Robust data collection can transform your artificial cross-platform marketing intelligence.
Bottom line improvement is an undeniable benefit, with a direct correlation between hyper-personalization maturity and conversion rates. Short attention span and the infinite options for web-based consumers can only be countered by quality and differentiation. Personalized content and product offerings have been proven to improve click-through rates, revenue, and ROI.
Hyper-personalization bolsters efficiency for both the customer and marketing team by removing complexity from the sales funnel and eliminating irrelevant options. For the consumer, this results in a faster, less stressful sales process. For marketers, optimized content can eliminate the need for ongoing multivariate or A/B testing aimed at finding a singular solution for a wide audience.
Artificial intelligence and machine learning allow an organization to deliver customized messaging on a scale once reserved for generic content. E-mail calls to action, social media content, and webpage layouts are tailored and adjusted based on the latest data. New interactions developed to hold customer attention are automatically deployed at the optimal time.
“Treat every customer as an individual” is the tenet behind the hyper-personalization pattern of artificial intelligence (AI). These methods are widely used by marketing departments to build actionable personality profiles on customers. As these profiles evolve and grow over time, new information about the customer's buying behavior and life changes glean from the data. Personalized calendars and customer journeys allow for more relevant product offerings
Accurate and meaningful data establishes a foundation for hyper-personalization. Timely customer data consisting of browsing history, direct interactions, and customer relationship management (CRM) database content can be used to discover and predict the needs and preferences of each individual. Effective artificial intelligence (AI) associates the pertinent customer data with optimized content delivered via E-mail, web, or social media platforms.
Hyper-personalization does not replace the conventional marketing activity of classifying and grouping customers based on common attributes. Instead, it refines and extends the segmentation funnel down to the individual level. An infinite number of customers do not necessarily translate into an infinite number of product variations or marketing campaigns. Traditional segmentation according to demographics and brand interaction should be completed before implementing ultra-personal messaging.
A successful hyper-personalization strategy is one that positively and actively influences engagement, defined as the level of interaction between the organization and the customer. Hyper-personalization tools favor metrics based on engagement rather than conversion percentages alone. This can be accomplished by delivering relevant, high-quality content that prompts return visits and more two-way dialogue with customers. Brand equity and familiarity then set the stage for the next round of hyper-personalized product offerings.
Personalized content and the resulting relationship are part of a “journey” that adapts and evolves over the course of the customer experience. Channels where the customer has spent their time or made past purchases become the logical areas to offer relevant content and product offerings that build upon past interactions. Loyalty and trust continue to grow as the customer journey unfolds and data analysis moves from descriptive to prescriptive.
Analysis of past results is essential for building a sustained and continuously improving hyper-personalization strategy. The chosen metrics should be “sanity checked” to eliminate potential vanity metrics such as page visits and followers that may or may not correlate with revenue. Behavior-based numerical scoring is an analytical method used to quantify customer value over time. This scoring can be useful for determining the allocation of hyper-personalization resources.
“AI can enable financial firms to segment product offers by market audience, and distribute them as part of an integrated, hyper-personalized omnichannel experience for customers.”
Chief Data Analytics Officer,
The impact of hyper-personalization is profound, seen often in context to a larger product strategy.
Real-time data brings context to the hyper-personalization approach with respect to time, place, and recent interactions. Accurately assessed context becomes a differentiator when discounts, dynamic pricing, and other time-sensitive elements are utilized.
As hyper-personalization narrows segmentation to the individual level, the number of unique slogans, images, and product iterations multiplies. Artificial intelligence cannot replace the insight and creativity needed to produce this diverse assemblage of content.
Conventional integrated marketing campaigns combine web, E-mail, and social media to support a unified message. Effective hyper-personalization takes the multi-platform approach to the next level by analyzing geolocation data from mobile platforms and ensuring continuity when customers transition between various devices and communication platforms.
The inherent value of hyper-personalization should not be overshadowed by the technology that enables it. Over-reliance on analytics and AI to deliver personalized messaging creates a void when customers seek feedback beyond what automation alone can provide. Well-informed human operators should be available to field phone and Email inquiries in a timely manner.
The trust gained through voluntary and transparent data collection is essential throughout the customer journey. Customers who develop trust in a brand are much more likely to share relevant and timely information, which feeds into more enlightened hyper-personalization strategies.
Having a team in place to build out the program ensures proper data collection, data sharing, and proper data security which is a critical component of database governance.
Quality rather than quantity of data is the key to effective hyper-personalization. Incomplete, inaccurate, or siloed data can detrimentally impact returns by feeding into equally inaccurate conclusions and campaigns. Even with the most advanced AI tools available, a combination of relevant and timely data sources in aggregate is required. With intelligent taxonomy, disparate data sources can be melded into an actionable customer profile.
Hyper-personalization based on anonymous IP tracking can make customers feel suspicious or uneasy. Although most consumers appreciate the efficiency gained through hyper-personalization, overly aggressive pop-up ads, spam Email, and surprisingly personal content based on private information can be unsettling. The threshold for privacy invasion differs for each individual but is likely to result in brand avoidance once that line is crossed.
Companies engaging in hyper-personalization must remain cognizant of local and international data privacy laws and regulations. Many of these laws require consumers to be notified when data is collected or shared and give them the right to opt-out of such practices. Transparent and ethical data collection methods establish customer loyalty and trust along with regulatory compliance.
Hyper-personalization can entail initial start-up and ongoing maintenance costs associated with data collection, analysis, and the marketing resources needed to develop personalized messaging. This combination of factors can lead to a higher customer acquisition cost (CAC) in the short-term that must be weighed against resource availability and the expected improvement in lifetime value.
Team Wavelabs. “How AI-Driven Hyper-Personalization Can Elevate Your Business,” January 5, 2022.
Senior consultants with previous experience with these types of projects can set the stage for a well-framed engagement.
A focused session on your specific software applications, platforms, or projects. Typically this includes technical resources from both sides.