One of the great myths of the digital world is that you can best measure a company’s success by its earnings before interest, taxes, depreciation, and amortization (EBIDTA) profits. Although this may be the gold standard for assessing traditional companies, it is not the way digital businesses think. It’s not that digital disruptors don’t make money — they do. But these profits are only part of the picture.
In their early days, many onlookers dismissed disruptors like Amazon, Dropbox, or Zalando, by claiming they didn’t make money, by which they mean they often produced limited or negative EBIDTA profits. What the critics failed to grasp is that these disruptors often operated based on a very different set of priorities. Unlike many traditional incumbents, successful digital disruptors focus on creating long-term value in a different way. Specifically, they focused on growth that was powered by:
Customer lifetime value (CLV) / customer acquisition cost (CAC)
CLV, in its simplest definition (based on historic data), is the value from a customer over the lifetime of engagement with the business. This can be calculated based on all historic aggregated data or based on specific customer segments. In addition to simplistic historic CLC calculation, sophisticated Machine Learning models can also be used to predict forward-looking CLV.
CAC is the cost to acquire a customer and usually takes into account not just the cost of how you reach a customer (e.g., advertisement cost) but related overhead (e.g., cost of marketing and/or sales team salaries).
End-to-end customer experience
The customer experience represents the journey a customer goes over the entire interaction with a company, from discovery and evaluation through purchasing and finally through retention and advocacy.
In an EBIDTA world turning on quarterly results, incumbents often tend to focus on different things, such as maximizing reach or converting traffic into transactions. Successful digital businesses, however, are focused on building sustainable longer-term relationships with customers by understanding them better — who they really are and what they care about — and driving richer, personalized and proactive interactions with them by anticipating their needs.
In an ideal situation, if the lifetime value of a customer is three times or greater than the cost of customer acquisition cost, then why return revenue as profits when you could multiply that revenue several fold in the next year or two? And if customers have a great experience with you, they will come back and stay with you, further magnifying their lifetime value.
Insight Center CollectionExecuting a Growth StrategyWhat value creation means today.
This powerful digital growth engine is part of what drives the valuations beneath some of the most disruptive companies. While digital disruptors have pioneered this approach, the established companies — both B2B and B2C — are actually often better positioned to take advantage of the digital growth engine because they have one thing startups lack: customers. Whereas startups have to go out and pay a high price to acquire new customers, incumbents can do more with their existing customer base to either a) increase the lifetime value of their existing customers, or b) lower cost of customer acquisition, or both. Add to this a greater knowledge of the customer journey, if existing companies are willing to make the necessary changes, they can kickstart their own digital growth engine.
To make the most of the digital growth engine, incumbents can use data, AI, and digital engineering to identify which existing customers are most valuable, understand their journeys, pain points and needs in new ways, and then create growth by solving the pain points and unmet needs and capturing repeat business with these customers. (In analog terms, this approach is similar to sweating your assets). Alternatively, incumbents can use data, digital engineering, and AI to understand the characteristics of high-value existing customers who buy their products or services and then use these attributes to more effectively find new customers at lower acquisition costs.
Kickstarting the Digital Growth Engine
Engaging the digital growth engine requires shifting focus from transactions to lasting relationships. We looked into a few case studies in retail, hospitality, and luxury industries, particularly multi-brand, multi-country complex global organizations often with physical locations as well as parallel online/ecommerce propositions. In addition, one of the authors of this article (Chakraborty) has actively led a number of complex global digital transformations and hence, added his personal experience to bolster our research. We learned that six critical actions are needed by an incumbent to deliver a disruptor-style digital growth engine and create lasting value.
1. Align your ambition for your customer experience with your financial ambition and operational reality.
The customer experience is the North Star that should guide all of your data, AI, and digital initiatives. All of these should align around a vision for your future customer experience, and you need to map out the small steps that are necessary to make that experience a reality.
For example, one multi-brand global retailer wanted to grow their revenue and EBITDA in double digits year-on-year over a five-year strategic period, and they wanted to do so while also increasing their customer Net Promoter Score and revenue from existing customers. The Chief Customer & Digital Officer — who was leading the customer experience program — worked closely with the Chief Financial Officer as well as the Chief Sales & Operations Officer to ensure that customer experience ambition was “executable,” had backing from CEOs of key brands, and was closely aligned with the company’s financial goals.
To decide where to focus resources, the team analyzed all the existing customer journeys and their pain points and identified what needed to change, weighing factors such as business value, customer need, global scalability, and technological complexity. Working with global and local management teams, the CCDO then created a multi-year cross-functional CX roadmap that connected the delivery of Future Customer Experience (in tranches) with projects across underlying digital IT, enterprise IT, retail and brand marketing, supply chain, data and AI, product innovation, and more. The CX roadmap also considered operational and financial constraints, such as: Do the local brands and markets have enough resources to be freed up to “land” these initiatives? Can they fund the ambitious CX program from their operational income? Can they build a global digital team fast enough and good enough to deliver an ambitious program under intense scrutiny and gain confidence and trust from the brands and markets?
