The CMO Survey found that “marketers are still challenged to maximize the potential value of analytics.” A high-performance research study by Accenture found that companies that invest heavily in their analytic capabilities outperform the S&P 500 on average by 64%. How are the high performers different?
They possess these four characteristics.
- Above-average analytical skills.
- Better decision support analytical tools.
- Highly value analytical insights, which seems obvious, or they wouldn’t have invested in the first two.
- They use analytics across their entire organization, including sales and marketing.
Julio Hernandez, a partner at Accenture, says, “Companies need to be analytically inclined and data-driven in order to turn insights into action for driving growth.”
4 Types of Analytics
The most common meaning of analytics is the use of a “scientific process of discovering and communicating the meaningful patterns which can be found in data” in order to improve decisions. Analytics involves logical analysis. Research, measurement, and analysis are the domains of analytics. You begin with a question, create a hypothesis, design a test, collect the data, measure the results, and identify patterns.
By using analytics, you can identify potential trends, evaluate options and/or performance, and assess the effects of certain decisions. The key thought here thoug,h is that analytics today entails the skills and process of deriving actionable, practical insights by applying math and statistical models against existing and/or simulated future data.
Analytics are commonly organized into four types:
- Descriptive analytics. Descriptive analytics answers the question of what happened by analyzing raw data from multiple data sources to give valuable insights into the past. From descriptive analytics, you can learn whether something is wrong or right, but not know the reason why.
- Diagnostic analytics. Through diagnostic analytics, you can drill down into the data to find out dependencies and to identify patterns. Now you can begin to understand why something happened.
- Predictive analytics. Once you have descriptive and diagnostic analytics, you can begin to detect tendencies, clusters, and exceptions in order to predict future trends. Predictive analytics is about understanding what will likely happen.
- Prescriptive analytics. With prescriptive analytics, we enter the realm of literally prescribing what action to take to address or offset a future problem or take full advantage of a promising trend.
Address 5 Growth Opportunities
Tom Davenport and Jeanne Harris in their classic book, Competing on Analytics: The New Science of Winning, introduced business leaders to analytics and explored how analytics is rewriting the rules of competition. They provided a road map for becoming an analytical competitor and how to use analytics to create value and growth. Value and growth fall into the purview of Marketing.
Marketers who want to leverage the value of analytics should address at least five growth opportunities:
- Acquisition of more valuable customers
- Acquisition of customers who will buy more from you
- Acquisition of customers who will buy your higher value products/services
- Retention of high-value customers
- Identification of marketing activities that have the greatest impact on accelerating customer acquisition and improving retention
2 Criteria for Prioritizing Your Projects
Because analytics is hard and time-consuming, and there are so many possible projects to evaluate its hard to know where to start. So we need a way to prioritize. One approach is to evaluate analytical projects against two criteria: ease of execution, from easy to hard, and value derived, from low to high. By scoring each project by its value and ease-to-execute you can organize projects into four categories.
- High-Value/Easy-to-Execute- Must Dos
- Low-Value/Easy-to-Execute – Quick Hits (things you can do in 30 days or less)
- High-Value/Hard-to-Execute- Transformative
- Low-Value/Hard-to-Execute- Nice to Have
Our vote is to first focus on the high-value/easy-to-do first. This is the way to demonstrate fast high value wins. Then tackle the easy-to-execute/low-value for the next set of fast wins while you put a plan in place to address the hard-to-execute/high-value projects. This approach to the business takes skills and resources. If you don’t have the analytics capabilities within Marketing, consider collaborating with folks internally or externally who do.
Scale Analytics with Marketing Operations for a One-Two Punch
An analytical approach to the business takes skills and resources and it is not easy to scale. How do you scale it? We believe the optimal way to scale is with Marketing Operations (MarketingOps). Marketing operations is oxygen for growth.
A properly chartered and resourced MarketingOps function facilitates an agile Marketing organization. Marketing organizations with an effective MarketingOps function reap a number of essential benefits:
- well-defined, efficient, and scalable processes, including data capture and management
- analytics employed to identify and recommend value-based Marketing investments, including developing models to optimize channels
- insights to facilitate strategic planning and growth
- skills to develop models, such as customer segmentation and attribution models.
Our research shows that many organizations have someone performing some part of the Marketing operations function. As we approach and prepare for the next year, perhaps it is time to invest in your MarketingOps beyond automation to achieve the next level of capabilities on your Marketing accountability and analytics journey.
Together, your MarketingOps and analytics create a powerful one-two punch for growth. Ready to create a charter for your MarketingOps or enhance your analytics? These are perfect opportunities for us to work together.
FAQ:
A: The CMO Survey reports marketers are still challenged to maximize analytics value. Accenture research found analytics-heavy companies outperform the S&P 500 by 64% on average.
A: They have above-average analytical skills, stronger decision-support tools, explicitly value analytics insights, and use analytics across the enterprise—including Sales and Marketing.
A: Analytics is a disciplined process for turning data into actionable insight: ask a question, form a hypothesis, design a test, collect data, measure results, and identify patterns—often using math and statistical models on historical and simulated future data.
A: Descriptive (what happened), Diagnostic (why it happened), Predictive (what will likely happen), and Prescriptive (what action to take to improve outcomes).
A: Acquire more valuable customers, acquire customers who buy more, acquire customers who buy higher-value offerings, retain high-value customers, and identify which marketing activities most accelerate acquisition and retention.
A: Score projects on two criteria—ease of execution (easy → hard) and value derived (low → high)—then sort into four buckets.
A: High-Value/Easy (Must Dos), Low-Value/Easy (Quick Hits), High-Value/Hard (Transformative), Low-Value/Hard (Nice to Have). Start with High-Value/Easy to prove fast wins, then Quick Hits while planning for Transformative work.
A: Pair analytics with Marketing Operations (MarketingOps)—the process, data, and performance backbone that enables scalable data capture/management, value-based investment decisions, channel optimization models, and capabilities like segmentation and attribution.
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