How to Mix Data Science and AI Without Expertise in Either (Expert Tips & Tools)

As a marketer, you know data is the fuel for smarter strategies and campaigns. But let‘s face it – wrangling all that data yourself is a huge time suck. Especially when you‘re staring down billions of data points across countless channels and touchpoints.

That‘s where the one-two punch of data science and artificial intelligence (AI) comes in. By marrying the two, you can automate the grunt work of collecting, preparing and analyzing data at mind-boggling scale and speed.

The best part? You don‘t need a PhD in statistics or Python wizardry to get in the game. A skyrocketing array of AI-powered tools are putting data science superpowers in the hands of the masses.

Ready to open up a can of data science and AI whoop-ass on your marketing? Let‘s dive in.

Data Science vs AI: What‘s the Difference?

Before we get to the how, let‘s break down the what. Data science and AI are often thrown around interchangeably, but they‘re two distinct domains:

Data science is all about extracting insights and knowledge from raw data through techniques like data mining, statistical analysis, and machine learning. The goal is to unearth patterns and trends to inform smarter decisions.

Artificial intelligence, meanwhile, focuses on developing computer systems that can perform tasks normally requiring human smarts – things like recognizing speech, translating between languages, and predicting outcomes. Machine learning, a subset of AI, uses algorithms to automatically learn and improve from data without being explicitly programmed.

While separate, data science and AI are two peas in a high-octane pod. Data science feeds the data models that power AI systems. AI, in turn, can juice every stage of the data science process, from automating data prep to serving up insights.

In short: data science makes AI possible, and AI makes data science scalable.

Why Data Science and AI are Marketing Game-Changers

The convergence of data science and AI is a match made in marketing heaven. By 2025, AI and big data are predicted to generate over $200 billion in business value.

For marketers, this dynamic duo delivers serious ROI across the board:

  • Automating manual data collection and prep (up to 80% of a data scientist‘s time)
  • Personalizing content, offers, and experiences based on real-time customer data
  • Predicting customer churn, lifetime value, and next best actions
  • Optimizing ad spend and marketing mix in real-time
  • Powering conversational AI chatbots and voice assistants
  • Enabling smarter social listening and sentiment analysis
  • Detecting and blocking fraudulent traffic and spam

The list goes on. With AI doing the heavy lifting, marketers can say sayonara to manual data drudgery and focus on high-impact strategy.

How to Blend Data Science and AI (Without a Rocket Science Degree)

Itching to inject data science and AI into your marketing mix? Here are five ways to get started:

1. Tap AI-Powered Data Collection & Analysis

Collecting and analyzing data at the scale and velocity needed for modern marketing is Sisyphean for mere mortals. AI and machine learning pick up the slack by:

  • Automatically ingesting and integrating data from disparate sources
  • Detecting patterns, trends and anomalies that humans miss
  • Generating predictive models and insights in minutes vs months

Take lead scoring. AI can analyze thousands of demographic and behavioral data points to predict which leads are most likely to convert. Tools like Infer and Node automatically crunch data from your CRM, marketing automation, and other systems to dynamically score leads in real-time.

Or look at churn prevention. AI can unearth subtle patterns across product usage data, engagement metrics, and support interactions that signal a customer is at risk of bolting. Apps like Churn Buster plug into your data and proactively flag likely churners so you can intervene before it‘s too late.

2. Automate Data Prep with AI

Ever marvel at how much time you waste just getting data into shape for analysis? You‘re not alone. Up to 80% of a data scientist‘s day is burned on data prep – reformatting, cleansing, and integrating raw data to make it usable.

Enter automated data prep. Platforms like Trifacta, Paxata, and Alteryx use AI and machine learning to intelligently format and clean data on autopilot:

  • Automatically structuring data (e.g. parsing key/value pairs)
  • Deduplicating and resolving conflicts across data sets
  • Detecting outliers, anomalies and quality issues
  • Batch data cleaning based on preset rules

With the grunt work outsourced, marketers can fast-forward to the fun part: extracting insights. Per Forrester, data prep automation reduces time spent preparing data by up to 90%.

3. Predict Customer Needs with Automated Machine Learning

Manually building and tuning predictive models used to be the domain of data science pros. No longer. Automated machine learning, or AutoML, uses AI to automatically cycle through thousands of algorithms and parameters to find the optimal model for a given data set – no PhD required.

