time to become more distinctive, it is also essential to gather consumer characteristic information like

Demographics (age, gender) User demographics refer to the various characteristics of individuals, such as age, gender, income, education level, location, and cultural background. In recommendation systems, user demographics can be used to personalize recommendations and improve user experience. In addition, it encourages engagement and loyalty to drive more business.
Psychographics (interests, value) Psychometrics is the scientific study of human mental processes and behavior and can be used to develop more accurate user recommendations. By tailoring recommendations based on personality, interests, and behavior, companies can increase users’ likelihood of engaging with products or services.
Feature Information (genre, object type) Feature information such as genre, object type, and keywords can also be used to personalize recommendations. By leveraging these characteristics, companies can create more targeted and relevant content for their users. For example, an online clothing retailer could use feature information to suggest items based on a user’s style preferences or the season.
Contextual Information (time of day, location) Contextual information such as time of day, weather conditions, and location can be used to determine what content is most appropriate for each individual at any given moment. For instance, if a user is located in a city that experiences hot summers and cold winters may be prompted with seasonal items or activities suitable for the local climate.

2. Data Storage

Data storage is essential for any business as it allows them to store and access customer information. By monitoring user data, a business can gain insights into its target audience, including purchasing habits and preferences.

 There needs to be enough scalable storage as you gather more data. Depending on the data you gather, you have various storage options, including NoSQL, a regular SQL database, MongoDB, and AWS.

Considerations for selecting the best storage alternatives for Big Data include portability, integration, data storage space, and ease of implementation.

3. Data Analysis

Data analysis is required to provide prompt recommendations. Therefore, the data must be probed and examined. Data analysis techniques that are most frequently used include:

  • Real-time analysis involves the system using tools to assess and analyze events as they are happening. Then, when we wish to make prompt recommendations, this strategy is typically used.
  • Batch analysis is a method for routinely processing and evaluating data. When we wish to send emails with recommendations, this strategy is typically used. 
  • A near-real-time analysis is practical when you do not immediately require the data. It allows you to process and analyze the data in minutes rather than seconds. The main application of the strategy is when we offer suggestions to users while they are still on the website.

4. Data Filtering

The data must be appropriately filtered after analysis to produce insightful recommendations. During filtration, Big Data is subjected to various matrices, mathematical algorithms, and rules for the best suggestion. The recommendations that result from this filtering depend on the algorithm you choose.

Recommendation engines rely on understanding relationships. Relationships give recommender systems a wealth of information and a thorough knowledge of their clients. Three different kinds typically exist:

User to product: When specific users have a preference or affinity for particular goods they need, there is a user-product relationship User to user: Users with comparable preferences for a given good or service form user-user relationships Product to user: Product-product relationships develop when two products are similar, whether through description or appearance. These are then shared with an interested user

How do Recommendation Engines Gather Data?

Recommendation engines gather data based on the following:

  • User Behavior: Information regarding user interaction with a product can be gleaned from user behavior data. Rating clicks and purchase history can all be used to gather it.
  • User Demographics: Users’ data, such as their age, education, income, and location, are tied to their demographic data.
  • Product Attributes: Information about a product’s attributes includes information on the product itself, such as the genre of a book, the movie actors, or the cuisine of a dish.

Challenges and Advantages of Using Recommendation Systems

Challenges: 

Implementing a recommendation system is rather complicated. The key challenges are: 

  • Data Collection and Integration: The first challenge is collecting data from various sources, such as customer profiles, product catalogs, purchase histories, social media accounts, etc., and then integrating the data into a unified format for the recommendation engine. The process can be time-consuming and labor-intensive.
  • Algorithms: Choosing the right algorithms for your recommendation engine is also challenging since you need to build an algorithm that understands user preferences and makes accurate predictions based on those preferences. Additionally, new algorithms must be implemented as customer preferences change over time.
  • Accuracy: Once you have chosen the best algorithms for your purposes, it can still be difficult to ensure accuracy in the system’s output. This becomes even more difficult as the amount of data increases.
  • Scalability: As your business grows, so does the volume of data, and it can be difficult to ensure that your recommendation engine can handle these large volumes of data without a performance decrease.
  • Data Storage: Storing all of the necessary data requires considerable space and resources. Additionally, the data must be kept up-to-date to ensure the system’s output accuracy.
  • Privacy and Security: Customers may be concerned about privacy when they share their personal information with a recommendation engine. It is important to have a strong security measure that protects customer data and prevents unauthorized access or use for malicious purposes.

