Enhanced Customer Experience Strategy for Enterprise Systems

Customer Experience Strategy

Successful digital transformation promotes the customer’s role and impact on the continuous nature of business growth and innovation. Ushering simplicity and operational excellence, businesses that can do more with less are ones that leverage responsive enterprise systems to enable priceless customer experiences strategy.

Customers are to be assured that their data is secure with you, utilized to enable easy access to your value proposition. Your goal as a business is to make the lives of customers easier with a major importance attributed to facilitating distinctive experiences. By fostering personalized experiences, companies can discover new ways to improve the customer journey, being the premise to build off the strengths of your products and services. 

Framework for Customer Experience Strategy

Binding confidentiality and transparency to customer sentiment, data quality governance is necessary for incorporating a framework that responds to customer needs promptly. Especially when computing on cloud or handling voluminous transactional data, better access to data manipulation tools and scalable AI programs helps define and achieve business-critical decisions. 

In addition to enabling ease of access, secure communications and operational expediency, high-performance enterprise systems are data-driven machines that need less operational expenditure and comprise a more coherent data architecture.

As the complexity of data architectures increases, so does the need for AI & ML models to train and deploy systems to perform better. 

Enterprise AI for Customer Experience Management (CXM) 

Imagine a sales strategist, Margaret just discovered a new extension for her sales team’s cloud-based CRM solution.

She believes that integrating an AI powered lead routing workflow with existing customer data collection methods will improve the efficiency of her sales team. She wants what’s best for her team, to ensure that customers gain uncompromised value. Her role as sales strategist is to refine existing processes by leveraging the latest of enterprise IT.

Just like how executing an excel formula can calculate multiple fields of data to make tallying easier, AI can simplify complex commands exchanged between networks, digital assets, and devices across your enterprise, to create automated workflows. The reward is that organizations can then unify multiple processes using AI and govern all relevant information sources via the same, simple, and responsive interface.

Here’s an illustration of how AI can use clearly defined parameters to create an automated workflow, in this case, for a systematic sales lead routing solution.

Sales Automation Example - Lead Assignment Solution

By controlling who has access to the records, Margaret can then define parameters to direct sales operations based on factors like response time, acquisition channel, historical records and even implement automated processes at varying stages of the customer journey.

The sales process automation Margaret discovered will undoubtedly reengineer the way agents interact with, onboard, and retain customers. 

Warranting Data Integrity for Seamless Automation 

With respect to data quality control, an AI algorithm can be deployed to handle numerous commands that organize data into buckets, ready for consumption, or to achieve specific tasks. This data is qualified against new and existing records to match and merge identical customer records, avoiding duplicate entries and organizing customer cases based on their unique journey. 

Example of Automation using AI

The flexibility of AI interfaces in enabling automated workflows depends on the integrity of data used. To ensure that data is not being replicated or lost, but administered for quality and security, the process pushes selected data sets through a clearly defined path in the data architecture. By enabling the seamless transition of data sets through the enterprise system, AI creates a feedback mechanism that runs securely, with minimal to zero human intervention. As a result, AI augments tasks for increased efficiencies like saving time on repetitive labor, reducing operational costs and reducing exposure to risks. 

The algorithm that connects functions between various enterprise assets is responsible for unifying the sales frontier with real-time insights obtained throughout. By improving the efforts taken to resolve customer cases, sales agents can direct more effort towards strategizing their operations based on customer contact points. A slight modification in the enterprise system via sales automation pivots around the urgency to respond to changing consumer behaviors backed by intelligence gathered from multiple acquisition channels.  

AI helps amplify the customer lifecycle value by helping agents predict the nature of customer interactions while ensuring accuracy in concerted efforts. 

Via sales automation, customer teams can reengineer the entire approach to front-end operations, enabling effective customer experience management based on tangible evidence of the various activities and acquisition patterns. 

Phased Modernization for Enterprise System Efficiency 

Implementing automated workflows at intervals in an existing enterprise system can impact the overall efficacy of your digital initiatives and can be measured and adapted to suit ever changing business needs. Margaret’s idea for sales automation unlocks more efficiencies down the value chain besides just improving the current sales process performance. 

With a dedicated interface to visualize fetched data, employees must simply update client briefs based on their previous interactions to ensure smooth lead assignments, follow ups and authorizations. 

Introducing Automation in the Value Chain

Knowing where the customer left off helps agents know where to continue the transaction with all available data at hand. This prevents data silos that would otherwise lead to inefficiencies in the enterprise system like duplicate customer records, privacy threats, that could tarnish your brand reputation. 

