The Marketing-Data Science Relationship

AI & Machine Learning|[wtr-time]

The Marketing-Data Science Relationship

The traditional marketing-data science dynamic is changing — and for the better.

The days of digital marketers having to rely on (or longing for) their resident (or outsourced) data scientist to collect, clean, organize, and prepare customer and prospect data from several different sources for analysis and activation are over.

Or, at least, they can be for marketers who use a customer data platform (CDP).

Even with a CDP and concerted data strategy in place, though, there are still several instances in which marketing needs a helping hand from data science, including:

  • Scrubbing dated and inaccurate data from various marketing databases
  • Assisting with the construction and deployment of machine learning models
  • Interpreting complex data analytics, diagnosing issues, and prescribing remedies

It’s evident data scientists continue to play a central role in marketing teams’ success today.

What’s not as evident to some of these individuals on both sides of the data-driven marketing equation and marketing-data-science relationship, though, is how they can better align on a daily basis to improve their shared, must-achieve business goals.

Common issues in the marketing-data science dynamic at companies today

More often than not, when you hear or read the phrase “operational efficiency,” it typically refers to an organization streamlining its business operations — product development, supply chain, customer support, etc. — to ensure top-to-bottom success.

When it comes to data science and marketing, however, operational efficiency refers to the unification of the two teams: the former (data-savvy experts) supplying the latter (everyday marketers) with the necessary resources and insights to succeed.

“For me, I’m doing my job right if I can give my company and stakeholders the data they need to make decisions,” an anonymous data scientist explained to Digiday.

Success for this marketing-data science relationship comes in many forms.

In this instance, it’s defined by marketing’s constant access to pertinent, up-to-date, first-party data they can activate with ease through real-time marketing, like:

  • Real-time bidding for programmatic advertising
  • Personalized emails to top customer segments
  • Individualized emails to prospective customers
  • Dynamically populated site and app messages
  • Triggered messaging based on geolocation data

Where things tend to go wrong with a marketing-data science relationship — as is often the case with cross-departmental relationships — is communication (well, lack thereof).

Data scientists need to hear directly (and often) from marketing to fully understand:

  • What types of data they need (e.g., particular segments they may need help creating)
  • How that data will be used (e.g., if custom, predictive models will need to be built)
  • Where the data needs to be stored (e.g., in separate sources or a centralized database)

In other words, the onus is (mostly) on marketers to document their specific customer data needs, then liaise with the data science professional or team on staff so they can determine how to best aid them: hands-on help or simply general guidance.

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Knowing customer data needs — and how to express them — is critical

And yet, many marketers fail to communicate effectively — often because they either don’t know exactly what they want data scientists to help them with or how to articulate their ask.

If you, an expert marketer, don’t know what data you need to make science-backed decisions, your friendly, neighborhood data science pro can’t help.

Here’s a common data science project marketers request of their in-house analysts today — one you’ll likely run into with your increased focus on data-driven activities:

  • You want to take a deeper dive into your customer data to discover what’s causing potential customers to fail to purchase after a lengthy customer journey and lots of digital engagement with your website — particularly, with some of your product pages.
  • You know there are numerous prospective buyers who visit your site often, stick around for long times each session and check out many of your product offerings. But, for some reason, they just never purchase — a common pain point for ecommerce brands today.
  • So, you turn to your data scientist (or data architect, data engineer, or data administrator — whatever title your colleague in question has been assigned) to have them comb through their advanced analytics regarding your users’ site interactions.
  • And then, you wait. And wait. And wait. Why? Because you didn’t properly specify the exact data you needed. You simply asked for insights on all segments’ behaviors — a time-consuming task for data scientists, particularly if they don’t have a CDP.
  • If you requested insights for specific segments (i.e., those with individuals who viewed at least X product pages in the last Y days, who’ve never bought before, and whose momentum scores continually increase), you would have received far more useful info.

This is a common state of affairs for marketers: the constant desire to detect what marketing elements are driving certain customer behaviors like this and modify them accordingly.

As long as you clearly lay out your specific ask, needs, and preferred timeline to your data scientist, you can more efficiently resolve issues like this sooner rather than later.

(Not to mention prevent lost revenue due to problematic aspects of your UI/UX, such as poor-converting/unintuitive product and service pages.)

Proactively sharing customer insights a crucial task for data scientists

Marketers certainly need to initiate and own most conversations with data scientists, given they’re at the proverbial wheel, driving their data-driven marketing programs.

But there are situations in which these data scientists need to meet marketers in the middle.

Situation #1: Your brand is stuck with dreaded data silos.

We’d like to think most marketers finally recognize the distinct value of a CDP today. However, we know many have yet to invest in a proper CDP and still have siloed data.

This means they must instead rely mostly, if not wholly, on data science to consolidate data for them. An arduous tasks for this team, given it accounts for 80% of their jobs.

Sometimes, though, marketers don’t know they don’t have access to the latest customer data and unwittingly end up asking for data they assume they can access.

Thus, data scientists need to regularly check in with marketing to provide fresh data.

It’s not just hands-on marketers who need consistent data updates, as 60% of marketing decision-makers struggle to access data and integrate it into a single source of truth.

Many data science departments have been built for this purpose, and it’s easy to see why:

Therefore, it makes perfect sense data scientists should proactively champion data unification — and find the most applicable, pure-play customer data platform for the job.

marketing data science

Situation #2: Your marketers need intricate data models.

Marketers who own and operate BlueConic’s CDP for their brands can take advantage of out-of-the-box machine learning models to predict customers’ and prospects’ behavior.

For instance, using Jupyter notebooks in AI Workbench, our customers can deploy (or schedule) models and train them  to anticipate things like customer lifetime value (CLV) for a particular segment (e.g., top buyers, subscribers with a high propensity to churn).

There are certainly advanced machine learning models, though, that are beyond the average marketer’s technological scope, both in terms of building and deploying.

Enter data scientists, who can assist with these AI marketing efforts and import custom notebooks into software like ours to allow marketing to take the wheel with deployment and, in turn, set them up for long-term success with their big data.

Situation #3: Your company simply has too much dirty data.

A Deloitte survey found the data brands collect about consumers was mostly inaccurate — particularly third-party data, which may have come from disreputable entities.

Meanwhile, a 2019 Experian study discover 95% of major brands said poor data quality undermines their business performance and worsens their bottom lines.

While many marketers now turn to first-party data to enhance their marketing, there’s still the occasional need to clean up one’s database and eliminate old or imprecise data.

If you’re a data scientist, make it a focal point to conduct regular audits of your data sources to weed out contacts who are likely to never convert in any way, shape, or form.

Your marketing department will love you for it, as they’ll be able to focus only on the customers who matter and not waste any time, effort, or money on unworthy prospects.

New and improved processes key to better marketing-data-science relationship

As veteran data scientist Katie Malone shared in her guest post for AdExchanger, there are several sources of tension between data science and marketing departments.

Arguably the biggest one? Not making the effort to learn one another’s “language.”

Malone’s solution is a sound one: Find someone to act as “a bridge across teams, representing both points of view in relevant discussions” to ensure both get what they need.

With new protocols like this in place to streamline communication between data scientists to marketers, misunderstandings can be avoided and data can be properly leveraged.

At the end of the day, that’s what both teams desire (and, frankly, what your CMO requires).

Learn how you can leverage a CDP with artificial intelligence capabilities to implement advanced AI marketing use cases in our on-demand webinar.

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See what BlueConic can do for you.

Whether you’re looking for operational efficiencies or improved marketing effectiveness through data activation, our customer data platform can help.