Turning Customers into Loyal Advocates with Agentic Marketing

As marketers, we face a fundamental problem: the ever-widening ‘execution gap’. Today’s customers expect more from brands than ever before. They want personal experiences, seamless handoffs, empathetic conversations, and for brands to adapt to their needs in vulnerable moments. 

Rapid advancements in AI are increasing these expectations. According to the State of the AI Connected Customer report, seventy-nine per cent of consumers say the brands they’re most loyal to must actively demonstrate they understand and care about them. 

However, executing on these expectations is overwhelming, especially when an incredible 41% of workers’ time is lost to low-value, repetitive tasks. Combined with challenges like fixed team capacity and traditional tech stacks, it is difficult for marketers to build meaningful customer loyalty.

The good news is that the tide is turning. Agentic AI bridges the gap between expectation and execution and provides an endless opportunity to build loyalty throughout the marketing lifecycle. It does this through the use of intelligent agents, which use machine learning and natural language processing (NLP) to handle a wide range of tasks. 

In this blog, I will share practical examples of how agentic AI can be used at each stage of this lifecycle to drive efficient execution and continuous loyalty. 

Agentic AI for every stage of the customer journey

Awareness: Creating target audience segments in minutes

The awareness stage is where a customer first discovers a brand, often through channels such as search, social media, or word-of-mouth. Traditionally, digital marketers spend hours manually segmenting audiences, recreating lookalike audiences, and handling data to drive discovery.

Agentic use cases: Agents can fill these operational gaps, allowing marketers to focus on what’s important. For example, with Agentforce, marketers can use natural language prompts to describe the target audience they want, and the agent will translate that into the appropriate segment attributes. 

Consideration: Personalising recommendations at scale

I recently moved into a new place and needed new appliances, so I headed to my local retail store. The salesperson guided me through the options, answered my questions, and helped me identify what was best for my needs. That’s what consideration is, helping customers weigh up options and make informed decisions.

In the digital world, the consideration phase involves setting up product recommendation engines, building customer journey flows, constantly A/B testing, and refining and personalising content to build engagement. This process is time-consuming and often inefficient.

Agentic use cases: Agents automate and scale engagement during the consideration phase. They use real-time behavioural data to serve personalised content, comparisons, and recommendations. So customers receive the right information at the right time, while marketers are free to focus on strategy.

Conversion: Optimising the path to purchase

Now the question is, did I buy those appliances? You bet I did. I converted in the moment because of the salesperson’s phenomenal customer service. 

Driving conversion online can be an ongoing, labour-intensive process involving building and optimising conversion funnels, setting up retargeting ads, and manually adjusting offers and pricing strategies to reduce drop-off. 

Agentic use cases: Agents can automate and optimise engagement and hyper-personalise offers to create a seamless transition from decision to purchase. They can dynamically adjust pricing, trigger urgency-based incentives, and streamline checkout by reducing unnecessary steps.

Retention: Streamlining loyalty programs & engagement

Retention is about keeping a customer engaged through personalised experiences and continued value. Loyalty programs play a key role. However, marketers often manage these programs manually, tracking customer activity and segmenting audiences for rewards. 

A Loyalty Management System (LMS) helps by centralising and streamlining program management, but requires access to unified data to deliver truly personalised experiences. 

Agentic use cases: For organisations without large development teams, agents can automate heavy-lifting tasks. For instance, agents can enable integration with third-party systems via event-driven architectures. Marketers can also chat with agents to analyse redemption and issuance trends. 

Salesforce Loyalty Management brings the power of agentic AI to loyalty programs, along with features like Global Promotions Management that streamline tasks like segmentation and enable personalised offers based on member data.

With humans and agents working together, organisations can drive loyalty forward with minimal resource requirements, fostering lasting customer relationships and increased customer lifetime value.  

Advocacy: Fueling organic growth with automated referrals

In the advocacy stage of the marketing lifecycle, a customer shares their positive experience with others, influencing organic growth. Marketers can also link advocacy to loyalty and uncover more ways to reward brand advocates.

However, marketers often track feedback and identify brand advocates manually. This is unfortunately often deprioritised due to the overwhelming number of data sources required.

Agentic use cases: Agents can make advocacy scalable and impactful. Agents can automatically identify satisfied customers, prompt them to share their experiences, and offer personalised rewards for referring a friend. Data Cloud powers these use cases, acting as the unified data decisioning layer. 

Accelerate your agentic future

Agentic AI is not hype, it’s a competitive advantage. If you’re not already strategically planning and integrating it into your daily operations, you’re falling behind your competitors. 

Here are some simple steps to help you move forward in your agentic journey: 

1. Audit your processes: List time-consuming marketing tasks related to the lifecycle pillars. This list becomes your action plan for transformation.
   
2. Map your martech stack: Identify which of your current platforms have built-in AI capabilities; see where agents can integrate and where the gaps are.
   
3. Evaluate and implement: Determine if you can use existing vendor solutions or if you need to go to market. Start with quick wins to measure the ROI of agentic solutions.
   
4. Optimise and scale: As you redistribute tasks to digital labour, measure the hours you get back to focus on strategic marketing. Refine your approach and scale agentic solutions for maximum impact.

The bottom line is that with agentic AI, the execution gap is no longer a barrier to meeting customer expectations. By embracing agentic marketing, you can unlock new levels of efficiency and create long-lasting customer loyalty.