Despite the steep implementation curve, companies continue to rely heavily on recommendation systems. This is because user recommendations have a very strong influence on user purchase decisions. The TWO major advantages of using a recommendation system will always outweigh all the challenges. 

Advantages: 

  • Deliver Personalized and Relevant Content

Recommendation systems enable brands to customize the customer experience by identifying and suggesting items that may pique the customer’s interest. Additionally, the recommendation engine allows you to analyze the customer’s previous browsing history and current website usage. With the help of these real-time data, brands can deliver the most relevant product recommendation. 

For instance, Instagram often suggests pages or brands you might want to start following based on your preferences. Similarly, based on the kind of reels you watch, Instagram keeps suggesting new ones. This keeps the addiction on as you continue scrolling through new recommendations you can’t deny liking. It boosts engagement via personalized content recommendations. 

  • Increase Sales and Average Order Value

You can significantly enhance your revenue and average order value (AOV) by bringing in more website visitors by adding recommended products.

A recommendation system enables users to drive more conversions and deliver a high level of relevance that will boost sales. It exposes the customers to more products they are more likely to purchase.

You can add multiple data sets to your recommendation algorithm using a recommendation engine. The data sets will help provide recommendations in real time.

For instance, when looking at a product on Amazon, you will always find two sections back-to-back – customers who viewed this have also viewed it, and customers who bought this also bought. These two sections will push you to consider different products that go well with the product you are trying to buy. 

Let’s say you are looking for a channel file on Amazon and have zeroed in on one of the options. As you scroll down to read more about its features and look at a comparative table (which Amazon always adds to propel better decision-making, ideally for the more priced product), you will come across two sections like this: 

Now, offline stores will call this push selling. This is exactly what Amazon is doing here. Recommending similar or related products often triggers human brains to explore those products and probably buy or add them to the cart. Towards the end of the purchase cycle, the order value automatically goes up from what it was when the cycle started. 

Filtering Algorithms Marketers Must Understand

To put a recommendation engine into proper functioning, marketers must also understand the algorithms that make up this system. Three major types of filtering algorithms are at play: Collaborative filtering content-based filtering Hybrid System

Collaborative Filtering

Collaborative filtering gathers information regarding customers’ activities or preferences to predict user interest. It is usually acknowledged based on their similarities with other users who might have the same preference. Similar customers are found using customer characteristics like demographics and psychographics. 

E-commerce platforms like Amazon and Myntra are pioneers in effectively implementing collaborative filtering. 

It works by gathering preferences from each user to determine a Customer X Product Matrix.

It is also known as the matrix factorization method and can be used to determine how similar user evaluations or interactions are. 

For example, according to the straightforward user-item matrix, Ted and Carol bought products B and C. Bob also searched for product B. Matrix factorization determines that users who enjoyed B also like C, making C a potential recommendation for Bob.

Collaborative filtering is divided as follows: 

User-item collaborative filtering Like-minded customers are spotted based on similar rating patterns. It is a method for recommending products based on ratings from other users.
Item-item collaborative filtering Similarities between multiple items are calculated. A recommendation approach that looks for comparable products based on the things consumers have already shown interest in or interact with.

Content-based Filtering

Predictions made by content-based engines are based on end-user interest in a particular product. The engine uses metadata to find and suggest related content items once a user interacts with a piece of content. 

You can spot this recommender system on news websites with prompts like “You may also be interested in reading” or “You may like.”

For instance, on Medium, you will always find recommendations for articles similar to the ones you are reading or have read. 

