B2B CMOs Should Strategically Apply AI in Marketing to Drive Growth
Summary
Using Artificial Intelligence (AI) to improve personalization and customer journeys are leading priorities to drive revenue growth for B2B CMOs. However, the most widely available applications of AI in marketing are for more tactical and operational use cases such as lead scoring and propensity to buy. Access to data of suitable quality and a more holistic approach to AI infused in the marketing technology stack is key to realizing revenue growth. This research details three key success factors and how to achieve them to enable AI-powered revenue growth in Marketing.
Actionary Take: Marketing leaders must change their approach to martech stack innovation, and address the fragmented data reality, else they will fail to realize AI-empowered growth.
B2B CMOs STRUGGLE TO DRIVE GROWTH USING AI INNOVATION
Forrester’s 2022 Global AI Software Forecast states that off-the-shelf and custom AI software spend will double from $33 billion in 2021 to $64 billion in 2025. It will grow 50% faster than the overall software market and 18% annually. Much of the spend will be by CMOs who agree that AI is central to marketing success. B2B buyers are leaving a large digital trail of actionable intent data. However, B2B CMOs are struggling to realize AI-powered growth because:
- CMOs need better data access. According to a Forrester study commissioned by Eyeota, on harnessing first-party data in customer engagement strategies, most companies lack a consistent, data-centric approach to onboarding and identifying new customers. Only 27% surveyed consider their data strategy mature. (see endnote 4) Businesses struggle with siloed and incomplete data, not of the right quality or providing the right level of detail to support potential Marketing AI use cases. Especially those for targeting strategic revenue growth such as AI transformed creative, content personalization at scale and conversational AI. Delivering the Next Best Experience (NBX) to improve customer lifetime value (CLV) requires data that informs a deeper connection and understanding of B2B buyer needs. (see endnote 6).
- CMOs have been slow to innovate the martech stack. Marketers are demanding solutions and features faster than existing martech platforms can provide them, and CMOs can afford. There are now nearly 10,000 martech solutions. There’s a tendency to adopt the next exciting or innovative marketing tool every few months on an operational or tactical basis. (see endnote 1) Fragmented investment means the martech stack is not being used to its full potential. A majority of B2B marketers — 69% of respondents in a recent survey — believe “the perfect marketing stack does not exist yet.” And it never will. (see endnote 3) CMOs are hesitant about investment in budget-draining AI implementations.
- CMOs lack confidence in evaluating AI technology. There are hundreds, if not thousands, of potential use cases for AI in marketing. In a recent survey, by the Marketing Institute of Artificial Intelligence, nearly half (45%) of all respondents say they’re still beginners when asked to classify their own AI knowledge and capabilities. (see endnote 2) The same report states only 29% of respondents have high or very high confidence in evaluating the AI-powered marketing technology that makes their desired outcomes possible and when asked what was stopping AI adoption, 52% cited lack of awareness of AI capabilities and use cases — up 6% from 2021.
THREE SUCCESS FACTORS WILL IMPROVE STRATEGIC USE OF AI
To optimize revenue growth, CMOs must offer B2B customers more personalization, more B2C-like intelligent customer journeys and realize improved staff AI augmentation benefits. B2B CMOs have three levers at their disposal:
- Data literacy: Learning to “speak” data. Achieving growth ambitions using AI in Marketing requires access to data of suitable quality, and the right level of detail. B2B CMOs have an extraordinary amount of responsibility for the procurement and management of customer data, however, AI solutions require not just clean data, but data that can be structured for ingestion by AI solutions correctly — there’s a usable data gap. Marketing team “citizen” data scientists are required to aid in the organizing and preparing of data. (see endnote 5) B2B CMOs will need to solve the data challenge to gain a competitive edge and achieve growth ambitions using AI.
- Martech alignment: Improved Martech stack alignment with AI goals. AI shouldn’t be viewed as a product over technology that can form part of an ecosystem of existing martech tools and make them better. B2B CMOs will need to lead investments, in existing stack technologies and new, such that each additional AI component complements others. This will take some trial and error to get right, however, today’s ‘try before you buy’ trend will make this possible economically. Platform, content management, CRM, analytics, social media apps are all candidates for improvement using AI technologies, however, there must be clearer alignment to benefits, based on each newly composed solution goals, to be easier to justify investment in.
- Use case clarity: Improved understanding of AI use cases. B2B CMOs must upskill through investments in the Marketing organization being more knowledgeable about AI-powered use cases beyond predictive and prescriptive AI solutions. To optimize revenue growth, CMOs must offer B2B customers more consumer-like intelligent customer journeys and staff augmentation benefits. Meaningful and precise problem definitions will help inform clearer and more relevant AI use cases: Instead of, ”How do we reduce churn?” CMOs must ask, “Given a budget of $x million, which customers should be targeted with a retention and growth campaign?” A clear understanding of how to improve marketing performance using AI will inform an ability to then evaluate which AI-powered marketing solution can best support use cases.
B2B CMOs MUST INVEST IN ALL THREE SUCCESS FACTORS TO DRIVE BETTER AI ADOPTION FOR STRATEGIC GROWTH
CMOs have a more hopeful outlook than other C-level executives who often believe that marketing leaders lack business and financial understanding. B2B CMOs must overcome this perception to participate in key decision making and activate three success factors of AI’s strategic use in marketing. B2B CMOs must:
- Form alliances and partnerships with data strategy peers. Marketing represents the voice of the customer. Executive leaders across enterprise functions need to consider that voice in their business decisions. B2B CMOs should form better alliances and partnerships, they haven’t historically had with other C-suite leaders, to ensure recognition as a key stakeholder in the overall company data strategy. Based on these renewed C-suite relationships improve data analysis and interpretation for AI applications within the marketing team and improve the ability for staff to “speak data”, invest in AI dedicated resources like citizen data scientists, collaborate with peers and amplify data and insights powered decision making. (see endnote 5) Get employee buy-in and trust for the data strategy for Marketing AI with inclusiveness and transparency.
- Use a framework for strategic martech investment. B2B CMOs should consider technology in the context of the core marketing capabilities required to support growth goals. Using Forrester’s Design Your Martech Stack To Deliver The Optimal Buying Experience research, B2B marketers can leverage the customer lifecycle as a design framework and consider their customer’s buying motion as a design muse to engineer the optimal buying experience. (see endnote 3) B2B CMOs should leverage automated machine learning (AutoML) solution providers such as Akkio, DataRobot and Google Cloud AutoML who offer ‘try before you buy’ SaaS-based solutions for forward looking martech investments and to execute more quickly on innovation ambitions.
- Invest in strategic growth Marketing AI use cases. Look to existing and new AI vendors who provide AutoML solutions and better support in designing growth focused machine learning models, to inform a strategic AI use case approach. B2B CMOs should sign up for demos with existing martech solution providers to learn about AI use case roadmaps and learn what’s coming in the industry. Hire, or migrate existing staff to become, citizen data scientists in the Marketing team. Set their goal to discover use cases that support overarching growth ambitions such as fine-tuning content, learning buyer preferences, offering relevant recommendations and assisting in real-time engagements.
