What does it take to power a truly real-time customer experience? More than just data collection. Let’s start there.
Simply delivering a digital interaction in real-time conversational channels such as web, chat or mobile with the technology that brands have today isn’t that difficult. The challenge is delivering something that is anticipated, personalised and useful—or, in other terms—relevant to the consumer at the current stage in their customer journey.
Brands recognise that this is a challenge. SAS, in a recent report alongside Accenture and Intel, found that more than 80% of executives surveyed by Harvard Business Review Analytic Services said they want to use analytics to generate real-time actions from customer data. However, only 22% of respondents in the same study said their organisations are effective at using analytics and data.
That is a huge gap between desire and reality. Why is this the case? It’s because being able to deliver contextually relevant real-time interactions across all channels can be a daunting task, and it’s a task that must account for many variables. It requires good data, a variety of analytics, integration with many endpoints, and the ability to execute at lightning speed. Easy to write, much harder to do.
Significance of CX in today’s business landscape
Continually improving the customer experience is one of the two main goals (along with increasing operational efficiencies) of broader digital transformation initiatives that many brands are currently undergoing. Brands should continually gauge their impact to provide relevant and contextual interactions along a customer’s journey with a brand—customer experience. Brands own the CX framework, with customers each managing their journey. Brands that can provide CX guardrails to usher consumers along this journey with relevant content, messages, and offers will win.
Consumers have come to accept sub-par CX from a lot of brands. The reality is that providing truly exceptional CX can be a challenge. It requires many synchronisations—from data to analytical insights to engagement channels. If synchronisation and orchestration aren’t present, the result becomes a siloed, disjointed CX delivered by the brand and experienced by the customer.
Challenges in delivering relevant, real-time CX
Simply put – people, processes, and technology.
People – Empowering employees to deliver on CX initiatives is critical. That means giving them insight and authority to address customer concerns.
Processes – Data processes, analytical insight distribution, and customer journey orchestration from a Martech perspective. Other CX processes, such as scheduling, commerce, and support/enablement, must be aligned as well.
Technology – The technology that supports both these people and processes must be synchronised for success.
Bridging the gap between consumer perception and brand reality in CX
How consumers want (great CX) and what brands want to provide (great CX) can be aligned. It starts with data and analytics practices and processes, some of which we have mentioned above. It also includes going beyond transactional, channel-based engagement and rethinking organisational models and processes. It involves thinking comprehensively about your organisation. This often involves having individuals and/or teams consider the “enterprise view” of customer experience.
- A more efficient organisation and a delighted customer
- Improved customer metrics such as net promoter score, customer lifetime value, and customer satisfaction
- Increased consumer loyalty and trust
- Improved brand equity in the marketplace
- Competitive differentiation and advantage
Leveraging technology for relevant, real-time CX
Data and analytics technology can make interactions along the customer journey more relational versus transactional. From a data perspective, many brands turn to CDPs to ingest, manage, and activate data holistically.
From an analytics perspective, a few analytical technologies include:
- Streaming Analytics: Collect data from event streams on IoT-style devices. This can include usage information and behaviour, location, device statistics, etc. Users would have to opt-in to this data collection.
- Text Analytics and Sentiment Analysis: This is a must-have for conversational AI. By analysing chat text strings and the sentiment in those text strings, brands can understand customer attitudes and intent.
- Natural Language Processing and Generation: The ability to process natural language data (NLP) from documents such as chats and convert speech-based conversations into natural language text (NLG) are also foundational components of conversational AI.
- Computer Vision: Images consumers share over conversational channels must be quickly analysed and tagged to provide additional context to the dialogue.
Measuring the success of CX efforts for brands
By leveraging both qualitative and quantitative methods.
- Qualitative techniques like surveying, value exchanges (if you provide your feedback, then you get a discount, etc.), and focus groups can be used.
- Quantitatively, using analytical methods to calculate things like customer lifetime value score, net promoter score, and customer satisfaction scores and tracking their trends over time can be used.