How Does AI Help Marketers?

[suneelgrover]

Marketers, advertisers and brands face growing challenges in making sense of complex data to drive actionable insights. This article introduces recent SAS efforts to develop a solution-oriented bridge to this cited gap, offering prescriptive recipes to address trending use cases for B2C (and B2B) brands. SAS is diligently exploring how data science and AI can bring forth incremental value to marketers across industries. The intent is to create synergy improvements between marketers and data scientists while elevating self-sufficiency in running analytics at scale through use case-driven solutions.

Image 1: Challenges Facing Brands Today

As a result of these recent trends, marketing research and customer analysis as a discipline should continue to explore the following considerations to unlock incremental innovation:

Treat all of your owned data assets as a priority. It is frequently mentioned in the martech industry that brands must maximize the potential of their "owned" customer data sources. However, this also means we should prioritize activating structured, semi-structured and unstructured data sources. Brands cannot deprioritize semi-/unstructured data because of a perception that these flavors of information are difficult to use, as the opportunities to harvest these ingredients for analytical innovation are front and center today. 

Recipes for data and analytical models. Retail data offers different analysis experiences from mining financial banking data. Similarly, a churn model in the discretionary fashion industry, where most customer behaviors indicating disengagement is silent, will likely differ from a churn strategy for a subscription-oriented streaming service, where churn is explicit (or observable). Recipes provide comprehensive use case-specific solutions to reduce adoption friction and increase the likelihood of success in leveraging customer insights within a marketer's workflow.

Optimize customer-level treatments. Machine learning models don’t make decisions — but they do identify actionable signals in noisy customer behavioral data. It’s up to our CX and/or marketing teammates to take prescriptive insights and execute data-driven targeting/personalization. But the decision isn’t always clear. Should a brand optimize on likelihoods, engagement, propensities, or profitability? The path forward will vary by a brand's unique business model, as well as the industry-vertical it operates in.

Transforming Marketing Organizations Into Analytical Factories

Numerous well-known martech vendors have attempted to aspire to this vision over the years. However, the theme across the big martech vendors has frequently gone like this. There are three segments we target our software and technology towards: marketers, analysts and data scientists.

In the world today, there are a large volume of marketers, moderate amounts of analysts, and a smaller subset of data scientists. The theme at major martech vendors has been to automate analyses on behalf of users using templates and agents to provide AI insights while masking manual workflow steps. While this can provide benefits regarding perceived speed-to-market acceleration, the auto-analysis behind these templates typically does not offer customization features to conform to a unique business model. The data science community understands that incremental opportunity is being left on the table with solutions like this.

This trend has resulted in a compelling insight for SAS, and a deep exploration of how AI is (and isn’t) being used in the martech landscape has resulted in the realization that there is a different way to approach this emerging paradigm.

Think about the magnitude of requests that come in from customer experience teams to their supporting analyst teams. The wish list includes actionable scoring for topics like acquisition, upsell, retention, segmentation, next-best-action (or experience), recommendations, lifetime value, pricing personalization, attribution and net lift.

The list could go much longer, but the point remains the same. Customer experience management has an insatiable appetite for data intelligence. This myriad of desires stratifies further when considering industry context. Let's take a moment and imagine we are in a requirement gathering meeting between two teams.

Everyday Scenario: Requirement Meeting Between Marketing & Data Science

The marketing and advertising teams responsible for the interactions between a brand and everyday consumers speak one language. The data science group likely speaks another. Terms like acquisition, cross-sell, churn, targeting, personalization, A/B tests, conversions, and impressions are the common tongue of the martech universe. Alternatively, words such as misclassification, precision, average squared error, confusion matrices, outliers, auto tuning, neural networks, and random forests represent the language of data science.

In other words, marketers do not typically think in terms of algorithms, and analytical jargon creates confusion, friction and inefficiency for those not trained in the discipline. This can be intimidating for many working professionals, and why data and analytical literacy across the enterprise is increasing in relevance.

The language of marketers is rooted in use cases and outcomes. Domain expertise, acceleration and simplifying the process of analytically injecting data-driven intelligence into workflows is the desire, and year after year, SAS clients share feedback on this challenge. If this is what the martech community craves, this is a call-to-action to my practitioners of data science across all industries.

You want to see your analytical assets bring rewarding impact to your brand, right? You want to observe your efforts making a significant positive difference in customer journeys, correct? Then the democratization of marketing team enablement via customer journey orchestration and prescriptive analytics benefits from speaking their language. Further, SAS AI development efforts targeting the martech community is to bring forth software that removes technical jargon and adoption intimidation.

Our vision at SAS is to empower brands to practice responsible marketing.

Introducing SAS 360 Marketing AI

From a software user's perspective, our motivation is to create an experience that unites what is special about data scientist and marketer talents. To achieve this, use-case-driven solutions that proactively guide these two types of anticipated users is the intended vision.

These articles introduce our development efforts to release a solution-oriented software application offering prescriptive experiences (i.e. recipes) to address trending use cases for B2C (and B2B) brands.

Image 2: Introducing SAS 360 Marketing AI

The concept of recipes and required ingredients, which lives at the center of SAS 360 Marketing AI's design principles, can be outlined as:
 
Data – What data do I need?​
Preparation – How does it need to be transformed?​
Use-case specific – Applicable ML/AI algorithm​(s).
Scoring​ - Segments, recommendations, propensities, etc.
Activation – Using the scoring in journeys and channels.

Our objective is to create synergy improvements between marketers and data scientists while elevating self-sufficiency in running analytics at scale that package the best of SAS capabilities in a simple-to-use interface. In other words, SAS is introducing AI and advanced analytic capabilities FOR marketing use cases acutely. For a moment, reflect on the idea of a software application that is:

• Designed for the domain space and themes of martech.
• Focuses on use cases while minimizing adoption friction related to statistical jargon frequently misunderstood by anyone outside of the data science profession.
• Uses the best of both worlds - GenAI blended with best-practice machine learning, predictive & segmentation capabilities in a no-code rapid-scoring mechanism that seamlessly integrates with the broader SAS CI360 solution, or external 3rd party martech tools.

As we continue to partner, guide and help find incremental value with our customer partners, SAS realizes the time in NOW to release a solution that bridges all the power and innovation of prediction, machine learning, and GenAI together.