Delivering seamless, consistent, and engaging experiences starts with a customer-centered digital strategy. This ongoing series explores the characteristics that make up a great digital strategy and how to deliver powerful brand moments that solidify customer loyalty and drive differentiation for your organization.
Ever-increasing and evolving customer expectations pose a challenge to businesses, regardless of their industry. Earlier in this series, we explored smart personalization as one approach to help brands provide more meaningful, relevant customer experiences.
Artificial intelligence (AI) is key to advancing personalization. It unlocks the potential for deeply understanding individuals, their preferences, and their journeys to create customer-focused experiences at scale. Organizations that want to maintain their competitive edge realize that embracing AI is a must.
31% of companies want to use AI to significantly improve the customer experience – Forrester
Among AI’s major benefits is the visibility it provides. Machine learning capabilities make it possible to cater to customers’ needs and exceed expectations. At the same time, AI’s predictive analytics can spot trends and opportunities for growth. Applying these insights ultimately makes it possible to create hyper-personalized messaging, deliver effective content, and precisely target customers.
To bring AI into your digital strategy, I’ll talk about the role it plays in delivering exceptional customer experiences, especially around the topic of customer support, and highlight examples of brands that are getting it right.
Get in the game with AI
AI is here to stay, but we’re only scratching the surface of its full potential. With an increasing number of experiences powered by AI, such as voice search, it’s reshaping how consumers behave and purchase items. This puts the impetus on leaders to continuously identify growth opportunities to meet the ever-evolving consumer expectations.
AI is also making an impact on the workplace. In fact, Gartner estimates that 70% of organizations will integrate AI by 2021 to assist with employees’ productivity. CMOs and others in the C-suite should recognize that embracing AI will make their brands relevant not only for customers but also for the teams they lead.
Much of the time-consuming, manual data analysis tasks marketers handle today can be alleviated with AI. Its ability to sort through large amounts of data and provide analytics is a game changer.
However, this aspect of AI also “requires a workforce with a higher level of digital aptitude than what most organizations have today. Employing digital marketing talent that can adapt to the shift of AI as well as leverage and properly interpret insights from AI is essential.” Before fully launching AI into processes and operations, you must have the right organizational structure in place – with the right roles and people to fill them.
Making sense of embedded AI capabilities
AI is also embedded in nearly every platform now, whether it’s to drive personalization, support asset management, or provide sentiment analysis. For example, Adobe has built Sensei AI services into a variety of its solutions, including its Experience Cloud, Creative Cloud, and Document Cloud. Within Adobe Experience Manager (AEM), you can classify images and the built-in AI capabilities recognize and automatically tag images or allow you to look for very specific images within your repository.
Considering this, you might need help understanding how to take advantage of these AI features within the platforms. That’s one area – among many – where we help clients maximize AI’s potential.
Boosting customer experience with AI
A few of the solution areas that fall under the AI umbrella include machine learning, natural language processing (NLP), predictive analytics, cognitive computing, and signal services. These solutions help companies improve understanding of customer intent, support customer service agents, and spot growth opportunities.
Machine learning to understand intent
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Many solutions you encounter today are trained to provide results but not answers. Chatbots and virtual agents are common examples we’ve seen within the past few years. However, there’s a significant difference between the two. The “intelligence” behind chatbots has been mostly scripted by developers, creating scenarios to anticipate common questions asked by customers.
For example, if you’re shopping for a new car, then you’ll likely start by visiting the auto manufacturer’s website. The chatbot on the site may prompt you with the usual, “How can I help you?” And you answer, “Which vehicles have all-wheel drive?” In this case, the chatbot responds with a list of results that show all the vehicles available with all-wheel drive. However, this puts the effort on you to refine and narrow your choices from this list.
If the website used a virtual agent instead, you could speak more conversationally and say, “I want to take this vehicle to the mountains.” Or, you might respond with a specific location in mind, such as, “I plan on driving to Pike’s Peak.”
The machine learning that powers the virtual agent understands the intent of your response. It learns and begins to understand the context of specific requests so that it presents you with options for all-wheel drive vehicles instead of you having to specifically ask that question.
You could try to manually script answers to every possible question a user could ask, but it would probably be an impossible task. Machine learning technology understands intent and how people ask questions. And over time, it gains that understanding so customers can engage virtual agents as if they’re talking to someone at a dealership. These agents understand what you mean and can make recommendations as you would have in real-life conversations.
Machine learning to improve search capabilities
We’ve also seen intelligence evolve with search engines. When you think of Google, you know you can ask the search engine a question and you’ll be presented with real answers, or even “people also ask” prompts to help you find the best answer.
Considering the intelligence behind search, you could use this to improve effectiveness of your internal applications or intranet solutions. You could ask, “How much PTO do I have left?” The search application would connect to your workforce solutions, see the time you’ve used and the total time you can accrue for your role, and come back with an answer. Then, the solution could connect you to a PTO request process because it realizes you’re probably asking about your PTO balance with the thought of requesting time off.
Listening tools to assist customer service agents
Some AI solutions lend an extra hand for customer service agents by listening to conversations with customers and presenting recommended answers, policies, and so on.
Rather than having the agent navigate between three or four applications at once, an AI solution embedded within the system can query multiple databases in real-time and present relevant information to the agent. This eliminates a step for the agent, allows them to focus on the customer, and reduces the call time needed to resolve or address the customer’s needs.
