Why you need to automate marketing analytics

In our last post, we took a closer look at marketing analytics – what it is, why you need it, and how to use it. However, there’s another important aspect to consider in any data analytics process: automation.

What is marketing analytics automation?

Rather than manually collecting and analyzing data from marketing activities to improve campaign ROI, which as we all know is unsustainable, you can use data automation to level up these processes.

Automation harnesses computational power to collect and analyze data, as well as carry out repetitive tasks at scale, such as sending marketing emails and generating social media posts. Marketing analytics software can also use AI to provide deeper insights, optimize outcomes, and increase ROI on marketing activities.

READ MORE: Realizing the ROI of data automation

It’s tempting to believe that marketing analytics automation is more useful at the middle of the marketing funnel – for instance generating leads via automated emails. In reality, however, marketing analytics should be integrated into the funnel from top to bottom, so that you can build more lucrative and long-term relationships with your customers.

Software for marketing analytics

To apply automation and AI techniques to your marketing data, you’ll need to implement specialist software. These tools enable you to see which factors are driving your KPIs, conversions, and brand awareness. Marketing analytics automation also improves the customer experience, as it enables you to serve personalized ads.

Marketing analytics software can use different methodology depending on the KPIs you need to track. For example, measuring conversions uses different data compared with measuring brand awareness. Types of marketing analytics models include:

• Multi-touch attribution (MTA) – This model involves measuring the impact of each touchpoint along the customer journey according to your particular KPIs.
• Media mix models (MMM) – Also known as ‘marketing mix modeling’, this uses multi-linear regression to find relationships between dependent variables, i.e., your KPIs, and independent variables, such as ad spend. MMM works best using at least three years of historical data, to account for seasonality.
• Unified marketing measurement (UMM) – If you need to manage multiple campaigns across multiple media channels, you need to move beyond a single model to measure attribution. UMMs integrate different models, such as MTAs and MMMs, to provide a unified view of all your campaigns and their impact on KPIs.

How to choose software for marketing analytics

The software works by recording user actions, such as navigation on your website, app, and campaign emails, then aggregating these into a database for modeling. All marketing analytics tools should include three core parts:

1. A central database – A storage location for all of your marketing data on user behavior. You should also be able to segment users into different audiences, so that you can target them with different messaging.
2. An engagement engine – This is a centralized location where automated marketing processes, including both online and offline channels, are created and managed.
3. An analytics engine – This is what produces the models for understanding your data and campaign effectiveness. Here, you can test, measure, and optimize campaigns to maximize your desired KPI, such as ROI.

When shopping around for your ideal marketing analytics solution, make sure it has the following features:

• Real-time marketing – This combines behavioral analytics and automated marketing to serve users with the right offer at the right time, based on their past actions. This is particularly useful in customer retention.
• Brand measurement – The ability to define a brand’s standing in the market, by examining its products and competitors. Metrics can include appeal, association, awareness, loyalty, reputation, recall, and trust.
• Granularity – This usually means analyzing data at the individual level, but can also mean the depth of data available for sub-campaigns, creatives, or keywords.
• Both online and offline – Enabling you to understand the correlation between online and offline attribution metrics.
• Contextualized insights – Customer and market insights can only be understood by building connections with other data points. This enables you to judge whether an insight is significant to your goals.
• Forecasted media recommendations – The ability to make informed predictions about the performance of future marketing campaigns.