The recommendation engine algorithms filter content based on the following:

  • User ratings: User ratings are of two types: Explicit and Implicit.  User profiles and star ratings are common sources of explicit data for recommendation systems (difficult to achieve). Implicit data is based on users’ engagement with the item. These are simple to obtain, including purchases, clicks, and views.
  • Product similarity: The most effective method for recommending products based on how much a customer would like a certain item is product similarity. Users may be shown comparable products if they browse or look for a specific item. Here’s how Amazon does it.
  • User similarity: It is a particular kind of algorithm for recommendation systems that suggest products based on product resemblances. Using data from previous user-product interactions, the algorithm generates suggestions. It assumes that individuals with similar interests will favor related goods. This is how Myntra does it: 

In Conclusion:

Note: The debate on privacy versus personalization has been hot since the recommendation systems rise. While such technologies have enabled organizations to provide tailor-made customer experiences through big data analytics, there is an inherent tension between these two concepts.

Companies must collect and store large amounts of data to generate personalized customer recommendations. Reports show that Big Data analytics has helped them increase revenue by 30%

On the other hand, companies must ensure that all collected data is securely stored and not misused. Survey results show 97% of consumers are concerned about their data being misused by companies and the government.

Ultimately, striking a balance between privacy and personalization should be the primary goal to ensure that customer data remains secure while providing an enjoyable customer experience. 

With this in mind, businesses can leverage big data-powered recommendation engines to offer a customized experience that will keep their customers returning for more.

Images from analytixlabs.co.in

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How To Create A Rapid Research Program To Support Insights At Scale

July 12th, 2023 No comments

While the User Experience practice has been expanding and will continue to balloon in the coming years, so have its sub-disciplines such as content strategy, operations, and user research. As the practice of UX Research matures, scalability will continue to be important in order to meet the rapid needs of iterative product development.

While there are several effective ways to scale user research, such as increasing researcher-to-designer ratios, leveraging big data and real-time analytics, or research democratization, one of the most effective methods is developing a Rapid Research program. In a Rapid Research program, teams are provided quick insight into key problems at an unprecedented operational speed.

Rapid Research-type support has been around for a while and has taken different shapes across different organizations. What remains true, however, is the goal to provide actionable insights from end-users at a quick pace that fits within product sprints and maintains pace with agile development practices.

In this article, I’m going to unpack what a Rapid Research program is, how to build one in your organization, and underscore the unique benefits that a program like this can provide to your team. Given that there is no singular ‘right way’ to scale insights or mature a user research practice, this outline is intended to provide building blocks and considerations that you may take in the context of the culture, opportunities, and challenges of your organization.

What Is Rapid Research?

Rapid research is a relatively recent program where typical user research practices and operations are standardized and templatized to provide a consistent, repeatable cadence of insights. As the name suggests, a core requirement of a rapid research program is that it delivers quicker-than-average insights. In many teams, this means delivering research on a weekly cadence where a confluence of guardrails, templates, and requirements work to ensure a smooth and consistent process.

Programs like Rapid Research may be created out of a necessity to keep up with the pace of development while freeing the bandwidth of expert researchers’ time for more complex discovery work that often takes longer. A rapid research program can be a crucial component of any team’s insight ecosystem, balanced against solving different business problems with flexible levels of support.

Scope

Research Methods

In order to make research more rapid, teams may consider some research methodologies out of the question in their Rapid Research program. Methods such as longitudinal diary studies, surveys, or long-form interviews might suffer from lower quality if done too quickly. When determining the scope of your rapid research program, ask yourself what methods you can easily templatize and, most importantly, which best support the needs of your experience teams.

For example, if your experience teams work on 2-week sprints and need insights in that time, then you will need to consider which research methods can reliably be conducted in 1–2 week increments.

Sample Size And Research Duration

Methods alone won’t ensure a successful implementation of a rapid research program. You will also need to consider sample size and session duration. Even if you decide usability tests are a reasonable methodology for your rapid research framework, you may be introducing too much complexity to run them with 15+ users within 60-min sessions and analyze all that data efficiently. This may require you to narrow your focus to fewer sessions with shorter duration.

