In today’s fast world, using AI to identify problems in real-time quickly can help businesses stay ahead of competitors affordably. Increasing the value and impact for your end-clients increases profits and allows for growth despite external circumstances (pandemics, wars, geopolitical tensions).
Let’s dive into the new opportunities offered by AI for Customer Experience (CX).
Introduction: The Intelligence Layer
The Enterprise software stack has grown exponentially over the last 20 years, from a couple of vendors to hundreds of different solutions. These solutions may vary in their use cases, budget, implementation time, and financial impact. Regarding customer-related software, we can group these software vendors into two groups: Systems of Record and Systems of Engagement.
- Systems of Record: Systems of Record are critical databases and applications that manage key business functions, specifically customer, employee, and asset data, through CRM, HCM, and ERP/Financial systems respectively.
- Systems of Engagement™: Systems of Engagement™ are user interfaces that connect end-users to systems of record, controlling interactions and potentially evolving into systems of record by accumulating data. These systems have rapidly evolved across various platforms, from mainframe terminals to mobile and conversational UIs.
Zooming into the Customer Experience Management industry, these two groups are very easy to see:
- Systems of Record: CRM systems, Customer Feedback systems, CDP systems, etc.
- Systems of Engagement™: Surveys, Review platforms, Ticketing systems, etc.
This organization of the enterprise software stack has been updated with the arrival of AI and the introduction of a new type of system: Systems of Intelligence.
Systems of Intelligence are software that is not only based on analyzing engagement records but that can replace cognitive and time-consuming tasks. Unlocking a new level of productivity and usage of existing data within the organization.
Customer Intelligence is a subset of the Systems of Intelligence. Leveraging existing records and engagements with powerful AI workflows that allow faster improvement cycles within the organization and a new level of efficiency. Delivering consultant-level analysis in minutes, identifying pain points 24/7, automating feedback synthesis, etc.
Customer Intelligence Definition
Customer Intelligence is the process of collecting, analyzing, and leveraging data about customers to enhance decision-making and personalize interactions, ultimately aiming to improve customer satisfaction, loyalty, and business outcomes. This involves understanding customer behaviors, preferences, and needs through various data sources, including transactional, demographic, and interactional data.
Customer Intelligence goes further than traditional business intelligence (BI). While BI focuses on the business data, Customer Intelligence amplifies your customers’ needs and emotions with the related business impact. Using actual Intelligence (AI) to identify issues and draw action plans.
- The customer’s needs and emotions are coming from real-time customer feedback (reviews, surveys, and claims or tickets).
- The business data comes from systems such as CRM, ERP, or static files (for example: the organization structure, the list of products, the different accounts, etc.).
The problem with traditional Voice-of-Customer (VoC) and CX tools
The Voice-of-Customer era
Over the past decade, companies have been able to measure customer satisfaction at different points of the customer journey. From sending customer satisfaction surveys, sending NPS surveys, and getting online reviews, to simply having feedback forms everywhere.
Collecting records of customer feedback, having dashboards with key metrics, and triggering notifications have become a standard in experience management.
Yet, these tools also have major drawbacks:
- They are mostly survey-based, missing an important part of the customer experience. Customer feedback coming from online reviews, tickets, claims, etc. is often missed in the process.
- They mostly connect to only one source of business data (usually the CRM). The connection is expensive to set up, not agile, and ignores new sources of business data.
- They are designed to analyze structured indicators (CSAT, CES, NPS) but lack the business context to make the insights actionable.
- They require a lot of manual time to create actionable reports.
Voice-of-Customer dashboards are valuable tools, great for collecting records of customer feedback, but they differ from Actionable Intelligence. While dashboards present data, intelligence transforms that data into actionable business opportunities. Recognizing this distinction is crucial for making effective decisions.
Diving into the pain point detection process
Let’s dive into a concrete workflow that is the day-to-day activity of many CX teams. Identifying and solving pain points in the customer journey is a manual, repetitive, and time-consuming process.
It includes:
- Listening to the voice of the customer from all available channels and centralizing all feedback data. Problems: This action is often manual or extremely expensive.
