20 years of Unrealised Promises (Part 2)

Unrealised Promises (Part 2): Stopping the hype flywheel and setting ourselves up for a successful future!

This post is a follow-on to my previous exploration of the past “20 Years of Unrealised Promises” in marketing automation and customer data, where I highlighted some of the major trends that I believe didn’t quite deliver on their promises. As many of us have experienced, the evolution of technology doesn’t always equate to immediate success, and many “breakthroughs” fell short due to gaps in technology, data limitations, or over-hyped expectations. Now, as the MarTech landscape continues its move forward, new trends have emerged with similarly ambitious claims. 

So, how can businesses approach today’s trends in a way that maximises their potential without repeating the pitfalls of the past? In this piece, we’ll examine the current buzz in MarTech and discuss the strategic steps businesses should take to navigate these innovations with a clear-headed approach – bearing in mind that technology must keep pace for these trends to reach their full potential. 

The Power (and Reality) of Generative AI in Marketing 

Generative AI is the latest buzzword in marketing automation, with tools like OpenAI’s ChatGPT and Google’s Bard, as well as industry-specific solutions, promising to change how brands engage with customers. These systems can create personalised content, power chatbots, and even assist with customer segmentation. But using generative AI effectively involves more than simply adding it to your existing tech stack. 

Take steps to succeed: 

• Define clear use cases: Just as past AI tools were often adopted without a specific purpose, businesses should be mindful not to use generative AI just for the sake of it. Start by defining specific objectives, whether it’s enhancing email marketing, improving customer service, or creating more dynamic social content. 

• Invest in data quality: Like any AI, generative models thrive on high-quality data. Clean, accurate customer data will result in more relevant AI-generated outputs. Make sure your data is up-to-date and representative of your target audience. 

• Implement human oversight: AI-generated content should be treated as a draft, not a final product. Assign team members to review, refine, and ensure the outputs align with your brand’s tone and standards. 

• Use a feedback loop: Regularly review the content generated by AI and adapt its training based on what resonates with customers. AI’s strength is its adaptability, but only when it’s fed continuous feedback. 

Generative AI may hold transformative potential, but it’s essential to use it as an augmentation tool rather than a replacement. Human oversight remains critical to ensure the technology enhances rather than diminishes brand integrity. 

Customer Journey Orchestration: Moving from Silos to Seamless 

Customer journey orchestration is the latest take on omnichannel engagement, promising to connect every customer touchpoint in real-time. Platforms like Adobe Journey Optimizer and Salesforce’s Customer 360 aim to achieve this by adapting to customer behaviours seamlessly across digital and physical spaces. However, businesses need to move carefully to avoid the same challenges that hindered omnichannel marketing’s early promises. 

Take steps to succeed: 

• Audit data sources: For effective journey orchestration, you need a full understanding of where customer data lives. Conduct a data audit to map your data flows and identify any silos across systems. 

• Invest in real-time integration: Journey orchestration relies on real-time data to be effective. Consider prioritising integration efforts between your CRM, POS, and marketing platforms to create a cohesive picture of your customers. 

• Focus on high-impact interactions: Rather than overextending by connecting every interaction, start with touchpoints that have the most influence on the customer journey and invest in orchestrating those first. 

• Set measurable goals: Define clear KPIs, whether for reducing churn or increasing conversions, to ensure the technology delivers value. 

Customer journey orchestration has incredible potential, but businesses should start with manageable steps, monitoring outcomes closely before scaling up to more complex interactions. 

Predictive Analytics: Learning from Past Challenges 

Predictive analytics has evolved since it first gained attention, now leveraging advanced machine learning algorithms to deliver more accurate forecasts. Today, predictive tools claim to offer insights into customer behaviour, from churn prediction to next-best-action recommendations. But, as history has shown, predictive analytics is only as good as the data behind it. 

Take steps to succeed: 

• Prioritise data hygiene: Predictive analytics relies on accurate data. Invest in processes to ensure your data is clean, consistent, and up-to-date. Regular audits can prevent inaccurate predictions that could harm customer relationships. 

• Focus on relevant business goals: Predictive analytics has applications across many areas, but it’s essential to choose the most impactful use cases, such as upselling, customer retention, or inventory management. 

• Foster collaboration across teams: Predictive analytics doesn’t sit solely with marketing; its insights can benefit sales, customer service, and even product development. Involving cross-functional teams will enhance both implementation and impact. 

• Use predictions as a guide, not a rule: Predictive insights should inform decisions, not dictate them. Blend predictions with strategic thinking to ensure they align with broader business goals and values. 

When approached thoughtfully, predictive analytics can bring considerable value. But it should be one component of a broader strategy, not a standalone solution.

Personalisation at Scale: Aiming for Quality over Quantity

Personalisation is still evolving, and today’s platforms promise hyper-relevant messaging for each customer across channels. However, achieving high-quality, large-scale personalisation without compromising customer experience is a fine line, particularly with privacy concerns rising. 

Take steps to succeed: 

• Segment smartly: Large-scale personalisation doesn’t mean addressing each individual customer as a unique segment. Smart segmentation based on meaningful traits will make personalisation more manageable and effective. 

• Prioritise relevance: Personalisation is about delivering useful, timely information and not just inserting the customer’s name. Use data to understand what information is most relevant to each segment, and tailor content accordingly. 

• Maintain privacy compliance: Stay transparent about how customer data is used and ensure compliance with regulations like GDPR. Failing to respect privacy will hurt trust and, ultimately, your brand. 

• Test and learn: Personalisation should evolve with customer needs. Use A/B testing, monitor engagement metrics, and refine your approach based on actual results. 

Scaling personalisation is possible, but it’s crucial to prioritise relevance over sheer volume. Content that resonates will have a far greater impact than delivering “personalised” messages that feel impersonal. 

CDPs and the Pursuit of a Unified Customer View 

Customer Data Platforms (CDPs) continue to promise the elusive “single view of the customer,” essential for informed decision-making across marketing, sales, and customer service. While CDPs can help overcome data fragmentation, many businesses find the implementation process challenging and resource-intensive. 

Take steps to succeed: 

• Evaluate need carefully: Not all businesses need a full CDP to achieve data unification. Assess whether your existing tech stack can meet your current needs with minimal adjustments. 

• Ensure compatibility: A CDP is only effective if it integrates seamlessly with other systems. Ensure your CRM, email, and analytics platforms can connect to the CDP without sacrificing data integrity. 

• Emphasise data governance: CDPs rely on consistent, high-quality data. Establish data governance practices, ensuring regular updates and audits to maintain data accuracy. 

• Focus on actionable insights: The unified customer view is valuable when it translates into actionable insights. Define how you’ll use this data, and set KPIs to measure the CDP’s impact on business objectives. 

CDPs offer potential, but their success hinges on strategic implementation and ongoing data management. 

Moving Forward with Realistic Optimism 

As you saw in my previous post, the martech landscape is filled with promise – and often unrealised promises – but also risk. Today’s trends, from generative AI to customer journey orchestration, hold incredible potential, yet businesses must navigate them with realism and purpose. The success of these tools depends on setting clear objectives, ensuring high-quality data, and keeping a focus on the human element. 

By approaching these trends strategically, businesses can achieve sustainable progress and avoid the pitfalls of overhyped promises. As technology evolves, the key is to maintain a balance between embracing innovation and grounded execution – ultimately using technology as a tool to elevate, not replace, customer understanding and engagement.