Participant Recruitment

While there may be fewer and shorter sessions for each study, you also need to consider your participant pool. Recruitment is one of the most difficult aspects of conducting any user research, and this effort must be considered when determining the scope of the program. Recruitment can jeopardize the pace of your program if you source highly specific participants or if they are harder to reach due to internal bureaucracy or compliance constraints.

In order to simplify recruitment, consider what types of participants are both the easiest to reach and who account for the most use cases or products you expect to be researching. Be careful with this, though, as you don’t want to broaden your customer profiles too much for fear of not getting the helpful feedback you need, as UserZoom says:

“Why is sourcing participants such a challenge? Well, you could probably find as many users as you like by spreading the net as wide as possible and offering generous incentives, but you won’t necessarily find the ‘right’ participants.”

— UserZoom, “Four top challenges UX teams face in 2020 and how to solve them

Timing

Why Timing Matters

Coupled tightly with scope, the timing of your rapid research end-to-end process will be paramount to the program’s success. Even if you have narrowed the scope to only a handful of research methods with limited sessions at shorter durations and with specific participant profiles, it won’t be ‘rapid’ if your end-to-end project timeline is as long as your average traditional study. Care must be taken to ensure that the project timelines of your rapid research studies are notably quicker than your average studies so that this program feels differentiating and adds value on top of the work your team is already doing.

Reconsidering scope

If your timelines are about the same, or your rapid cadence is less than 50% more efficient than your average study, consider whether or not you’re being judicious enough in your scope above. Always monitor your timelines and identify where you can speed things up or limit the scope in order to reach a quick turnaround, which is acceptable. One way to support shorter project timelines is through compartmentalization.

Compartmentalization

About Compartmentalization

One way to balance scope, timing, and consistency is by breaking up pieces of your average study process into smaller, separate efforts. Consider what your program would look like if you separated project intake from the study kick-off or if discussion guides were not dependent on recruitment or participant types. Splitting out your workflow into separate parts and templating them may eliminate typical dependencies and streamline your processes.

Ways To Compartmentalize

Once you’ve determined the set of research methods and ideal participants to include in your program, you may:

  • Templatize the discussion guides to provide a quick starting point for researchers and cut down on upfront preparation time.
  • Create a consistent recruitment schedule independent of the study method to start before study intake or kick-off to save upfront time.
  • Pre-schedule recurring kick-off and readout sessions to set expectations for all studies while limiting timeline risk when at the mercy of others’ calendars.

There is a myriad of opportunities to do things differently than your typical research study when you reconsider the relationships and interdependencies in the process.

Consistency

Expectability

While a quality rapid research program takes into consideration scope, timing, and compartmentalization, it also needs to consider consistency. It would be difficult to discern whether or not the program was ‘rapid’ if, on one week, a study takes one week, and on another week, a study takes 2.5 weeks. Both may be below your current study average. However, project stakeholders may blur the lines between the differences in your rapid studies and your typical studies due to the variability in approach. In addition, it may be difficult to operationalize compartmentalization or rapid recruitment without some form of expected cadence.

More Agility

As you and your team get used to operating within your rapid cadence, you may identify additional opportunities to templatize, compartmentalize or focus scope. If the program is inconsistent from study to study, it may be more difficult to notice these opportunities for increased agility, hindering your program from becoming even more rapid over time.

A Rapid Research Case Study

While working at one of the largest telecommunications companies in the US, I had the privilege of witnessing the growth of the UX Research team from just four practitioners to over 25 by the time I left. During this time, the company had matured its user experience practice, including the standards, processes, and discipline of user research.

Identifying The Need

As we grew, human insight became a central part of the product development process, which meant an exponential increase in its demand. While this was a great thing and allowed our team to grow, the work we were doing was not sustainable — we were constantly trying to keep pace with product teams who brought us in too late in the process simply to validate their ideas. Not only did we always feel rushed, but we were stuck doing only evaluative work, which not only stifled innovation but also did not satisfy our more senior researchers who wished to do more generative research.

