Over the past few decades, Customer Relationship Management (CRM) has become the cornerstone of businesses looking to leverage data: according to statistics, more than 91% of companies with ten employees or more use CRMs to manage customer information. How many of them gain actionable insights from their CRM database, though?
This is not many, say studies. The 2022 State of CRM Data Health Report reveals that about 95% of companies face data quality issues that prevent them from fully leveraging their CRM database. Poor data quality has caused about 44% of companies to lose 10% of their annual revenue, while 75% of organizations have faced customer loss that could have been avoided through better data.
CRM data enrichment – i.e., improving, cleaning, and refining your CRM data using tools – is the best way to address such data quality issues and usability roadblocks. However, enriching highly qualitative and unstructured information, such as customer feedback, and deriving insights from it can be challenging. That is where customer feedback analysis gains significance as a powerful information enrichment tool.
So, what exactly is customer feedback analysis? How does it help you make the most of your CRM data? How do you analyze feedback from customers – should you do it manually, or are there any tools you can use to automate the process? If there are tools, how do you choose the right one?
Read on to find answers to all these questions and more. In this post, we talk about how to use customer feedback analysis for data enrichment.
What Is Customer Feedback Analysis?
Simply put, it is the process in which companies break down customer feedback using tools and turn it into meaningful, actionable, and useful insights. It opens a direct doorway to customers – you can make better decisions based on what you learn from them, offer them products and services they want, and improve the overall customer journey.
Let’s try and understand it further. Studies show that using the insights obtained from customer data for decision-making is a priority for more than 85% of businesses. And, what is one of the largest pools of customer information readily available to them? Yes, you guessed it right, feedback from customers.
You receive customer feedback in different formats, such as text, speech, videos, graphics, images, etc., through multiple channels. These include:
- Support conversations
- SMS and email
- Feedback forms
- In-app and on-site surveys
- Customer reviews on third-party websites
- Social media posts
- Live chats
Despite being abundant, customer reviews might not be meaningful or usable in its raw form. Imagine that you are running an eCommerce company. The product feedback you receive from customers through calls, chats, and online reviews will be in cluttered language or informal speech, making it hard to decipher the emotions, intentions, and sentiments behind it.
In other words, though you have large volumes of unstructured, qualitative feedback data in your CRM, you lack the methods or tools to analyze and process these data sets for insights and patterns. Customer feedback analysis is one such knowledge-structuring tool to extract relevant details and gain usable knowledge from customer reviews collected in different formats.
Why Is CRM Data Enrichment from Customer Feedback Analysis Important?
As we have already said, customer content in its original form – natural speech or text – would rarely give you any actionable insights. Why? Here are a few reasons.
- Complex language
A group of customers who give the same feedback about a product or service might do it in strikingly different ways. Check out the examples below:
- Customer 1: Your payment process is too complicated.
- Customer 2: It took me ages to complete the payment.
- Customer 3: Your payment page is difficult to navigate.
- Customer 4: I have never seen a payment page as non-intuitive as yours.
Here, all the customers point out the same issue and express the same sentiment – frustration. However, their choice of words and syntax vary, which makes it hard to extract useful information from such feedback.
- Feedback quality
Customer feedback is qualitative, and hence its quality differs. Some customers might give you specific and instantly usable feedback, while others offer generic, vague, and unusable responses. Here are two examples:
- Example 1: Your checkout process was a nightmare.
- Example 2: I couldn’t add more than three products to the cart. The coupon code was also not working. Then again, I couldn’t use credits from previous purchases. I am disappointed!
Here, the first response doesn’t add any value to your analysis, while the second feedback gives you relevant information on the issues or glitches in your system. Sorting loads of such data sets and identifying which are insightful and which are not can be a laborious task.
- Multilingual feedback
Customers might choose to leave their feedback in different languages. For instance, a France-based company is likely to get customer feedback in both French and English. To make it usable, you must first translate the text or speech from one language to another.
- Multichannel feedback
When your business has a presence on multiple platforms, you receive customer feedback through more than one channel. While some of these channels, such as support calls, are speech-based, others are text-based. Collating and streamlining feedback data spread across channels can be tricky unless you use tools designed for that.
