By Jeanne Roué-Taylor
There’s a truth in customer experience management brought about by rapidly changing technology—marketers are better off 80% right today than 100% right three months from now. Today’s marketing math delivers better results from fast turnaround with good data rather than perfect data that takes far too long to assemble. Traditional marketing approaches looking for perfect data segments simply aren’t competitive any longer.
Intersection of Big Data and Real-Time Information
The math has changed because of a combination of new technologies like mobile, social, and in-memory storage, all creating and managing enormous amounts of data that feeds remarkably powerful analytics. At the intersection of Big Data and real-time information, marketers are finding that analytics changes the game of interactions in the moments that count, in the context of the customer.
Visualization and self-discovery performed against a growing list of available digital information shows patterns we could never see before that allow us to test and learn with every interaction. But there are challenges to taking full advantage of marketing’s new math. The biggest challenge comes from the fact that customer data is found in many places, never in just one.
Customer Systems Evolved Separately
As computerization of commerce progressed over the past few decades, the many things we need to know to provide the best customer experience evolved separately. Information today is stored in many disparate systems, which are even located both on and off premise. Waiting for change won’t help—no monolithic system is going to show up that can replace what already exists, and anything that does is too rigid to be useful as consumers and markets evolve rapidly.
Connecting The Dots
This is precisely why integration is hot technology for marketers, feeding analytics with all kinds of current and historical information that allows marketers to be mostly right very quickly—right enough and fast enough to make a difference.
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By Jeanne Roué-Taylor
Customers are rarely a homogenous group. They differ greatly in revenue potential, levels of loyalty, and frequency of contact. What makes loyalty work, therefore, is an ability to segment your customers using powerful analytics. Segmentation allows the right amount of effort and right offers to be applied in the right moments to the right customers.
Knowing enough to segment properly takes powerful analytics. Unless analytics are a key part of your loyalty platform, odds are you won’t be able to do much more than old-fashioned transactional loyalty.
But segmentation isn’t such a simple thing. There are common segments based on how people shop—like price or convenience—but below those broad categories, there are more subtle ways to segment based on what is required to engage a customer in the right moment with the right offer. This is the description of behavioral segmentation, and takes a capability to test and learn that wasn’t available until recent times.
Test and Learn
Test and learn provides a way to find segments that may not otherwise be visible to marketers. By choosing existing segments and testing offers on a smaller scale, new segments can be developed and marketed to in a larger way. The opportunity to test and learn is a benefit of a customer loyalty platform that includes powerful visual analytics.
In the end, everyone shops for a different reason. Marketers armed with the tools to not just segment along traditional lines of age, race, and other factors—but also to test and learn—have a distinct advantage over those who don’t.
By Jeanne Roué Taylor
There’s so much customer data around us, and it gets richer and faster each and every day. People are talking non-stop about Big Data and its many uses in various parts of the market. But what’s not really getting as much attention is how analytics are used to make loyalty platforms effective for meaningful customer engagement.
Powerful analytics are on the critical path to understanding customers and when to engage, something we call right-time marketing. Right-time marketing is more nuanced than real-time marketing because it takes into account the data that supports the best time for the right kind of customer interaction. It isn’t a simple action-reaction cycle that turns customers off and can even come across as creepy and opportunistic.
Analytics Done Right
Analytics, used appropriately, allow a brand to understand many aspects of customer engagement, including:
By understanding this kind of data, which is often ignored or unavailable because of silo’d systems, brands understand exactly when, where, and how to interact with their customers. This changes the model for customer engagement, and makes the customer much more than a pitch and transaction.
It Takes a Platform
It takes a loyalty platform to bring together, analyze, and respond appropriately to all of the fast-moving, rich data available today. Are you ready?
By Jeanne Roué Taylor
The enormous amount of ink being spilled around Big Data often includes the phrase “amazing insights.” At the same time, there’s plenty being said about the need to know and respond to your customers in the timeframes that matter, aka customer loyalty management (CLM).
While these customer loyalty management ideas seem intuitive, too often, the ways we gain insights into customer loyalty aren’t directly connected to the action side of the equation. This happens either because the technology that supports insight-to-action or the skills/individuals required to do this efficiently and effectively are artificially separated…or both.
The latency or customer blindness this creates is what differentiates great customer loyalty management from the mediocre. So what can be done to make insight turn into actions?
Models are great tools for bringing together diverse data sets and preventing insights and actions from “falling through the cracks.” A great model brings together data from several sources, feeds analytics, makes predictions, enables test-and-learn scenarios, and allows the organization to adapt as data, channels, and other factors change over time. In a nutshell, models support an organization’s ability to be dynamic and responsive in their customer loyalty management.
Just as important as the model is the ability to spend less time on reports and dashboards while increasing the transparency of program performance. That can only be done when the marketer’s toolset can dynamically segment and show purchase propensities. Segmentation isn’t a new idea, but the ability to adapt segments and prediction to what’s happening in real time is very ‘new-generation’.
As we move closer to the moment of decision, thanks to mobility and faster data, our ability to know, predict, and then respond must also be fast. A customer is only on the website until they’re not, in the store until they’re not, and waiting for your interaction until they’ve moved on to something else…or engaged with your competitor.
Models, analytics, and execution are core components of today’s capable customer engagement platform. Any less is too slow and lacks coherence within the brand and worse, across to the customer. Are you ready?
by Jeanne Roué-Taylor
We’ve all heard the phrase, “Better to beg forgiveness than ask permission” used as an excuse to move forward without the official nod from the higher ups. While there are situations where that works well, marketing is increasingly not one of those scenarios.
As the Internet matures and its users become more sophisticated, asking permission becomes the way to open lines of communication to consumers. Skipping the permission step is an easy way to be ignored or even blocked by the intended audience.
There are those who would argue this point and say that unsolicited offers are still working well, but if you peel back the onion, you’ll see two clear facts: 1) the more often email marketers use their tools, the less effective those tools become, and, 2) email is in decline as an effective marketing tool. Email marketing simply doesn’t scale.
Fortunately, there is a way to ask permission that scales remarkably well: Loyalty programs. A well-executed loyalty program creates a relationship between the seller and buyer that allows for implicit mutual benefit: I will reward you for engaging more closely with you and in return, I will offer you a higher level of service, exclusivity and, in some cases, better pricing on the things you buy.
I say ‘some cases’ because it doesn’t have to be about better prices. We engage with a brand because we feel a level of affinity that doesn’t necessarily come from economic benefits. Each consumer is different and while some are motivated by discounts, others are drawn in by increased sense of worth, common goals, and even game mechanics, where achieving levels or benefits is the outcome of a competitive framework.
Measure and modify your program
What truly makes loyalty work is the ability to create, test and modify loyalty programs. Loyalty programs that improve constantly will increase consumer commitment, increase spend, and create a loyal fan that stays around and has a much higher lifetime value to the brand.
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