What do you get when you cross a data scientist with a marketing pro?
Larger returns and lower acquisition costs.
While this may sound lofty, it’s actually pretty simple.
Think about it. Data science exists to create better business outcomes. Marketing teams want to do the same thing.
Data scientists love sifting through data to find trends and insights. Marketers collect a ton of data, more data than most branches of any organization. Website analytics, email campaigns, buyer personas, PPC performance, you name it – marketers, or at least savvy, smart, data-driven marketers, are always looking at data.
The problem is that these marketers aren’t usually that good at extracting the right information from the massive amount of data they aggregate. At the very least, they’re not as good as a trained or professional data scientist would be.
That’s why there’s such a huge opportunity for data scientists to come alongside marketers.
Below, we’ll show you data scientists some specific ways you can join forces with marketers to create some awesome business outcomes.
Before data scientists get too far into this whole marketing thing, it’ll be helpful to learn some of the marketing language.
Marketers love acronyms, and while this list doesn’t even begin to scratch the surface, it will definitely help you get started.
CAC stands for Customer Acquisition Cost. It measures how much spending it takes for a prospect to convert to a customer. Marketing expert Neil Patel calls it “the one metric that can determine your company’s fate.” So, yeah, it’s a pretty big deal.
LTV is the lifetime value you get out of a customer. This piece requires making sense of all sorts of data including customer lifespan, retention rate, profit margin, and others.
Having a good understanding of CAC and LTV enables marketers to create this important ratio that sets a benchmark for how much the company can spend on acquisition while still making a profit.
The ROI, or return on investment, is all about analyzing the efficiency of an investment.
Think of it as “counting your cost.” Investing $100 into something with a return of $10,000 is a pretty high ROI. $110, dollars, on the other hand, would be a pretty dang low one.
You’ll mostly hear about conversion rate from digital marketers. This measures the amount of times that a specific web page achieves its desired result. For instance, have you ever visited a website with a big button that says “join now” or something similar? Every time someone joins, they have been “converted.” Divide that number by the number of visitors and boom! You got your conversion rate.
Churn is the amount of customers lost through cancellation divided by the amount of total customers. Knowing this helps business’s identify why they’re losing certain customers.
Click Through Rates (CTR) measure how often someone visits a website based on an advertisement or link relative to the amount of times that link appeared on browsers.
Net Promoter Scores calculate overall brand satisfaction by surveying how likely customers are to recommend the company to others.
These numbers show how much recurring revenue a company brings in by the month or year.
You’re probably already noticing how data scientists can bring value to marketing teams, but let’s look a little closer at 5 specific opportunities.
Marketing efforts that don’t perform well add no value to the organization and eat away at the company’s budget.
Data scientists can analyze the various channels –blogs, social media, print, web advertisements, etc.– and help identify which ones lead to actual results. This information will enable marketers to optimize the successful channels and quit wasting time (and money) on the ones that yield little to no results.
Lead scoring helps drive the right leads to the sales team, so sellers can spend more time converting and less time with uninterested prospects.
This requires intense analysis, though, making it difficult for marketers to properly score their leads. Having a data scientist involved could drastically improve lead scoring effectiveness, resulting in better leads for sellers and more sales for the company.
Marketers often struggle with developing a great pricing strategy. Data scientists who team up with marketers can evaluate things like customer preferences, external economic factors, and buying histories to set the best prices possible.
Social Media functions as one of the most powerful marketing channels out there, but marketers may fail to optimize their efforts if they don’t have the right understanding of data.
Analyzing which leads engage with the company’s page, which pages lead to the best outcomes, and what content pieces received the most clicks can help marketers establish an effective social strategy.
Web advertising is like a minefield of data possibilities. Crafting the right message requires a skill. Creating conversions by getting that message in front of the right people requires deep analysis of customer search habits, keywords, and ad effectiveness.
With the constantly changing world of the internet, this analysis needs ongoing evaluation to make sure the marketing team is up to date with all they’re offering.
Today’s marketers rely on data to make decisions, but their strongest skills aren’t in data science. They can easily get overwhelmed by all the numbers, causing them to leave holes in the bigger pictures.
Data scientists can help fill this void and deliver marketing initiatives that result in better business outcomes. When these two worlds come together, companies will reduce their spending on acquisition and increase their overall returns on their marketing.
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.