Customer Analytics
Leverage Customer-Centric Insights, Personalize Customer Experiences, and increase Customer Loyalty with AI-powered Customer Analytics
The key to success in today's customer-centric business landscape is the ability to personalize every interaction with customers. This requires businesses to leverage customer data from multiple touchpoints to understand their preferences, behaviors, and needs.
With Valiance's customer analytics solutions, you can unlock the power of your data to create personalized experiences, improve marketing ROI, and boost customer satisfaction. We empower you to optimize marketing campaigns, offer relevant cross-sell and upsell opportunities, and deliver exceptional customer service.
Our expertise in data integration and analytics empowers you to gain a complete 360-degree view of your customers, so you can make informed decisions that drive results
Why Customer Analytics Matters
Customer Retention
Customer analytics empowers businesses to understand the key drivers of customer loyalty and satisfaction, so they can focus their retention efforts on the areas that matter most. For example, based on the customer profile, the AI-powered system can identify patterns and trends, such as decreased usage, missed payments, or many complaints, to identify at-risk customers. Based on this information, companies can provide personalized retention offers and incentives to encourage customers to stay, such as discounted rates or additional service features. They can target marketing campaigns to build stronger relationships with their customers and increase their likelihood of returning.
Personalization
Personalization is a powerful tool for increasing customer satisfaction, loyalty, and retention. It shows customers that businesses understand their needs and will go above and beyond to meet them. However, companies must deeply understand their customers' needs and preferences to implement customer personalization successfully. They should also have access to comprehensive customer data and analytics.
Customer 360
Businesses can improve engagement and retention by leveraging a customer 360 view and implementing a unified omnichannel operating model, increasing customer satisfaction and loyalty. A customer 360 view offers a complete picture of the customer's data and interactions across all touchpoints and channels, so businesses can understand the customer’s behavior and preferences, predict future needs, and identify crossselling and upselling opportunities
Use Cases
Customer Retention
Personalization
Voice of Customer
Customer Retention
Customer retention analysis focuses on predicting which customers are at risk of leaving and identifying the reasons behind their potential churn. Businesses can develop targeted retention strategies to address the underlying causes of churn and increase customer loyalty. It can also help businesses identify high-value customers and tailor personalized retention programs to keep them engaged and satisfied. It is a powerful tool to enhance the customer experience, reduce churn, and boost revenue.
Personalization
Personalization and recommendation engines help brands create targeted and personalized customer experiences. Customer analytics makes it possible to collect and analyze vast amounts of customer data from various sources, such as social media, customer feedback, and online behavior, to understand customers’ needs, preferences, and behaviors. As customers continue interacting with different channels, the AI system can adjust the recommendations and promotions based on real-time data, providing a continuously evolving personalized experience. Use cases include personalized product recommendations on e-commerce websites, content recommendations on media streaming platforms, and targeted marketing campaigns based on customer preferences and behavior.
Voice of Customer
Companies can use VoC analytics to identify pain points and areas for improvement from customer feedback, enhance their existing products and services, or develop new ones. Customer feedback from various sources, such as social media, customer reviews, and surveys, is analyzed through AI algorithms, and common themes and sentiments are identified. As the company continues to collect customer feedback, the AI system can adapt and improve its analysis based on real-time data, providing continuous insights to inform the company's product development strategy. It can also inform brand reputation management, offering insights into competitors and helping companies develop strategies to differentiate themselves in the market. Analyzing customer feedback helps companies identify trends and recurring issues in customer service interactions to train their customer service teams, measure campaign effectiveness, and improve the overall customer experience.
Our Success Stories
Customer 360 For Digital Marketing Company
Learn how Valiance achieved data-driven customer engagement and increased ROI for a prominent Indian digital engagement solutions company with a reach of 500 million plus end-customers by implementing a text mining and machine learning platform that could discover and predict customer attributes from unstructured data collected via their digital channels.
The Key Challenges
The client's digital platform generates nearly 5 TB of unstructured data per month, containing a wealth of information about consumer lifestyles and preferences. The primary challenge was to discover and mine this dataset at scale and leverage text mining and natural language processing capabilities to understand unstructured texts and predict customer preferences wherever they were lacking.