Plug in data around customer profiles and behavior and AutoML tools like DataRobot, Google Cloud AutoML, and H2O Driverless AI will spit out predictions for individual customers‘:

  • Likelihood to convert, churn, or purchase again
  • Potential lifetime value and loyalty
  • Propensity to respond to specific offers or creative
  • Product or content affinity and recommendations
  • Next best action or offer across touchpoints

Continuously updating models as new data flows in keeps predictions scary-accurate. Case in point: travel site Hopper uses AutoML to predict airfare and hotel prices with 95%+ accuracy.

4. Put Data Insights on Autopilot with AI

Even the juiciest data insights are worthless if no one sees or acts on them. AI-powered analytics and business intelligence tools seamlessly surface needles in data haystacks and translate them into plain English for marketers to grok and use.

Narrative Science, for instance, uses natural language generation (NLG) to automatically spin data into easy-to-digest stories, reports, and articles. On the viz side, tools like Automated Insights and Lucid instantly transform data into interactive dashboards and infographics that would make your Tableau-slinging analysts jealous.

Automated alerts and triggers make sure hot-off-the-press insights get in front of the right eyeballs and spur action while they‘re still fresh. Set it and forget it.

5. Democratize Data Science with No-Code AI

What if you could empower everyone across marketing to be data-driven without making them learn Python or SQL? No-code AI platforms like Aible and Noogata abstract away the underlying complexity of data science and machine learning behind user-friendly, drag-and-drop interfaces:

  • Connect data from spreadsheets, databases, and cloud apps
  • Automatically discover trends and patterns in a few clicks
  • Build and deploy AI models without writing a line of code

Suddenly, Susan in content marketing can analyze keyword data to brainstorm high-converting blog topics. Derek in demand gen can build lead scoring models on his own. No more bottlenecks or hand-holding required.

With data science and AI accessible to all, insight-driven marketing becomes a team sport. BCG predicts no-code AI will expand the number of "citizen data scientists" 5-10X in the near-term.

Making it Stick: Data Science & AI Best Practices

Marketers may be eager to hop the data science and AI bandwagon, but success doesn‘t happen by just flipping a switch. Some key best practices to keep in mind:

  • Start with a use case, not a capability. Don‘t adopt AI tech for tech‘s sake. Identify your biggest data challenges and opportunities first – THEN pinpoint how data science and AI can help. Solving real problems out of the gate builds momentum.

  • Optimize for speed, then scale. Resist the urge to take on massive AI initiatives from the jump. Start with quick-win pilot projects to show value before ramping up. Per PWC, 54% of execs cite quick, small AI wins as a key to broader scaling.

  • It‘s all about the data. Data science and AI are only as good as the data you feed them. Invest heavily in unifying, standardizing, and cleaning customer data across silos. High-quality data in, high-quality insights out.

  • Make trust a top priority. Achieving transparency and trust is key to AI adoption. Incorporate explainable AI (XAI) techniques that shed light on the "why" behind machine-made decisions. Practice good AI ethics to mitigate bias and discrimination.

  • Cultivate a data culture. Executing successful data science and AI strategies requires buy-in outside IT and analytics. Educate stakeholders from the top down on the value of data-driven marketing. Empower teams with self-service tools. Celebrate data wins far and wide.

What‘s Next for Data Science and AI in Marketing?

Looking ahead, the impact of data science and AI on marketing will only accelerate. As data volumes continue to rise and AI gets smarter, more marketers will lean on machines to drive decisions and personalize CX in real-time.

We‘re already seeing glimpses of an intelligent, automated marketing future:

  • Real-time hyper-personalization enabled by AI and edge computing
  • 1:1 customer interactions powered by conversational AI agents
  • Immersive AR/VR experiences generated by synthetic AI
  • Automated optimization of marketing mix and creative based on live data
  • AI-driven, omnichannel orchestration across the full customer lifecycle

With AI expected to unlock $3.3 trillion in business value by 2024, pioneers who pair data science and AI today will have a massive edge over laggards tomorrow.

Become a Marketing Data Science & AI Superhero

Data science and AI are quickly becoming table stakes for marketers. But diving in doesn‘t demand deep technical chops. With the latest AI-powered tools, any marketer can harness data to work smarter, faster, and at mind-bending scale.

Will you be among them?