More mechanisms are available now than ever before for customers to provide feedback about their experiences – and share their thoughts loudly – through public forums like Facebook, Google, and Yelp, and traditional customer feedback channels like surveys. Simultaneously, customer expectations are increasingly challenging to manage due to greater business complexity. Businesses today serve customers through more channels and in more customized ways, with a greater variety of products or menu items, a wide range of promotions, enhanced loyalty programs, and more.
These variables have made customer experience (CX) measurement more complex. Organizations need to be able to see, digest, and react to feedback from many different mechanisms. Yet gathering all of this customer feedback in one place to analyze it collectively and meaningfully can be a challenge.
New technologies, new data sources, and the emergence of artificial intelligence (AI) can enable new measurement approaches and increase companies’ ability to make a rapid, positive impact on customer experience.
Wavicle’s Chief Strategy Officer, Sue Pittacora, sat down recently with Jola Oliver, CX Leader at McDonald’s Corporation, for a fireside chat about the future of customer experience measurement and the impact of emerging technologies. Throughout the discussion, they covered the latest innovations in customer experience measurement, including how companies use AI to gain deeper customer insights, the importance of gathering hyperlocal data to evaluate competition, and how businesses leverage new tools and technologies to improve the customer experience.
Take a behind-the-scenes look at Sue and Jola’s conversation and dig into the future of customer experience measurement.
Sue Pittacora: Jola, since joining McDonald’s Corporation, you have pioneered social insights processes, systems, and text analytics capabilities; measured social campaign performance; and synthesized social voice of the customer (VoC) insights across channels, markets, and feedback sources. With more than 30 years of experience as an analytics, insights, and customer experience leader, you’re uniquely positioned to see the changes in the industry and the opportunities that new technologies are creating. To start the conversation and provide some background, can you briefly explain how you think about customer experience in your role at McDonald’s?
Jola Oliver: As a brand, you need to think about why the customer is coming to you and make their goal as easy as possible to reach. Customers at McDonald’s want to come in and order quickly, and they want to be able to spend money or pay somebody back easily. You also have to consider how customers are feeling. Customers today are constantly being physically and digitally bombarded, and we want to help them escape the noise by making their experience simple. We focus on the digital journey and building brand trust by providing simple, memorable experiences.
It is also critical to know who your best customers are and to focus on maintaining and increasing their customer lifetime value. This includes thinking about what kind of messaging they receive, how we serve them, how we get them the best deal possible, and how we can reward them to increase their buying frequency.
Sue: You’ve articulated McDonald’s objectives to make things simpler for the customer given the complexity of the business and, more importantly, the number of decisions and amount of information all of us are bombarded with in our day-to-day lives. Trying to help customers deal with that and offer a simple, easy, memorable moment is probably more difficult today than ever before. What challenges do you face while striving to simplify the customer experience and gain insights to inspire action and drive revenue growth?
Jola: First of all, reaching these objectives requires a whole new approach to data ingestion. It can be disruptive, but it is absolutely crucial to ingest and analyze all of our customer data holistically in order to make the necessary decisions. At McDonald’s, for example, we have more than 40 customer journeys to manage. It is critical to leverage new technologies and innovative approaches to analyze customer experience data at that level and at that volume.
The second challenge is that the complexity of running a restaurant has also reached unprecedented levels. There are countless feedback mechanisms, and data comes in many forms from many sources like internal CSAT scores, Twitter, Google reviews, and more. Plus, much of that data is in an unstructured text format. We had to find innovative ways to tackle this type of data analysis.
To effectively navigate this data landscape, we also must clearly understand what we need to track and the quality of the data we collect. With that knowledge, we can then sift through our customer analytics, separating the noise from the valuable insights and prioritizing initiatives that offer the greatest return on investment.
We have a complex environment with many moving parts, and our goal of providing a simple, memorable experience and keeping up with changing customer expectations is increasingly difficult. It has been important for us to have a strong partner on the data and analytics side that understands not only data, analytics, and AI, but also the nuances of customer experience measurement. Our partnership with Wavicle has been of huge value for our customer experience initiatives, as we can trust the Wavicle team with both our business objectives and our technology needs.
Sue: It sounds like you have a massive challenge in trying to analyze many complex journeys and tease out all of the different themes from the feedback, cluster them together, and identify the salient details to be able to inform your strategy and operations. That complexity leads to a tremendous opportunity to capitalize on advances in technology, AI, and new data sources to gain sharp customer insights that drive action. Can you tell us a bit about how you’re leveraging AI in VoC measurement?
