Transitioning to Data Activation in Digital Marketing

This is the third article in my series about Digital Marketing Transformation . After that, I covered the complexity of the marketing technology world .

The effectiveness of digital marketing is determined mainly by the level of your creativity and the extent to which you use data and insights to know how to engage your audience.? Success comes down to something quite simple: be informed and make the right decisions.

Easier said than done, like everything in the digital world. Being informed means understanding and leveraging data as you shift towards data-centric marketing strategies, which start with data analytics and then expand into data activation. Many digital marketing executives are adept at using analytics tools like Google Analytics, but the transformative step towards data activation often seems like a bridge too far.?

So, what does it mean to shift from data analytics to data activation?

The Current State of Analytics

Most marketing teams have integrated analytics tools into their strategies. These tools provide essential insights into consumer behaviour, campaign performance, and other key metrics. This is great, but it is important to recognise that analytics is predominantly about hindsight, about understanding past behaviours to infer trends.? Here are some categories of analytics.

• Surface Analytics: Examining historical data for insights.
• Predictive Analytics: Forecasting future trends based on past data.
• Prescriptive Analytics: Suggesting actions to achieve desired outcomes.

Only a handful of marketing teams can look you in the eye and tell you that they are excellent at any of these, which warrants some comment about the possible reasons.

1. Skill Gap: There's a significant skill gap when it comes to data analytics. Not every marketer can interpret data or have the statistical background to derive actionable insights from data sets.?
2. Resource Constraints: Many marketing teams need more resources. They may need more time or personnel to delve deeply into analytics and move beyond surface-level insights.
3. Overwhelming Data: The sheer volume of data can be overwhelming. Marketers may struggle to distil vast amounts of data into actionable insights.
4. Lack of Real-Time Capabilities: Traditional analytics often involve looking at past data. However, the real value lies in real-time analytics and reacting quickly to consumer behaviours. Not all analytics platforms offer robust real-time analytics features.
5. Silos: Data silos within an organisation can hamper the effectiveness of analytics. When data isn't shared freely among different departments, getting the holistic view necessary for more advanced analytics is hard.
6. Resistance to Change: Transitioning from a traditional marketing mindset to a data-driven approach can meet with resistance. Change is hard and requires a shift in culture, attitudes, and practices.
7. Lack of Clear Objectives: Without clear objectives, it’s challenging to know what data to focus on and how to interpret it. This lack of direction can lead to the underutilisation of analytics tools.
8. Technology Limitations: Sometimes, the limitation lies in the technology itself. Not all analytics tools are created equal; some may lack the sophisticated features needed for more advanced analytics.
9. Privacy Concerns: With increasing regulations around data privacy, some marketing teams might be cautious in how they collect and use data, which might restrain their analytics capabilities.
10. Insufficient Integration: When analytics tools are not well-integrated with other marketing technology, friction can lead to under-utilization or incorrect interpretation of data.

Knowing which afflictions are your own is the first step towards elevating your marketing team's analytical capabilities and transitioning from mere data collection to actionable data activation.??

But now that you are suitably depressed let’s move on and talk about the main topic of this article because there is hope.? I believe you can get significant wins by having basic surface analytics in place and focusing on data activation instead. And without meaning to sound like there is even more silver lining, if you’re interested in leveraging Generative AI in your marketing workflow, then the effort towards activation will give you double dividends.

The Leap to Data Activation

Data activation is the process of utilising data insights in real-time to drive marketing strategies. It moves beyond mere analysis to operationalise data for optimising campaigns, personalising interactions, and improving overall marketing performance.

• Real-Time Engagement: Engage with audiences in real time using data-driven insights.
• Segmentation and Targeting: Use data for precise audience segmentation and personalised messaging.
• Performance Optimization: Adapt campaigns continuously based on real-time feedback.

Below is a table that contrasts the features of a conventional digital analytics tool such as Google Analytics with those of a Data Activation Platform, for example, a Customer Data Platform (CDP).

Digital analytics tools like Google Analytics primarily focus on collecting and analysing data to provide insights, and Data Activation Platforms like Customer Data Platforms (CDPs) are engineered to unify data from various sources and activate it in real-time to drive marketing strategies. Transitioning from analytics to activation entails leveraging insights gleaned and operationalising these insights across different marketing channels in real-time, which is where a Data Activation Platform, particularly a CDP, shines.

Implementing Data Activation

Transitioning to a data activation mindset involves more than a technical shift. It requires an organisational culture that values data as a dynamic asset.

• Invest in Technology: Opt for platforms that facilitate real-time deployment of insights.
• Cultivate a Data-Driven Culture: Encourage teams to operationalise data, making data activation a standard practice.

Here is a scorecard that can help marketing teams understand their readiness for data activation.

Hit everything in the right column, and you’re ready to go.?

Motivating for Data Activation Budgets

Here are some of the ways investing in data activation will please your CFO:

1. Customer Retention Rate: Using data to understand customer behaviours and preferences, marketing strategies can be fine-tuned to improve customer satisfaction and retention.
2. Conversion Rate: Data activation can help in personalizing marketing messages, thus potentially increasing the conversion rate of leads into customers.
3. Customer Lifetime Value (CLV): With more personalized interactions and better customer experiences driven by data activation, the lifetime value of customers can be enhanced.
4. Return on Marketing Investment (ROMI): By optimizing marketing campaigns through real-time data insights, the return on marketing investment can be improved.
5. Average Order Value (AOV): Data activation can help offer targeted upsells and cross-sells, increasing the average order value.

If you don’t believe me, give this case study of my team's work using SalesForce Marketing Cloud a read.

The journey from data analytics to data activation is a crucial phase in digital marketing transformation. It sets the stage for leveraging emerging technologies like Generative AI and Machine Learning, which promise to automate and enhance data activation processes.

As I go deeper into digital marketing transformation in the following article, I will look at the fusion of AI, ML, and data science with digital marketing. This blend augments data activation and opens avenues for automating and personalising consumer engagement at an unprecedented scale. Stay tuned.

Read part 1 here: Why Digital Marketing Needs Transformation

Read part 2 here: Navigating the Spaghetti of Marketing Technology