You could boil the challenges customer service will face over the next five years down to two questions:
- How do we engage customers as they interact across digital touchpoints?
- How do we embed intelligence into servicing applications to take advantage of the ability of algorithms to optimize far beyond what we've achieved to date?
Orchestrating Digital Touchpoints
The move to digital touchpoints, micro-moments of attention, conversational interfaces and messaging-based interactions is causing major challenges for many organizations. One reason is that customer services apps (and processes) today are designed for single channel, synchronous and dedicated session interactions.
So orchestration of digital interactions is very difficult... but it's not an option to sit it out because that is where the consumer/customer is.
Algorithms Driving Apps
The shift to algorithms driving interactions might be an even bigger challenge. It is very likely that this trend will significantly improve servicing benchmarks (costs, CX) and become competitive differentiators for your business.
Again, it's not an option to sit it out because that is where competitors are going and what consumers will expect.
How, then, do you begin to re-constitute every app you run in the contact center to take advantage of new algorithms that drive these apps in real-time?
There are several functional areas (routing, WFO, CRM, knowledge) to where you could start , but one with strong promise and excellent early results is Intelligent Assistants.
Intelligent Assistants (IA) and Chatbots overlap in definition but both do two functions: (1) Rapidly recognize or anticipate intent and (2) Respond with the best action or answer. Chatbots do this in a messaging app. IA can work outside messaging.
Both use algorithms to drive the interactions. The algorithms can be from simple rules to complex machine learning models.
"Intelligent Assistants are automated self-service resources offering consistent answers and responses to queries or instructions on behalf of brands or enterprise companies" (Opus Research)
"Bot or Chatbots are automated, conversational software agents deployed most often inside of a messaging apps" (Opus Research)
Guide to Intelligent Assistants
To help me better understand Intelligent Assistants I got my hands on a copy of the new report “Decision Makers’ Guide to Enterprise Intelligent Assistants” from Opus Research. It’s very good and highly recommended.
You’ll need to purchase the report but here are a few highlights.
Key Findings of IA Market
Opus says 1,800 customers have deployed 2,700 IA deployments, meaning the technology is moving into the main stream. The report is based on insights, surveys, interviews and analysis of vendors and IA deployments. The key findings from the report are:
Successful IA implementations reduce operating costs while improving customer experience and customer satisfaction scores by automating the handling of routine queries and optimizing person-to-person for both agent and customer when interactions require a human touch.
To support competitive differentiation and omnichannel strategies, the age of Intelligent Assistance is being thrust upon Marketing, Customer Care and Digital Commerce executives around the world and across multiple industries; it is no longer a matter of “if” but “when.”
Corporations and brands face the challenge of unifying disparate, single business unit bot projects into a focused bot strategy that embraces omnichannel deployment.
Decision-makers pursue a holistic approach that leverages existing investment in knowledge management, CRM, analytics, Web commerce, chat and contact center and offers it through conversational interfaces that support today’s channels and those to emerge in the coming years.
Companies with existing intelligent assistant solutions are ahead of the game and well positioned to continue leveraging and improving their investments.
Solutions span two broad technological domains: “Conversational Technologies,” spanning speech recognition, text input, avatars, emotion detection and biometric authentication and “Intelligent Assistance Technologies” melding natural language processing (NLP), machine learning, and semantic search with conversational analytics and knowledge management.
Propelled by the “age of the bot,” NLP-based intelligent assistant solutions, have entered the mainstream.
Bot development suites represent a new category of emerging tools that brands may want to explore as they develop innovative strategies to reach customers and prospects on popular messaging platforms.
The report covers IA technology components, tech options and considerations, and vendor evaluations.
IA Technology Stack
We've talked about our view of the technology stack for Chatbots in this post. Opus provides abroader view of the IA and Bots Tech Stack. They break the technologies into four layers and discuss each component.
- Conversational technology
- Bot platforms
- Emotions and sentiment
- Biometric authentication
- IA technology*
- NLP, ML Semantic search
- Knowledge Management (KM)
- Intelligent Assistants and Smart Bots
- Personal assistants
- Personal advisors
- Virtual agents
- Employee assistants
- Meta bots
IA Technology Definitions
The discussion of these components provides insights into options such as types of Chatbot platforms to consider. One important consideration that often gets overlooked by people outside customer service is that service is a mission critical operation. So any technology used must also support mission critical performance. That is a different ball game from fun, brand-building apps.
28 Vendors Profiled
Opus narrowed the field of vendors down to the top 28 then assessed each against a 7-criteria matrix. The findings and commentary for each are provided in the report. (I can’t give out the ranking but our partner, NextIT, is really good).
- Artificial Solutions
- Creative Virtual
- CX Company
- Digital Genius
- Do You Dream Up
- IBM Watson
- Living Actor
- Next IT (a Vertint partner)
- Sundown AI
- True Image Interactive
The report concludes with a recommendation of getting started now.
"The decision to integrate intelligent assistance (both a conversational user interface and AI-like understanding and response) is inevitable. The challenge for enterprise decision makers is to learn from the experience of theirs and avoid some common mistakes while identifying automation opportunities, choosing among multiple vendors, and organizing to manage implementations."
Overall the report is an outstanding read and will help you map out an IA strategy. Many analyst reports cover general machine intelligence trends but this one addresses customer service head on.
Here is where you can get a of the report: “Decision Makers’ Guide to Enterprise Intelligent Assistants” from Opus Research.
Our Approach to IA
We're excited to be working with leading brands to address the two questions we opened this blog with. Our take is IA is critical part of the answer.
Our approach to IA is based on:
- Orchestration of enterprise messaging and digital engagement
- Automation through Chatbots (the right FAQ Chatbot can "respond to 80% of customer queries" and we can extend from there)
- Automation through IA for additional intelligence
- Use of algorithms to drive servicing apps in real-time
Let me know if you want to know more about our approach.