CX strategy

Customer experience can give you a headache but it’s also a great inspiration for business. With Reddit influencers, Elon Musk’s tweets, and today’s ‘cancel culture’, customer experience is something companies can’t ignore but need to find new ways to optimise and enhance it.

For this reason, SMBs and enterprises seek to adopt customer experience technologies that can help them get a holistic view of customers and optimise the entire customer journey.

One of the major tasks of CX technologies is to help the company move from the situation where each department operates independently, creating technological silos and slowing down the adoption of a customer-centric mindset. A key part of this endeavour is to create an integrated ecosystem and nurture a collaborative environment by connecting separate technological systems:

Systems of engagement: all channels and touchpoints where customers can communicate with the company, like phone, chats, email, social media, messengers, etc.
Systems of record: customer data accumulated by different departments, like personal details, transaction and browsing history, preferences, service tickets, etc.
Systems of things: data accumulated from sensors, beacons, POS systems, wearables, and other connected devices.
Systems of intelligence: systems that process and analyse accumulated data and provide all kinds of insights.

Systems of intelligence serve as the brain of the entire technological structure, analysing data across all the systems. Now let’s look closer at their components.

Artificial intelligence

Artificial intelligence underpins all the systems of intelligence and serves as the key component of smart automation and customer experience personalisation. It provides such capabilities as natural language processing, speech recognition, customer journey orchestration, dynamic recommendations, virtual assistance, and more.

Also Read: Transformation tenet: The digital customer experience is key to “stickiness”

As customer data grows exponentially, AI is continuously learning to provide more and more accurate insights and forecasts into customer behaviour over time. As a result, customer-facing departments are able to connect with customers on a hyper-personal level, offer them highly relevant content, boost upselling, and provide self-service—all of which nurtures customer loyalty and trust.

Emotional intelligence

As more and more customers shift to the online realm, instead of face-to-face conversations they communicate with chatbots, write emails, and leave feedback via forms. Sometimes remote communication can be misinterpreted, which can cause customer frustration.

To overcome such problems, businesses should implement cognitive systems able to read emotions in real-time via text, voice or video channels. When customer-facing systems are empowered with this instrument, they can foster satisfaction and turn negative emotions into positive ones.

It’s true, machines can’t interpret emotions the same way people do, but they are able to analyse big amounts of data and tell between different tone and voice inflections or micro-expressions in images and associate them with particular emotions.

By learning from each interaction, emotional intelligence systems can understand not only what people say but what they feel, interpret their intent, understand jokes, and more.

The prominent use cases of emotional intelligence systems are:

Brand sentiment analysis of social media and online content
Human-like conversations via chatbots
Emotion interpretation during phone and video calls
Mental health monitoring based on the patient’s voice, additionally coupled with body temperature and heartbeat measured by wearables

Customer analytics

In 2020, Google acquired Looker, a data analytics company, and Salesforce purchased Evergage, a customer data platform. Why so? Customers’ growing need for tailored experiences and real-time omni channel interactions make companies view customer data and analytics as an important part of their operational and marketing strategies.

Customer data is actually everywhere—browsing history, transactions, saved items, support tickets, loyalty memberships and subscriptions, location sharing, and more. But it’s useless to run AI algorithms on plain data you accumulate—you can’t get energy from a river unless it’s dammed. For this reason, companies need to understand what customer data they need for their specific goals and segment data flows.

Also Read: Why a customer-centric digital marketing strategy is the way to go?

Once there’s a pool of meaningful data composed of relevant data sources, it’s necessary to create a data hub to gain 360-degree visibility into customers and let every customer-serving team have access to it.

This way, by visualising data, building predictive models, and using AI for insights and forecasts, companies can meet their customers where they are and provide personalised experiences.

In connection with this, we should expect two trends:

In pursuit of agility and innovation, companies will try to minimise their reliance on third-party analytical agencies and maintaining data scientist teams and build in-house customer data solutions based on low-code platforms and tools. It will allow them to boost data literacy and let more employees, particularly those less tech-savvy, use data to make informed decisions.
Active data mining will trigger more security and privacy concerns and, as a result, more privacy laws and regulations will see the light.

Workforce optimisation

Companies have started to look into their workforce optimisation (in terms of timekeeping, scheduling, training, workload, KPIs, hiring, etc.) to drive business growth, as happy employees mean happy customers. Against the common perception that AI is going to replace human workers, it’s actually used to augment human workforce and facilitate their daily tasks:

Workload forecasting: AI helps to foresee changes in the workload and suggests staffing schemes for certain periods of time based on available resources. It allows companies to serve each customer during the busiest times, like seasonal sales, while minimising overtimes for employees. This capability also helps to deal with unanticipated events and long-term uncertainty when habitual prediction models and schemes seize to work. It allows probing for even the weakest activity impulses, embracing this opportunity, and measuring the results.
Smart staffing: AI can forecast the number of customers, users, callers, or shoppers overall or during specific periods and determine the corresponding number of employees of certain skills needed to meet this demand.
Process automation: AI streamlines workflows and automates time-consuming tasks, letting employees focus on more meaningful work.
Smart scheduling: In case of distributed teams and remote work, cookie-cutter schedules become an outdated concept. To work out a personalised schedule for a large multi-skilled team across multiple work streams, AI can analyze all the variables, like preferable time, task priority, types of work, take into account all the dependencies, and offer schedules tailored to each worker.
Intelligent performance assessment: AI helps monitor overall and individual performance, provide unbiased assessment, calculate KPIs, and more. It can forecast drops in productivity, diagnose them, and prevent them from becoming chronic, for instance, by suggesting additional training.

Customer-centricity makes companies turn to artificial intelligence and incorporate various systems of intelligence into their customer-facing processes. As customer data is the fuel that powers these systems, companies need to develop a data strategy that embraces data accumulation, processing and analysing, along with promoting a data-driven culture.

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