Marketing transformation: Data is a prerequisite
This blog post is part of a four-part series that will attempt to give you a better idea of the key components that you need to take into consideration to accelerate your marketing transformation with data. It will focus specifically on the data component. Upcoming blog posts will focus on other areas like culture and processes, talents, and finally technologies (platforms).
In his book Digital Transformation: Survive and Thrive in an Era of Mass Extinction, Thomas M. Siebel defines digital transformation as the intersection between cloud computing, big data, the internet of things, and artificial intelligence. In other words, digital transformation is the process of using digital technologies to transform your business processes and your organization.
According to the American Marketing Association (AMA), marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large.
Marketing transformation can be defined as the process of using digital technologies to transform your marketing processes (activities). These processes support the following goals: acquiring, growing, and retaining valuable customers.
The marketing function is faced with multiple challenges that need to be tackled in the coming months and years.
- Acceleration of digital transformation initiatives: COVID-19 has accelerated the volume of digital transformation initiatives. There is a growing shift of marketing budgets towards digital channels. According to Gartner, 72% of marketing budgets are allocated to digital channels (Gartner: Annual CMO Marketing Budget Report 2021). Massive investments are directed towards IT to accelerate initiatives linked to the digital economy (e-commerce, online learning and on-boarding, automation, collaboration tools). Building cross-functional initiatives between IT and marketing is key to ensuring the success of marketing technology initiatives.
- Continuing pressure to deliver results: CMOs are expected to deliver more results with almost slightly the same year-over-year annual budgets (Gartner: 5 Strategic Priorities for CMOs). Intensifying competition and demanding customers force organizations to focus on improving their customer experience while looking for faster returns on their marketing investments.
- Growing difficulty of measuring return on digital marketing investments: The changing technological ecosystem (ITP 2, cookie apocalypse, App Tracking Transparency) linked to privacy regulations (GDPR, CCPA) is forcing organizations to adopt new technologies and methodologies to measure the performance of their digital initiatives.
- Need to reduce operational costs: Following the financial impacts of COVID-19 on all organizations, most of them are looking to optimize or reduce their operational costs through automation to drive efficiency (do the same with less).
- Lack of digital talents: This forces organizations to reassess how they operate. Usually this lack of talent tends to slow digital initiatives.
Typical business questions that marketers want to answer:
- Who is my target customer?
- How can I acquire a valuable customer?
- How can I grow the value of my customer base?
- How can I retain my valuable customers?
The role of the data is to answer all these key questions that impact the performance of an organization.
Usually you will need to acquire the following categories of data in order to answer the above questions:
- Customer data: socio-demographic profile, location, life stage
- Financial data: customer profitability and lifetime value
- Competitive data: market share, competition performance
- Behavioural data: mobile, web, offline, email, product usage
- Advertising data: Google Ads, Facebook Ads, etc.
- Sales data: monetary transactions
- Product and promotion data: product pricing, promotions, and discounts
- Attitudinal data: needs and wants of the customers, brand perception
The role of marketing science or marketing analytics is to transform raw data into valuable and actionable insights for marketers and the organization at large. Marketing analytics can encompass many well-known disciplines, such as:
- Market research
- CRM and marketing database analytics
- Data engineering
- Data science
- Digital analytics
- Experimentation (A/B testing, multivariate)
- And more
Unfortunately for all platform and technology lovers, the success of an analytics project doesn’t start with the adoption of a specific technology or platform. The first and most critical step is defining your data strategy. This strategy can cover the acquisition of first-party data, advertising data, competitive data, etc. The data strategy ensures that your data are supporting your business objectives (the only path to ensure that your data become a business asset). A successful data strategy should show that the data will bring value to the business.
You need a plan to define what data you need to support your marketing and business objectives, how you will use them, and how you will acquire them while ensuring that you are following all laws and privacy guidelines. Once this step is completed, you will need to build a detailed road map. The choice of your technology (platform) and talents (people) will be guided by your business and data strategy. Don’t underestimate the impact of your organization’s culture—it can derail any execution of your data strategy (look for champions to help you sell the strategy).
Focusing on technology before the data strategy is a recipe for disaster. Make sure you define your data strategy before embarking on a customer data platform, marketing data warehouse/lake, self-service analytics, or AI project.
A successful use of data in the context of marketing transformation should touch on the four pillars of marketing performance:
- Marketing strategy: business goals, marketing goals, tactics and initiatives
- Marketing and campaign planning activities: budgets, targets, initiatives
- Measurement and analysis of marketing initiatives: dashboards, reports, insights
- Optimization of marketing initiatives: A/B tests, scenario analysis
The combination of data, cloud computing, talents, and AI should make these processes more efficient (cost reduction) while keeping them effective (reaching the organization’s business goals). The approach is to centralize data and automate most of the activities related to the data life cycle in order to accelerate time to value through data activation (insights, targeting, personalization, self-serve analytics).