Here are 6 ways to build a customer-centric and data-driven culture

Delivering customer growth and value starts with creating a culture of learning.

As every marketer today knows, the ability to collect, analyze and act on available data is increasingly vital to any brand’s success. Companies at all levels of data maturity are investing in data analytics and marketing methods to personalize and improve customer experiences at scale and in real time. And yet, transforming brand and product experiences to increase Customer Lifetime Value takes more than just a commitment to data and statistics.

CMOs today are being asked to drive sales and revenue growth, improve CX and marketing effectiveness through data analytics, all while also leading brand, product and customer marketing. Creating meaningful impact and growth across these areas starts with defining your brand strategy from the outside-in.

With that overview in mind, here are six steps companies can take to improve CLV and create a more customer-centric and data-driven culture:

1. Build an outside-in, CLV-focused strategy

Today’s most successful brands embrace the fact that every customer is different, with continually evolving tastes and preferences. A fundamental lens for this perspective is Customer Lifetime Value – a prediction of future purchases with your company based on past transactional and behavioral data, viewed through the lens of predictive analytics.

Too often, we see companies that are focusing largely on marketing execution to improve CLV, continually searching for the next piece of content or martech tool, instead of starting with a holistic strategy that’s grounded in customer and competitor insights. In other words, they’re taking an “inside-out” vs. an outside-in approach.

Brand strategy starts from the outside-in, understanding the underlying needs and motivations of customers through data (both quantitative and qualitative), in the context of competitive and market dynamics, and translating those insights to strategy – and then action.

2. Build your culture around the customer

The first thing to remember is customer centricity is not about treating all customers the same, or trying to get the highest Net Promoter Score across all customers. Rather, it is about serving your target customers.

While many organizations say they are customer-centric, the reality is the companies that are truly committed to customer-centricity stand out. They passionately believe their target customer comes first and this mentality permeates throughout the entire organization. They can make intentional trade-offs to ensure resources are focused on their strategic target customers, and try to minimize distractions from others.

To get to this level of customer-centricity, companies need to live it – literally. They have to transform their organization and gear their team to not only understand what their target and most valuable customers want but also use available first and third party data to understand their near term and future needs.

Warby Parker’s rocket ship growth is a great example. When Warby Parker founders were still at the University of Pennsylvania’s Wharton School, they conceived their company, refining their home-try-on program, arguably the key to getting people to purchase glasses online. By connect­­ing directly with their most valuable customers (MVCs), gathering data about their purchase behavior and preferences, they’re able to deliver a unique experience across customer journey, building affinity and loyalty for their brand.

3. Evolve your organization around the customer journey

At its core, CLV is a function of how a brand uniquely creates and delivers products and services for a target set of customers which causes those customers to choose and pay more for the firm’s offerings vs. a competitive alternative. Once your MVCs are identified and you’ve developed a differentiated strategy to reach them, your go-forward success depends on delivering value across the entire customer lifecycle and journey which then minimizes both acquisition costs as well as churn.

To successfully do this day in and day out, CMOs need to invest in developing a data-driven culture, competencies and best practices across their team for turning available data and insights into action.

  • What’s happening with customers at the moment and how can we improve the message, the content and the experience we’re delivering?
  • Why aren’t some of our intended target customers choosing us?
  • Are their adjacent, high-value customer segments who could be attracted to the brand or product extension?

To effectively answer these questions and improve CLV, marketers need to experiment (ideally via randomized experiments), while also separating the signal from the noise. These signals can be quantitative data (internal and external data) and should also be informed by qualitative research and feedback such as surveying front line employees, customers, partners, etc. One additional source of data is user-generated content (UGC) which allows firms to hear the “voice of the customer” using text data obtained from ratings, rankings, reviews, etc… and then tie the numerically coded text, via natural language processing methods (NLP) to consumer activity, hence CLV.

4. Identify your most valuable customers

Companies need a differentiated strategy that puts their most valuable customers at the center, or bullseye, and that strategy is a living process, one that is continually tested, verified and improved.

So how does a company go about identifying who their MVCs are? For existing customers, as Wharton Marketing professor and author Peter Fader notes, identifying your MVCs starts with asking the right questions: “Based on what a customer has done in the past, can we make a pretty accurate projection of what they are likely to do in the future?”

What segments and customers are most attractive for your brand to target and build products and services for? How do you understand the customer journeys for your most valuable customers?

Through research and analytics, companies can not only identify who their potential new and existing MVCs are but also what they care about, what motivates them and what types of differentiated products, services or offers they’re most likely to want in the future.

5. Reward learning and experimentation

There’s no better way to learn about your customer than to see what actually works and what doesn’t. While big data and machine learning are great to business intelligence, a well-controlled experiment can deliver far more value.

Finding the most impactful experiments to run starts with asking the right questions and maintaining a test and learn mindset where you’re constantly evolving to improve the experience for customers. The iterative adaptation based on these experiments builds momentum.

Before brands dive into any experimentation, they need to ask themselves the following questions:

  1. How is the org structured and how is innovation and experimentation rewarded? Do team members embrace continual experimentation and iteration?
  2. Do they have a specific team that is focused on designing and running experiments, or is it shared and expected broadly across the organization? If the latter, how are people measured and incentivized to experiment?

One of the most important aspects of successful experimentation is having transparency and visibility into what people are learning. Encourage your team to experiment, fail fast and share what they’re learning.

6. It’s about better data, not big data

An ongoing challenge organizations face today is what we call “better data, not big data.” This is a conversation that’s happening more and more at the CMO, CIO and board level.

Collecting more data doesn’t necessarily lead to greater business intelligence – and in many cases can expose the brand to issues that impact customer trust. And yet, too often we see companies collecting data for data’s sake or trying to leverage the wrong data to understand or improve the customer experience.

The key for strategic marketers is only to collect data if it allows for better prediction of future behaviors, or helps optimize the MVC’s experience with the brand. So, what data are truly needed to improve CX and CLV over time? And, what value are we delivering customers in exchange for any personal data?

Why this all matters

In today’s market, on-demand experiences are everywhere, interwoven across every facet of our daily lives, and are becoming expected. The brands that are the most successful obsess over the target customer experience. They steer every aspect of that experience by developing an outside-in strategy and anticipating and predicting the needs of their target customers through better data and analytics.

Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.

About The Author

Jeremy Korst is the president of GBH Insights, a leading marketing strategy, consumer behavior and analytics consultancy. In his role, Jeremy works closely with Fortune 500 brands and CMOs to solve marketing challenges, improve customer experience and create strategies for growth. Prior to GBH, Jeremy held CMO or senior executive roles with Avalara, Microsoft, T-Mobile, among other brands. Korst holds a BA in economics from the University of Puget Sound and an MBA in finance and strategy from the Wharton School, University of Pennsylvania. He serves on boards of both institutions, as well as those of several technology startups. Eric T. Bradlow is the chairperson of Wharton’s Marketing Department, K.P. Chao professor, professor of marketing, statistics, economics and education, and co-director and co-founder of the Wharton Customer Analytics Initiative. He is also the co-founder of GBH Insights, a leading marketing strategy, consumer behavior and analytics consultancy. He has won numerous teaching awards at Wharton, including the MBA Core Curriculum teaching award, the Miller-Sherrerd MBA Core Teaching award and the Excellence in Teaching Award. Professor Bradlow earned his Ph.D. and master’s degrees in mathematical statistics from Harvard University and his BS in economics from the University of Pennsylvania.

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