Data, Analytics, and Benchmarks — Making Sense of Experience Business Data

by John Bates

posted on 12-13-2017

A large international retailer in the discount clothing and houseware niche came across an interesting piece of data while looking through their analytics. This company primarily targets shoppers of department stores, an audience that values quality at reasonable prices. You can imagine their surprise when, on February 12, 2015, they discovered a 300 percent increase in a metric called “cart-remove revenue,” a metric that tracks potential revenue lost when a customer removes an item from his cart and doesn’t follow through on purchasing it.

With analytics tools, this retailer caught the metric immediately, finding it statistically significant against the benchmark of how that metric historically performed and its predictable expectations. In turn, using contribution analysis, the retailer identified the “why” behind the increase within a matter of seconds. Contribution analysis auto-analyzed tens of millions of queries and applied machine learning to reveal why the rise occurred.

As it turned out, their third-party tag management solution had a bug in it, which automatically booted trending ball gowns and boyfriend jeans from customer carts before they could purchase them. Because the demand for these items was high, the company was losing an estimated $1.2 million per day leading up to Valentine’s Day.

With contribution analysis data insights in hand, the retailer immediately pushed out a patch to fix the bug, thereby resolving the issue for their customers. The solution solved the problem that affected each individual customer while implementing a solution scaled to the entire online market. That, in short, is what experience data is all about — fixing broken customer experiences and looking for ways to proactively reach out to consumers to continually improve the customer experience.

Like our retail customer, let’s look at the steps experience era brands can take to collect experience data, analyze it, then turn it into actionable insights to continually improve the customer experience.

Tackling experience data in the “Experience Era”

A decade ago Thomas Davenport wrote a book that revolutionized the way businesses competed by using analytics. In the book, aptly titled “Competing on Analytics: The Science of Winning,” Davenport argued that the frontier of analytics-driven business strategy had shifted. That was in 2007, and it is shifting once again with what the industry is calling “experience data.”

Experience data gives insights to finding and resolving customer problems. In turn, these insights can be used to create better customer experiences based on revolving optimization. Think of competing on experience in the same way you would think about competing on analytics. For both, brands need data and lots of it. The data that’s ultimately needed can be derived from any moments that exist between a brand and a customer, a prospect, or a user, regardless of where that moment occurs. These moments might happen on an Internet-of-Things device, via a call into a call center, in a store, on a mobile device, or at a digital check-out. Regardless of the touchpoint, in every micro-moment, the brand’s goal is to fully understand the context of the individual and provide an unparalleled experience that reflects that contextual understanding.

Redbox is an excellent example of a company adopting modern marketing technologies and data analytics to provide better customer experiences. With more than 60 percent of its traffic coming through a mobile channel, the company uses analytics to measure the effectiveness of mobile campaigns and continuously iterates the experience to drive engagement and rental behavior.

The Redbox App is a result of that data, and acts as a hybrid between in-store and mobile experiences. Since sending out the first push notification in 2012, the company has seen a 100 percent growth in app downloads. As they continued to refer to their data, Redbox found the optimal time to send out each message, and even implemented a “welcome series” promotional offer designed to engage with customers immediately following a product download. The welcome message garnered a successful 56 percent open rate among Android and iOS users.

Gathering various types of experience data

For these types of results, experience businesses look at different types of data, extract insights from it, then use it to continually optimize the customer experience. Both structured and unstructured data are needed to discover where and how to provide better experiences. Structured data is more predictable than unstructured, and is easier to understand. A good example of this is tracking whether or not someone watched a video, or clicked on an article posted to social media. It’s easily quantifiable. Unstructured data is less predictable, like voice or text that comes from a conversation, emoticon interpretation, or comments on a video. This type of data is often more qualitative in nature, but not always. Experience brands need both types of data to get a holistic view of the customer.

So, it follows that brands also need to actively collect both qualitative and quantitative data. There’s a lot of overlap between structured and quantitative data, which also focuses on numeric type values. Qualitative data is usually less objective, and generally depends on a user response. This, for example, is the type of data that would be pulled from a customer survey or social media engagements.

Gathering a breadth of data from a plethora of online and offline touchpoints, as well as different types of engagements, can ultimately set your business on a path to making smarter, customer-centric decisions aimed at providing better experiences. However, don’t be afraid of integrating data sources as they become available, even if it doesn’t create a complete customer view. The longer you wait to start integrating what you have, the longer you wait to begin receiving actionable insights from it.

Data resolves concerns and offers solutions

After assessing all available data sources, the next step is to extract insights from that data. There’s a wealth of information to be gleaned from data analytics as you seek to resolve customer concerns and improve experiences.

