This is the second blog in a two-part series that explores how to use advanced digital technologies to create revenue lift. Part one examines the transformative power of embedded AI and machine learning to engage with a customer with a personalized customer experience in the context and cadence of an omnichannel journey.
A previous blog on the topic of AI and machine learning as vital tools for producing revenue lift referenced an Aberdeen Group study that found brands that “create personalized experiences by integrating advanced digital technologies and proprietary data for customers” are seeing revenue increase by 6 percent to 10 percent.
In some cases, that estimate undersells the revenue-generating power of advanced digital technologies. According to Gartner, the difference between up to a 10 percent lift and a 20 percent lift more in line with what some Redpoint customers achieve is the difference between being “persona-centric” and “customer-centric”. In the former, an enterprise uses transactional, preference, historical, and purchase data to form an engagement strategy. In the latter, an enterprise also looks at behavior across devices, analyzes device usage, IoT and sentiment analysis, and first-party, second-party, and third-party data across an anonymous-to-known customer record. In other words, what the Redpoint customer engagement platform delivers.
One Redpoint client directly attributes our platform to a 19 percent revenue lift. Another produced a 3X ROI on the entire system in the first year. This is the reality of what embedded, in-line analytics and machine learning models produce; automated revenue-generating interactions that directly impact the bottom line. These and other Redpoint customers are executing on customer engagement strategies to drive revenue with personalization, and with machine learning as an indispensable partner to achieve the ultimate objective: capitalizing on the moment of interaction with each customer in the context and cadence of an individual customer journey.
From “Nice to Have” to “Must Have”
Many companies misunderstand or mislabel advanced analytics and machine learning as strictly a cost concern, which can be understandable if the goal or use case is to produce a superficial personalized experience on par with a ‘smart’ drive-thru at a fast-food eatery.
A truer cost consideration considers the price of inaction, combined with the cost of existing manual engagement systems, processes, and strategies that fail to seize on the moment of interaction. Existing technology that cannot offer a consistent personalized experience across an omnichannel journey has two strikes against it.
One, it’s ineffective. According to a Harris Poll survey commissioned by Redpoint, 37 percent of consumers said they will no longer do business with a company that fails to offer a personalized experience. Two, it is counterproductive. Failing to keep pace with the consumer in the channel of their choosing – such as an online offer for an item the consumer recently purchased in-store – introduces friction into the customer experience. In the same Harris Poll survey, 34 percent of consumers say it is “very frustrating” when a brand does just that, with 33 percent also frustrated when a brand sends offers that are not relevant.
Viewed through this lens, there is a sunken opportunity cost marketers must factor when they’re unable to seize the moment of interaction. Taking a longer view, automated machine learning models that retrain themselves based on current data and are always primed with a next-best action regardless of where and when the customer appears in their buying journey are indispensable for augmenting a customer’s lifetime value (CLV). Opportunity, then, must be weighed not only against the cost of a standalone personalized customer experience but rather against the customer loyalty and lifetime value a consistent, personalized CX delivers.
According to research from the Aberdeen Group, companies with an extremely strong omnichannel customer engagement have an 83 percent customer retention rating, versus a 53 percent rating for those that do not. In a 2018 Boston Retail Partners survey, more than half of consumers (51 percent) said that it is important to have a personalized experience across all digital channels within a brand.
An increase in orders over a customer’s lifecycle, a greater average order value, and growing wallet share from a loyal, growing customer base are all provable ROI metrics from introducing AI and machine learning.
Leave No Data Behind
A casino offers a real-world example for how embedded AI and a next-best action drive revenue. One casino approached Redpoint with a problem: it was taking 36 hours or longer to ingest and aggregate customer data to create an identity proxy to be used for customer engagement. If a weekend guest earned a craps windfall on Saturday afternoon, for instance, that information wouldn’t be in an updated customer record until long after the guest had left the premises, making real-time offers to help the guest spend their winnings in the casino – a discount on a luxury fur, a room upgrade, a spa treatment – no longer relevant to the customer’s experience.
The Redpoint customer engagement platform consists of three layers that enable personalization at scale, in real time, in the cadence of the customer. A next-best action spurred by machine learning begins with the golden customer record or 360-degree customer view, a persistently updated record that captures data from any source or type in real time. The instant data comes in it moves through a model and is ready to be used to form a relevant engagement with a customer in the context and cadence of where the customer is in their buying journey or path-to-purchase, independent of channel.
The lack of latency between ingestion and activation of the golden record is a key functionality of the Redpoint Customer Data Platform (CDP). As the above casino example illustrates, knowing everything there is to know about a customer in real time is the foundation for providing a next-best action that is hyper-relevant to the customer experience. The crucial importance of running advanced analytical models on an updated customer profile across an unknown-to-known state is a unique enabler of the Redpoint CDP to enterprise customer engagement at scale.
Real-time decisioning powered by Redpoint Automated Machine Learning (AML) is the second layer of the Redpoint Customer Engagement Hub that, in concert with the CDP, breathes life into a unified customer profile by personalizing an omnichannel journey. AML runs 24/7 looking for opportunities to monetize the golden record; a simulation engine continuously monitors campaign models, and when a model predicts a better outcome than an existing model, a new model will automatically be put into production. This lights-out modeling capability can be tuned to any business goal, whether it’s acquisition, cross-sell, retention, customer value score, or another metric.
Intelligent orchestration is the third and final layer, activating the real-time decision with a next-best action relevant to the customer’s real-time experience, regardless of channel. A customer’s cadence may dictate an email or a push notification, for instance, but the key point is that an action is not determined in advance. Rather, a dynamic next-best action is perfectly synchronized to that precise moment of interaction.
Shed Complexity with a Single Point of Control
The customer referenced earlier which attributed Redpoint software to a 19 percent revenue lift has a service level agreement (SLA) that stipulates a 50 millisecond response time to engage with a customer with a next-best action independent of channel, with the platform regularly returning a next-best action in roughly 15 milliseconds. The immense complexity of continuously updating a golden record, applying real-time decisioning, and intelligently orchestrating a next-best action happens, literally, in the blink of an eye. This is made possible because the platform offers a single point of control for all data, decisions, and interactions that is indispensable to keeping pace with an omnichannel customer journey.
Because a personalized customer experience is largely overtaking price and product as a competitive differentiator, marketers can no longer rely on intuition to create engagement that moves the needle. Automated machine learning in a platform finely tuned to a customer’s needs, wants, and desires unlocks revenue opportunity for customer-centric, data-driven organizations.
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