Delivering an improved customer experience with AI

Tapping AI’s Potential for Growth

Across many industries, companies using Artificial Intelligence (AI) in marketing and customer experience (CX) are simply outperforming those that don’t. The AI impact is real and quantifiable.

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Now, as we consider how applying intelligence to commercial models makes sense for life sciences, CMOs and CXOs at today’s leading manufacturers are often left wondering, “What’s the tangible value AI can bring to my organization to help me build differentiating customer experiences and ultimately win in competitive markets?” “Can we utilize AI the same way other industries have?”

These are fair questions that we have heard. We hear about how AI is reshaping life sciences but it’s often in the context of R&D or other non-commercial functions. Perhaps you’ve heard how AI is drastically reducing the lengthy drug discovery phase. Or maybe you’re familiar with how AI combined with extended reality is allowing manufacturing teams to visualize the optimal floor layout.

But when it comes to AI’s potential to create growth opportunities in marketing and next level customer experiences, where exactly are we?

The state of AI in life sciences

It may not feel like it, but we are further than you might think when it comes to leveraging AI for marketing and enhancing the customer experience. Today, conversational AI is being deployed by marketers and sales reps to create deeper and more personalized interactions with their target HCP populations. Marketers are also using Next Best Action/experience algorithms to help both sales and marketing teams drive more impactful interactions.

But when it comes to modernizing commercial by tapping AI’s massive potential in customer experience, we know our industry has more complexity, and as a result, has plenty of room for growth.

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Learning through the lens of other industries

We don’t need to look far to envision a life sciences organization using AI to power truly bespoke and ultra-engaging customer experiences. In a statement that will surprise no life sciences marketer: other industries are ahead in using AI to enhance their marketing presence and customer engagement. Yes, we have our own barriers when it comes to innovating, and for good reason. Still, even within the bounds of our industry, there is so much more value to leverage through AI. To get an idea of that value, let’s look at a few examples in other industries. These use cases were developed and implemented by Accenture and our clients.

 A major tech company is using natural language processing and machine learning to understand customer sentiment to recommended and automatically craft email communication layouts and contents. Using an AI model that’s trained on over 500k unstructured data requests from inbound customer emails, this company is delivering a customer experience that’s not only curated to each individual, but it’s also continuously learning and delivering even more relevant communications. By using NLP and ML (both are different types of AI), the company saw a 35% increase in measured customer satisfaction and a 40% decrease in required time to produce and deploy communications.

A leading telecommunications company built an intelligent bot using AI to anticipate which customers are going to contact the service center, and then pre-emptively contacted them about their issue. The company completely revamped their customer experience by proactively reaching out to their customers! The AI bot learned what customers needed help with, and when they were most likely to reach out based on past behaviors. Less than a year after deploying this AI-powered solution, inbound calls were down by 1.5 million, digital channel engagement was up 26% and customer satisfaction hit an all-time high.

A major cruise line used AI to build hyper-relevant experiences for thousands of guests at a time, based on what activities they previously completed while on-board. Instead of bringing documentation and waiting in long lines to board, guests used an AI-powered program that curates a unique itinerary. The result is that guests complete most pre-departure steps online—reducing wait times by 90% (from 10 minutes to 30 seconds).

The common thread across these AI CX applications is that they include the development of interactions based on the unique needs of the firm and even the business unit. The lesson here is that through creativity and a strategic approach, we can find applications for life sciences marketing and CX that will advance a brand(s) into the market.

Advancing your CX journey with AI

It’s true that the case studies above didn’t start producing results overnight. However, they cement the idea that you’re much closer to tapping the true potential of AI than it may seem. Here are three actions to either start or advance your AI marketing and CX journey:

1. Start small, then go wide. If you haven’t left the starting blocks on your AI journey yet, don’t fret. Align on a challenge that AI can address, like improving the experience with HCPs that practice within certain integrated delivery networks or increasing the digital engagement of branded materials with a select segment of patients. The possibilities are many and deciding where to start can be the hardest part. Include a cross functional team of AI and go-to-market leaders who can help to choose the right place to show that AI can deliver value via CX. AI is like compound interest. The longer you leave it in market, the more it grows. You just need to pick a spot to invest. And while you are moving on the initial starter application, you can plan for the bigger play.
2. Scaling your AI investment. Scaling starts by considering all the areas your organization can reap the benefits from AI. While an ideal use case may have been considered for one key brand or channel, it may have applications across other brands or areas of marketing. A key use case could even have applications across other business units where the cost can be shared (e.g., the pharma team can share costs with the consumer products team). By planning AI investments for multiple areas, we can increase our ROI, but we still need to consider how the needed infrastructure can be most efficiently deployed. An analysis of the use cases categorizes them into groups that will allow for better sharing of data and tech resources within those groupings. Each group is focused on use cases that operate in specific domains (e.g., content operations, segmentation and targeting, ad operations).
3. In-Market Pilots. Planning for scale is still not enough to ensure a smart investment in AI. Much of the tech is nascent and should only be introduced broadly after pilots can prove the most basic premise of the most compelling use cases. Agile methods also help to ensure the pilots are deployed in a way that balances business and technical requirements and accelerates pilot launch timing through well planned sprints.

 Imagining the Possibilities

With the blueprints already published across multiple industries, your marketing organization has the potential to start re-shaping the customer experience, today. Our industry is one of complexity and numerous hurdles, especially when it comes to personalized engagement, but that does not have to be the case when seeking real results from AI.

[1] https://www.accenture.com/us-en/services/applied-intelligence/solutions-ai-marketing