Harness the insights of data analytics to deliver great customer experience for your clients, from customer profiling to analyzing feedback. Read more.
Steve Cannon, former CEO of Mercedes-Benz USA once said, “Customer experience better be at the top of your list when it comes to priorities in your organisation. Customer experience (CX) is the new marketing ” and it still holds true.
CX is quintessential to retaining customers and businesses of various industries are placing more importance on data analytics to better understand customer behaviour, preferences, and needs as this information can be leveraged to create better products and services. In this article, we'll explore how data analytics can help improve customer experience.
How Data Analytics Can Help Improving Customer Experience Data analytics can help companies improve customer experience from various sides, such as personalisation, demand prediction, and customer service.
In more detail, here is the explanation:
1. Creating Personalised Marketing Data analytics enable you to segment your customers based on their behaviour and preferences. By analysing customer data such as income level, purchase history, and values and segmenting them based on those characteristics, you can customise your products, services and communication to each segment, which translates into a great brand experience for them.
To simply put, you know what makes customers happy and what ticks them off. As an illustration, a fashion retailer could leverage data analytics to recommend products based on a customer's browsing and purchasing history, eliminating the need for customers to search through a potentially time-consuming product catalogue.
The word “personalisation
It claimed that 75% of viewer activity is powered by recommendations. This content personalisation strategy has generated over USD 1 billion for Netflix. Furthermore, it utilises time-based KPIs such as viewing time and overall satisfaction to gauge its engagement and churn rate.
Aside from Netflix, a Fortune-500-ranked Malaysian oil and gas brand experienced success in customer acquisition through the use of data analytics. Its petrol stations and convenience stores suffered poor footfall traffic during the onset of the COVID-19 pandemic.
During the recovery period, by using ADA’s Consumer Profiler , customer footfall trends were better understood, and different buyer personas were created, allowing hyper-personalised creatives and messages to be served to them. The campaign has led to high engagement and motivated consumers to patronise their stores.
2. Predicting Customer Demand Predictive analytics can facilitate the ability to anticipate customer demands by detecting recurring patterns and trends that indicate what customers are likely to require or desire in the future.
An example would be a financial services company using predictive analytics to anticipate a customer's financial needs at different lifecycle stages, offer personalised financial advice, and promote products or services based on his or her needs and financial state.
It is imperative for luxury retailers to strike a balance between brand experience and elegance. To build and foster brand loyalty, a French skin-care brand, L’Occitane combines machine learning and predictive analytics to antedate what their customers might buy in their next purchase.
Referring to their previous purchase history, returning customers might see a restocking suggestion based on the stock turn of the products they previously purchased. L’Occitane also created a sense of urgency to incentivise their customers to purchase sooner by showing them the fast-selling products on their website.
A large Vietnamese fashion retailer wanted to maximise reach to highly affluent women, explore consumer engagement opportunities, and identify potential store locations. With ADA’s Location Planner , we helped our client to identify potential store locations with a high concentration of target audience based on their age, gender, and affluence.
Besides that, this tool also helped an Indonesian commercial bank to assess the distribution of the Syariah segment as their target audience and map out the ideal locations to address their Syariah banking needs. The said bank has since integrated ADA’s Location Planner into their 5-year branch transformation blueprint.
3. Improving Customer Service and Process Data analytics facilitates the acquisition and analysis of customer feedback, providing valuable insights into areas that necessitate improvement. Customer feedback, obtained from sources such as surveys, social media platforms, and websites, serves as a first-party data source that allows for the identification of pain points and satisfaction factors.
During the early phases of COVID-19, the number of passenger count taking rail transit dropped. Even in the new norm, the passenger count was still approximately half of what it was before the pandemic.
With ADA’s IFA-Targeted Survey , our client, a Filipino rail transit provider was able to identify the pain points of current commuters and reasons non-commuters taking alternative transportation options – to save time. With this insight, our client can focus on improving their service and win back the passengers who stopped using its commuters since the pandemic started.
It is a known fact that a customer’s complaint can be deemed as a gift in the service industry. How so? With customer service data, you can identify and proactively tackle the problems before they exacerbate. Additionally, you will be able to pinpoint the performing and non-performing customer service representatives who warrant more training or are probably caused by other factors such as time-consuming, manual processes that hinder their performance.
For example, simple requests like account details updates and balance-checking can be done by customers themselves via online and mobile banking applications instead of customer service officers of a bank. This not only reduces the call waiting time for customers but also allows customer service officers to attend to other more complicated issues.