Identifying trends among heaps of data
Other AI use cases include combing through data to pinpoint trends and opportunities. It can analyze interactions occurring on your site, such as which products people view and items for which people are searching. Session replay tools provide funnel analysis and capture every interaction customers have from start to finish. You can review actions taken, where conversions happened, and whether or not the transactions were completed.
While there’s tremendous value in seeing how customers behave, consider the volume of recordings to review. When you have thousands – or even millions – of sessions, how is it feasible to manually comb through all that data? You can’t have one or two people sit there and watch them. Decibel Insights is an AI tool that can identify trends among hundreds of thousands of sessions. For example, it may see customers having problems on a specific page or viewing a specific product. AI excels in this case because it aggregates data and points to potential issues.
Similarly, AI is great for sentiment analysis. With the ability to do large-scale processing of datasets and customer reviews, organizations can put these tools in place and spot the trends and growth opportunities.
Infusing AI into your digital strategy
Even though AI is among the technologies that enable remarkable digital experiences, it can be embedded throughout your digital strategy. AI can be included in everything from content management and customer support to front-end experiences.
AI’s ability to anticipate customers’ needs and be more predictive is among the most significant outcomes. You can identify trends and behavioral patterns and use them to predict next steps.
Additionally, it can tackle personalization on a massive scale. As personalization has grown in recent years, brands have segmented audiences to five or six groups, and then established and applied rules to create different experiences. But this “basic” personalization won’t stand the test of time. If your organization wants to deliver relevant, personalized experiences to everyone, the only way to do that is with artificial intelligence. Cognitive solutions understand where someone is in their journey and the different factors and traits to deliver truly individualized experiences.
Sorting fact from fiction
Not every company is on the leading edge with artificial intelligence. Some still regard AI as “magical,” so there are some misconceptions to address. Contrary to what some may think, artificial intelligence can’t figure everything out.
In fact, with machine learning solutions, you need to train them. Some platforms provide built-in AI capabilities that may help interpret and understand intent. However, the technology on its own can’t figure this out. You must train it to perform and operate in the way that best suits your needs. You have to establish a foundation, add some building blocks, and evolve the solution over time.
We typically recommend starting with a small, narrow use case and then expanding upon it. Once you begin to see success with AI, you’ll identify other challenges to resolve and prove value over time.
You can implement AI in a small way because most of it is service-based. You don’t necessarily have to invest in implementing an entire platform. If you’re testing out improving a single product or solution using AI, you may not require the buy-in from leadership that larger efforts would need.
Once you’ve proven the value on a small scale, you’ll see bigger investments in the technology to expand and scale it across the customer experience.
Seeing success with AI
We recently helped one of our clients, a leading auto manufacturer, create a virtual agent to provide a differentiated – and improved – buying experience for customers. The overall objective was to help customers with their research and make the experience less of a hassle.
The company’s leadership believes that most vehicles made today are built with quality in mind and features that better connect the driver and car. They want their brand to stand out from the competition – not based on their products – but instead with a unique buying experience. The brand is known for its “shopper assurance,” which offers transparency on the pricing of its vehicles.
To provide that level of transparency, the company wants to provide customers with tools that will make this information as easy as possible to obtain. We’ve developed a tool that uses artificial intelligence to help customers find and purchase the right vehicle.
Most customers today are challenged by their limited knowledge and understanding of the terminology dealers use. The dealer view of products relates to model numbers, trim packages, and options within those packages.
As a customer, you don’t care about the terminology. Instead, you’re looking for specific features and functionality – leather seats, all-wheel drive, a sunroof, and so on. Wouldn’t it be easier to research vehicles using an online tool that provides choices by looking at your preferred features instead of predefined packages?
We helped our client look at this situation through the customers’ eyes and develop common questions asked when researching vehicles, such as number of seats and storage capacity. For example, if a customer says, “I need a car that seats seven people,” the tool pulls the list of vehicles that meet that priority. Then, it asks follow up questions around those options to narrow the choices.
As a result, we’ve helped this client on one phase of the customer journey to ease the car buying experience and stay true to delivering “shopper assurance.”
While not one of our clients, Verizon provides strong example of a brand that’s seeing success with AI. The virtual agent within its mobile app does a nice job of understanding intent by asking the right questions. Additionally, the agent excels at keeping customers in the experience to accomplish what they need to do, rather than redirecting them to its website or their customer profile.
For example, you might ask, “How do I add a new line?” And the agent responds, “I understand you want to add a new line. You can do this in your account. Do you want to do that?” Then, it allows you to continue within that experience and guides you through the end of the process. However, you can also ask questions along the way at any time.
In this case, AI serves as a copilot to walk you through processes in the same way that a human customer service agent could, but it’s all powered by technology – pretty incredible.
Final takeaway for CMOs and marketing leaders
Advancing technologies, like AI, make it possible to be successful marketers and meet customers where they are. You have to think about the best ways to leverage AI for the experience you’re trying to create. It should be considered for every solution, but there are many untapped applications for AI because not all the use cases are defined.
It’s on you as marketing leaders to understand your customers, their expectations, and their journey to find opportunities where you can incorporate AI and take advantage of its potential for creating better solutions and experiences.
Creating stand-out digital customer experiences that attract, engage, and retain customers is a tall order. Perhaps you’ve already done some of the foundational work, and you need help with the next step.
When working with clients, we help make sure you know your customers and understand their journeys. Through design-thinking tools, industry research, and pragmatic ideation to execute from end-to-end, you will have what it takes to deliver experiences that surprise and delight your customers.
Ready to get started with your digital strategy? Dive in for more resources.