- Enriching with business data so the feedback is actionable.
Problems: It requires expensive integrations and IT roadmaps are always stacked up. - Identifying pain points.
Problems: Analyzing text feedback is either too generic when achieved automatically or achieved manually in hours of manual work. - Creating action plans to solve the pain.
Problems: It’s very time-consuming to have precise action plans for every pain point and the action plan impacts are often manually assessed. - Presenting the insights and action plan to the operational teams.
Problems: Business impact is hard to measure for CX teams and creating a PowerPoint presentation for every improvement plan is a repetitive assignment.
This is a key example of a process that can be automated with AI to increase CX team productivity and increase net retention revenues.
The Customer Intelligence process
Step 1: Capitalize on existing data
To transform customer feedback into actionable intelligence, your CX team needs two types of data.
First, measure customer feedback data at different touchpoints of the customer journey.
Customer feedback records can come from various sources, such as:
- Satisfaction surveys (NPS, CES, CSAT)
- Research surveys
- Online reviews (Google Reviews, Trustpilot for example)
- Incidents, and tickets
We use customer feedback data to understand existing customer feelings and potential customer needs.
Second, enrich with business data, from various sources, to identify correlations. Business data can come from various sources, such as:
- CRM software
- Operational systems (ERP/Databases)
- Static files (that might contain organization structure, employee records, etc.)
- External news (such as competitor updates, press releases, etc.)
We use business data to understand customer engagement, and customer behavior and get customer information that helps uncover insights.
So, the good news is that you likely already possess the main requirements to begin taking Voice of Customer insights to the next level.
Step 2: Apply operational context
Improvements are made by the operational team on the ground. Therefore, Customer Experience (CX) teams must incorporate the operational context when analyzing insights and sharing them with the operational teams. This ensures that the operational teams have all the necessary context to make informed decisions.
So examples of operational context are:
- Excluding noisy topics, which are not actionable from the operational side.
For example, for an airline company, delays-related issues are in control of the operational teams, however, anything that would be related to pricing would not be actionable for operational teams at the airport. - Integrate business value to the different pain points and recommendations, helping operational teams prioritize where to focus while aligning with their business challenges.
For example, having the Customer Lifetime Value (CLV) attached to the insights is important information that helps Customer Intelligence solutions automatically draw the financial impact of every recommendation. - Adding role-based rules to limit the visibility of the insights and recommendations.
Step 3: Leverage AI to go from Customer Data to Customer Intelligence
AI technology helps your CX team turn customer feedback and business data into extremely useful insights. Insights that would have taken hundreds of hours to detect without the proper technology.
How is AI a tech enabler that increases operational excellence?
Let’s dive into 4 examples that are key to Customer Intelligence.
Example 1: Analyze Text feedback at scale
How does one efficiently analyze hundreds of text feedback, without bias? AI has simplified Natural Language Processing (NLP), for companies of all sizes, and saved many hours along the way.
Text analysis has traditionally been handled by consultants or extremely expensive software, but AI has made it more accessible and efficient. Companies can now analyze text insights on their own with great precision and scale.
Example 2: Automate time-consuming tasks
Improving products or services requires analyzing customer feedback, but this process takes time, and many of the tasks involved are highly repetitive.
AI easily automates tasks such as text synthesis, pain point detection, generating improvement ideas, creating action plans, and identifying the most mentioned topics.
To conclude, while the AI cannot do the actions on the ground, it can drastically improve the speed needed to identify these actions.
Example 2: Build consultant-level reports faster
Decisions run companies, and reports and their insights drive those decisions. Creating reports for weekly meetings, monthly boards and specific workshops has always been a time-consuming task. From finding the insights, and visually presenting them, to commenting on them with annotations that are action-driven.
AI performs a first-level analysis that, when given the right context, can be used to:
- Generate the report’s structure
- Generate intelligent annotation based on graphics and results
- Identify anomalies in metrics
- Generate action plans based on text feedback
Why does Customer Intelligence matter in 2024? Unlocked benefits.
Customer Intelligence bridges the gap between customer satisfaction and competitive advantage.