Jola: We’ve been using a text analytics tool that Wavicle built and trained specifically for McDonald’s. It helps us reveal the nuances and sentiments hidden within unstructured text, aggregating and synthesizing feedback across various sources. Using AI-powered natural language processing (NLP), applying sentiment analysis techniques that classify customer comments as positive, negative, or neutral is easier and faster. This empowers us to gauge the overall customer sentiment and uncover areas for improvement. In addition, AI enables text mining and analytics, delving into unstructured customer feedback and extracting valuable insights through techniques like topic modeling, clustering, and keyword extraction. This helps us quickly discern emerging trends, identify common issues, and uncover customer preferences.
It might help to have an example because it can be very nuanced. Suppose a customer says, “I ordered two Big Macs. They were good, but the staff was rude.” AI can tease out that “they were good,” but our trained text analytics model is able to co-reference that “they” refers to the Big Macs. It can also assign the topic of that feedback to the menu category of burgers or Big Macs and identify if the input is positive or negative – “Big Macs were good” is positive, but “staff was rude” is negative. The customized AI model can recognize that these are two different sentiments and capture the feedback correctly. Without AI – and a model tailored specifically for our business and customers – this level of detail wouldn’t be possible at scale.
Another critical component is how we manage feedback and customer experience insights. Decisions must be made on who the correct recipient is and what is the highest priority. To truly understand CX feedback, you need text analytics in place to provide context and extract insight throughout the customer journey. You need to be able to distill themes and cluster them together for a more holistic picture. Leveraging AI also requires training the model to understand how people talk and associate themes and nuances around a particular experience – for instance, so the AI can identify when commentary about the parking lot or waiting in line is related to the drive-through experience rather than in-store ordering.
Sue: Getting those insights quickly and accurately is crucial to taking corrective action in the business – whether that be additional training for crew members, correction of the way a sandwich is being prepared, or a fix for another operational issue. AI can also help you perform predictive analysis in a more meaningful way. How are you thinking about using this data in a predictive manner?
Jola: Predictive analytics give businesses the power to understand their customers like never before. AI algorithms can delve into vast amounts of historical customer data and behavioral patterns, from feedback to purchase history and demographics. With the help of machine learning models, AI can even predict future customer behavior, identifying potential churn and suggesting personalized actions to boost satisfaction. This all contributes to a better understanding of the customer experience. We can use past behavior to predict a customer’s subsequent behavior, give them the best customer experience, and even enhance how we market to them. This can be the ticket to turning a poor experience into a great one.
The most important thing to remember here, again, is that you must train the model to meet your specific business – to pick up the nuances across different themes within the customer’s experience. The more you can train your predictive model to understand your business and your customer experience, the better it will be able to provide insight.
Sue: Another trend we’re seeing is the strive to gain hyperlocal competitive intelligence, which is becoming more accessible due to technological advances and data availability. For example, new access to foot traffic data can contribute to a better understanding of the local market, needs, and competition. So much can be done with that type of information to augment customer experience measurement. What do you see as the role of hyperlocal competitive intelligence?
Jola: Hyperlocal data offers businesses location intelligence that allows us to understand each restaurant’s trade area better to maximize growth opportunities. You can augment customer feedback with foot traffic data to understand how promotions, holidays, seasonality, and events impact your business compared to your competitors. It can also improve staffing accuracy, sales forecasting, demand forecasts, and profit and loss efficiency. This data is now available to help businesses capture unmet customer demand and increase sales in ways they couldn’t before.
Sue: With all that data and the company’s investment in customer experience, can you tell us a little about what kind of impact your team has had on the business by leveraging analytics around customer experience measurement?
Jola: We have had so many examples of how McDonald’s is leveraging AI and analytics tools to gain insights, improve our customer experience, and drive action. We’ve used these technologies to identify operational issues for new product launches, determine where additional training or increased staffing can measurably improve service, and drive accountability for operational excellence.
One of the strongest indicators of our CX program’s strength is in our loyalty program. We have more than 40 million loyalty members now, which accounts for about 20% of our guests. We are working to double that number in the next few years, and we’re focused on impacting the highest-value members, which helps both the company and our best customers thrive.
Sue: It is impactful to hear your story of how analytics and AI boost your customer experience insights and initiatives. So much can be done with AI and hyperlocal competitive intelligence to transform how we understand and serve customers across the industry, and continuing innovation will open up new opportunities and abilities in the years to come.