SAS Customer Intelligence 360: DataOps, AI & ModelOps for Hybrid Marketing
Rapid digital transformation over the past couple of years has resulted in a hybrid customer engagement model that is changing how brands shape, manage and deliver superior customer experiences (CX). Throw in the upcoming loss of the third-party cookie and marketers must double down on the data they do have: their own first-party data.
It wasn’t long ago where brands operated across the customer lifecycle of acquisition, cross-sell, and retention using assumptions based on years of practitioner experiences. Then up and coming analytic talent prior to the data science explosion began challenging those conventions with hard quantitative data. The shift to today is the expenditures with capturing and storing 1st party digital data has decreased significantly opening a new realm of innovative possibilities. For decades, SAS has been perceived as a technology that can flexibly absorb a variety of data sources. Now, SAS can help in the capture and creation of new 1st party digital data sources for your brand to exploit.
(Image 1: Capturing & Contextualizing 1st Party Digital Data)
Moving on, if the marketing & customer analytics market were a soccer game, we'd be in the second half. The first half was dominated by general-purpose toolkits that data scientists used to build custom models from scratch. Due to the scarcity of these data scientists, the martech ecosystem saw the emergence of solutions with prepackaged analytics geared towards marketers. Now we are in the second half of the game. The commoditization of many core analytical capabilities is well underway. But the game is far from over. SAS recognizes the critical importance of serving multiple enterprise personas.
This spectrum ranges from business users who want out-of-the-box analytics to savvy analysts who want to build models from scratch. It is extremely challenging for any brand or supporting vendor to predict if a do-it-yourself (DIY) approach vs. a do-it-for-me (DIFM) approach will be more effective. SAS observed, accepted and used this challenge to inspire our software’s design principles to enable capabilities to reflect the balancing needs between marketers, analysts and data scientists within an organization.
No matter what type of marketing or customer experience team you are a part of, there is likely a business leader is wrestling with a challenge. Typically, this translates into a question (or set of questions) that inspire research, projects or new assignments for others. Ultimately, the finish line is a decision. Because if a decision is never made, no value can be derived as a result.
Call it decision science, business analytics or marketing AI…it doesn’t matter. We need to exploit 1st party data to it’s fullest potential to answer the questions that keep senior leaders up late at night. Segmentation, recommendations, next-best-actions, attribution or prediction…we are all working on customer journey optimization.
The translation of this process between questions and decisions can be represented by three components:
- DataOps
- Artificial Intelligence
- ModelOps
(Image 2: DataOps, AI & ModelOps)
SAS provides end-to-end capabilities across the entire bridge between questions and decisions. Through no-, low- and high code- interfaces, as well as support for multiple programming languages, access to these capabilities satisfies the needs of developers, data scientists, business analysts and marketers alike. For every question a senior leader poses to their broader team, SAS delivers decision-oriented solutions through AutoML and embedded analytics that accelerate the timetable to deployment, as well as customizable modeling recipes and patented procedures that optimize the in-house AI talent your brand employs. Let’s gently walk through these capabilities…
Learn more about how the SAS Platform can be applied for customer analytics, journey personalization and integrated marketing here.
