Data Science and Marketing Analytics: The Formula for Better Marketing
Data science and advertising analytics play a critical role in understanding your audience and what motivates them to buy so you can develop and execute successful marketing strategies. These technologies enable marketing professionals to gain powerful insights into buyer behavior by leveraging big data. According to Deloitte, “Data science and analytics are driving big shifts in marketing. In fact, the possibilities are unfolding so quickly that new applications for data science-led marketing are emerging nearly as fast as marketers can imagine them.”
Marketing analytics is nothing new. How we go about getting the data for those analytics, however, has dramatically changed over the years. In the past, marketers looked at basic sales data and rudimentary data to create their own customer profiles and marketing strategies. They analyzed data mostly manually, inferring all kinds of assumptions based on the best, albeit limited, data they had. Data science wasn’t even a thing. My, how things have changed.
Today, we have web analytics, predictive analytics, artificial intelligence and machine learning to not only access big data but crunch the data for us in ways we couldn’t have dreamed of just a decade ago. These technologies help marketers save time and money while building more successful marketing campaigns that reach the right people at the right time on the right channels.
Related: 3 Ways to Make Your Data-Driven Marketing Strategy Deliver
Data science and marketing analytics are powerful marketing tools. They used to be a differentiator but have now become table stakes. Marketers who aren’t leveraging these technologies are not only at a major disadvantage, they are likely in danger of becoming completely obsolete. According to Deloitte, there are several data science and analytics trends, including digital advertising, micro-targeting and micro-segmentation, success validation, and real-time experimentation.
Digital Campaigns
Digital Marketing Institute says digital marketing has changed business forever, citing its ability to provide instant communication and lasting intimacy with customers via personalization. While the use of data science and marketing analytics is allowing companies to reach customers in innovative ways, it also creates a massive amount of data that have to be sorted through for the nuggets that matter – and those bits of details and how they can be used is constantly changing.
For a digital campaign to be successful, it’s less about the amount of data you have and more about the quality of that data as it pertains to what you want to do with it. You must know what your overall business objectives are before you can align your marketing strategy and desired outcomes to them – then you must know which metrics are important to measure, those that matter to the business and will justify investment into the marketing efforts.
Marketing analytics will help you get control over all of this data. You may have a robust martech stack that has all kinds of valuable data in them, but the key is to integrate them so you can enrich, manage and better leverage their data to streamline workflows, automate processes and operationalize data faster. When all of your data is consolidated and analyzed, your team can make more informed, faster decisions about which audience to target, which channels to prioritize, and what types of personalized content will get their attention and have the best chance to convert them to customers – for each campaign.
Micro-targeting and Micro-segmentation
Forrester found that 80% of U.S. online customers are comfortable sharing some form of personal information to personalize their retail experience. Forrester also says, “Done well, personalization captures customer attention and creates value for your customers, which in turn drives customer loyalty and long-term profitability…All of that data you’re capturing is the bedrock of personalization. Bringing that data together and refining it through analysis unearths the signals that guide personalization.”
Related: How (and Why) to Nail the Market Segmentation Process
Data science and marketing analytics give you insights into your target audience by enabling you to break down or segment your audience into groups based on your chosen characteristics. You may want to put your audience in buckets based on gender, buying habits, physical location, job or title, and dozens of other characteristics. Chief Marketer says there are four main categories that these types of characteristics fall into for segmentation purposes:
- Demographics – Gender, marital status, age, income level and level of education
- Geographical – Location data, such as country, state, regional area, city and neighborhood
- Psychographic – Values, attitudes, beliefs based on social status, personality type and lifestyle
- Behavioral – Loyalty to a brand, purchase history and purchase intent
Having a smaller segment to work with enables you to further personalize your marketing content and efforts. Mass targeting is old school, an inefficient use of marketing funds and efforts because it results in poor outcomes. When you micro-segment your audience, you can micro-target them with content and messaging they are more likely to engage with and respond to. And that’s the goal of any marketer.
Success Validation
The most effective way to maximize ROI is to track, measure and analyze your marketing activities across your portfolio. You have to know if your strategy and execution are hitting their mark so you can pivot if and when it’s necessary. To do this right, you need the help of marketing analytics.
Forbes quoted a professor at Drexel University who said, “In the end, analytics won’t tell you the next big creative idea; it will tell you when the next big creative idea is working.” The insights you get from data science are connected to marketing results. You will have to justify continual investment in your marketing efforts and to do that, you need the measurement data to quantify your success. Even if these measurements point to failure at some level, you need to know when to cut your losses and move on to higher-value projects. By doing so, you prove to the business the value of marketing and give executives confidence that you have the systems in place to keep your department expenditures in check as you continually focus efforts and spend on those projects that drive business value.
Real-time Experimentation
Deloitte explains real-time experimentation and the need for marketing analytics as this: “Scenarios and experiments can now be tested in real-time rather than in hindsight or on an intermittent basis.” Marketing analytics provide an excellent method to understand customer sentiment, but it’s even more powerful when you can use marketing analytics to fuel experimentation.
Innovative companies win. Running the same types of campaigns as everyone else doesn’t attract, engage or convert. It doesn’t differentiate a brand. With so much big data now accessible, marketers are having to find ways to use it to their advantage to stand out. The only way to test ideas without committing resources is to through real-time experimentation. HBR says that A/B testing gives marketers “a powerful way to gain insights into the impact of potential changes on different customer segments and markets.”
Focus on the word “potential” there. Ideas are all potentials but until you test ideas in real-world scenarios, you’ll never know which ones, if any, to prioritize. Using data from your data science and marketing analytics, you can test, measure and gather feedback, and test again. This continuous measurement and feedback loop allows marketers to whittle down their list of “potentials” to a more refined list of the most viable marketing ideas – using data to back their decisions.
Even for ideas that have made it to executable and in-flight campaigns, marketers can utilize this real-time experimentation to model adjustments and tweaks before they’re actually made. This gives your team greater freedom to innovate without worrying about backlash if their idea failed. That’s how you create a culture of innovation.