Here are the 4 top benefits brought by Customer Intelligence:
Benefit 1: Improved Customer Experience across your customer journeys.
Customer Intelligence delivers more precise and more frequent analysis of the different pain points encountered in your customer journeys. While a CX consultant could deliver 10 to 20 reports per month, a Customer Intelligence Platform can deliver 10x more reports, with greater precision, accurate business impact, and clear remediation plans.
By understanding customer needs with more clarity and better timing, your CX team can drive better service and products. Metrics such as NPS, CSAT, or CES are going to improve.
Benefit 2: Increased Revenue.
Increasing Net Promoter scores has a double financial impact on your company’s bottom line:
- Lowering the number of detractors has a direct impact on the Net Retention Rate (NRR), closely related to loyalty. Loyalty increases revenues from the existing customer base and lowers the cost of churn (customer service costs).
- Increasing the number of promoters increases the net earned growth, which represents the growth coming from customers’ word-of-mouth and not paid channels.
Interested in the correlation between NPS and revenue growth, check out our NPS 3.0 guide here.
Benefit 3: Improve Efficiency in your CX team.
Having an AI Analyst do the reports for your team has a measurable impact on the team’s efficiency.
Let’s say a CX consultant or full-time CX analyst can do 16 reports per month for a $80k annual salary. Making the cost per report at USD 400. Now, using a Customer Intelligence Platform, an AI could easily 80 reports per month for a $40k annual cost. Making the cost per report at USD 40, so a 10 fold cost reduction.
Benefit 4: Increase Competitive Advantage.
Customer Intelligence creates a competitive advantage on three different timeline levels:
- Short term: Having your Sales and Marketing team have access to the right customer signals (low NPS, segment at risk, etc.) will increase the conversion rate across all your go-to-market functions.
- Midterm: Deeper customer insights lead to a better ability to identify strategic actions. Identifying opportunities in customer needs regarding development of new services, products, etc.
- Long term: Gain a competitive edge by accelerating innovation with Customer Intelligence, enhancing your processes, products & services at an unmatched scale.
Bad Customer Intelligence vs Good Customer Intelligence
As the last part of this guide, let’s dive into what makes a good Customer Intelligence solution and the errors to avoid when unlocking the Customer Intelligence opportunity.
| Red flags 🟥 | Best in class 🟢 | |
|---|---|---|
| # of data sources | Surveys based and 1 business source | Capture all sources of VoC data and all sources of business data. |
| Integration cost | High cost with external integrators. | No cost, handled 100% by software |
| Text analysis engine | Generic topics | Very precise topics based on your use cases and current environment |
| Recommendation engine | Generic recommendations | Very precise recommendations based on your use cases and current environment |
| Reporting engine | Extra work is needed to create presentations. | Provides ready-to-use reports that require no extra work. |
| Business value | No clear business value, focus on CX indicators only | Include direct business output ($) within the software |
| AI-provider | No information is shared | Full transparency on the AI used and the storage locations |
| Security certification | No certification | ISO 27001:2022 or SOCII |
| Project implementation | Externalized with CX consultants | Built-in in the software offering |
Conclusion
In conclusion, leveraging AI for real-time problem identification can give businesses a competitive edge by enhancing efficiency and delivering superior customer experiences.
The evolution of enterprise software stacks, from Systems of Record and Engagement to Systems of Intelligence, highlights the transformative potential of AI in customer experience management. AI-powered Customer Intelligence solutions offer significant benefits, including improved customer satisfaction, increased revenue, enhanced efficiency, and a stronger competitive position.
By integrating and analyzing diverse data sources, these solutions enable companies to swiftly identify and address pain points, ultimately driving growth and resilience in a dynamic market.
About Feedier
Looking to reduce the number of detractors? Feedier’s Customer Intelligence Platform simplifies customer data mastery (from both non-structured feedback data and structured business data) and accelerates decision-making at the operational level, empowering our users to lead with impactful insights. With Feedier, they harness their customer data and take advantage of AI, and automated reports to illuminate the path forward to rd, ensuring they’re not just competitive but ahead with confidence and clarity.
