Marketing's Age of Relevance: How to read and react to customer signals

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The new world of customer lifecycle management (CLM) is killing off two traditional and outmoded marketing concepts: the funnel and the calendar.

Traditional marketing communications—aligned with a calendar of holidays, product launches, and other marketing-defined events—is an unresponsive model organized around the company, not the customer. It doesn’t account for the many daily opportunities to effectively engage with customers on their terms, when they’re ready to engage. Increasingly, customers just aren’t listening to the barrage of traditional outbound, irrelevant messages. They TiVo past television commercials, sign up for Do-Not-Call lists, install ad blockers on their PCs, and automatically divert email offers to their spam folders.

The funnel—the classic linear progression in which customers narrow their buying options as they advance from product awareness to purchase—is becoming a lot less relevant because customers are engaging in a much more iterative and dynamic decision journey. They add and narrow options, turn to a wide range of sources for help in making choices, and they are vocal—to both the benefit and detriment of brands—after their purchase. In addition, once customers are impressed by a highly personalized, responsive, and relevant experience with another brand (think of Amazon’s two-day free delivery, one-click purchasing, and “for you” recommendations), they bring those expectations to all their brand relationships.

As the sun sets on the calendar and funnel, the pressure is rising on marketers to become receptive and responsive. What that means in practice is that they must listen for the signals that indicate customers are ready to engage and design programs that respond to those signals quickly. This requires both advanced customer analytics to read the signs of customer intent and a response system that reacts to those signs by reaching out with relevant content across touchpoints and channels.

Becoming receptive

To build an organization that’s receptive, companies need to focus on developing a comprehensive program that incorporates and integrates data. McKinsey’s DataMatics 2013 survey shows that companies that are receptive, i.e. use customer analytics extensively, are more than twice as likely to generate above-average profits as those that don’t. They also outperform their peers across the entire customer lifecycle, are nine times more likely to enjoy superior customer loyalty, and a whopping 23 times more likely to outperform less analytical peers on new-customer acquisition (see exhibit).1


To be receptive, companies need to excel at:

  • Spotting Intent. Predicting customer behavior boils down to spotting the signals of customer intent sooner than your competitors do and acting on the information. In that arena, customer analytics are an invaluable tool. Companies need to establish necessary “receptors” in the form of well-designed triggers (e.g. signing up for a coupon, asking questions about a product on social media) to detect and respond to customer signals. Marketers who spot the moments when intent first develops have a tremendous opportunity to capture customer attention and steer it to their products.

    Insights can come from new sources of data. Life insurers, for example, use emerging categories of third-party data sources to provide deeper insights into applicant lifestyles, health situations, and risks (e.g. recreational activities, weight, travel behavior). Combining these data sources allows them to tailor policies to each individual’s risk profile (e.g. crime rates in his/her neighborhood, affinity for bungee jumping), creating data-driven underwriting capabilities. Spotting intent is also about spotting changes in consumers’ lives that may lead to significant purchases—for example, a family moves into a bigger house and can now find space for the home theater system they’ve been thinking about, or someone receives a bonus or inheritance that creates the need for new banking products. Similarly, life-event triggers often lead to fundamental changes in shopping behavior: The birth of a child can lead consumers to start shopping in discount stores for diapers, trips that can lead to shifts in purchase behavior across a whole range of unrelated categories.

    Spotting intent can be more subtle. Consumers can gradually develop the intent to purchase an item through their everyday interactions, whether by seeing it used by the person next to them on the subway or by interacting online with friends about it. Advanced analytics that can “sense” burgeoning interests can get a head start on the process of guiding customers to an eventual purchase. In the old CLM model, these types of data-mining analytics were done ad hoc once a year to prove a hypothesis. In the new CLM, it’s a core competency tied to real-time processes and personalized marketing.
  • Anticipating Intent. Spotting intent is great, but anticipating it is even better. Advanced analytics systems are good (and relatively cheap) enough to dramatically improve predictions of what a customer will buy next after making an initial purchase, and when s/he is likely to do it. In the simplest example, someone who has bought a high-end TV will likely be interested in surround-sound speakers in the future. One leading Southeast Asian telecom player invested in a “next-product-to-buy” tool and boosted upsell revenue from existing customers by 30 percent.
  • Creating incentives to volunteer data. Consumers are already vocal about their opinions. The Pandora phenomenon (where consumers tell you exactly what they like / don’t like) is here to stay as consumers follow up on recommendations and channels formed by their friends’ tastes and habits. Companies need to be creative in developing applications and offers that reward customers for sharing data. Customers use the Nike Fuelband, for example, which collects data on their workouts and then recommends relevant products. Car configurators, which help shoppers design the exact car they want, are a great source of information about customer priorities. Companies can be even more direct about collecting data. Our research shows that 35 percent of online buyers are willing to share personal information in exchange for promotional coupons.2

    Companies that want to turn data to their competitive advantage need to move aggressively. Nearly three quarters of companies believe their budget for customer insights is too low, according to our recent survey of almost 700 senior executives. Even more disturbing, only 6 percent of companies surveyed say they understand customer needs extremely well, while 45 percent admit their understanding of how their customers interact with them digitally is limited-to-none.3

Becoming responsive

Being responsive is about having the systems, guidelines, and content in place to react with relevant messages or offers when opportunity arises. Timing and relevance are critical. Too many companies mine customer data but fail to get the insight out of the datamart and into the appropriate channels or the hands of frontline agents quickly enough to drive better, more valuable interactions. The objective of responsiveness is to turn new customers into loyal repeat buyers, or to ensure that a long-time customer whose loyalty is wavering doesn’t leave for a competitor. Our research shows that this kind of personalization can deliver five to eight times the ROI on marketing spend and lift sales 10 percent or more.

