We’ve been talking for more than two decades about the “experience economy,” in which the companies that provide the best customer experience (not just the best products and services) generate the most value for their customers and shareholders. Experiences become the currency that great companies give to their customers and employees.
Companies have spent billions of dollars trying to improve experiences for their customers, employees and partners. But in most cases, these investments haven’t paid off. While 95% of retail CEOs said personalizing the customer experience is a strategic priority in a McKinsey & Co. study, only 23% of consumers believe retailers are excelling in their personalization efforts.
The Differentiating Factor
A phenomenon we’ve noticed is that many companies are taking a very similar approach to customer analysis and segmentation, have access to the same pools of data and are leveraging a common technology stack. So why are some so far ahead in providing exceptionally relevant and valuable experiences?
We believe it’s because they start by imagining the experiences they want to create for individual customers, rather than for broad customer segments or fictional “personas.” Then, they combine technology, customer data and artificial intelligence to deliver contextually relevant and truly personalized experiences at scale and speed. We call this mix of data, technology and decisioning tools enabled by AI the “experience operating system.”
In our recent research with ESI ThoughtLab, customer service and experience was found to be the area most likely to generate a positive return from an AI investment. But to be successful, businesses need to base the experience strategy not on “what do we have?” but on “what should we deliver?” Successful companies also understand the quid pro quo of data: What type of experience or convenience would customers consider to be a good trade in exchange for data about themselves and their activities?
With these insights, leaders can combine the often disparate elements of how an experience is delivered (data and insights, context and content, technology and processes) into an effective experience operating system.
Nine Essential Questions
By asking themselves the following questions, businesses can guide the development of their experience operating system:
- What experiences are you trying to create, improve or recreate, and for whom? Why are they important and what must they deliver? We worked with a pharmaceutical company that wanted to show customers how their aesthetic treatment would help them look more youthful. We used our LEAF Evolutionary Neural Network framework, along with the client’s existing code and datasets, to develop an AI/machine-learning-based prediction model that enables customers to visualize their estimated appearance using the product.
- What are the key drivers for each individual you’re targeting? It’s crucial to understand the individual consumer and tailor experiences for them. Online consignment store Thredup, for instance, uses AI to tailor selections of clothing items that match the individual customer’s style.
- What are the highest value interactions, and what’s the most appropriate channel for those interactions? Which interactions should be automated, and which augmented with AI? To answer these questions, businesses need to identify how and where their customers engage with them. For a luxury car maker, we used AI to identify not only the next best offer, but through which marketing channel to offer it.
- Are your employees focused on tasks that improve the individual customer experience, and do they have the needed tools and information? If speech recognition shows a customer is frustrated, for instance, customer service agents need the business’s customer relationship management or service tracking systems to provide specific information to help that customer.
- If you don’t have the needed data in-house to personalize experiences, how can you gather it from internal or external sources? Furniture retailer West Elm uses AI to scan customers’ Pinterest boards to understand their personal style and create a list of recommended home décor and furniture options.
- How will you identify, acquire, host, prepare and speed the delivery of data to the people and software that make up the experience operating system? For the luxury car maker, we used AI and machine learning to analyze a variety of data sources – including customers’ purchase, sales and service history data, website visits, and external sources such as social media – to identify the “next best offer.”
- Can you support the speedy delivery of a new customer experience with your existing application architecture, development and testing tools, infrastructure and databases? For a major bank, we leveraged an agile data platform to move all customer data to the cloud to enable delivery of more personalized experiences.
- Have you reimagined your business processes to reflect your consumers’ expectations, along with the technology (such as augmented or virtual reality) to support these processes? Wiivv Wearables uses AI to create a digital map of a customer’s foot to analyze how they walk, as well as agile manufacturing to create custom footwear for each customer’s needs.
- How will you measure the success of the enhanced experiences you offer, and continually improve upon them? For the luxury car company, success metrics included doubling the email open rate to 45%, doubling the email click-through rate to 20% and generating 50% more sales prospects in the first year.
The Ways and Means of Purposeful Experiences
An outside pair of eyes –as well as specialized frameworks and platforms – can help enterprises deliver game-changing user experiences. We apply the following approaches when working with clients:
- Create a data acquisition strategy to support the development of an experience operating system.
- Develop AI analytics and recommendation capabilities to help customize each stage of the user experience.
- Build an agile and scalable platform to enable organizations to access and analyze the right data at the right time and work within a framework of data privacy and ethical AI governance.
- Perform an end-to-end analysis of the business’s ability to deliver valuable experiences at scale, and identify the highest priority needs.
However you approach it, winning with customer experience requires an unrelenting focus on creating experiences that make it worthwhile for customers to share their data, a willingness to re-think technology platforms and business processes, and the ability to perform the right analysis on the right data. Above all, it means focusing on the needs of the individual customer rather than broad customer segments.
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