Our Winning Moves
- Valiance partnered with the client to develop a methodological approach comprising a text mining framework, machine learning approach, and technology platform.
- Business analysts, data scientists, and data engineers worked closely with the client's business and product team to validate the approach for the accuracy and completeness of results.
- Created a Hadoop-based infrastructure to process TBs of unstructured data.
- Developed text mining rules and NLP algorithms to discover customer attributes, including age, gender, spending pattern, number of credit cards, number of kids, travel frequency, mutual fund info, and other useful attributes from a marketing standpoint.
- Trained ML algorithms to predict missing customer attributes using discovered attributes and experimented with different algorithms to determine the best one.
- Results of text mining and machine learning predictions were regularly shared with the client's team for feedback and improvement.
Outcome
- 5 months: the first version of results with robust and accurate customer data made available.
- 450 customer attributes discovered and exposed to the marketing team through open APIs.
- 10% increase in customer engagement from digital interactions within two months of testing.
Customer Feedback & Sentiment Analysis For Entertainment Company
Valiance helped a theme park and production studio in the United States, which attracts millions of visitors each year, achieve a better customer experience by implementing a machine learning-based solution for customer feedback analysis.
The Key Challenges
The client faced challenges in consolidating and categorizing customer feedback from multiple sources, such as YouTube, Facebook, Google Reviews, TripAdvisor, Yelp, and Pissed Consumer. The feedback collected from these sources was mostly unstructured, making it difficult to derive meaningful insights.Reviews needed to be tagged under various themes, and feedback had to be categorized as positive, negative, or neutral.
Data from ten theme park rides from the last three years had to be crawled.
Our Winning Moves
Valiance developed a machine learning-based solution that segmented customer feedback from various data sources, using custom web crawlers to scrape user feedback data for each ride and from each source. With this sentiment analysis approach, the company could extract ready-to-use, clean, and structured data with variables such as review title, complete text, URL, rating, user, location, and date.
The N-gram analysis technique summarized the subjective feedback extracted from different data sources. Bigrams, trigrams, and quadgrams (tuples of two words, three words, and four words, respectively) were used to get the complete context of the sentence while analyzing the sentiments of the feedback
A taxonomy was created, and 20,000 reviews based on the approved taxonomy were tagged along with recognizing and classifying the sentiment as positive, negative, or neutral. This allowed us to create a sentiment analysis model with tagged reviews.
Outcome
25% increase in ROI due to reduced manual intervention in customer feedback extraction, cleansing, and topic identification.
Personalized Consumer Engagement For credit Card Customers
Learn how we helped an Indian fintech startup fulfill its vision of providing delightful, relevant, and personalized experiences to retail banking customers by leveraging machine learning and artificial intelligence.
The Key Challenges
- The company wanted to provide personalized experiences to its customers, enabling them to save more and spend on the right things. However, it lacked in-house expertise in machine learning and analytics.
- The company, therefore, wanted to engage an analytics provider with the necessary expertise to implement personalization and recommendation.
Our Winning Moves
- Valiance worked closely with the client to co-create a product that fulfilled its vision of providing personalized experiences to retail banking customers. Our team identified key areas in consumer engagement where machine learning could be applied, including using credit card transaction data to discover consumer preferences, enriching merchant metadata to create deep customer profiles, and serving relevant offers based on customer preferences.
- Merchant metadata was manually enriched to create deep customer profiles and serve relevant offers, group merchant categories by transaction frequency and necessity vs. discretionary nature, develop customer spend preferences under categories, and enrich merchant offers with similar tags used for the merchant to enable mapping.
- We used scalable technologies like Hadoop and Spark for data processing and machine learning to scale with larger transaction datasets.
- Our solution enabled the client to save customers more and spend on the right things at the right price, enhancing customer satisfaction and loyalty.
Outcome
Our solution enabled the client to develop a robust MVP within cost constraints and scale it to the next level. The MVP included modules for data cleaning and transaction enrichment, customer preference creation, rules for event-based recommendations, and collaborative filtering-based recommendations. We also participated in pitching the product and its analytics capabilities to private sector banks, enhancing the client's reach and impact.
