Eye-popping ROI: 5 examples of data-driven brands
By Giselle Abramovich
Posted on 09-25-2020
In 2014 Wired published the powerful headline: “Data is the new oil of the digital economy.”
Six years later we can all acknowledge that data has, indeed, had an immense impact on business, guiding innovation, fueling business intelligence, and even uncovering areas to invest or reduce costs.
Below we take a look at how five brands are using data to guide their decision-making and better meet customer needs.
1. NASCAR: Deepening fan engagement with data
The marketing and customer experience teams at NASCAR use data to personalize the fan experience, deepen connections with loyal enthusiasts, and engage new ones. When fans visit nascar.com or interact with the mobile app, the content each person receives over time will shift through hyper-personalization efforts. In other words, no two experiences are the same.
For example, data tells NASCAR that some fans enjoy videos and articles about their favorite drivers, while others prefer a quick way to access schedules and scores. To achieve this, teams are constantly analyzing data to uncover the most popular user journeys and then optimizing the experience accordingly. In a typical month, hundreds of tests are run to optimize content, and small but impactful changes are made across digital properties.
Artificial intelligence (AI) and machine learning play a helping hand in understanding a massive data set, working behind the scenes to help each fan’s experience become more personalized and more compelling. The results have been incredible: Video views across NASCAR’s digital platforms are up 18 percent year-over-year (YoY), and visits to the mobile app are up 16 percent. Fans are more engaged as well, with the amount of time spent across digital channels up 10 percent YoY.
2. RS Components: Forecasting demand for PPE
RS Components is a global supplier of industrial components. According to Andrew Morris, head of global insight, the key to RS Components’ resilience during COVID-19 has been its use of data insights. By looking at trends in countries that were affected by COVID-19 early on, the company was able to predict how other populations would react and prepare its supply chains to meet local needs.
More specifically, in December 2019 RS Components noticed a shift in consumer behavior on its Chinese website. Data revealed a significant spike in demand for PPE product lines for the month, particularly in the city of Wuhan, China. Once it became clear the Coronavirus was spreading, the company began to draw on data from countries where the COVID-19 infection had escalated, like China and Spain, to predict and proactively address needs in markets that would soon be fighting an escalation in the pandemic. This allowed RS Components to ramp up awareness for its products at a local level and prepare its supply chains to meet demand.
After data from Italy revealed that the shift to remote working led to higher sales of headsets and office chairs, RS Components conducted in-depth data analyses to forecast demand in other countries and adapted its strategies to align.
Data allowed RS Components to get ahead of these trends and be more proactive. With better information, the company’s marketing, sales, and supply chain teams can prepare ahead of time to deliver essential products where and when they are needed.
3. Staples: Finding meaningful data to drive purchases
Elisha Heaps understands the value of data. She is, after all, principal data scientist at Staples, the office supplies retail company, where her responsibilities include developing AI and machine-learning techniques to drive its e-commerce business.
Heaps said every product or SKU offers demographic clues that help predict how the person contemplating an online purchase is going to behave. Staples employs clustering algorithms that predict other products a person might be interested in.
“Not every single data point is going to be constructive,” she said in a previous interview. “If everybody is buying the same ream of paper from Staples, it tells you that’s a really great product, but that’s not really going to give you a sense of how that person may be different from anybody else who walks in the door. But when you see that that person has made another purchase, like buying even something as simple as glue or a particular type of pen, now you have more nuance about them.”
That insight goes a long way in fueling product recommendations, Heaps said.
“There are a lot of different ways of grouping items together that are not necessarily associated with each other ‘on paper,’” Heaps said. “You have to observe what’s borne out in terms of what people are purchasing together, what they’re viewing together, and once they’ve purchased something that maybe is a little bit rare or maybe a little bit more unique — perhaps an unusual color choice — that’s going to be the ticket.”
The key, according to Heaps, is to find the meaningful piece of data you can then latch on to and make the customer’s experience more personalized.
4. Food Network: Data to quantify the impact of trends
The Food Network’s content, marketing, product, and analytics teams use data to understand the overall user journey across touchpoints. With a digital audience of 40 million monthly visitors, signals are analyzed to identify trends and give credence to gut instinct. And while the team has built a level of sophistication in predictive capabilities, the last few months were hard to see coming …
COVID-19 meant people were staying in and eating at home more, creating a surge in online traffic. It drove interest in home cooking unlike ever before. In mid-March, Food Network began to see a spike on its digital properties. It was one indication that people were supplementing news consumption with comfort and guidance from their favorite lifestyle brands, and the traffic continued to grow – hitting another record a month later on Easter.
This generated a fire hose of data, and the team went to work. They featured and created new content that helped people take advantage of pantry staples. Data allowed them to quantify the size of each trend and identify nuances in audience segments. Along with industry research, an uptick around side dishes, for example, could indicate that people were rounding out takeout meals. The team could drill into the expectations of different individuals and cater cross-platform experiences accordingly. Visitors to a Food Network property likely will not realize that they are in an A/B test, but they receive its benefits through a more personalized experience in the future.
AI capabilities like anomaly detection provide a real-time pulse on which types of content receives unexpected upticks. These signals have been leveraged to acquire customers in a new way for the brand, through a direct-to-consumer subscription product called Food Network Kitchen.
5. City National Bank: Personalizing digital financial services
One of the inherent challenges for City National to provide a personalized experience online, is its wide variety of services and the different audiences it serves. In a traditional face-to-face setting, it is easier for a banker to tailor the experience and get to the heart of what clients might need—including a baseline understanding of who they are and services they already use. While this is trickier to do online, there are data signals that can help.
City National uses analytics data from it digital and physical touchpoints to paint a picture of where clients need guidance. While the data itself is aggregated and anonymized, it helps create different segments that can be personalized against. Take a recent campaign, which delivered a different home page on the website for different segments. By customizing the experience based on data, City National saw a 157% lift in form completions (a core metric for any bank).
The bank has also began experimenting with AI capabilities, which help automate cumbersome processes and allows for deeper analysis of data (a difficult task with limited human talent). It enables teams to create more relevant customer segments, while also automating personalization for millions of clients.
According to Linda Duncombe, EVP and chief marketing, product and digital officer of City National, teams can now see how leads are moving through the marketing funnel, or which promotions in branches drove the most engagement online. She can also get a constant pulse on mobile performance, desktop traffic, and more.
Topics: CMO by Adobe, Analytics, Customer Stories, Personalization, Digital Transformation, Experience Cloud, Information Technology, Marketing, Customer Intelligence,