A recent McKinsey article claims that a “tech-enabled transformation” across the commercial function for industrial companies could generate close to $300 billion in additional revenues. A commercial function playbook details how an investment in analytics will result in commercial excellence across four sources of value: e-commerce and digital marketing, digital sales, advanced pricing, and analytics-driven marketing.
While marketing directly accounts for two of the four value sources named in the article, it’s notable that analytics underlines each area as the driving force for achieving sustainable growth. A recognition that analytics must be accompanied by broader change, that it should be led by the business and not IT, and that the best-performing companies build small but highly skilled analytics teams are among several core tenets identified as key to a tech-enabled transformation.
While the ideas presented are in the context of the commercial function, they mirror the efforts of data-driven marketing organizations that are using advanced analytics such as automated machine learning for excellence in the area of customer engagement. With a re-reading of the article in this lens, an argument can be made that McKinsey puts artificial constraints on marketing by limiting it to just two of the four value sources for growth potential. If we instead think about marketing as responsible for delivering a holistic customer experience across all channels and every interaction with a brand, it is impossible to separate this experience from commercial excellence.
Personalization is the Common Denominator
By accepting that a customer’s entire experience with the brand is inseparable from commercial excellence, then tech-enabled transformation should start with marketing because of the potential to drive growth across the enterprise.
Customer experience, in this light, extends beyond a customer’s omnichannel journey across online and offline channels. It also entails interactions with a brand that have not been considered marketing-related in the traditional sense, such as customer service and account management. Customer experience, in a nutshell, extends across every aspect and covers every possible reason someone would interact with the enterprise.
Personalization is the thread that ties all these interactions together, showing the customer that the brand recognizes they are the same customer across channels. It is why personalization is now considered table stakes. In the Harris Poll survey commissioned by Redpoint, 63 percent of consumers said that personalization is a standard service they expect. Asked how a brand can demonstrate it recognizes a customer as an individual, 43 percent said it was important the brand recognize them across all touchpoints.
From the marketing perspective, then, “commercial excellence” is better understood as “personalization excellence,” which means providing a customer with an unbroken chain of engagement across all touchpoints. According to Rusty Warner, principal analyst with Forrester, there are five key elements of real-time decisions. Recognition, Context, Insight, Execution, and Optimization are all important characteristics of the personalized customer experience. While it may seem to be a simple list, each step requires serious thought and attention to achieve the personalization excellence that drives revenue. Any break in the chain interrupts the experience created by ubiquitous personalization, akin to seeing a Starbucks coffee cup in a Game of Thrones scene. It breaks the spell, and introduces friction into an otherwise seamless customer experience.
Start with Customer Data
To achieve the level of personalization that customers increasingly expect, a tech-enabled, data-driven transformation using automated machine learning needs to begin with customer data. To deliver a personalized customer experience across every touchpoint, brands need a broad, AI-driven platform for complete, holistic customer experience management.
By ingesting data from every source and every type – structured, unstructured, semi-structured, first-party, second-party, and third-party – a brand has a unified customer profile, or golden record, that gives them a 360-degree customer view that includes all behaviors, preferences, transactions. Applying automated machine learning to the golden record using code-free data models tuned to optimize a customer experience across all channels provides marketers with a next-best action for a customer that is always in the context and cadence of a unique customer journey.
