Brands are leveraging cutting-edge technology to deliver an exemplary customer experience (CX) at every customer touchpoint to be ahead of the competition.
According to the Customer Experience Live April 2021 report, 81% of organisations plan to adopt a customer experience-centric approach to deliver growth post-pandemic.
The customer behaviour has evolved markedly post-pandemic, from wanting budget-friendly products the focus has shifted to quality and sustainability as consumers rally in their quest to be positive, impactful, and experiential. These trends are challenging companies to innovate their pre-existing CX models and invest in the latest technologies to cater to evolving customer needs and wants.
The quest for data
There is a quintessential thirst for data as business leaders want to get a complete picture of customer preferences and behaviours to formulate their growth strategy. It’s no surprise that surveys and customer feedback are forming the backbone of all CX interventions at this point.
Budgets and resources are being allocated to getting access to quality data that can "close the loop" on customer feedback, thus allowing senior executives to plan strategies, based on insights, that deliver growth and capture market share as organisations look to rebound post pandemic.
There is further agreement that survey data does not meet the CX needs within organisations, but it helps predict customer demand and thus is an essential tool for market research.
According to a survey by McKinsey of more than 260 customer experience heads in the United States, 93% of respondents stated that a survey-based research model (such as customer satisfaction score and customer effort score) is a fundamental tool for measuring CX. The data also suggested that a large proportion of leaders (85%) were dissatisfied with how their company was measuring CX. Less than 6 per cent thought that their CX measurement process enabled strategic and tactical decision-making.
The challenges they encountered in leveraging data to improve strategic decision-making are:
- Limited access to quality data
- Majority of data is still unstructured making it difficlut to draw insights
- Uncertainty about performance drivers, and
- Unclear link to financial value
How can data enable organisations to deliver strong growth?
Tech giants such as Amazon, Google, Facebook, Apple, Microsoft have historically invested in big data thus allowing a streamlined approach to making strategic decisions based on the analysis and interpretation of data. The companies can regularly, lawfully, and seamlessly collect and interpret data from their customer, financial, and operations systems, which analyzes how humans interact with smartphones, computers, or use machinery to uncover patterns and trends.
With plenty of analytical data at hand, businesses are able to have a mature vision and proactively design services that satusfy customers' needs and wants before they even know their desires. This advancement delivers a personalized customer experience that resonates with customers and increases brand loyalty.
Let’s explore the four critical steps that CX leaders may wish to consider as they plan a data-driven implementation.
Any organization that initiates a data-driven strategy will face barriers and organizational resistance. However, with a clear vision, even companies with undeveloped CX systems, restricted data, and a lack of data specialists can convert their CX programs to deliver strong customer experiences. The efficient approach to strengthening CX is creating personalized customer journeys because the one-size-fits-all strategy is extremely outdated and not geared to deliver results.
1. Enabling access to diverse data
Data-driven companies rely on insights and information to steer growth. They utilize their big data to create value, and aim for operational excellence to streamline processes and that enhance seamless service delivery. Volumes of structured data are analysed to draw inferences including data from business transaction systems, customer databases (such as purchase behaviour and preferences), clickstream logs (digital behaviors), social media activity, medical records, and others. The patterns from the data analysed allow the company to improve its operations, enhance customer service, personalise marketing campaigns, and increase profitability.
2. Predictive modelling
Further, refining and leveraging data to aid decision-making allows alignment with business strategy and provides operational flexibility across departments to drive change effectively.
Whilst the specific implementation of data structures varies by organisation. the predictive modelling approach follows three core principles:
i) Customer level data sets – the customer level data sets contain vast amount of raw data. This is data collected from different sources such as web systems, mobile applications, social media, IoT devices, and pre-existed data run in real-time modes. The company processes raw data and stores it in a cloud-based platform where encryption supports and compresses it in a cost-effective manner. After data is processed, the comprehensive customer-level data sets allow for an in-depth analysis of interactions, transactions, and operations to help refine customer journeys.
ii) Predictive customer scores – By leveraging data analytics and machine learning tools, the customer level data sets can deliver actionable insights into customer satisfaction, business performance, or predicting an accurate outcome. Algorithms can give predictions with a high probability of occurrence when substantial data is available from past events. This allows CX leaders to determine ROI for particular investments and work with variables that are most likely to produce desired business outcomes.
iii) Database Management System (DBMS) – this step requires the software to serve as an interface between databases and employees, ensuring that data is constantly organized to create, protect, read, and update to allow personalized customer experiences and improve CX outcomes. The data collection allows predictive feeback that companies can use to measure their CX performance; they also improve strategic decision-making.
3. Predictive analytics enabling the omnichannel strategy
Organisations have embraced the omnichannel strategy pretty well to boost performance, even though digital channels were highly accelerated during the pandemic. The ‘back to basics' approach has been adopted by a lot of organisations when formulating their omnichannel strategy - constructing customer data sets to identify, correct, record and influence customer satisfaction and business performance across priority customer journeys.
By leveraging predictive analytics organisations can create interactions with the customer, both current and potential, at all customer-facing touchpoints: organizing, automating, and synchronizing when interpreting outcomes and responses. Whether it be a website, social media, online ads, a retail interaction, or an online purchase, predictive analytics can level up the ability to direct customer insights for recommending actionable improvements in the digital customer journey.
4. Predictive CX Platform – Statistics-driven analysis allow organisations to see the qualified and organized view into the difficulties, prospect areas, and channel interactions across millions of customers, enabling them to support a recurring journey-improvement cycle. By combining data collection processes, analytics, and machine learning algorithms, business leaders can use data from the past to predict the future. Thus, CX transformation should focus on tracking customer behaviour online, raising red flags in a customer journey, assessing missed opportunities, this will allow predictive analytics to suggest personalized digital experience solutions to supercharge customer retention.
The system can also be recognized as a survey engine to collect valuable customer feedback across all customer touchpoints and collate the responses into the CX platform. Through these mechanisms, the team can use the analytics platform to achieve cost savings and revenue growth eg reducing interaction and operation costs by 10 to 25 per cent due to CX and digital transformation.
3 key points to consider when designing data driven experiences
Organisations must focus on customer experience as a sum of interactions and understand consumer retention is the direct outcome of all interactions combined.
There are 3 main points to consider when designing data-driven experiences – Consumer, Data, and Analytics.
i) Consumer- Organisations rely on surveys to gather customer data. But CX must be measured at various customer touchpoints across the entire customer journey, not just at the point of conducting a survey. By developing a 360-degree view of the customer through insights, organisations can improve service delivery, influence future product development and enhancement, and track marketing automation and spend, based on customer segmentation and consumer characteristics.
ii) Data – Data is a fundamental requirement if an organisation wants to capitalize on profitable insights to create value, deliver growth and achieve operational excellence. By discovering patterns in data, organisations can forecast trends and buying behaviours, fast track product launches or modifications, thus putting them ahead of the competition and gaining market share.
iii) Analytics – Data analytics allows granular predictions of consumer behaviour from the structured data at the organisation’s disposal. It can forecast buying behaviour and ensure favourable outcomes.
Delivering an exemplary customer experience creates huge benefits for all businesses. So, how can data fast-track advanced CX delivery? Read on
