Creating personalized experiences for customers should infuse everything a marketing organization does, in part because it has to. Customers, whether B2B or B2C, now expect tailored messaging, recommendations and offers. The penalties for not meeting these expectations can be severe. Increasingly, customers punish brands for undifferentiated experiences and irrelevant communications, Gartner research indicates. That puts a high price on getting personalization right.

The problem is, many brands don't get it right. For most marketers, achieving personalization goals remains elusive. Sixty-three percent of digital marketing leaders indicated that delivering personalized experiences to customers presented a moderate or significant challenge when executing their company's digital marketing strategy, according to Gartner's 2021 Digital Marketing Survey. As challenges go, this ranked second behind complying with privacy and security standards. What's more striking is that the severity of the personalization challenge has increased appreciably since 2019: The share of respondents citing personalization as a significant challenge rose 53% in that time frame.

Personalization challenges

Several related factors may explain the level of advancing difficulty when it comes to personalization, the first of which is that effective personalization involves the synchronization of a lot of moving parts. It requires digital marketing leaders to set strategy, define resources, prioritize tactics, integrate data and test and optimize content to motivate audience behavior. Although a comprehensive personalization strategy and roadmap can be deciding factors in the results marketers achieve from personalization efforts, a majority of marketing organizations lack an effective personalization strategy, let alone one that is explicitly linked to desired business and customer goals.

Likewise, personalization typically entails the use of multiple technologies, many with overlapping functionality. Personalization requires four core sets of capabilities: data management, analytics, decisioning and execution, so it's often preferable to think of personalization technology in terms of overall architecture, rather than a single solution that's going to do everything for an organization.

The challenge here is that digital marketers tend to overbuy and underutilize the technologies that will help them deliver the personalization outcomes they seek. Driving successful personalization outcomes usually isn't contingent on increased spending on personalization technologies. Rather, achieving those outcomes depends more on maximizing technologies through more effective use. Similarly, marketers need to wring value from accessible data, available content and existing organizational talent before making new investments. Personalization programs that require the marketing organization to spend heavily on tools, content development or talent just to get started bring greater risks around the size, speed and certainty of payoff.

Enter AI

Artificial intelligence (AI) and machine learning (ML) embedded within a range of martech solutions supporting data management, analytics, decisioning and marketing execution hold the promise of facilitating marketers' personalization goals. These solutions include customer data platforms (CDPs), multichannel marketing hubs (MMHs), personalization engines and A/B/n testing tools, to name a few of the most prominent. Embedded AI and ML within MMH solutions, for example, support a broad range of personalization scenarios. These include segment discovery; campaign and journey path generation based on business goals; channel propensity models; predictive content and offer recommendations and autonomous campaign optimization capabilities.

Among emerging technologies that marketing leaders are using to improve digital marketing execution, AI/ML lead the pack, according to Gartner's 2021 Digital Marketing Survey. Yet, only 17% of marketers are deploying AI/ML widely to support a variety of marketing functions. Thirty-eight percent of respondents characterize their efforts as being in the planning and piloting stages. For organizations beyond these stages, 44% are deploying AI/ML on a limited basis for a few specific applications. In other words, we are still in the early days of AI/ML's impact on marketing execution.

Trust, specifically trust using AI/ML to make important decisions, is a key inhibitor to more widespread deployment of AI/ML technologies in marketing organizations, even among brands that are currently using them. However, increased usage brings a progressive acceptance curve. Whereas 75% of respondents piloting AI/ML worry about trusting the technology, that number drops to 53% among those broadly using AI in the marketing organization.

Staffing gaps are another critical stumbling block to successful AI/ML deployments. Digital marketing leaders looking to advance their organization’s use of AI/ML and other emerging technologies that may disrupt — but ultimately benefit — established workstreams should do so with an eye to broader change management. The success of deployments will depend on adequate training to existing staff, hiring new team members where necessary and awareness of the impact new technologies will bring to the organizational culture.

Use of AI/ML is tied to personalization goals

Broadly speaking, digital marketing leaders view the impact of AI/ML through the prism of personalization. Eighty-four percent of respondents in the Gartner survey agreed or strongly agreed with the statement that using AI/ML enhances marketing's ability to deliver real-time, personalized experiences to customers. When asked about the most important use cases for AI/ML-enabled tools, respondents focused on the value of such tools in bringing automation, scale and efficiency to marketing activities across channels. They cited specific activities that connect to broader personalization efforts, including:

• Delivering predictive content (45%)
• Creating campaign/journey paths based on business goals (45%)
• Developing channel propensity models driven by customer profiles, behavior and preferences (45%)
• Identifying audiences and segments most likely to engage (43%)

Succeeding at personalization requires an understanding of what customers are trying to achieve in their interactions with your brand. This insight should inform the strategy for how personalization can help customers reach their objectives, and how to align customer needs with business goals.

Personalization demands a deliberate, considered use of a mix of technologies, specific skills and the right team structure to manage complex workflows. Desired capabilities include strategy, planning, analytics, martech adoption, campaign orchestration, content creation and project management. Marketing leaders should maximize what they can achieve by leveraging existing tools in conjunction with available data and content before committing to new technologies. Use AI and ML to mature efforts by driving greater relevance in marketing engagement and increasing influence over customer behavior.