This is the second part of a two-part series: According to BPMonline Insights, nearly 40% of companies struggle to convert data into actionable insight and we know from our research, and that of others, that there is increased pressure on Marketing to measure its contribution and optimize investment and decisions.

Adam Berke, president and CMO of AdRoll recently echoed our mantra, “marketers need to look for smarter and more sophisticated ways to connect their activities to actual business metrics.” Plus, the demand for predictive performance analytics is rising, as organizations try to anticipate future business scenarios with in-depth analytics of past and present performance data. According to the latest CMOSurvey.org study, spending on analytics is expected to increase from 5.5% of Marketing budgets to 18.1% in the next three years.

Read our first section 'Should You Use A.I. in Your Marketing?'

In Part 1 Should You Use A.I. in Your Marketing?, we briefly outlined the value of Artificial Intelligence (A.I.) for Marketing and some initial steps you can take to prepare you and your team. Artificial Intelligence allows you to understand the data generated by customer and prospect interactions so you can develop and implement more effective Marketing strategies and programs, improve your Marketing performance, and outperform your competition.

The ultimate power of A.I. is to be able to drive better decisions through the intelligent use of data. In the words of Tomer Naveh, CTO of Albert, “data is unquestionably the domain of A.I.” Thank goodness, because as of last count, we are creating 2.5. quintillions of data daily! It’s no wonder organizations feel crushed by the deluge of data.

Intelligent tools enable you to swiftly react to market changes, optimize mix models, and improve your processes, especially those that affect customer buying patterns. However, before you succumb to the lure of the latest artificial intelligence shiny new tools, be sure your data and analytics skills are up to snuff. According to research by the CMO Council, marketers are far less data-savvy than they may think.

In their study, the CMO Council and RedPoint Global claim “They [marketers] don’t know what data they have at their disposal, and they don’t know how to use it. While today’s omnichannel customers are more connected than ever before, organizations are failing to keep pace with customer expectations for frictionless experiences, despite the multitude of data, analytics and engagement systems in place.” Tools are not the issue. According to the CMO Council, over the past five years, 42% of marketers have installed more than ten solutions across marketing, data, analytics or customer engagement technologies.

Four steps to take before trying to apply A.I. to your data:

1. Build your data and analytics skills. Data is the DNA of Marketing. Regardless of role, every marketer needs a solid base in data. Whether your hire it or outsource it, you need strong data and analytics skills to address the increasing need to generate insights from the constantly increasing volume of the data and the expanding number of measures and metrics being used to assess and drive Marketing performance. The CMO Council laments that Marketing analytics — measuring and analyzing marketplace activity and marketing performance to improve decision-making — are employed for 37.5 % of business decisions, but less than 2% firms say they have the right people in place to leverage the information.
2. Create a Data Management strategy and a consistent set of standards. The purpose of data is to facilitate decisions. Therefore, you need clarity around which decisions are applicable for A.I.. Then you can build your data management strategy to address how data will be acquired, prepared and normalized. You will want to define how models will be developed, tested, and deployed.
3. Fix your data silos. For any tool to be properly deployed to help you transform data into insights, the systems that house your data need to be connected. It’s difficult to create seamless customer experience when data is siloed.
4. Establish an analytics center of excellence (CoE). Your analytics center of excellence is comprised of a team of business and technical professionals who enable the ability to drive best practices around methodologies, tools, models, and techniques. The objective of any CoE is to improve effectiveness and gain efficiency across to the different business units.

Remember the end goal is to be able to extract patterns from data so you can make intelligent and actionable decisions and improve operational excellence.

Is A.I. the next norm for strategic Best-In-Class Marketing Organizations? Only time will tell. In the meantime, stay tuned for continued insights and tips from our experts. Subscribe to our blog for instant updates and check out our How Data Moves You from One-Size Fits All to Segmentation-Based Marketing recording.

Laura Patterson is president and co-founder of VisionEdge Marketing, Inc., a recognized leader in enabling organizations to leverage data and analytics to facilitate marketing accountability. Laura’s newest book, Marketing Metrics in Action: Creating a Performance-Driven Marketing Organization (Racom: www.racombooks.com ), is a useful primer for improving marketing measurement and performance. Visit: www.visionedgemarketing.com

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