The internet is filled with big data that marketers can use to target customers and improve their marketing strategies.
Every minute there are 400 new Facebook users, 4.2 billion search queries, 60 thousand images uploaded to Instagram, and 200 million emails sent. By 2025, there will be 175 zettabytes of data, which would take one person 1.8 billion years to download at current internet speeds.
Advancements in machine learning and artificial intelligence have led the rise of sophisticated data mining techniques, which enable companies to identify hidden patterns in data that they couldn't have seen before.
Marketers can gain meaningful insights from mined data and use their findings to improve conversion rates and add new customers.
But there's one big problem.
Many marketers never utilize these data sets because they either don’t know they're out there or they don't know what to do with them. Beyond this, it’s also hard to differentiate signal from noise.
That's why marketing departments need to start thinking about how they can utilize data science to make strategic, data-driven decisions at scale.
Data science is a field that incorporates statistics, data visualization, and mathematics to take a scientific approach to analytics by creating actionable insights from customer habits.
When done right, data science can greatly reduce excessive spending, help businesses optimize their digital marketing efforts, and ultimately, attract new customers.
Data science takes a great deal of skill, but expertise is not always needed. Below, we will highlight 10 simple ways any marketing department can utilize data science to improve their marketing effectiveness and outcomes.
Customers stop using a product for a number of reasons, but without proper data analysis, these reasons will remain a mystery.
Data science can help marketers find the trends and user-patterns that lead to churn so they can pinpoint the problem before losing the customer. It’s all about exchanging lower churn rates for greater customer loyalty by knowing how (and what) to market to your existing customers.
Creating positive customer experiences and keeping users engaged are big parts of customer retention.
Data science can help you reach out to the right people with the right information at the right time so that they stay invested with your brand.
That concept may sound simple but think about all the marketing analytics that go into it. Knowing who to contact, what to say, and when to say it all requires a deep knowledge of your users and their habits. You can’t sit down and interview every one of them, so where do you get this information? Through data.
The last thing any company wants to do is spend a ton of money on something that’s not working. That’s why they often force marketers to work on a tight budget, just in case their plans don’t lead to solid ROIs.
Data science can help marketing departments ensure that they always maximize their return. Once they are equipped with the right marketing data sets like acquisition costs, lifetime value, and conversion rates, marketing teams can build spending models that get the most out of their budgets.
Sentiment analysis helps businesses identify what people believe about their brand and how they feel about it. Marketers typically find this information through feedback surveys or social media mentions.
Data science can help marketing teams sift through all the historical data like social posts and mentions so they can identify the trends that surface and make informed decisions based on their findings.
Marketing efforts that don’t perform well drain the budget and offer no actual value to the company.
Marketers can analyze their various marketing efforts –SEO, blogs, social media, print, web advertisements, etc.– and identify which ones yield the highest returns. Having this information enables marketers to set achievable KPIs as they focus their energy on the right channels and ditch the wrong ones.
Effective lead scoring ensures that only the best leads get to the sales team so sellers can spend their days converting customers instead of allocating resources to uninterested prospects.
Developing a lead scoring system is a lot of work, though. It requires sifting through tons of marketing data and finding trends, making it a challenge for marketers to properly score their leads. If the leads aren’t scored right, the sales team will continue to spend their days on ineffective conversations.
Marketers can use data science to improve their lead scoring process and make sure that only the best prospects get a conversation with the sales team.
Marketers love to brainstorm and come up with creative ideas for content, but how can teams be sure that what they come up with will actually work?
Through data analytics.
Looking at things like page views, reactions, and shares help marketers see which pieces performed the best. Then, data science can help them identify the trends of the most successful content so they can build on those models over and over again.
Marketers often have a hard time coming up with prices because they do not always know where to start. Evaluating marketing data sets like customer preferences, external economic factors, and buying histories can help them set the best prices possible.
It doesn't matter if you're using Facebook, Linkedin, Google, or any other ad-hosting service; making sure your company’s message gets in front of the right people requires an understanding of customer search habits, keywords, and ad effectiveness.
Because of the constantly changing world of the internet, each of these data sets requires ongoing analysis. Things become outdated real quick in the digital world, and companies cannot fall behind. Giving data the attention it deserves helps marketers stay on top of search trends so they can put their efforts towards a strategy that will guarantee success.
Data science gives marketers the meaningful insights they need to send contextualized emails to target audiences.
Data sets like who reads what kind of emails, the types of content that get the most engagement, and the optimal time for sending help teams focus only on the best practices to ensure greater success.
Utilizing data science in marketing doesn’t take an expert data scientist – though one definitely wouldn’t hurt. All it takes is paying attention to the data sets surrounding your existing marketing efforts. Applying data science to these 10 opportunities will lead to more informed decision making and improved results.
Shanif Dhanani is the co-founder & CEO of Apteo. Prior to Apteo, Shanif was a data scientist and software engineer at Twitter, and prior to that he was the lead engineer and head of analytics at TapCommerce, a NYC-based ad tech startup acquired by Twitter. He has a passion for all things data and analytics, loves adventure traveling, and generally loves living in New York City.