- Categorizing the feedback
Customers don’t thematically structure their feedback. A customer giving feedback might talk about billing issues, product quality, and refund issues in the same call or message. Without extracting these themes/topics and labeling them, you cannot take any action for improvement.
- Cluttered texts
Text-based feedback, such as social media posts, chats, and online reviews, is often cluttered. Typos, emojis, structural mistakes, grammar errors, repetitive content, improper use of punctuations and cases, usage of slang, presence of URLs, stop words, and figures of speech make it complex to understand. Such information in your CRM database needs cleaning before you can use it.
How Does Customer Feedback Analysis Work?
The process of customer feedback analysis makes large and cluttered bodies of information usable by categorizing them into insightful and non-insightful data. There was a time when companies used to do it manually, but today there are AI-based Natural Language Processing (NLP) tools to automate the whole process.
Such a tool comes with a suite of analytical tools that help identify key data patterns and trends from qualitative and unstructured feedback. The information and knowledge thus extracted automatically enrich your CRM data during the process.
Listed below are some of the key tasks that an AI-based feedback analysis can automate for you:
- Data collection from various sources and channels
- Data cleaning
- Text categorization
- Machine-based translation
- Survey analysis
- Sentiment analysis
- Speech-to-text and text-to-speech conversion
- Keyword analysis
- Topic analysis
- Contextual extraction of texts
- Synopsis generation for large texts
- Dictionary and vocabulary building
Benefits of CRM Data Enrichment from Customer Feedback Analysis
The primary objective of customer feedback analysis is to pull out usable and granular-level insights from a largely unstructured data set. You can use the insights thus gained to enhance your operations, sales, and revenue. Here are some of the ways you can benefit from the process:
- Improve your products and services
Performing CRM data enrichment through feedback analysis, through surveys for example, is a sure-shot way to improve the quality of your products and services. An enriched CRM database will indicate bugs that need immediate fixing, features and functionalities that need improvement, and features that are missing from your product or service suite.
- Personalize your products and services
Customer expectations regarding personalization are soaring high, creating pressure on businesses to offer curated experiences for users. According to a 2021 report by Mckinsey, 71% of customers expect personalized experiences based on their past interactions, while 76% of them feel frustrated when brands do not offer them.
Customer feedback analysis is a great way to enrich your CRM data for personalization. It helps create comprehensive and updated customer profiles based on which you can offer curated experiences and differentiate your company in a highly competitive market.
- Enable your sales team to sell and upsell
Sentiment analysis, an integral part of customer feedback analysis, opens up opportunities for your sales team. You can optimize your sales strategies based on the insights it gives and identify the right time to sell a product or service. AI-based analysis indicates whether a customer is in a good mood to buy, thus allowing your sales team to nurture leads and pitch a product or service at the right time.
- Improve customer service and reduce churn
Customer service matters; quality customer service matters more. A report by Zendesk reveals that nearly 50% of customers are likely to switch to a competitor after one bad experience from a business, and the number shoots up to a staggering 80% with more than one bad experience.
Analyzing the feedback, surveys and tone of a customer will equip support agents to adopt suitable strategies that increase customer satisfaction. For instance, if you notice that customers are unhappy with delayed deliveries, you can offer them a promo code for the next purchase as a gesture. Likewise, for a customer who is happy with a purchase, you could try upselling products that further enhance their journey.
- Improve your customer satisfaction metrics
All businesses must track a few metrics to understand what customers feel and experience about their offerings and brands. Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Health Score (CHS) are among the most crucial metrics. Analyzing feedback is a great way to measure and track these metrics and improve your offerings based on them.
How to Choose a Feedback Analysis Tool for CRM Data Enrichment?
There is a good range of market-leading tools for customer feedback analysis, each with different features, functionalities, and pricing. How do you choose one that is worth your investment? Ask yourself these five questions before selecting a tool:
- What are your business requirements?
The tool you choose must suit the size of your company, the volume of feedback, the scale of operations, and your budget. Standard third-party tools are ideal for SMEs and enterprise-level businesses that want to understand large volumes of information. If you are looking for a tool that automates the process end to end and offers qualitative insights, an AI-powered analysis tool is your best choice.
- What are the features and functionalities?