How It Fits In

Once diagnosing this issue, our leadership initiated several new processes to build a more well-rounded research portfolio that supported iterative research while enabling generative research. This included a democratization program, quarterly planning, and my initiative: Rapid Research. We determined that we needed a program that would allow us to take on mid-sized projects at the pace of product development while providing a new opportunity to hire junior researchers who would be a great talent pool for our team and provide a meaningful way for those new to the field to grow their skills.

Getting Started

In order to build the rapid research program, I audited the previous year’s worth of research to determine our average timelines, the most common methodologies used for iterative and mid-sized projects, and to identify our primary customer who we do research with most often. My findings would be the bedrock of the program:

  • Most iterative research was lite interviews and brief usability tests.
  • Many objectives could be covered in 30-minute sessions.
  • Mid-sized projects were often with just a handful of current customers.
  • Our average study time was 2–3 weeks, so we’d need to cut this down.
  • Given the above constraints, study goals should be highly focused.

Building The Program

At first, we did not have the budget for hiring new junior researchers to staff the program team. What we did have, however, was a contract with a research vendor who we’ve worked with for years, so we decided to partner with researchers from their team to run our rapid research program.

  • We created specific templates for ‘rapid’ usability tests and interviews.
  • Studies were capped at two objectives and only a handful of questions in order to fit into 30-min sessions.
  • Study intake was governed via a simple intake form, required to be filled out by EOD every Wednesday.
  • We scheduled standing kick-off and readout sessions every Friday and shared these invites with product teams for visibility.
  • To further establish our senior researchers as Portfolio Research Leads and to protect against scope creep, we required teams to formally request ‘rapid’ studies through them first.
  • We started our rapid cadence at two weeks and were able to cut it down to just one week after piloting the program for a month.

Strong Results

We saw the incredible value and strong results from building our rapid research program, especially alongside the other processes our team was standing up to support varying insights needs.

  • Speed
    We were able to eventually run three research studies simultaneously, enabling us to deliver more research at twice the pace of a traditional study.
  • Scale
    Through this enablement of speed, consistent recruitment, and templatized process, we ran over 100 studies & 650+ moderated interviews.
  • Impact
    Because we outsourced rapid research to a vendor, our team was freed up to deliver foundational research, which doubled our work capacity.
  • Growth
    Eventually, we hired junior researchers and transitioned the program from the vendor, increasing subject matter expertise & operational efficiency.

How To Build A Rapid Research Program

The following steps outline a process for getting started with building your own rapid research program in your organization. Exactly which steps you choose to follow, or if you decide to add more or less to your process, will be entirely up to you and the unique needs of your team. Follow the proceeding steps while considering the above guidelines regarding scope, timing, compartmentalization, and consistency.

Determine If You Even Need A Rapid Research Program

While seemingly counter-intuitive, the first step in building a rapid research program is considering whether you even need one in the first place. Every new initiative or tactic intended to mature user research practice should consider the available talent and capabilities of the team and the needs or opportunities of the organization it sits within. It would be unfortunate to invest time to build a robust, rapid research program only to find that nobody uses or needs it.

Reflection On Current Needs

Start by documenting the needs of your experience teams or the organization you support by the different types of requests you receive.

  • Are you often asked to deliver research faster?
  • What are the types of research which are most often requested?
  • Does your team have the capability or operational rigor required to deliver at a faster pace?
  • Are you staffed enough to support a more rapid pace, even if you could deliver one?
  • Is delivering faster, rigidly-scoped research in service to your long-term goals as a research team, or might it sacrifice them?

Gather More Information

Answering these questions should be your first step before any meaningful work is done to build a rapid research program. In addition, you might consider the following information-gathering activities:

  • Audit previous research you or your team have done to determine their average scope, timeline, and method.
  • Conduct a series of internal stakeholder interviews to identify what potential value a rapid research program might hold.
  • Look for signals for where the organization is going. If leadership is hiring or training teams on agile methods or demanding teams to take a step back to focus on discovery can help you decide when and where to invest your time.

These additional inputs will either help you refine your approach to building a program or to steer away from doing so.

