By Eric Bradlow and Jeremy Korst
As every marketer today knows, the ability to collect, analyze and act on available data is increasingly vital to any brand’s success. CMOs today are not only being asked to lead brand strategy, product and customer marketing, but also drive sales and revenue growth. Companies at all levels of data maturity are investing in data analytics and marketing methods to personalize and improve customer experiences at scale.
And yet, without the right strategy in place, organizations can and most likely will fail to leverage available data and tools in an impactful way.
At GBH, we believe brand strategy starts from the outside-in, understanding the underlying needs and motivations of the brand’s target customers through data (both quantitative and qualitative), in the context of competitive and market dynamics, and translating those insights to strategy – and then action.
Today’s most successful brands also embrace the fact that every customer is different, with continually evolving tastes and preferences. A fundamental lens for this perspective is Customer Lifetime Value (CLV) – a prediction of future purchases with your company based on past transactional and behavioral data, viewed through the lens of predictive analytics.
In this post, we discuss the key steps companies and marketing leaders can take to improve CLV and create a more customer-centric and data driven culture:
1. Build an outside-in, CLV-focused strategy.
Brand strategy is the north star and defines every facet of your company from product development and marketing to the customer experiences you deliver. Your overarching strategy also informs your hypotheses about the customer journey as well as tactics to improve their experience and CLV – which can be implemented and tested using data, analytics and technology.
Too often, we see companies that are focusing largely on marketing execution to drive customer acquisition and transactions, instead of a holistic strategy to maximize CLV that’s grounded in customer and competitor insights. In other words, they’re taking an “inside-out” vs. an outside-in approach.
2. Build your culture around the customer.
The first thing to remember is customer centricity and maximizing CLV is not about treating all customers the same, or trying to get the highest NPS scores across all customers. Rather, it is about differentially 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 are passionate and truly believe their target customer comes first and this mentality permeates throughout the entire organization from top to bottom. And, they understand that it is impossible to attract and serve all customers, so this forces them to make important choices and prioritizations across the business – from strategy, product development, marketing, customer support, etc.
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 internal company and third party data to understand and predict 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, 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 target customers are identified and you’ve developed a differentiated strategy to reach them, your go-forward success depends on continually and consistently 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.
- Have we effectively defined our high value target customers such that it drives prioritization and focus across the entire company?
- Do we have a data-driven, continual feedback loop that allows us to know what’s happening with customers in 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 a 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/or 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:
- How is the org structured and is innovation and experimentation explicit objectives?
- Do team members embrace continual experimentation and iteration?
- Is there a specific team that is focused on designing and running experiments, or is this capability 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 – and do do so across internal company organizational boundaries. 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 to only collect data if it allows for better prediction of future behaviors, or helps optimize the target customers’ 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 – and are we acting to maintain their trust?
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 are those that obsess over a differentiated experience for their highest value target customers. They steer every aspect of that experience by developing an outside-in strategy, and then anticipating and predicting the needs of their target customers through better data and analytics.
Follow up questions for our readers:
- How has your marketing organization used CLV to drive strategy and execution?
- What programs and tactics have been most impactful for your company in improving CLV with your customers?
- Other than what we covered in this post, what other factors are important to build a customer-centric and data-driven culture that maximizes CLV?
*Note: A condensed version of this article originally appeared in Marketing Land