Invest in a tool that offers all essential features and functionalities, such as text collection, cleaning, labeling, vocabulary customization, and sentiment analysis. Features like multi-language translation and transcription are ideal for companies that collect feedback in multiple languages and through speech-based and text-based channels.
- Does the tool analyze feedback from across channels?
Not all platforms analyze content the same way. While some analyze only Net Promoter Scores (NPS), others analyze text-based feedback, like surveys. Standard third-party solutions often don’t cover speech-based feedback like support conversations. Opting for tools that support all communication channels is always a great idea. That way, you can enjoy scalability and flexibility. It also makes spotting trends and identifying patterns across channels easier.
- How granular do you want the feedback insights to be?
Some tools do broader-level analysis, while others do granular-level analytics. Both help you in decision-making, but granular analytics platforms don’t require you to do any manual tasks to understand the insights. AI-powered tools with sentiment analysis are also a good choice for gaining accurate and granular insights into customer mindsets.
- Does the tool allow easy CRM integration?
It is a no-brainer, right? You are investing in an analytics software for CRM data enrichment, so what use is it if the tool does not offer CRM integration? Choose a tool that allows easy setup and integration with your CRM, text analytics tools, and other APIs and management software.
Best 5 Customer Feedback Analysis Software for Your Business
While choosing feedback analysis software for your company, the best approach is to do your research, shortlist a few, and book a demo to identify the one that suits your business. To help you save time, we have shortlisted five industry-leading customer analysis tools.
Lettria is a no-code AI platform that allows companies to make the most of their textual data through Natural Language Processing (NLP) automation. It requires minimal configuration and offers an intuitive user experience and easy integration with CRMs. Users can use the platform to collect textual data of all kinds and formats from multiple channels and analyze it, like as an NPS for example. The platform runs a gamut of powerful features, ranging from text collection to cleaning to annotation to vocabulary management.
Pricing and plans: Customizable plans are available at competitive pricing. Click here to get a quote.
Glanceable, a B2B SaaS solution for customer review analysis, is ideal for SMEs and large businesses. It consolidates all your customer review information into a single platform and generates AI-powered insights that you can use to drive sales and revenue. They offer solutions and products for brand perception, thematic analysis, survey generation, review management, and internal benchmarking, to name a few.
Pricing and plans: Plans start at €499. To know more about them, click here.
Qualtrics is a customer experience management (CXM) platform that lets you gather customer feedback information through the digital journey and surveys and consolidate it into a single platform. The tools offered by the company help you understand the collected information from surveys for usable insights and improve your customer journey. They also have product, employee, NPS, and brand experience solutions, which makes it a good choice for businesses that need a 360-degree experience management solution.
Pricing and plans: Subscription-based customizable plans are available. Contact the sales team for more info.
- IBM Watson Studio
IBM Watson Studio is a leading machine learning and data science platform for AI-powered businesses. It offers a suite of data science tools for users to import surveys and analyze data sets, including visual data, using several analytical techniques. From NLP services for advanced text analytics to speech-to-text and text-to-speech tools to language translators to natural language classifiers, they offer a range of services and products to get knowledge from user data.
Pricing and plans: Multiple licensing options, pay-as-you-go, and all-in-one packages are available. Click here for more details.
Chattermill is a Unified Customer Intelligence platform to unify and analyze customer feedback information, including support and conversation data, obtained from across multiple channels like surveys, etc. It uses deep learning AI for customer review analysis, detects sentiments, and offers unbiased and result-oriented insights. The salient features of the user platform include feedback collection, content aggregation, tagging, thematic analysis, data visualization, sentiment analysis, NPS, and more.
Pricing and plans: Free and subscription-based plans are available. Click here for more details.
Start Enriching Your CRM Data Today
Your CRM database is a goldmine, but if not enriched using the proper tools, it can become a minefield that harms your sales, revenue, and reputation. Why suffer from data usability roadblocks when you can get insights with ease and accuracy using a customer feedback analysis tool?
Whether you are a start-up without a technical team or an established company with teams of experts, you can leverage the power of such AI-enabled analysis platforms. Invest in a customer feedback analysis tool like Lettria and harness the power of data to stay ahead of the game.
Learn more about Lettria