How AI Improves Marketing Results and ROI

Summary

  • Artificial Intelligence (AI) is an underlying marketing technology that improves both the efficiency and effectiveness of almost every marketing program.
  • Research and empirical evidence show that when AI is applied to marketing campaigns, sales lead acquisition growth improves by double digits.
  • Demonstrating a clear and measurable AI ROI is best done using a 3-step sequence.
Johnny Grow Revenue Growth Consulting

Research performed for the Marketing Transformation Report found that the Best-in-Class marketing leaders (i.e., the top 15 percent) used AI 2.5X more frequently than their lower performing peers. That's a significant difference that should not go unnoticed by marketers seeking revenue growth. Among the Best-in-Class marketers, AI was described as a "game changer" and driver of improved marketing results.

But technologies only become sustainable when they show a clear and measurable ROI.

So, to demonstrate an ROI for this technology, it's important to begin with high impact use cases that drive the most important marketing outcomes and then leverage AI for technology automation, performance measurement and continuous improvement.

And one more thing. There's no need to seek out new problems or opportunities to validate AI. A better approach is to apply AI to your existing marketing challenges and opportunities.

A 3 Step Approach to Maximize Artificial Intelligence ROI

The Johnny Grow Artificial Intelligence Framework fully integrates prescriptive use cases, measurable business outcomes and AI technology.

Here's our three-step sequence to apply AI to drive the most important marketing performance results.

1

Prioritize the Highest Impact Marketing Use Cases

Succeeding with AI for marketing is less about the technology and more about the results. That's why we start with prioritized business outcomes and then assemble the use cases, data, and technology to achieve them.

Several AI marketing use cases are shown below.

Einstein AI for Marketing

See the Marketing use cases and business outcomes that are improved with #AI technology.

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To organize and add context to use cases, Gartner recommends companies adopt an information capabilities framework. We've standardized on this type of framework to support multiple types and categories of marketing use cases and make repeated use of a common set of interfaces and technologies.

We typically create use cases in agile user story formats so that they have specific business goals, stated beneficiaries and clear acceptance criteria. Use cases vary by client and industry but all should be supported with evidence-based best practices that fully leverage AI to achieve one or more of the five overarching marketing outcomes shown in the above diagram.

2

Prepare Your Data

Data quality is a prerequisite to AI success.

Customer data can be marketing's most valuable asset. However, it decays at about 2 percent per month so without a CRM data maintenance program that asset can quickly become a liability.

A CRM data quality program ensures lead and customer data is accurate and complete, secured and available to those who need it, and in compliance with company policy and regulatory requirements.

And because different data becomes compromised at different times, you will also need continuous data quality measurement. To aid data quality programs we generally use a data quality dashboard.

Customer Data Quality Dashboard

The customer data dashboard surfaces data quality variances in near real-time so they can be remedied before they exacerbate and spread to multiple systems.

Recognize that a 10% increase in data quality will
outperform 100% improvement in an AI algorithm.

Marketers have lots of data.  But most do not effectively use it.

In fact, while many companies claim data is their most valuable asset, a research report from IBM shows that for most companies only about 2 percent of data is unearthed and made actionable (Source: IBM Institute for Business Value analysis; The Cognitive Enterprise, Part 1.)

So, once you have accurate and complete data, you need to assemble a data transformation process to convert raw data into insights and actions that improve marketing programs. A sample marketing data transformation solution architecture is shown below.

Marketing Data Transformation Pipeline

Data becomes an asset when it is converted from a raw material to a finished product of information or insight. Only then will the data provide answers to powerful questions such as how to improve campaign conversions, acquire more leads, accelerate lead conversions, improve brand reach and increase marketing ROI.

3

Apply AI Technology

Now it's time to apply artificial intelligence technology.

This step is greatly facilitated for marketers using popular marketing clouds such Adobe Customer Experience with its Sensei AI, Salesforce Marketing Cloud and Pardot with its Einstein AI and Microsoft Dynamics 365 with its Azure Machine Learning. These marketing automation systems combine CRM and AI to remove technical barriers and put AI configuration into the hands of business analysts and marketing managers.

The Point is This

The most successful marketers are defined by their ability to collect and curate accurate customer and campaign data, transform that data into actionable insights and prescriptive recommendations, and apply those insights with every customer communication, offer and message.

It's a complex undertaking for sure, which is why those who succeed will achieve competitive advantage over those who don't.

Our experience in implementing AI for marketing is that clients achieve an average payback period of 5.5 months and an average ROI of 95 percent at the end of the first year. ROI increases in subsequent years.

Data is the fuel, AI is the engine, and insights are the destination.