Ever wonder why Amazon consistently receives recognition for their customer service? The answer is big data and data analytics. Simply look at how they optimise their supply chain management. Their shipping optimisation allows their customers to select their preferred carrier and know the expected delivery time. Amazon heavily invested in its distribution infrastructure that allows them to offer same-day delivery and one-to-two-day ground delivery in the USA.
4. Understanding Customer Journey You can also use customer analytics to do customer journey analysis. Analysing the customer journey, from initial contact to purchase and post-sale interactions, helps identify pain points, bottlenecks, and areas for improvement. By using analytics, you can understand where customers drop off or experience difficulties, allowing you to optimise the journey.
5. Analysing Customer Behaviour Analysing customer behaviour on your website, app, or in-store provides valuable insights. You can track which pages they visit, what actions they take, and where they spend the most time. This data helps you understand customer preferences and areas where you can enhance the experience
6. Gaining Insights from Sentiment Analysis Use natural language processing (NLP) techniques to analyse customer reviews, feedback, and social media mentions. Sentiment analysis provides an understanding of how customers feel about your brand, products, or services, and helps you address issues or capitalise on positive feedback.
7. Prediting Churn Analytics can predict which customers are at risk of churning (leaving) based on historical data and patterns. By identifying these customers early, you can implement retention strategies and prevent churn.
8. Finding High Value Customers Data analytics can help you to analyse the CLV of different customer segments. This helps in focusing resources on high-value customers and optimising acquisition and retention strategies.
9. Identifying Touchpoints Data analytics allow your business to identify the most influential touchpoints in the customer journey. This helps allocate resources effectively to improve the customer experience at critical moments.
10. Analysing Customer Feedback in Real Time Implement real-time analytics to capture and analyse customer feedback as it happens. This allows you to respond quickly to issues and make immediate improvements.
By leveraging these analytical approaches, businesses can measure, monitor, and enhance the effectiveness of customer experience, leading to increased customer satisfaction, loyalty, and business growth.
How to implement data analytics to improve customer experience Implementing data analytics to improve customer experience requires a structured approach that integrates data-driven insights into your customer-facing processes and strategies. Here's a step-by-step guide on how to implement data analytics to enhance customer experience:
1. Define Customer Experience Objectives Start by clearly defining your customer experience goals. Determine what specific aspects of the customer journey you want to improve, such as response times, personalisation, or reducing customer churn.
2. Identify Relevant Data Sources Identify the data sources that can provide insights into customer behaviour, preferences, and interactions. This may include CRM systems, website analytics, customer feedback, social media, sales data, and customer support logs.
3. Data Collection and Integration Ensure that data is collected accurately and integrated from various sources. Implement proper data governance to maintain data quality and consistency. At this stage, you can build a customer data platform (CDP) to help you organise and analyse all the data you collect.
4. Develop Customer Experience Metrics Choose relevant metrics to measure customer experience. This may include NPS, CSAT, retention rates, conversion rates
5. Implement Data Analytics Tools Choose and implement the appropriate data analytics tools and platforms for your needs. This may include data visualisation tools, machine learning platforms, and customer analytics software.
6. Hire or Train Data Analysts Employ skilled data analysts who can interpret the data, identify trends, and extract actionable insights. Alternatively, upskill existing staff to handle data analysis tasks.
7. Analyse Customer Journey Use data analytics to analyse the customer journey from start to finish. Identify pain points, drop-off points, and areas where customers may face challenges. Understand the touchpoints where improvements can be made.
8. Use Insights to Create Personalised Marketing Leverage data analytics to segment your customer base and provide personalised experiences. Use past behaviour, purchase history, and preferences to offer tailored product recommendations, content, and promotions.
9. Continuous Improvement Customer experience optimization is an ongoing process. Continuously monitor customer feedback, analyse data, and iterate on your strategies to keep improving.
10. Employee Training Train your employees to leverage data-driven insights. Ensure that your customer-facing teams are equipped to use customer data to provide better service.
By following these steps and integrating customer experiedata analytics into your customer experience strategy, you can make informed decisions, deliver personalised experiences, and ultimately enhance customer satisfaction and loyalty.
Delivering next-level CX with data analytics Data analytics is transforming the way companies operate, attract, and retain customers. With an improved understanding of customer behaviour and preferences via data analytics, you can customise your interactions and offerings to meet or exceed your customers’ expectations. Equip your business with the right dataempl analytics tools and techniques with ADA’s data solutions