Because agents are fully aware of the customer’s journey, the process changes that follow can include diverse functions to scale their AI solution based on proven outcomes. 

For example, predefined SLAs can be uploaded into the application and linked to the lead routing mechanism to automate follow ups or automate responses based on specific customer cases, their attributes and history of interactions. This results in less time spent performing manual tasks, and more accuracy in qualifying leads that are time bound and require immediate attention. 

Besides saving on time lost between exchanging briefs, an automated lead routing system fetches data of customers in the sales pipeline and segments them based on priority, ensuring that agents only deal with leads that require their personal involvement. In the long run, establishing a workflow that saves costs also helps govern and refine their sales strategy.  

Because Margaret leverages AI for sales automation, the system can then be further engineered to sort data into fields based on relevance to other processes in the business value chain. By simplifying the processes, Margaret’s team can allocate more resources to accommodate more complex demands of customers and thereby meet enterprise objectives. 

Regardless of the volume of data Margaret is dealing with, her sales follow-ups will go in a timely fashion, because agents are always aware of updates in the client list and are positioned to meet their requirements based on quality data. All information is stored securely, accessed and used to improve the quality of the customer experience. Now, that’s customer success! 

AI Enhancing Customer Experience Management

Implementing AI workflows in any one of the processes of an enterprise system can lead to new opportunities for business improvements in other areas. In the case of the above-mentioned lead routing AI, improved data quality leads to sales efficiency, trickling down to superior customer data governance and security.  

Besides combining the many benefits of automation with multiple processes, AI has the capabilities to address pressing concerns of business logistics that directly impact the customers and employees.  

Deploying an AI solution to automate any one of your processes will reap immense rewards if your main goal is to accelerate business agility. 

Uses of Business for Enterprise Transformation

Using AI, the people behind sales operations, customer services, and crucial decisions can confidently make their stand without wasting any time on the intricacies of operations. The many benefits are accrued with every customer interaction, ensuring your business gives customers what they need, and exactly when needed. 

The incorporation of AI in the sales process like Margaret wants, directly impacts customer experiences through measurable changes, like: 

  • Enables more efficient customer communications 
  • Improves speed and quality of responses 
  • Improves ease of work and employee morale 
  • Generates actionable insights for customer experience management (CXM) 
  • Creates relevant and personalized experiences for each customer 
  • Discovers new areas of improvement in existing enterprise system capacities 
  • Boosts the overall yield of enterprise transformation efforts 

 

Artificial intelligence embedded enterprise systems go hand in hand in with business growth, at an age where automation is synchronous to profitability and presence in the industry. For enterprises, this means effective customer experience management sets the course to additional revenue generation. 

Download White Paper on AI & CX Transformation

Scaling Your Enterprise System Capabilities with AI 

The complexities of customer service management are set by fluctuating demand driven markets, and enterprises who match these expectations of customers are those who involve the end user in the process of innovation. A customer can thus go beyond the intricacies of a transactional system and acquire more value from your products and services through personalized experiences, tailored to their unique sentiments. 

But the ball does not stop rolling there. By incorporating AI into the enterprise system, the functionality improves drastically. The biggest advantage is that you discover new opportunities to improve the system, and its processes with increased flexibility in resources employed to carry out a specific function. The direct impact of digital transformation makes way to the customer, who is benefitted by the agility and responsiveness of businesses to their needs. The same intelligence can be utilized to create new enterprise IT architectures and integrate relevant technologies to support your plans for expansion in the future. 

Cognitive AI Example

If you are a business leader looking to enable bottom line efficiencies through systematic changes in existing data and system architectures, AI is within reach and ready to be used. The widespread adoption of responsible AI is redefining the way humans perform tasks and interact with each other. 

Artificial intelligence is responsible for implementing commands and feedback in the enterprise system, empowering a universal and organized flow of quality data and processes. Leveraging fine-tuned means of governing data on the cloud, enterprises of all sizes can now benefit from the advances in AI that discover new use cases each day.

That is because you can choose a custom solution, suited to your unique business challenges and augment processes as you discover new breakthroughs in the customer journey.  

AI helps businesses realize their ideal customer with intelligent insights, apps and devices built to transform end-to-end enterprise activity. For this reason and many more, AI is revolutionizing the way we perceive customer experience management (CXM) today, for tomorrow. 

Related posts