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How to Transform Marketing with the Membership Economy and Advanced Analytics

Posted by John Mack and Sarah Anderson on Wed, Nov 30, 2016

membership economy_blog.jpgEveryone wants to belong. But how can that basic human need coexist with the commercial needs of a business so that both the customer and the business find the relationship beneficial? Big Data analytics makes it possible while also opening new possibilities.

History is replete with examples of human beings finding ways to connect with one another. We form tribes, congregations, clubs, and entire societies. We develop communications channels and pass specialized content through those channels. Even efforts to divide these groups and disrupt these channels simply engender new ones and actually help strengthen our identity as individuals. This pattern has continued to evolve with the advent of the digital age and extends to peoples’ relationships with products and services. Are you loyal to Mac or PC? Do you use Facebook or Instagram? Are you enrolled in Amazon Prime? Are you a Netflix subscriber? This need to identify one’s self with a larger group is a primal human instinct no matter how contemporary the group.


Many companies have tapped into this innate human desire for membership by implementing “membership models.” The tremendous growth that many have realized as a result attests to the viability of this as a key element of a business’ strategy. Amazon launched Prime in 2005, for instance, and within only three months, the company had already broken even. By 2012, the typical Prime member was already spending 2.4 times as much as a regular Amazon customer. Management consultant and author Robbie Kellman Baxter has studied this phenomenon across multiple industries and has coined the term “The Membership Economy,” which is the topic of her book of the same title. 

Big Data analytics plays a key role in enabling membership models and is essential to make such efforts successful and sustainable. As customers traverse the customer lifecycle, they generate indicators of intent to purchase, data points describing product or service usage and experience, signals of intent to repurchase or upgrade, and expressions of enthusiasm or disappointment that can influence the purchase behavior of others. When aggregated across the entire base of prospective, existing, and former customers, this data set is massive. Only Big Data analytics technologies and techniques have the capacity and capability to extract actionable insight from such a large and constantly changing information repository.

So how can a business successfully transform its customer relationships into “memberships?” For those businesses that do accomplish this, how do they keep members satisfied and engaged with the brand? What role can Big Data play in orchestrating the customer experience across the lifecycle? Equally critical, how does a business measure the tangible impact of these efforts?

What the Membership Economy Is and Why Members Matter 

The Membership Economy represents a shift in the customer’s mindset from ownership to access. This shift also implies evolution from one-time payments to recurring payments, transactions to relationships, and one-way communication to feedback and community. It’s also important to point out that members are more than just loyal customers or subscribers. They’re connected not just by a formal agreement (which can be as simple as creating a username and password) but also by an emotional connection and a sense of community and belonging. Churches, diet programs (e.g. Weight Watchers), digital social networks (e.g. Facebook), and fitness clubs are some entities that embody this ethos. Similarly, Netflix and Amazon Prime offer original content that generates a community of fans, who can discuss their favorite shows and relate with one another in authentic and meaningful ways.

In the Membership Economy, members are more valuable than regular customers because they require less investment and effort to generate sales. Businesses experience the highest cost per transaction with new or occasional customers, who are therefore the least valuable to the business. Members occupy the opposite end of the spectrum and are the most valuable to the business. Not only is the cost-to-serve far lower for members, the churn risk is far lower, making risk-weighted revenue much higher. Of course, membership not only benefits the business, but it also benefits the customer. 

In one literal example of membership, a member at a country club need not decide where to spend time and money on most weekends. Likewise, if the member’s children need golf or swimming lessons, enrolling them at the club instead of at other local facilities is by far the most convenient — and probable — outcome. Membership means transactional decisions are often predetermined or require very little consideration. As a result, the business needs to exert little to no effort to capture those transactions. 

But how can a business in any industry sector create and sustain a membership model? In her book, Kellman Baxter outlines some key steps: 

  • Remove friction by ensuring that signup is easy, such as by offering free trials. Welcome new members and thank them for joining.
  • Deliver immediate value by engaging new members right away and providing an initial benefit. Also ask for and provide feedback, letting them know how they compare with other customers.
  • Encourage and reward desired behaviors such as referrals and purchases. Find new ways to optimize their experience.

