Client Experience

Channel Strategies

Effectively managing how a client/customer can make contact and get the services they need or desire form you is essential to the satisfaction of those clients. The private sector has kept raising the bar over the years, and the government has been challenged in keeping pace. With ongoing health concerns for direct contact, it isn’t just digital natives who demand new choices in channel options. Providing users a variety of high quality channel options is an effective way to demonstrate how you value them.

Lintu Solutions’ approach to public-sector channel strategies is a modified commercial approach driven by cost-effective value delivery while taking into account the experiences users have come to expect from private sector interactions. Lintu focuses on developing a channel strategy to improve service delivery and compliance, movement towards a holistic channel approach with the customer in the center, and consistently keeping channel cost awareness at the fore front. Providing user-friendly public sector services that are accessible, understandable and secure, helps to strengthen citizen/business voluntarily compliance and thereby contributes to improvements in overall levels of legal and regulatory compliance.

User segmentation enables differentiation by grouping users with common needs, attitudes and behavior to maximize benefits for users and the organization and increase value to users. Segmentation approaches typically categorize customers into mutually exclusive and statistically valid groupings. Users need to be provided with more effective channels that meet their preferences and give them increased access to high value-added services.

Examples of user considerations

  • What channel choices are available
  • Effectiveness of the channel/service
  • Confidence the user has in the channel
  • How secure the channel is
  • What the channel costs are

Customers are attracted to lower overall costs in using the channels, including reduced need for contact, reduced time spent in contact and lower direct costs of using channels. The challenge in today’s multi-channel environment is defining innovative channel combinations that best meet users’ expectations in a cost-efficient manner. Often, reducing the costs of contact volumes is achievable by channel migration – redirecting contacts to a more appropriate channel. We take balanced approach to short-term performance and long-term outcome goals and metrics. Our approach also includes qualitative analysis tied to users’ experiences, attitudes and actions.

  • Accessibility of the channel/service
  • How omni-directional the channel is
  • How inclusive the channel is
  • How adaptable or customizable the channel is, especially over time

UI/UX Optimization

Whether enticing new clients or serving current ones, delivering a world-class experience sets the tone for all interactions. Lintu engineers persistently monitor human-centric trends in financial and government realms to drive product development and modifications for client engagement.

Our methodology commences by defining usability standards based on client business services, performing usability testing sessions, and collecting survey data to funnel in most used interactions. We iteratively fine-tune customer interaction usability and feature enhancements to enable ease of customer experience. Human Centered Design is at the core of the methodology with access to the world’s best human factors research labs where application is warranted.

  • Live Assistance/AI
  • Machine Learning
  • Dynamic Adaptation
  • Client Experience Performance Metrics

Live Assistance

Artificial Intelligence driven Live Assistance technology harnesses automation in identifying human interaction patterns, user types, most accessed inquiries, demographic driven information access patterns, most used device profiles, and survey trends derived by using AI processes on real users.

Lintu Solutions’ Live Assistance technologies bring a multi-faceted approach where we merge the highly effective user trends to compile live assistance content. Our customer support model transformation approach spans the entire customer relationship driving client value:

  • Deep analysis of interaction with systems based on user data collection and pattern analysis using Reinforced Machine Learning
  • User-enablement to address their inquiries through role-based self-service options
  • Tailored interactions that effectively work for the individual based on profiles
  • Support any technical and administrative difficulties through automated workflows
  • Empower product improvement through customer feedback using support teams

Our core focus is not only the highest levels of client satisfaction while securing identities of our customer interactions, but also staying transparent throughout the workflow to enable a sense of trust and reliability in the Live Assistance program.

Live Assistance/AI support:

  • Machine Learning
  • Dynamic Adaptation
  • Client Experience Performance Metrics

Machine Learning

Machine Learning is the process of training a piece of software to make useful predictions using a data set. There are several models of Machine learning that can be utilized based on the data types and level of interaction of humans applied (supervision). Lintu specialists can assist with the development of strategies for different learning models appropriate for the chosen data sample types to ensure the model chosen will achieve the desired machine learning outcomes:

  • Supervised learning – Basic machine learning most applicable where the data sets are labeled or pre-categorized by humans or other systems. The learning system uses patterns associated with known categories to identify new samples that fit the categories to which it was exposed in the learning cycles.
  • Unsupervised learning – Best used on large unlabeled datasets where there is not human intervention, but rather the system uses clustering and associations to derive the patterns. It is an iterative approach that forms associations based on algorithms for the system to do the classification based on patterns detected.
  • Reinforcement Learning – This is a version of supervised learning where a behavioral model of successive trial and error events is guided by reinforcing success in problem solving.
  • Deep learning – When the data is unstructured or abstract, deep learning works in successive layers to learn from data in an iterative manner. Deep learning most closely models human the way a human brain works to learn patterns from abstract sources.