2. Optimize your customer data strategy, focusing on a few valuable use cases.
Data is the fuel for the digital growth engine, and you need to be thoughtful about your strategy across your entire data value chain, including capturing quality data to drive desired outputs (with relevant consents and marketing opt-ins), cleaning and transforming the data, harmonizing and standardizing the data, creating a 360 view of the customer by stitching various customer interactions together into one unique customer identity, and creating actionable insights using AI/ML models, or using simple analytical tools to recommend operational actions.
We noticed in our study that different companies have different strengths and weaknesses across their data value chains. Some were bad at data collection while others were sub-par at data related insights generation, and still others didn’t do well in creating clean data pipelines due to the complexity of their internal data sources and lack of data standardization across brands and markets. But most of the companies struggled with identifying and prioritizing valuable customer experience use cases, and with breaking down the underlying data, AI, and digital engineering requirements for bringing these use cases to life. This was particularly true in contexts when the companies managed multiple global brands across multiple markets.
For example, a multi-brand, multi-category, multi-market group holding company decided on a use case where a customer from one portfolio could be targeted by a CRM-generated “cross-sale” email after their first purchase by another portfolio brand for a complementary product category in the same market. The goal was to drive ecosystem effect for the parent company and amplify CLV. The marketing teams were excited about it, but their ambitions lacked granular details and tangible actions. When it didn’t immediate work, they blamed data and IT teams. Frustrated by lack of execution, the Global Chief Growth Officer built a cross-functional team of external experts and selected internal employees with functional representations from marketing, ecommerce, store operations, data and IT. The team defined the global cross-brand cross-category use case to granular detail, gained buy-in from brand CMOs, orchestrated commercial agreements between two brand CEOs to share their customer bases, defined rules of engagement, broke down the use case into data requirements (quantity, quality and velocity), AI/ML modelling requirements as well as digital IT & technology requirements.
Another retailer in our case study, decided to build on their strengths and deliver “quick wins.” They had good quality data coming from their store Point of Sale (POS) systems as well as ecommerce systems. However historically, they failed to capitalize on that data to generate actionable business insights and drive tangible use cases. They analyzed the last five years of transactional data to understand their Most Valuable Customers (MVCs) and then split those customers into meaningful cohorts by the most relevant variables for their business (e.g., by year/month/week of intake, and/or by product type, and/or by specific segments, etc.).
Next, they did some analysis to identify the churn and retention patterns of these cohorts over time. Their marketing teams worked closely with digital IT and data teams to slice and dice the data to identify how these patterns correlated to different product categories, different geographies (e.g., store locations), different brands, employee history of customer interactions (or lack of), and customer history of regular visits to stores or calls to customer service contact centers or other activities such as clicking on CRM email marketing campaigns, etc. Creating a cross-functional team including marketing, sales operations, ecommerce operations, data and IT, with an overarching strategy layer, was game changing for them to implement their data strategy and deliver valuable use cases to drive business growth.
3. Differentiate experience engineering, data and AI infrastructure engineering, and enterprise engineering.
Based on the retailers and luxury companies we studied, three types of digital engineering are typically required in a global organization trying to beat the disruptors in their own game.
First, experience engineering, which involves constantly improving all the customer-facing experience layer — for example, the ecommerce website experience, physical store experience, omni-channel experience (e.g., click & collect), CRM email marketing experience, customer care experience, paid advertising experience, etc.
Second, enterprise engineering, which is focused on digitalizing, automating and modernizing “behind the curtain” employee-operated systems and associated manual processes such as ERP, finance, HR, warehouse management, assortment planning etc. Typically, the end customers are rarely exposed to these systems directly.
Lastly, data and AI infrastructure engineering is focused on moving all the valuable data from various source systems within the company in a harmonized way — think of it as digital plumbing. Done well, it stitches customer interactions at various channels and touchpoints to create one 360 view of the customer and makes that customer data accessible to AI experts, data scientists, and precision marketers. With this data, these users can then build models that can help with specific use cases across marketing, sales, customer care, product development, etc.
Most of the companies we studied struggled to differentiate the three capabilities at the start and made expensive talent and organization mistakes. Many put all three capabilities into one IT bucket and tasked enterprise engineering people with experience and data and AI responsibilities, even though they lacked the expertise for these roles. Many companies also were reluctant to recruit the necessary technical talent — they didn’t want to offer competitive salaries, found top candidates too opinionated or threatening to established practices (and existing employees), or opted for less experienced candidates. When companies did recruit top talent, these new hires often left after few months due to slow progress, internal politics, unclear deliverables and lack of career progression path within an old-school incumbent trying to transform. The biggest struggle, however, was finding talent with the skills, experience, and bandwidth for a complex transformation and instead made hires that stuck to the company’s old ways of doing things and played it safe with old-school technology choices despite the fact that the transformation strategies required fundamentally different decisions.
