Customer experience can be a headache, but it is also a great source of inspiration for business. For Reddit influencers, Elon Musk’s tweets, and today’s ‘culture of cancellation’, customer experience is something companies cannot ignore but need to find new ways to optimize and raise it.
For this reason, SMEs seek to adopt customer experience technology That can help them have an overall view of the customer and optimize the entire customer journey.
One of the main duties of CX technology is to help the company move from situations where each department operates independently, creating technology silos and slowing down the adoption of customer-centric thinking. An important part of this effort is to create an integrated ecosystem and foster a collaborative environment by connecting discrete technology systems:
- Interactive system: all the channels and points of contact with which a customer can communicate with the company, such as phone, chat, email, social media, messenger, etc.
- Profile system: customer data accumulated by various departments, such as personal details, transaction and browsing history, preferences, service tickets, etc.
- Systems of things: data is accumulated from sensors, beacons, POS systems, wearable devices, and other connected devices.
- Intelligent system: the system processes and analyzes the accumulated data and provides all kinds of detailed information.
The intellectual system acts as the brain of the entire technological structure, analyzing the data on all systems. Now let’s take a closer look at their ingredients.
Artificial intelligence underpins all intelligence systems and serves as a key component of intelligent automation and personalize the customer experience. It provides capabilities like natural language processing, speech recognition, customer journey coordination, dynamic recommendations, virtual assistants, etc.
As customer data grows exponentially, AI continually learns to provide more and more insights and more accurate forecasts of customer behavior over time. As a result, customer contact departments can connect with customers on a hyper-personal level, provide them with highly relevant content, increase sales, and provide self-service. – all of which nurture customer loyalty and trust.
As more and more customers turn online, instead of chatting in person, they communicate with chatbots, write emails and leave feedback via forms. Sometimes remote communication can be misinterpreted, which can cause customer frustration.
To fix those problems, businesses should take action Cognitive systems can read emotions in real time via text, voice, or video channels. When customer-oriented systems are empowered with this tool, they can drive satisfaction and turn negative emotions into positives.
It’s true, machines can’t explain emotions the same way humans do, but they can analyze large amounts of data and distinguish between different melodies and voices or micro-expressions in images and associate them with specific emotions.
By learning from each interaction, emotional intelligence system can understand not only what people say but also what they feel, interpret their intentions, understand jokes, etc.
Prominent use cases of the emotional intelligence system are:
- Analyze brand sentiment on social media and online content
- Human-like chats via chatbots
- Interpret emotions in phone calls and videos
- Mental health monitoring is based on the patient’s voice, along with body temperature and heart rate measured with a wearable
In 2020, Google acquired Looker, a data analytics company, and Salesforce bought Evergage, a customer data platform. Why? Customer ‘ an increasing need for a tailored experience and omni-channel interactions in real time that make companies view customer data and analytics are an important part of their marketing and operations strategy.
Real customer data is everywhere – browsing history, transactions, saved items, support tickets, loyalty registration and membership, location sharing, etc.But it will be useless to run AI algorithms on the pure data you accumulate – you can’t get energy from a river unless it gets beaten. For this reason, companies need to understand what customer data they need for specific goals and data flow segmentation.
Once you have a meaningful set of related data sources, you need to create a data center to get 360-degree visibility to your customers and allow every customer service team to have access to it.
This way, by visualizing data, building predictive models, and using AI for insight and prediction, companies can meet their customers where they are and provide experiences. personalized.
In relation to this, we should expect two trends:
- In pursuit of agility and innovation, companies will strive to minimize their dependence on third-party analytics and maintain data scientist teams and build data solutions. Internal customer data is based on low-code platforms and tools. It will allow them to improve data understanding and allow more employees, especially those less tech savvy, to use the data to make informed decisions.
- Active data mining will cause a lot of security and privacy concerns, and as a result, many privacy laws and regulations will come to light.
Companies have begun to look workforce optimization (in terms of timekeeping, scheduling, training, workloads, KPIs, recruitment, etc.) to drive business growth, because happy employees mean happy customers. Contrary to the popular perception that AI will replace human labor, it is actually used to strengthen the human workforce and facilitate their daily jobs:
- Workload forecast: AI helps to anticipate changes in workloads and propose staffing options in certain times based on available resources. It allows companies to serve individual customers during the busiest times, like seasonal sales, while minimizing overtime for employees. This ability also helps to cope with unforeseen events and uncertainties over the long term when habitual prediction patterns and plans begin to work. It allows to probe even the weakest of action pulses, seize this opportunity and measure the outcome.
- Smart staff: AI can forecast the overall number of customers, users, callers or shoppers over specific periods of time and determine the number of employees corresponding to certain skills needed to satisfy this need.
- Process automation: AI streamlines workflows and automates time-consuming jobs, allowing employees to focus on more meaningful work.
- Smart scheduling: In the case of distributed teams and remote work, cookie cutting scheduling becomes an outdated concept. For personalized scheduling for a large, multi-skill team across multiple workflows, AI can analyze all variables, such as timing appropriate, task priority, job type, taking into account all of them. Factors depend on and provide the appropriate schedule for each employee.
- Smart performance evaluation: AI helps monitor overall and individual performance, provide unbiased reviews, calculate KPIs, and more. It can predict productivity declines, diagnose them and prevent them from becoming chronic, for example, by recommending additional training.
Customer centricity drives companies to turn to artificial intelligence and incorporate various intelligence systems into their customer-oriented processes. Since customer data is the power source for these systems, companies need to develop a data strategy that encompasses the accumulation, processing, and analysis of data, along with promoting a culture based on on the data.
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