Limitations Of Rapid Research

Finally, when considering if you should build a rapid research program in the first place, you should consider what the program cannot do.

  • What a rapid research program might save on time, it cannot necessarily save on effort. You will still need researchers to deliver this work, which means you may need to restructure your team or hire more people.
  • If you decide to make your rapid research program self-service, you likely will still need ResOps support for recruitment and managing the intake process effectively.
  • It is also possible to hire a research vendor partner to lead this program, though that will require a budget that not every team may have.
  • As mentioned above, a good rapid research program is tight and focused in its scope, which limits the type of projects it can accommodate.

Identify Your Starting Scope, Timing & Cadence

Once you’ve decided to pursue a rapid research program, you’ll need to understand what form your program should take in order to deliver the highest value to your team and those you support. As mentioned above, a right-sized scope should consider the research methods, requirements, session quantity & duration, and participant profiles, which you can confidently accommodate. And you will need to determine the end-to-end timing and program cadence that differentiates from current work while providing just enough time to still deliver sustainable quality.

Determine Participant Profiles

Start building your scope backwards from the needs gaps you’re filling within your team based on the answers to the discovery questions above. You’ll want to identify the primary type(s) of end-users this program will research.

  1. Audit the past 6–12 months of research you or your team has done, looking at the most common customer type with whom you do research.
  2. Then, couple that with any knowledge you may have of where the business or your experience teams will be focused for the following 6–12 months.

For example, if your audit revealed that your team had focused most frequently on current customers over the past year, and you also know that your business will soon focus on the acquisition of new customers, consider including both current customers and prospective customers in your rapid research scope.

Remember the important note about consistency above? Once you’ve identified potential participant profiles, make sure you can consistently recruit them. For example, if you use a research panel to source participants for research studies, test the incidence of your participant profiles. If you find they don’t have many panelists with the attributes you need, you might spend too much time in recruitment and jeopardize the speed of the program.

A balance should be struck between participant profiles that are specific enough to be useful for most projects and those broad enough to reach easily.

Determine Research Methods

You can conduct the same audit and rough forecasting when determining the research methods your program ought to support but with two additional considerations:

  1. Team strategy,
  2. Individual career development.

User researchers tend to focus their work further upstream, where they’re driving product roadmaps or influencing business strategy. This can bode well for your rapid research program if it is focused on evaluative research projects, which are often quicker and cheaper to conduct.

The ultimate goal is for the rapid research program to be a complement to what your team provides or as an enabler for freeing up their bandwidth so that they can focus on the type of work they want to do more of.

Right-size Research Methods

Once you’ve determined which research methods you want to include in your rapid research program, consider the level of rigor you need to balance effort and complexity.

Determining Timelines

Project timelines within a rapid research cadence are directly affected by the above scope decisions for participant profiles and research methodology. Timelines can also compound in highly regulated industries such as healthcare or banking, where you may be required to gather legal & compliance approval on every moderation guide. In order to call this a rapid research program, the end-to-end project timelines need to be shorter than a typical project of a similar scope, or at least feel that way.

  1. Scope current minimum effort
    Start by jotting down the minimum amount of time it takes a researcher on your team to do each sub-step in your current non-rapid research process. Do this for the same participant profiles and methods you want to include in your rapid research program.
  2. Dependencies
    Now, identify which sub-steps are dependent on others and think of ways to program them in order to build efficiency. For example, if you need legal approval on every moderation guide before data collection, which takes 2–3 days, see if Legal will commit to a change to a 24-hour SLA for rapid research-specific projects. Another example is if you typically give stakeholders a few days to provide feedback on moderation guides, change this for rapid research projects to cut down dependency time.
  3. Identify compartmentalization
    In addition to programming project dependencies, consider the above guidance for compartmentalizing some of the programs in order to remove dependencies entirely, such as with recruitment. Identify what parts of the process don’t have the same dependencies in your rapid research program and can be started earlier. By removing dependencies entirely, you may be able to do several things simultaneously to speed up project timelines.