Customer analytics can generate consumer-decision-journey “heat maps,” for example, that reveal opportunities and battlegrounds on the brand’s properties or elsewhere on the web. Those heat maps then need to be segmented by customer, often at a high level of granularity, based on a broad range of criteria (e.g. behavior, demographics, location, age, stage in buying journey, etc.). Once detailed customer segments are identified, the best companies develop a rich catalog of personalized messages. The Gilt Groupe, for example, has put analytics at the center of its personalization efforts, and at noon every day, they send more than 3,000 versions of a message to customers, based on what they’ve already shopped for, what they like, even what sizes they wear.

Responsiveness isn’t limited to digital channels. One Asian retailer personalized its printed flyer to address the characteristics of six broad customer segments. It had a set of objectives for each segment (such as retention, increasing shopping-basket size, driving repeat visits) and supported them with personalized incentives ranging from thank-you offers to coupons aimed at driving purchases in new categories and increasing purchase frequency. The result was a three-point lift in same-store sales, a 300 percent improvement in cost-to-sales ratio, and 100 percent ROI on the program.

Being responsive also means arming inbound channels and employees with information relevant to providing a more personalized customer experience. Amazon and other digital leaders have accustomed us to this kind of service online. We also see a wave of personalization in person-to-person interactions with customers, particularly in retail stores or with call-center agents. The trick is providing the front line with enough intelligence to deliver a tailored experience, but with enough flexibility to take into account what is learned from the most recent customer interaction.

Building the right capabilities

We see profound implications from these trends for how marketing organizations prioritize and build new capabilities to manage customer lifecycles. We believe that marketing leaders need to:

  • Get your data house in order. Often the best consumer triggers are behavioral touchpoint data already available but not organized or mined to be actionable in close to real time. Event trigger data (e.g. customer purchase in a related product category or clicking on a display banner for your product) has been shown to be five- to ten-times more predictive and powerful than externally appended demographic data. Proven analytical software tools can track the occurrence of a pattern of events across channels, so the brand can treat flagged customers differently. A customer-service rep who gets a call from a high-value customer, for example, might route the call to a queue or agent who can then cross-sell the caller relevant products or services based on the interests suggested by his or her online shopping behavior.
  • Arm your channels with analytics for better decision making. Often companies invest in sophisticated analytics that stay hidden in the marketing department or customer datamart for use only during outbound targeted campaigns. This is a missed opportunity to arm the rest of the organization, especially sales agents, with detailed, relevant, and useful customer intelligence. Customer-facing personnel should be equipped with relevant information about a customer’s history, accompanied by a recommended “treatment path,” e.g., offering an upgrade. A major insurance company has improved its profits by integrating customer analytics with its fraud-detection system. During the claims-handling process, agents use simplified customer analytics to fast-track claims for those customer segments that have a low likelihood of fraudulent activities, simultaneously boosting satisfaction and reducing operational costs.

    Critical to making this system work is ensuring that channel partners, business units, and frontline reps are involved in the design of “recommendation engines” or related tools. In our experience, companies need to pay as much if not more attention to the people and processes that translate insights into well-designed offers as they do to generating the insights in the first place. Salespeople do not react well to black-box solutions with counterintuitive suggestions for their sales pitch. They rightly look for some level of context and customer information to understand why this offer or treatment makes sense for a given customer. Even better is providing salespeople with the ability to dynamically adjust the offer or select from a list of possible offers based on what is learned from the live customer conversation.
  • Measure customer lifetime value. One of the hardest changes to adapt to in the new world of CLM is moving measurement and reporting from an emphasis on short-term conversion to one that reflects the business impact of engagement. Customer measurement needs to move from blunt units to a more finely tuned and sophisticated set of specific metrics such as “increase in share of wallet,” “lifetime loyalty/net present value” and “customer-segment profitability trajectories.” As the interaction between the brand and its customers becomes more complex and personalized, the measurement of those interactions needs to be more discreet with the goal of learning and then driving policy and on-the-spot offers and actions. The ultimate goal is to measure the (expected) impact on the Customer Lifetime Value (CLV) of your efforts to boost customer profitability and reduce churn. Once established, a CLV metric can then be used in day-to-day decision-making processes. For example, offering a $20 per month promotion to a high-value cable subscriber calling in to end service is a small price to pay to secure longer-term profitability.
  • Upgrade talent. Raising the “data IQ” of an organization is essential if Big Data principles are ever going to move from the lab to the front lines. Finding the right talent, however, can be difficult. Just 3.4 percent of CMOs surveyed by McKinsey in 2013 believed they currently have the right talent to fully leverage marketing analytics. Companies need to fill multiple roles with specific skill sets. Specialist competence is essential but not sufficient. Look for specialists who are also “translators,” able to bridge different business functions by comfortable and effective communications. This need is driving demand for what have been called “two-sport” managers—those with two complementary skills such as computer programming and finance, statistics and marketing, psychology and economics. Some traditional companies such as Walmart and Allstate have established outposts and innovation hubs in Silicon Valley to gain access to the types of talent and skills needed to excel at the new discipline customer lifecycle management.

The new CLM is about understanding customer intent, then reacting quickly, relevantly and profitably. Marketers who can adjust their tactics to not only identify those customers with the highest potential lifetime value, but also to act effectively on the next best action to take will have the edge in the marketplace.

  1. DataMatics 2013: Using Customer Analytics to Boost Corporate Performance (McKinsey, January 2014)
  2. iConsumer 2012, Insight No. 1
  3. “The Funnel is dead. Long live the customer decision journey,” David Edelman and Francesco Banfi, The Economist, February 14, 2014