Successful customization requires a balance of strategy, tactics, technology and skills
The following is a guest article by Noah Elkin, vice president analyst and chief research officer at Gartner. Opinions are those of the author.
Creating personalized experiences for customers should permeate everything a marketing organization does, in part because it has to. Customers, whether B2B or B2C, now expect tailored messages, recommendations and offers. The penalties for not meeting these expectations can be severe. Increasingly, customers are punishing brands for undifferentiated experiences and irrelevant communications, says Gartner research. This puts a high price on good personalization.
The problem is, a lot of brands don’t get it right. For most marketers, achieving personalization goals remains elusive. Sixty-three percent of digital marketing leaders indicated that providing personalized experiences to customers was a moderate or significant challenge when executing their company’s digital marketing strategy, according to the Digital Marketing Survey from Gartner in 2021. As the challenges grew, it came in second behind meeting privacy and security standards. What is more striking is that the severity of the personalization challenge has increased significantly since 2019: the share of respondents citing personalization as a significant challenge increased by 53% during this period.
There are several related factors that can explain the level of difficulty in advancing in customization, the first being that effective customization involves the synchronization of many moving parts. It requires digital marketing leaders to define strategy, define resources, prioritize tactics, integrate data, and test and optimize content to motivate audience behavior. While a comprehensive personalization strategy and roadmap can be decisive factors in how marketers achieve results from personalization efforts, the majority of marketing organizations do not have an effective personalization strategy without talk about a strategy explicitly linked to the desired business and customer objectives.
Likewise, personalization typically involves the use of multiple technologies, many of which have overlapping functionality. Personalization requires four basic sets of functionality: data management, analysis, decision making, and execution. So it’s often better to think of customization technology in terms of the overall architecture, rather than a single solution that will do it all for an organization.
The challenge here is that digital marketers tend to overbuy and underuse the technologies that will help them achieve the personalization results they are looking for. Achieving successful personalization results generally does not depend on increased spending on personalization technologies. Rather, achieving these results depends more on maximizing technology through more efficient use. Likewise, marketers should take advantage of accessible data, available content, and existing organizational talent before making new investments. Personalization programs that force the marketing organization to invest heavily in tools, content development, or talent just to start with come with greater risks in terms of size, speed, and certainty of payoff.
Artificial intelligence (AI) and machine learning (ML) integrated into a range of martech solutions supporting data management, analytics, decision making and marketing execution promise to facilitate business goals. personalization of marketers. These solutions include Customer Data Platforms (CDP), Multichannel Marketing Hubs (MMH), Personalization engines, and A / B / n testing tools, to name a few of the most important. The AI â€‹â€‹and ML integrated into MMH solutions, for example, support a wide range of personalization scenarios. These include the discovery of segments; generation of campaigns and journeys based on business objectives; channel propensity models; Autonomous predictive content and offer recommendations and campaign optimization capabilities.
Among the emerging technologies that marketing leaders are using to improve digital marketing execution, AI / ML leads the pack, according to the 2021 Gartner Digital Marketing Survey. Yet only 17% of marketers widely deploy AI / ML to support a variety of marketing functions. Thirty-eight percent of respondents describe their efforts as being in the planning and pilot stages. For organizations beyond these stages, 44% deploy AI / ML on a limited basis for a few specific applications. In other words, we are still in the early days of the impact of AI / ML on marketing execution.
Confidence, especially confidence in using AI / ML to make important decisions, is a key barrier to the more widespread deployment of AI / ML technologies in marketing organizations, even among brands that use them. currently. However, increased use results in a gradual acceptance curve. While 75% of those driving AI / ML fear trusting the technology, that number drops to 53% among those who use AI extensively in the marketing organization.
Staff shortages are another critical stumbling block for successful AI / ML deployments. Leaders in digital marketing looking to advance their organization’s use of AI / ML and other emerging technologies that can disrupt – but in the end enjoy – established workflows should do so from a broader change management perspective. Successful deployments will depend on properly training existing staff, hiring new team members as needed, and raising awareness of the impact new technologies will bring on organizational culture.
The use of AI / ML is linked to personalization goals
Generally speaking, digital marketing leaders see the impact of AI / ML through the prism of personalization. Eighty-four percent of those polled in the Gartner survey agreed or strongly agreed that the use of AI / ML improves marketing’s ability to deliver experiences personalized in real time to customers. When asked about the most important use cases for AI / ML-based tools, respondents focused on the value of these tools in bringing automation, scalability and efficiency to marketing activities across all channels. They cited specific activities related to broader personalization efforts, including:
- Provide predictive content (45%)
- Creation of campaign journeys / journeys based on business objectives (45%)
- Develop channel propensity models based on customer profiles, behavior and preferences (45%)
- Identification of audiences and segments most likely to engage (43%)
Successful personalization requires an understanding of what customers are trying to achieve in their interactions with your brand. This information should inform strategy on how personalization can help customers achieve their goals and align customer needs with business goals.
Personalization requires a deliberate and thoughtful 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. Marketers need to 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 improving the relevance of marketing engagement and increasing influence over customer behavior.