First, analytics will give you a good insight into the “state” of your customer. “State,” in this context, is where the consumer is in the buying cycle. You can glean from analytics where your customers are — whether in the discovery and awareness stage, ready to buy, or anywhere in between.

This data needs to be flexible and dynamic if you are going to succeed. To achieve this, tear down the walls of big data silos to share data between partners and internal stakeholders. Removing traditional data barriers will ultimately build loyal relationships with your customers and delight those customers who are getting to know your brand and understand the value of every customer interaction.

Understanding where our customers or potential customers are in this journey allows us to be “context aware,” whether it’s in-store, online, or elsewhere. In planning its push notifications, Redbox understood that most of its customers browsed products on mobile devices, even when they didn’t purchase in this context. So, they paid attention. By capturing mobile-browsing history, they could better optimize for successful mobile messaging which, in time, resulted in better experiences across all channels when it was time to purchase.

Pairing data and target groups to measure meaningfully

Analytics also allows brands to start measuring meaningfully, tracking what ultimately matters to customers and, in turn, the enterprise. You can do this by pairing the data you’ve gathered with different target markets to derive insights. Analyzing the data correctly helps identify new target groups or people not previously identified, or those that once couldn’t be identified.

As an example, using data to identify new groups has become apparent in the personal wealth management and investment industry. Historically, the vast majority of the population didn’t have access to wealth management, such as you’d find with Goldman Sachs or Fidelity. They traditionally targeted the top 1-5 percent of rungs on the economic ladder. But now you see other wealth enterprises — like Personal Capital, Wealthfront, Digit, Betterment, Robinhood, and others — enter the industry to provide the middle-class person with similar transparency and control over their financial lives. They’ve done this through combining technology, automation, state-of-the-art tools, and robo-advisors to scale the provision of a similar “white glove” experience to the less-affluent market at a fraction of the cost.

The market always existed, but the industry needed to adjust its strategy to compete on experience in order to reach that new audience. By benchmarking against more affluent target markets, those brands that traditionally targeted the middle class derived insights to improve their customer experience offerings with similar services as was once only offered to the top five percent.

Benchmarking helps companies turn data into insights

Similarly, in all industries, companies can reach new audiences and improve existing customer relations through benchmarking. Typical benchmarking involves an aggregated summary to help you compare a business against other businesses within the same industry, for example. While this high-level benchmarking still has its place, it is becoming less impactful for businesses that are truly transforming into leading with experiences. It’s not about competing with the industry. It’s about competing on customer focus at every stage of the customer journey. This is because, according to a Walker report, by 2020 customer experience will overtake price and product as the key brand differentiator.

The next type of benchmark — which proves to be more impactful in experience business strategy — is benchmarking individuals in one of two ways. The first is to pit one customer against other customers who look like them, thereby comparing one customers’ potential lifetime value versus another. The second is to benchmark individuals against themselves to identify how frequently they purchase from or engage with your brand. Using unstructured and structured data will help you derive complete and therefore, accurate insights, as well as a holistic understanding of similar customers or groups of customers by comparison. Without this holistic approach, it’s easy to derive erroneous insights.

We can look at wireless providers as an example. These brands often look at lifetime value of the customer and then cross-tabulate that with the customer’s propensity to complete some future action. Let’s take, for example, the propensity to cancel at the end of term. No mobile carrier wants to see valuable customers leave. For the high lifetime-value, but short-term customer, brands must provide a different customer experience than they would for a customer that has a lower lifetime value and a higher propensity to terminate their contract.

Let’s consider a low-value customer who always calls in with concerns or problems and is more likely to cancel her contract at any moment. It’s possible that the servicing brand may not be making money off her, so her contract cancellation may benefit the brand. For this customer, the brand may not intervene with a custom experience offering. In contrast, for high-value customers, this same brand will benefit from creating personalized experiences and maybe even spending a little extra to do so. This may mean reaching out via their call center to provide white-glove treatment.

As wireless providers benchmark high-value customers against themselves, they can tailor marketing efforts into more personalized campaigns. Additionally, they can let go of pushing high-value campaigns (in terms of cost) at lower value customers.

Think of the one, scale to the many

Investing in new technologies and analytics will help your business compete on experiences. Advances in technology have allowed for customer-centric campaigns like those provided by our international retail customer, middle-class financial service providers, and Redbox. When coupled with a vision to become context-aware in all marketing efforts, these brands could create the meaningful experiences customers expect and, thereby, grow their businesses.

Ultimately, it comes down to this: when just one person is having a bad experience, leverage technology to be a brand that thinks of the one and still scales to the many. In doing so, you can achieve experience era success.

_Learn how company leaders can prepare their brands to compete in the Experience Era, and r_ead more in #ExperienceBusinessPlatform series.__

Topics: Analytics

Products: Analytics