The simplicity of this framework obscures a more complex truth: Taking these actions is impractical or impossible without employing an effective Big Data analytics strategy and technical capability. 

How Big Data Analytics Facilitates a Personalized Membership Strategy 

When a business measures the customer experience journey, it generates massive quantities of data. The magnitude of the data management challenge becomes strikingly evident when there are multiple products, services, channels, customer segments, and geographic markets, as well as different pricing options and a fast-moving competitive landscape that necessitates constant adjustments to the levers that a business uses to interact with its customers. Managing this complex data environment can quickly overwhelm human capabilities and capacity, rendering any attempt at a manual process slow, incomplete, inaccurate, not readily scalable, and hence, completely inadequate.

Capturing the mass quantities of data that define the customer journey is only the first step in establishing control over the customer experience. To transform customer interactions into membership, a business must effectively extract insights from the data, apply those insights to the customer journey, update those insights as the data picture evolves, and reapply viable insights to future business situations systematically. To accomplish these tasks effectively and as a repeatable operational capability, a business must be able to store insights and refresh them frequently. Borrowing the concept of a “technology stack” from the enterprise IT world allows us to create a term for the repository where this data resides. We call it a “customer intelligence layer,” one of many layers in this proverbial “stack.”

The customer intelligence layer holds the synthesized insights that Big Data analytics derives from customer data through constantly refreshed mathematical transformations of that data. It is a repository of behavior, feedback, characteristics, needs, preferences, and multiple other variables that define the customer and the customer journey. Timely, precisely tailored offers and communications for each customer are essential to enable and sustain the perception of membership. By using Big Data as an intelligence asset, the business can structure personalized, relevant interactions that forge dynamic customer relationships in which both the business and the customer participate, communicate, and share experiences. 

This highly personalized approach, which incorporates customer feedback into the master set of data reflecting behavior, testing, and outcomes enables the business to engage more fully with the customer and in more effective ways — not just in terms of messaging, but also in terms of channel, frequency, location, and even device. Only by applying Big Data analytics to this complex set of parameters can a business develop and consistently manage personalized interactions at scale and improve its ability to do so over time.

An Example from the Retail Industry 

So how does this work in the real world? Let’s examine a department store retailer, which may not fit the traditional definition of a “membership” business. The retailer offers many different types of products in multiple categories, serves a wide array of customer segments, and operates stores in multiple locations and online. Big Data analytics allows the retailer to accurately illuminate high-interest categories and products, factors driving purchase behavior, trade-offs, relative levels of perceived value, trigger thresholds for purchase, and drivers of post-purchase satisfaction. The retailer can orchestrate the customer experience across the entire journey — from discovery to purchase and customer service and extending to post-purchase evangelism and repeat purchases. At each stage of this journey and across any channel, device, or location, the retailer captures data that enriches and evolves the customer experience profile. 

Nordstrom provides an excellent example of this approach in action. As far back as 2002, Nordstrom began investing in technologies that enable its employees to deliver an integrated, tailored, and constantly improving customer experience. These investments allowed Nordstrom to offer customers a consistent multi-channel experience long before other retailers were able to do so. In addition to using standard market research and point-of-sale data, Nordstrom constantly experiments with different techniques and technologies, such as using customers’ mobile phones to identify individual customers who enter its stores, where a customized application pushes customer profile to the salespeople on the floor.[2] This real-time, customer-specific information allows salespeople to engage with customers in a highly personalized way, strengthening the customer’s perception of membership and increasing the likelihood of both a favorable customer experience and a purchase. 

Nordstrom has also used natural language processing technologies to mine user-generated content on the Internet, helping the company identify issues and key trends that would be difficult to detect otherwise. In addition, the company has a fully integrated Website and mobile app that tie into its inventory management system and social apps. This creates an integrated fabric that enables employees and systems to more precisely orchestrate the customer experience. 