After these initial failures, most then decided to partner with Human Resources teams to apply different org and talent rules to the different parts — which, in a number of cases, required making significant changes to their Human Resources and recruitment teams first. They wrote job descriptions for roles that never existed, new career development plans and job gradings, new ways of assessing talent, and new org structures. They created new recruitment and retention strategies for high-value contractors and freelancers who would have never worked for that company previously, giving up some control in order to access top talent. They also installed faster internal sign-off processes for recruitment and contracting decisions. Most of them realized that the first two years of transformation require different kinds of “wartime executors” compared to “peacetime status quo” talent at the latter years of transformation where things can be put on autopilot. Making cohesive teams that combined internal and external talent working as one team” was critical to their success. Gradually, many companies internalized selected roles without compromising on talent quality.
4. Develop an “electronic brain” powered by predictive AI models (with feedback loops).
AI is simply a tool for understanding patterns in data and making educated predictions out of it. It can help you identify patterns from the past and predict possible customer actions in the future. It can also help you create an “electronic brain” that creates a dynamic picture of each customer and stitches all the interactions you have into one Customer 360 record that auto-updates. This electronic brain then fires specific instructions to downstream marketing, sales, and service channels.
For example, a luxury retailer created an AI-driven “electronic brain” that segmented the customer base. They used sophisticated machine learning models to calculate “Predictive Customer Lifetime Value” of certain high-value customer segments and proposed differentiated and personalized levels of commercial offers, product offerings, discounts and vouchers, communication timings, channels and communication styles to these different segments of customers via their online My Account as well as via CRM email as well as via store employees. Based on success or failure of the personalized offerings and communications above — the machine learning models (behind the curtain) auto-updated their accuracy and performance using a “feedback loop” and the AI-driven “electronic brain” was updated and continually improved on a regular basis.
5. Launch a data and AI-enabled omni-channel customer outreach program.
Sometimes the word “omnichannel” gets relegated as marketing lingo when reality it just reflects the reality that we perform most activities in a fragmented digital world that needs to be stitched together. Most of the successful businesses we studied, used their AI-powered “electronic brain” to launch an omni-channel customer outreach program for their customers. Their marketing teams ensured that the messaging to a single customer across different channels are coordinated, relevant, timely, and brand-consistent.
Store: Sales people, digitally empowered by a 360 view of the customer at their fingertips were trained to identify a customer profile (e.g., Gold customer), their preferred channels, preferred styles and designs, preferred messaging and tone of voice, etc. They reached out with friendly questions like, “How did you like your purchase of X?” or “Given your interest in Y, we wanted to let you know of an exclusive launch of Z before anyone else.” Because these interactions were predicted based on deep customer insight from the data and AI models, supplemented by human touch, they yielded significant repeat purchases and at the same time increased customer satisfaction (NPS).
Customer contact center: In addition to store sales associates, customer care contact center agents were trained to have a full 360 view of the customer the moment they had customers contacting them via phone, chatbot, email, WhatsApp, or webform to provide predictive-AI powered precision advice as well as sales pitches. They also used gen AI enabled chatbots for B2C customer interactions, with possible handover to human agents. These human agents were, in turn, empowered by internal knowledge management systems that used gen AI to make information more accessible.
CRM-generated email and SMS: One retailer used a sophisticated Customer Data Platform (CDP), a data lake with ML/AI models, and a CRM Marketing Automation systems to facilitate customers getting personalized CRM emails (and/or SMSs) to click on and complete transactions either online or at-store.
6. Transform KPIs, structure, and incentives.
To get the most out of your assets, you also need to have the right targets, structure, and incentives. For example, in the luxury retailer example, store employees were incentivized to use a customer outreach program to offer AI-augmented personalized products and services. They were also incentivized for assisting ecommerce customers in obtaining store-based service. But getting these store employees to use it required new KPIs and a new sales process and sales training programs.
New KPIs need to be introduced and tracked to measure the success or failure of the digital growth engine. These included customer experience KPIs across all different channels of customer interaction, Customer Lifetime Value/Customer Acquisition Cost KPIs as well as hard financial KPIs. A concrete plan must be put in place to operationally act on the KPIs on a regular basis, identify root causes, course correct, and make rapid trade-off decisions, and reallocate resources and budget.
. . .
Of course, the above data, digital engineering, and AI-driven approach needs to be matched with a good customer experience at all different channels of customer interactions whether it is in-store, online, contact center, CRM email, live chat, or something else. However, using the above approach allowed all the companies we studied (or worked at) to increase customer lifetime value and enter the age of AI.