Successful personalization demands: Customers punish brands for undifferentiated experiences and irrelevant communications, so personalization comes at a high cost, writes Gartner’s Noah Elkin.
Below is an article by Noah Elkin, vice president-analyst and head of research at Gartner. Opinions are those of the author.
Creating a personalized customer experience should permeate everything a marketing organization does, in part because it’s necessary. Customers, whether B2B or B2C, now expect customized messages, recommendations and suggestions. The penalties for not meeting those expectations can be severe. According to Gartner research, customers are increasingly punishing brands for undifferentiated experiences and irrelevant communications. This puts a high price on getting personalization right.
The problem is that many brands aren’t doing it right. For most marketers, achieving personalization goals remains elusive. Sixty-three percent of digital marketing leaders said that providing personalized experiences to customers is a moderate to significant challenge when implementing their company’s digital marketing strategy, according to Gartner’s “2021 Digital Marketing Survey.” The challenge ranks second only to privacy and security standards. What’s even more striking is that the severity of the personalization challenge has increased markedly since 2019, with the percentage of respondents who cited personalization as a significant challenge increasing by 53% in that time.
Personalization challenges
Several interrelated factors can explain the level of increasing complexity of personalization, the first of which is that effective personalization involves the synchronization of many moving parts. It requires digital marketing leaders to strategize, resource, prioritize tactics, integrate data, test and optimize content to motivate audience behavior. While a comprehensive personalization strategy and roadmap can be critical factors in the results marketers achieve through personalization efforts, most marketing organizations do not have an effective personalization strategy, let alone one clearly tied to desired business and customer goals.
Similarly, personalization typically involves multiple technologies, many of which have overlapping functionality. Personalization requires four core sets of capabilities: data management, analytics, decision making and execution, so it is often preferable to view personalization technology in terms of an overall architecture rather than a single solution that will do everything for the organization.
The problem here is that digital marketers tend to overbuy and underutilize technologies that will help them achieve the personalization results they seek. Achieving successful personalization results usually doesn’t depend on increasing spending on personalization technology. Rather, achieving those results depends on maximizing technology by using it more effectively. Similarly, marketers need to capitalize on available data, existing content and existing organizational talent before making new investments. Personalization programs that require a marketing organization to spend heavily on tools, content development, or talent just to get started are fraught with greater risks in terms of size, speed, and certainty of return.
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Implementing Artificial Intelligence
Artificial intelligence (AI) and machine learning (ML) embedded in a number of martech solutions that support data management, analytics, decision-making and marketing execution hold promise for helping marketers meet their personalization goals. These solutions include customer data platforms (CDPs), multi-channel marketing hubs (MMHs), personalization engines and A/B/n testing tools, to name a few of the best known. Embedded AI and OD in MMH solutions, for example, support a wide range of personalization scenarios. These include segment discovery, campaign and route creation based on business objectives, channel propensity models, predictive content and offer recommendations, and offline campaign optimization capabilities.
According to Gartner’s “2021 Digital Marketing Survey,” AI/ML leads among the new technologies that marketing leaders are using to improve digital marketing execution. However, only 17 percent of marketers are widely adopting AI/ML to support various marketing functions. Thirty-eight percent of respondents describe their efforts as being in the planning and piloting stages. As for organizations beyond those stages, 44% are implementing AI/ML on a limited basis for a few specific applications. In other words, we are still in the early stages of the impact of AI/ML on marketing execution.
Trust, namely trust in the use of AI/ML to make important decisions, is a key barrier to wider adoption of AI/ML technologies in marketing organizations, even among brands that are currently using them. However, increased usage is leading to progressive adoption of the technology. While 75% of respondents who have tried AI/ML worry about trust in the technology, that number has dropped to 53% among those who are widely using AI in a marketing organization.
Staffing gaps are another major stumbling block to successful AI/ML adoption. Digital marketing leaders looking to expand the use of AI/ML and other new technologies that can disrupt – but ultimately benefit – established workflows must do so with broader change management in mind. Successful implementation will depend on adequately training existing staff, hiring new team members where necessary, and being aware of the impact new technologies will have on organizational culture.
The use of AI/ML is tied to personalization goals
Overall, digital marketing leaders view the impact of AI/ML through the lens of personalization. Eighty-four percent of Gartner survey respondents agreed or strongly agreed with the statement that using AI/ML enhances marketing’s ability to provide customers with real-time, personalized experiences. When asked about the most important use cases for AI/ML-enabled tools, respondents emphasized the value of such tools in enabling automation, scaling and efficiency of marketing activities across channels. They cited specific activities that involve broader personalization efforts, including:
Providing predictive content (45%)
Creating campaigns/paths based on business objectives (45%)
Developing channel propensity models based on customer profiles, behaviors and preferences (45%)
Identifying audiences and segments most likely to engage (43%).
Success in personalization requires an understanding of what customers are trying to achieve when interacting with your brand. This understanding should inform strategies for how personalization can help customers achieve their goals, and how to align customer needs with business goals.
Personalization requires deliberate, thoughtful use of a set of technologies, specialized skills and the right team structure to manage complex workflows. Desirable capabilities include strategy, planning, analytics, martech implementation, campaign orchestration, content creation and project management. Before embarking on new technologies, marketing leaders should maximize the use of existing tools in combination with existing data and content. Use AI and OA to improve efforts, increasing the relevance of marketing interactions and enhancing the impact on customer behavior.