These data analytics techniques and technologies strengthen a membership capability that many experts already regard as the best in B2C overall, not just in the retail industry. Nordstrom has instituted one of the most effective loyalty programs in the market. It has nearly 5 million members, who drive an impressive 40% of the company’s total sales. In May of 2016, the company announced plans to add 5 million more members to its loyalty program over the next 12 months. Nordstrom’s approach of using its integrated website and mobile apps to constantly adjust offers and other factors that shape the customer experience allows the company to continuously improve the membership experience — another element that Kellman Baxter stresses in her book. The level of investment in both analytics and membership illustrates how much faith Nordstrom has in the power of these two commercial enablers working in concert.

Big Data Analytics in the Membership Economy: Use a Platform

To maximize the effectiveness of its customer intelligence layer, a business should use Big Data analytics technologies to synthesize insights into customer intelligence Signals. Signals are mathematical transformations of data that take the form of modular units of intelligence. Instead of attempting to weave together the outputs of multiple analytics solutions that perform discrete data preparation and transformation tasks across the end-to-end analytics workflow, the business should consider using a single platform that unifies all of those discrete workflows in a single IT environment, as such an approach delivers superior outcomes. Data preparation and transformation are less cumbersome on a unified platform, allowing data scientists to spend the majority of their time focusing on analysis rather than on the steps leading up to it. This rebalancing of work activity accelerates analytics workflows and increases the volume of business situations that a business can analyze. If the platform integrates seamlessly with the enterprise systems that the various internal business functions use to do their jobs every day (e.g., marketing, sales, and customer service applications), then customer intelligence Signals flow directly into the downstream workflows that directly shape the customer journey. 

Beyond the efficiency benefits, a platform-centric approach to customer analytics can also improve the quality of the analysis and the associated business results. Reusability of insights and constant, automated updating of those insights are key capabilities to seek in a world-class analytics platform. 

Opera Solutions' Signal Hub is an analytics platform that both generates customer intelligence Signals and supports Signal reusability, allowing the business to not only predict future customer needs, preferences, and behavior with high accuracy, but also to quickly adjust factors that influence the customer journey. The business can reuse Signals that have demonstrated high efficacy in driving business results and deemphasize or retire Signals that no longer deliver such results. If the analytics platform incorporates machine learning technologies, Signal quality improves further and maintains high efficacy over time as the platform “learns” how changes in customer and market parameters influence the customer journey. An analytics platform with such capabilities allows the business to more effectively orchestrate the customer journey and transform routine interactions into membership-building engagement. 

Taking such a platform-centric approach to Big Data analytics can help build a customer intelligence layer that… 

  • Creates a holistic “digital portrait” of the customer, with detail extending far beyond basic demographics
  • Reveals the customer’s known needs, unknown (or subconscious) needs, and need states, allowing accurate prediction of future purchasing behaviors
  • Allows the business to package customer interactions as a structured “curriculum” so that the business can serve the right offers at the right time through the right channels while orchestrating a consistent, high-quality customer experience at all times.

Only with a dynamic and capable Big Data analytics platform can a business effectively synthesize customer intelligence from its data assets at scale, package the intelligence as Signals, readily share those signals across the business, evolve signals over time, and use this Big Data workflow as an ongoing operational capability to orchestrate the customer journey. When a company can do all of these things with Big Data, it is no longer just driving transactions with its customers. It is also creating membership. Once customers see themselves as members, the business has created enduring relationships that establish a powerful foundation for sustainable revenue growth and enhanced profitability. 

Want to learn how you can implement a Big Data analytics platform to transform your business? Watch our Webinar on Transforming Marketing for the Membership Economy.

 

Transforming Marketing in the Membership Economy

 

John-Mack_6460-Print-square-1.jpgJohn Mack is the Executive Vice President of Marketing at Opera Solutions.

Sarah-Anderson_7688A-Linkedin.jpgSarah Anderson is the Director of Marketing at Opera Solutions.


This article was originally published at Martech Advisor. 

Topics: Big Data, Analytics, Marketing