Connecting the dots with artificial intelligence

Tips for omnichannel and digital assistant success

• Enterprises will need a well-defined process automation roadmap. They should plot the most significant areas of the business that are suited for automation and assign a focus group of business process owners to discuss and prepare a future blueprint with strategic objectives in mind. Also, ensure support for human interaction when necessary and that data privacy standards are being met with transparency about how that is being achieved.

• Chatbots can provide measurable improvements in availability, accuracy, and efficiency, as they can operate 24 hours a day, 7 days a week and can quickly leverage data from various sources, such as knowledgebases and historical and annotated call data. While chatbots can be very effective, they aren’t a customer support panacea. They excel at automating the handling of routine questions, but when it comes to more complex problems, they must transfer the interaction to a live agent. That’s why the goal of chatbots shouldn’t be to replace agents, but to free agents to address the most serious and complicated customer problems.

• Contact centers that have yet to invest in chatbots should start small. Test and use results to expand outward across the enterprise and customer engagement channels. Also, look beyond point solutions to strategize how to engage the wider enterprise and to interact with users through the entire customer journey from the initial contact to after sales care. This will help to enable frictionless customer experience.

Artificial intelligence will become more pervasive throughout enterprises and contact centers. A customer can engage in hundreds of micro moments along their journey. When it comes to optimizing each micro moment, the key is ensuring that the consumer journey is tailored to the platform or device they are using. AI contextualizes and dives into the micro moments, targeting customers’ needs along the journey. To be successful, customer touchpoints must be embedded with machine learning algorithms that can be used to identify the patterns in the customer data from different sources and can help correlate a customer behavior to a matching persona in real time and then be layered with AI to enable automation and intelligence throughout an enterprise.

This can provide a holistic customer experience throughout the customer journey, from the first touch to the final sale and beyond. For instance, the traditional approach to predicting customer lifetime value is based solely on customers’ historical data. But customer lifetime value models powered by artificial intelligence take into consideration a combination of factors, including the monetary value of the purchase and inference of future actions, for example, to make better predictions.

When done well, enterprises can leverage AI to respond to key moments in real time and proactively anticipate customers’ next moves, understand which channels they intend to use, and be ready with relevant content, offers, or remedies.

Machine learning algorithms help marketers and customer experience managers identify trends, patterns, insights, commonalities, and abnormalities in data. After the data is gathered and sorted, it can be analyzed to ensure that the best content and messages are not only reaching but are also positively affecting the appropriate audiences along the journey.

According to Omdia research, 26% of companies surveyed are planning a strategic investment in artificial intelligence in 2022, and 37% are planning a minor upgrade to their existing investments, totaling 63% that are planning to invest in artificial intelligence in 2022 (see Figure 6).

Figure 6: In 2022, 26% of organizations have a strategic investment plan in place for AI.

2022 next-best-action marketing /AI

Do not have

No investment planned

7% 6%

26% Strategic investment planned

Maintain existing investment 24%

37%

Minor investment planned

(Source: Omdia’s 2022 IT Enterprise Insights survey. Sample size: 4,757)

Going back to the fashion retailer example, imagine if the retailer were able to leverage fully integrated omnichannel support, self-service automation, and machine learning capabilities. The customer could start a conversation with an automated chatbot, which can conduct an account lookup and try to answer the customer’s question by searching the company’s knowledgebase. If unsuccessful, the chatbot can transfer the conversation and customer information to a live agent, who can pick up where the chatbot left off, without asking the customer to repeat any information. As with the earlier example, the chat would appear in the same window the agent is already using to support the customer, so there’s no need to toggle between windows.

By leveraging omnichannel support and self-service automation with machine learning, the retailer would notice the following benefits:

• routine inquiries are handled by the bot, giving agents more time to focus on more complex inquiries and

• agents can pick up where bots leave off, so customers don’t have to repeat information or troubleshooting steps.

By focusing on handle time, the retailer can think more broadly about customer handle time, which includes automated self-help and live agent support, to improve operating efficiencies and enhance customer experiences.

Best practices to obtain long-term value from AI

• Seeking greater intelligence requires clean data. When seeking AI developments, it is helpful to remember that data comes first. Enterprises must conduct data cleansing and tagging before initiating their AI efforts.

• Additionally, enterprises will need integrated, unified customer data. Customer data management is fundamental to the effective use of AI, which needs to be networked across customer journeys to orchestrate relevance at each step. Unless brands have unified historical, transactional, and behavioral knowledge and data about what customers do at each stage of their journeys, brands will not succeed at delivering personalized and relevant interactions.

When businesses have properly integrated data, they can recognize who they are interacting with and respond appropriately.

• Processes must be re-engineered. Identify areas of the enterprise that are affected and design or re-engineer related processes. Enterprises must clearly define their programs and retrain customer-facing employees to assume other higher-value-added tasks. Forgetting people, and their interactions with AI, would lead to the derailing of AI technology as a vehicle for positive change.

• Getting value out of AI requires a well-rounded workforce. Employees must have the proper mix of technology and business skills. Enterprises must prepare their teams for the impact AI will have on skillsets, expertise, and the nature of work. Consider how their roles evolve and how their jobs will change.

Bring it all together: Cloud transformation with Edify and Google Chrome OS

Edify on Chrome banishes legacy contact center complexity by letting users interact with colleagues and customers via phone, text, email, and video just like they do with friends – in a single window –with the simplicity, security, and ease today’s enterprises need.

With Edify on Chrome and Chrome OS devices, users can:

• Use one window for all contact center and unified communications applications, databases, and channels to improve productivity and experience.

• Protect their enterprise’s digital assets with OS, browser, application, firmware, data, and device security.

• Get up and running in minutes on one cloud-native solution inside of one screen without sacrificing quality or capability.

• Easily work at the office, from home, and on the go, making work-from-anywhere an efficient, reliable, and secure reality.