In a previous blog post, we explored the rapid growth of marketing technology applications and the corresponding explosion of marketing data. Marketing teams today have a proliferation of data silos supporting a diverse portfolio of marketing technologies. It’s not unusual for an enterprise to be using twenty or more best-of-breed apps for marketing in addition to enterprise applications like ERP, customer relationship management, and workforce management.
A recent article in McKinsey Quarterlysuggested 90% of all the data in the world has been collected in the last two years and, of that, only 1% has been analyzed. While the ratio may not be exactly right, this clearly applies to data for marketing. In fact, in a separate report, McKinsey Analytics found that, while Big Data and analytics were having some impact across all industries, sales and marketing was identified as the one business process affected across all industry sectors. In assessing the impact of marketing analytics on business performance, Forrester reported sophisticated marketers are investing in integrated marketing platforms. These marketers understand linking marketing technology solutions and data sources is critical to establishing and maintaining leadership in their industries.
A Complete Marketing Analytics Framework
The modern marketing data warehouse has the flexibility to collect, transform and store data of many types from multiple sources. It accommodates structured and unstructured data streamed in real-time or delivered in batch. When deployed in the cloud using Google’s BigQuery, for example, it can scale with the needs of the business, and do it affordably. Using tools like Cloud DataPrep and Cloud DataFlow to ingest, clean, and align data, marketing teams, and their IT support can flip the traditional data warehouse model on its head. Where once they spent 80% of their time managing, collecting, and cleaning data, they now spend 80% of their time on analysis and implementing marketing programs. Read more about Maven Wave’s approach to a modern data platform in this white paper.
Analytics and visualization extract insights from the marketing data warehouse and deliver them in a form that’s understandable and actionable. Marketers can see how their programs are performing, fine-tune offers and creative through testing, adjust to changing conditions, and take advantage of new opportunities based on constantly-refreshed data. Modern analytics and visualization tools like Google’s Data Studio put data in the hands of the marketing team, further reducing time-to-insight.
Advanced analytics, driven by machine learning and supported by scalable cloud computing resources now give marketers unprecedented power to extract insights. In the 1980’s, when modern point-of-sale systems were introduced, available computing power limited analysis of grocery shopping data to just a handful of stores. Today, data science teams at leading retailers are applying machine learning techniques to data from millions of shopping baskets at thousands of stores. They’re fine-tuning product assortments at the store level to give shoppers what they’re looking for and delivering valuable, personalized offers.
Scalable cloud computing power and machine learning techniques also help marketers activate offers in innovative ways. Predictive models help marketers automatically select the next best action for each step of a customer’s journey and deliver it through the right marketing channels like content marketing, website experiences, contact center scripts, location-specific mobile marketing, and social media. Integrating these model-driven actions through marketing technology applications closes the loop with the customer. Knowing their preferences and behaviors helps marketers deliver a customer experience that maximizes their lifetime value to the business.
Implementing this Marketing Analytics Framework requires a partnership between marketing and IT. It must balance showing results in the short term to secure continued support from company leadership with long-term marketing and technology strategies. We’ll explore this topic in the next blog post in this series.