Data Matters: The Importance of Data Science in Digital Marketing
Data Science in Digital Marketing
At a staggering 2.5 quintillion bytes of generated data each day and growing, the amount of data available to be analyzed is abundant, accessible, and valuable. Data science in digital marketing helps make sense of all those bytes by identifying, collecting, segmenting, interpreting and analyzing a seemingly chaotic cluster of 1s and 0s, and turning the insights into actionable results.
While the benefit of those insights is available to nearly any industry, the digital marketing sector has an especially significant upside. At a time when consumers demand more personalized content than ever, marketers face an unprecedented requirement to deliver targeted, informed campaigns. Data science gives digital marketers the insights to do it.
The data science industry is as expansive as it is profitable, so a review of the entire data spectrum isn’t as helpful as knowing why data science is important for marketing campaigns. For the martech industry, what matters is segmenting the audience to deliver relevant content, identifying potential for growth, interpreting the data to make sense of it, and applying the stats toward an intelligent campaign that converts leads into sales. As we’ll see, data science helps digital marketers do that and more.
Data Segmentation
All marketers understand the need for campaigns that connect with the consumer, but the digital marketing sector is unique in that it depends more on user interaction than others. The digital age gives consumers greater control over what ads they choose to engage with, making individualized, relevant campaigns imperative.
Market segmentation enables brands to focus their messaging to reach specific audiences, and the evidence of its effectiveness is convincing more organizations to prioritize it. 62% of martech experts say their top priority is improving their segmentation approach. Without a solid understanding of who their audience is, it is impossible to create the relevant campaigns customers demand. To that end, marketers typically subdivide their audience in four different ways:
- Demographic (age, sex, income, education, etc)
- Psychographic (values, emoticons, priorities)
- Geographic (nation, region, climate, urban/rural)
- Behavioral (online habits, visit frequency, clicks per page, purchase rate)
Several other segmentation methods exist—firmographic, generational, lifestage, and value, to name a few—but the point of all of them is the same: to classify audience members in order to deliver ads to them that resonate.
Data Benefit 1: Superior Segmentation
Since classification is fundamental to any science, it makes sense that data scientists would have much to offer to marketers seeking better segmentation of their base. The massive computational power associated with data science enables users to gain deep insight into trends and subgroups that would otherwise have gone undetected.
As an example of the use of data science in digital marketing, consider a clothing company marketing multiple lines of apparel. Sales records may show that women ages 25-40 purchase the majority of the company’s products, but that still leaves marketers with a broad audience. Women in this age group are likely to have a wide range of preferences that generalized content will fail to capture.
Data science yields a deeper dive. Upon further segmentation analysis, the company may find that among the female 25-40 age base, 68% are well-educated suburban mothers, and over half live in northern climates where warmer clothing sells better. After checking behavioral statistics from their website, the company also discovers that women under the age of 35 are 3x more likely to click through to the next landing page and have a higher visit frequency and purchase rate than women over 35, who seem to prefer in-store shopping over online purchases.
Any digital marketer worth their salt knows that the above data is a treasure trove of insight. By more precisely segmenting their audience into the educated suburban mother demographic, marketers know to calibrate their campaigns toward a practical yet slightly chic style preference, while a geographical segmentation shows that they’re better off promoting a winter lineup than lighter attire. Behavioral segmentation delivers further insights. Marketers now know to target their online energy towards younger women who are more likely to repeatedly visit and purchase from their site, and direct in-store advertisements to the older population who are more likely to shop there.
Before beginning to deliver insights on the efficacy of one campaign over another, you can use data science in your digital marketing efforts to categorize your base with razor-sharp precision. With such clarity available, you can speak to consumers with relevant content that is more likely to resonate.
Identification
Companies that fail to use data science in digital marketing segmentation are wasting opportunities to increase revenue. Data science does more than help you segment your audience by shared characteristics, but it also identifies what’s going wrong and where.
For the digital marketing sector, one of segmentation’s most critical components is recognizing who you are not reaching. Marketing primarily to a specific demographic carries with it the benefit of a stable role in the industry, but it undercuts any potential your business may have of expansion if you don’t seek out potential new markets. By applying basic data science tools, you can see not just who your main consumers are but how you can spread their brand further.
Data Benefit 2: Outliers Revealed
Let’s return to the example of our clothing company. While 68% of the consumers may be well-educated suburban mothers, if the marketing team is left sculpting their campaigns to this demographic, they will find themselves neglecting nearly a third of their audience—and likely losing profit.
It turns out that the other 32% consists largely of women from ages 55-65, 78% of whom are suburban grandmothers, and 22% live in rural areas. Behavioral segmentation shows that neither of these subgroups engage much with landing pages or links, so campaigns directing them to their local stores are best. Data also reveals that both suburban and rural grandmothers are likely to purchase children’s clothing in stores.
If the company in question is content to market mainly to the 25-40 crowd, they may miss out on potentially expanding their clothing line not only to appeal more to older women but perhaps to launch a new children’s line as well. Having looked at the data, the potential for cultivating a broader revenue stream emerges, giving you access to an entirely new market.
By using data science in digital marketing to reveal which segments of your audience are bringing in the most revenue, the contrast between the most and least dominant demographics emerges. You can easily identify neglected population bases that could turn into valuable new revenue streams.
Application
Market segmentation and growth identification are assets in themselves, but they’re useless without a plan for how to capitalize on them.
Data science is popular because it is practical. The insights gained lead to improved business goals and inform the plan for how to make the data actionable.
Data Benefit 3: Applicable Insights
Again, we’ll look at our clothing company. Nearly every byte analyzed in data science goes toward formulating a strategy. Identifying a segment of suburban mothers allowed marketers to craft a campaign that is most relevant for this specific demographic. Click rates on the company’s website reveal which pages are converting traffic so that other links may better drive viewer engagement.
By recognizing that there is a 32% revenue share consisting of suburban and rural grandmothers that marketers previously neglected with a trendy brand tone that didn’t resonate, marketers can now craft a campaign that specifically appeals to this base too. While cultivating relationships with a secondary audience, they may even discover a new market for entry. From segmentation to identification, data science in digital marketing isn’t a numbers game; it is about finding a path that achieves results.
Data Science: A Better Way Forward
From sales to logistics, departments across the board have much to gain from the actionable insights that data science provides. The digital marketing sector finds itself especially dependent upon the knowledge gained from a coherent data deep dive.
Relevant campaigns and newly-realized market opportunities are just a few ways marketers use data science to advance their brand; the results they obtain help them craft interactive digital messages that serve as the new norm in digital marketing today. As we see social media and consumer individualism continue to develop, the importance of such data-driven custom campaigns will only rise with them.