Published in 2007, Tom Davenport’s groundbreaking work Competing on Analytics: The New Science of Winning explored the then relatively new science of harnessing customer data for competitive advantage. CIO Insight lauded it as one of the top 15 most groundbreaking management books.
The first chapter focuses almost exclusively on Netflix as an example of a data-driven company that parlayed business intelligence into a (then) innovative new business model – mailing DVDs to customers and recommending titles for their queue. As we now know, the book’s publication pre-dates several more innovations from Netflix, which underscores the rapid pace of digital transformation and the danger of inaction (see Blockbuster).
Rules-Based, Automated Machine Learning Models
The current entertainment streaming wars further illustrate how much has changed since the incipient times of 2007 with the use of analytics to compete on customer experience. With price and product largely commoditized, companies in retail, finance, healthcare, travel, and industries across the board recognize the urgency to use data and analytics to create a personalized omnichannel customer experience.
Creating a personalized customer experience for the always-on, connected customer requires two-way communication across every channel. The traditional approach to analytics that created a static list of customers for an outbound marketing campaign based on a fixed data model is incapable of keeping pace with an omnichannel customer journey.
To support a consistent, personalized customer experience across channels, decisions need to be rules based, not list based. While this entails a real-time element, which requires that a brand take the optimal action at the precise moment a customer appears in a channel, it also entails dynamic flexibility. Unlike static lists that that cannot respond to new or changed data, rules-based decisions can now account for the totality of customer behaviors up to the customer appearing in a given channel.
All of this is made possible through real-time data, or a golden record, for each customer coupled with automated machine learning. Lights-out, evolutionary programming makes dynamic flexibility possible. With a continual ingestion of customer data from every source and of every type, automated machine learning models (code-free) are tuned to optimize a dynamic, personalized customer journey without human intervention across channels.
Competing On Customer Journeys and Experience
Dynamically managing a customer journey is required to provide customers with the type of online and offline experiences they increasingly demand from the brands they frequent. Results of a 2019 Harris Poll survey commissioned by Redpoint Global clearly show that competing on customer journeys and experiences is an imperative; brands that ignore this reality run the risk of alienating core customers.
According to the survey, 63 percent of consumers agree that personalization is part of the standard service they expect. For instance, they expect a brand to know they are the same customer across all touchpoints (in-store, email, mobile, social, call center, etc.). Further, consumers were unsparing in their critique of brands that fail to deliver personalization, with 37 percent claiming that they will stop doing business with a brand that does not offer a personalized experience.
It’s easy to see why static lists, which are familiar to any marketer who has run an outbound, drip campaign based on segments, are unable to keep pace with a dynamic customer journey. A typical non-sequential, non-linear customer journey includes many touchpoints, and an ability to deliver a personalized experience throughout the journey, across any channel, requires knowing everything there is to know about the journey. Consider a customer who logs in to a retailer’s website. Which pages the customer views and page durations vary with each visit and for each customer. A list derived from an analysis of past behaviors will not account for subsequent behavior that may fundamentally alter the next-best offer, recommendation, or action, and thus will not by synchronized to a customer journey.
Personalization failures, which an Accenture study estimates costs US firms $756 billion annually, clearly do not go unnoticed – or unpunished – by the savvy consumer. A reliance on static, list-based analytics could easily result in a customer receiving an offer for a recently purchased product, or a recommendation that is irrelevant to the journey in that precise moment in time.
Keep Up with a Dynamic Customer Journey
Rules-based approaches eliminate these types of customer frustrations by being in pitch perfect sequence with a customer journey regardless of which direction it takes. One Redpoint customer, a specialty retailer, uses automated machine learning within the Redpoint Customer Data Platform to deliver an innovative, personalized buy-online, pay in-store (BOPIS) experience. Within minutes of purchasing a product on-line, the retailer is able to deliver a relevant, personalized experience to the customer even before the customer arranges for the in-store pick-up. Personalization touches include emails with pick-up instructions (directions, store hours, etc.), relevant offers for product accessories, and website content tailored to the customer’s preferences.
Another Redpoint client, a web services company, uses the platform’s millisecond response time to know everything there is to know about a customer in real time. When a customer calls into the call center, for example, an agent will be aware of every action that customer has taken – up to and including the call. If the customer is currently having an administrative issue on a hosted website, the call center agent will have a record of it and have the information needed to resolve the issue.
These outcomes stem from the ingestion of all customer data – every source and type – in real time, and rules-based, hands-free automated machine learning models that offer the same dynamism present in every customer journey today.
In 2007, the year it launched its streaming service, Netflix customers still happily waited a few days to receive DVDs in the iconic red envelopes. Today, of course, customers expect to be able to stream content of their choosing at any time on any device. Relying on list-based analytics is the equivalent of mailing red envelopes and hoping your customers are okay with an experience that may have been delightful a decade ago, but is archaic by the standards of modern technology. Customers demand a relevant, personalized, and seamless experience across channels. Meeting this expectation results in more satisfied, loyal customers which directly translates into revenue.