Once you’ve documented your current research process (steps, dependencies, timing) and the changes you need to make to build efficiencies or remove dependencies, document what ‘must be true’ in order to consistently deliver identified changes. Create a table to document all of these details, then sum up the total timelines to compare your typical end-to-end research project timeline with your potential new ‘rapid’ timeline.

Ask yourself if this seems ‘rapid’ when stacked against your average study duration.

  • If not, look back at the guidance above. Ask yourself if there are other customer types that may be easier to get in front of that you haven’t considered. Consider whether you need to create a new process, expedite existing processes, or create new relationships in order to make your timelines even more rapid.
  • If so, congratulations! You might have just landed on the right scope for your rapid research program. Consider whether this new rapid timeline is something that you can deliver consistently and reliably over time and whether or not you have enough access to participants, and enough budget, to carry out this cadence long-term.

Build Infrastructure, Standards & Rules

It’s time to set the foundation. Return back to the tables you made above and create an action plan with the following steps and a timeline to build the infrastructure required to bring your program to life. As part of this, you’ll need to establish the rules and standards for communicating with partners. You might consider a playbook and formal scope document to inform others of the ins/outs of the program.

Gather Buy-in

Prioritize any work that requires buy-in, generating understanding, or acquiring budget first before spending your time and energy building templates or documentation. You wouldn’t want to create a 20-page scope document outlining the bandwidth for two researchers, a limit to 1 round of stakeholder feedback, and a 24hr SLA for legal approval, only to find out others cannot commit to that.

Create Templates

You’ll need plenty of templates, tools, and processes specific to the scope of your program.

  • If you’re limiting moderation guides to a maximum of 10 questions, then create a specific discussion guide template reflecting that.
  • If your data analysis will be sped up by using structured note-taking templates, create those.
  • If you’ve determined that all rapid research projects only require an executive summary one-pager, make that too.

Staffing

As mentioned above, even a drastically reduced version of your typical research processes still requires effort to support. You’ll need to determine, based on the expected scope and cadence of each rapid research project, how many researchers and/or research operations coordinators you’ll need to support the program. While all rapid research programs will require dedicated effort, there are creative ways of staffing the program, such as:

  • A dedicated team of 1–2 researchers and 1–2 Ops coordinators to deliver projects with the greatest efficiency and quality.
  • A dedicated team of 1–2 researchers who also handle the operations of running the program itself.
  • A self-service program, with 1–2 Ops coordinators for supporting anyone doing the research work.
  • Outsourcing the entire program to a vendor.

Work with your leadership, HR, and TA professionals on securing approval for any team restructure, needed headcount budget, or to onboard a new vendor. Then, take the appropriate steps to hire your next researcher or secure the staffing help you need to support your program.

Coaching And Guidance

Consider training, coaching, and check-in meetings as part of your infrastructure.

  • If you are staffing new researchers to this rapid research program, make sure they understand the expectations and have what they need to succeed.
  • If you’re implementing a self-service model, provide brown-bag sessions to partners to explain the program do’s and don’ts.
  • Schedule quarterly check-ins with partners and leadership to discuss the program accomplishments and any needed adjustments to ensure it stays relevant.

Pilot, Get Feedback, And Iterate Over Time

No matter how much preparation you do or how much time and effort you spend building the alliances, infrastructure, training, and support required to run your rapid research program effectively, you will learn that there are improvements you should make once you put it into practice.

There are many benefits to piloting a new program in an organization. One benefit is that it can mitigate risks and allow teams to learn quickly and early enough to make positive enhancements.

“Piloting offers a realistic preview experience for users at the earliest stages of development. It allows the organization and design team to gather real-time insights that can be used to shape and refine the product and prepare it for commercialization.”

— Entrepreneur, “Tasting As You Go: The 5 Benefits of ‘Piloting’

This means setting expectations early. Consider your first few projects as pilots and expect them to be rocky and imperfect. Use this to your advantage by asking stakeholders you’re closest with to be your trial projects and let them know how important their honest feedback is throughout the process. Ensure that you have clear mechanisms to gather feedback at each project milestone so that you can track progress. It is especially important to capture what might be slowing you down along the way or putting your ‘rapid’ timelines at risk.