Live Assistance Integration: Lintu Solutions’ Machine Learning methodology for our Live Assistance client experience solutions leverages a version of Supervised Reinforcement Learning with concepts including using a natural language engine for categorizing user inputs to predict types of requests needed, then identify and detect response inputs to formulate automated responses, which are reinforced by supervised reviews.

We recognize and correlate previously unidentified human interactions with various digital customer support technologies to form specific use cases and eventually drive our iterative enhancements of Live Assistance.

Live Assistance/AI support:

  • Machine Learning
  • Dynamic Adaptation
  • Client Experience Performance Metrics

Dynamic Adaptation

In support of our Live Assistance capability, layered upon and supported by Reinforced machine learning, we also incorporate role-based Dynamic Adaptation to the client. Live Assistance responses are tailored by additional intelligence about the user’s profile and service subscriptions.

Our Adaptation methodology uses stored profile data about the client to guide the responses of the Assistant to assure accuracy and relevance to the automated responses. This predictive tailoring assures the highest value responses are provided the client based on what is most relevant to them.

Live Assistance/AI support:

  • Machine Learning
  • Dynamic Adaptation
  • Client Experience Performance Metrics

Organizational Change

Change is inevitable, and is full of exiting opportunities if managed well. The disruptive pace of change in the global landscape of digital services requires organizations to anticipate and keep pace to assure organizational readiness to handle change, whether evolutionary or revolutionary.

Lintu’s experienced change managers have lived the change cycles in a variety of environments. We’ve learned how to develop an enthusiasm for change that not only increases an organization’s tolerance for change, but actually helps them thrive on change and grow the appetite for improvements. Using the ADKAR model has proven valuable across industry segments.

Lintu solutions goes far beyond helping you manage individual changes. We help organizations develop a healthy culture of embracing change and even desiring the realization of positive outcomes change makes possible. An organization that embraces and fosters change is ready for whatever lays ahead.

Imagine what you could do if change was seen by your workforce as a desirable aspect of your organizational culture.

ADKAR principles:

  • Build Awareness for Change
  • Enhance Desire to embrace Change
  • Build Knowledge in Individuals
  • Foster the Ability to Implement Change
  • Reinforce and Sustain a Change

Process Transformation

Inherent in optimization of your service workflow is process transformation. In the context of client experiences, it is effective service delivery built on effective underlying processes that consistently achieves outcomes that exceed client expectations and eliminates re-work.

Organizations develop processes and practices over time as their service models evolve. However as inputs and outputs, requirements, tools, and opportunities for improvement change, so must the functional processes change. Some of these factors are regulatory or other external pressures to change and some are identified opportunities for streamlining and potentially automating, or a need to increase quality and reliability.

Lintu solutions takes a systemic integrated approach to process analysis, looking at the existing processes and evaluating each aspect of the process including the human factors, digital touchpoints, effectiveness metrics, and input/output content and quality. Often process output quality improvements and exception reductions can be achieved with incremental technical controls which include stored procedures and data validation implemented at the human-digital touchpoints.

Looking at larger opportunities such as robotic process automation or new overall digitalization efforts can completely transform a process to leverage transactional effectiveness for reliable repetition. These can greatly reduce manual interactions for reduced effort, increased accuracy, and overall efficiency. Whatever the drivers for transformation

Client Experience Performance Metrics

To continually improve our Live Assistance accuracy, relevance, and value delivery, we apply a maturity model approach that derives optimization actions from data intelligence. This approach is based on our recent direct experience guiding an organization to achieving CMMI level 5 for Services.

Leveraging client-focused experience, activity and feedback metrics, our processes are continually improved based on quantitative analysis of the common causes of variation inherent in the Live Assistance supporting processes of Machine Learning and Dynamic Adaptation.

Lintu’s overall process improvement approach focuses on continually improving process performance through both incremental and innovative technological improvements. Our quantitative process-improvement objectives for client organization are collaboratively defined and continually revised to reflect changing business objectives, and used as input criteria in managing process improvement activities.

Live Assistance/AI support:

  • Machine Learning
  • Dynamic Adaptation
  • Client Experience Performance Metrics