How Data Science Has Changed Marketing
As soon as consumers began engaging with digital media, marketing tactics and strategies changed. Or was it the other way around? What we do know for certain is that consumers are highly complex, generate a ton of valuable data, and want more personalized content. It is up to marketers to deliver what they want, when they want, how they want it. Even better, the most effective marketers know how to predict the what, when and how, often before their target audience even knows they want it. This is where data science marketing comes into play.
In the past, marketers had little to work with outside of basic sales data and gut instinct. “Spaghetti on the wall”, “spray and pray”, “shotgun marketing” are all common marketing terms used to describe the attitude of that time: throw what you have at the largest audience and see what sticks, what resonates, what results in conversions. Only then could you determine if your campaign strategy worked.
Today, marketers have much more in their arsenal to work with. Technology is enabling marketers to leverage some of the 2.5 quintillion bytes of digital data created every single day to personalize marketing content. Why? Because research continues to show that the vast majority of consumers not only want personalization, they now expect it. And while privacy is always going to be an issue, more than half of consumers say they are willing to share their personal data with companies who use it to create a more personalized experience.
Consumers are telling companies what they want and they are willing to shift loyalties if they don’t get it. But with so much consumer data floating around in different systems, how can marketers take advantage of it to make critical decisions? Forbes says, “Data can only be gathered if you have the technology to do so.”
Marketers need data science to help them wrangle together all of that big data, make sure it’s clean so it can be trusted, and analyze it so it is operational. Those who invest in data science to underpin their marketing strategies will reach, attract, retain and convert more customers over the long haul than those who don’t.
Data Science Marketing Use Cases
There are dozens of ways data science helps marketers create, execute and manage successful campaigns. We put together just a few of the data science marketing use cases to give you an idea of its value.
Before you can begin any marketing campaign, you need to know your budget. Going over-budget only feeds the common sentiment from the rest of the business that marketing is a cost center instead of a value center. In order to achieve the highest ROI and gain the respect you deserve, use data science to do a deep dive into past and current marketing spend versus return, then model where best to distribute money going forward.
Audience segmentation and personas
Data science is fantastic for helping you dilute your market size into manageable buckets based on common characteristics as well as determine the type of person who is most likely to be in one or more of those buckets. When you know who you want to target and can pull them aside from the rest of the group, it’s much easier to personalize your marketing to what is most likely to resonate.
A Pew Research Center survey discovered that 92 percent of adults say they go online daily and 32 percent they are online nearly constantly. While that’s important to know, what’s truly valuable is knowing where they are going online so you can go where they are. Going a step further, data science sheds light on which channels are seeing the most lift so you can focus on the right channels at the right time with the right messaging to the right segment.
Market basket analysis
One of the more interesting data science marketing use cases is market basket analysis. This technique relies on an if-then approach, analyzing everything a customer purchases, determining the relationship between those items, and building a persona based on that data. This can be a powerful tool in understanding more than just personal buying habits but the type of products that persona gravitates towards and when, giving you insight into the ideal time to promote and cross-promote products.
Lead Targeting and Scoring
Imagine being able to identify, score and focus on consumers who are the most likely to engage and buy. Data science collects various consumer data points, such as demographics, purchase history, previous product interest, etc. to uncover exactly that, saving you time, effort and dollars going after low-scoring consumers.
Social Media Trend tracking
Social media has a knack for revealing potential trends before they become trendy. There is no human way to track all of the social media posts, likes and comments across all channels, but data science can, in real-time, all of the time. It can show you what content is getting attention, what type of people are engaging, and what behavior and sentiment it is invoking. You can also see if its popularity has any correlation with the time, frequency and type of post so you know exactly where to be and when with what type of content.
There is no shortage of statistics proving how vital personalized content is for a consumer. Just a few stats for you:
- 72% of consumers in 2019 only engage with marketing messages that are customized to their specific interests
- 70% of millennials are frustrated with brands sending them irrelevant marketing emails and prefer personalized emails over batch and blast communications
- Brand loyalty among millennials increases by 28% on average if they receive personalized marketing communications
But content isn’t as simple as knowing what to say. It’s also knowing who to say it to, when, where, and how often. That takes a lot of data. You have to understand exactly what your segmented target audience wants to hear, the channels they are most likely to be on at what time of day (or night), and how many touches they’ll tolerate before they get annoyed. Data science will also help you know how to word things so your message can be found organically online.
All of these data science marketing use cases leverage real-time and predictive analytics, taking the data already out there (historical and real-time) and then making predictions based on that data. When you can combine what’s happened in the past with what’s happening now, you can predict what you need to do next with greater certainty.
Getting The Business to Listen
If you’re like many organizations, wanting data science technology to boost your marketing efforts is one thing; getting executive buy-in is quite another. As any marketer knows, you have to speak the language of your target audience when you are justifying why an investment into data science technology is worth it.
Instead of talking about the technical part of data science, talk about the outcomes having this capability will have on the business. Show them a dashboard or two (finance loves dashboards), an infographic (marketers love those), and a comparison explanation of what you are able to do now with current capabilities and what you would be able to do using data science. Show them what your competitors are doing and where the marketing industry is heading. Show them this article. Don’t give up. An investment into data science is an investment into the future of the business.