Program Evolutions, Impacts & Considerations

Potential Evolutions & Variations

While I’ve outlined a process for getting started, there are many ways in which your rapid research program may evolve over time to meet the needs of your organization better.

  • After a few periods, you might identify volume isn’t as high as you anticipated, so you extend the 1-week timeline to every two weeks.
  • After a few months, your business might launch a new product line, requiring you to consider a new set of customer profiles in recruitment.
  • You may decide to leverage your rapid cadence for individual segments of a longitudinal diary study to accommodate new methods.
  • You might use rapid research projects to exclusively evaluate in-market products while others on the team focus on in-progress / new products.
  • Rapid research projects could be a stage-gate for larger projects — proving a customer need before larger time investments are made.

However your rapid research program takes shape, revisit its goals, scope, and operations often in relation to your organizational needs and context so that it remains relevant and delivers the highest impact.

Solid Impacts From Rapid Research

Building a rapid research program can have a big impact and can contribute positively toward your team’s long-term strategy. One impact of instituting a rapid research program could be that now your team is freed up to focus on more generative research, which unlocks your ability to deliver deep customer insights that pave the way for innovation or strategy. And due to your new rapid pace, you may be able to keep pace with agile development and conduct end-to-end research within 2-week sprints. Another impact is that you may catch more usability issues further upstream, saving you over 100x in overhead business cost. A final impact of a rapid research program is that it can double your team’s throughput, allowing your team to deliver more research, more frequently, to accommodate more organizational needs.

Be sure to track these impacts over time so that you not only get credit for the hard work you put into building the program but so that you can sustain and grow the program over time.

Considerations When Building A Rapid Research Program

As mentioned in this article, there are many benefits to building a rapid research program. That being said, there are limitations to rapid research in regard to its pros and cons when it should be used, and if you have the available time to stand up a program yourself.

Pros And Cons

As with building any new program, one should consider both its benefits as well as drawbacks. Here are a few for rapid research programs:

Pros:

  • Can free time for foundational work;
  • Rapid studies may keep a better pace with development cycles;
  • Can create meaningful opportunities for junior staff;
  • Can double project throughput, increasing output volume.

Cons:

  • Still requires work and dedicated bandwidth;
  • Another thing to diligently track and manage;
  • Not great for all types of research studies;
  • May cost more money or resources you don’t have.

Guidance For Using The Program

Rapid Research programs are best for specific types of research which do not take a long time to complete or require rigorous expertise. You may want to educate your partners on when they should expect to use a rapid research program and when they should not.

  • Use rapid research when:

    • Agility or quick turnaround is needed;
    • You need simple iterative research;
    • Stakeholder groups are easier to rally;
    • Participants are easy to reach.
  • Do not use rapid research when:

    • The study method cannot be done quickly without risking quality;
    • A highly complex or mixed-methods study is needed;
    • A project requires high visibility or stakeholder alignment;
    • You have specific, hard-to-reach participants.

Ramp Up Time

While the exact timeline of building a rapid research program varies from team to team, it does take time to do it right. Make sure to plan out enough time to do the upfront work of identifying the appropriate scope, timing, and cadence, as well as gathering consensus from leadership and appropriate stakeholder groups. Standing up a Rapid Research program can take anywhere from 3 months to 1 year, depending on the following:

  • Legal and compliance limitations or requirements.
  • The number of stakeholder groups you need buy-in from.
  • Approval of budget for outside vendors or for hiring an in-house team.
  • Time it takes to build templates, guidelines, and materials.
  • Onboarding, training, and iteration when starting out.

Conclusion

A rapid research program can be a fundamental part of your team’s UX Research strategy, enabling your team to take on new insight challenges and deliver efficient research at an unprecedented pace. Building a rapid research program with high intention by determining the goals, appropriate scope, and necessary infrastructure will set your team up for success and enable you to deliver more value for your organization as you scale your user research practice.

Don’t be afraid to try a rapid research program today!

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