The Data Science Dilemma: Should Marketers Build, Buy, or Both?
August 8, 2017
Data science, artificial intelligence, machine learning and algorithms have become the business buzzwords du jour. And with good reason. Organizations adopting data science-driven automation see up to 10x improvement in business process speed, 72 percent reduction in customer churn, and 40 percent reduction in new customer acquisition costs.
It’s the so-called sexiest field of the 21st century as both enabling technologies and brands are fighting for the top talent that will allow them to modernize and scale their marketing efforts. But much like the big data that fuels the practice, data science for marketing is challenging to get a grip on.
Having a data science team doesn’t mean that marketing gets to take advantage of the expertise. Macro level insights and micro level optimization can’t always both be a focus.
What should be built? What should be bought and managed? When do you combine building and buying?
Build if you’re like Stitch Fix
If you’re going to build a data science team and data products, be sure you’re using your resources to build something that’s truly valuable and differentiating. And be sure you can continuously support the products and innovate. It’s not enough to set it and forget it — your data products and the algorithms and models that serve as their foundation must be updated as they are used.
Take Stitch Fix for example. They’ve built an 80+ person data science team because data science is critical to their total product and competitive differentiation. This is a company where success relies on machine learning and algorithmic product selection based on multiple levers — wisdom of the crowd, predictive analytics, and both explicit and implicit data — and where data science is critical to business processes across the organization, including supporting marketing.
Buy if you’re like RevZilla
Every modern marketing organization — and company for that matter — is data-driven. But that doesn’t mean that data science is core to the business model itself. When you’re looking to apply data science to marketing or business functions, there are often technology partners that have already built the solution.
RevZilla is the nation’s leading retailer for motorcycle jackets and gear. The founders were frustrated with the lack of customer-centric experiences for motorcycle enthusiasts — they didn’t just want to shop on price, they wanted to buy from retailers who shared their passion and would advise, not just sell. Instead of waiting for this to happen, they created RevZilla.
Every individual in RevZilla’s audience has unique preferences for riding. From sport touring, adventure, and even racing, the gear needs for their buyers are incredibly wide-ranging. While the RevZilla team maintains a “local bike shop” feel in every store visit, phone call, or customer service email, the team wanted to scale that approach to their total marketing program.
RevZilla partners with Sailthru for email marketing and uses their personalization algorithms to serve up the right content to individual buyers based on their behaviors and interests. With Sailthru’s personalization capabilities, every single one of their customers receives email newsletters that are unique. The end game is increased email response, engagement, and revenue.
Selecting the right partner is challenging and most marketers are faced with a choice of buying multiple point solutions or a single platform. When it comes to marketing technology, you want to identify a partner with a strong data science foundation with applications for multiple channels. Working with individual channel technologies and separate solutions for data science layers will be overly cumbersome if your goal is a connected, seamless experience for your audiences. Data science applications perform best when the output can impact your marketing in real time.
Sailthru is the world’s largest sender of personalized email and it delivers and personalizes messaging for websites and mobile applications. With Sailthru, retailers can predict the specific items a customer will purchase, when they will buy them, how much they will spend, and whether they’ll respond to email. Cross channel campaigns are then automatically adjusted and personalized for every individual customer to ensure the predicted behaviors are actualized.
For companies like RevZilla, Business Insider, Rent the Runway, and Frank & Oak, Sailthru is the partner of choice for outsourced marketing campaign management and data science-based products.
Build and buy if you’re like JustFab
In the case of a company like TechStyle — parent to JustFab, ShoeDazzle, Fabletics and other fashion lifestyle brands — it’s not a question of whether to build or buy, it’s a question of what to build in house and what to buy from partners.
Tech and data are core to JustFab and every brand in the TechStyle family, but their structure allows for internal analytics and customer insight teams to focus on projects that move the entire portfolio strategy forward while giving individual departments autonomy to buy products that deliver growth.
The JustFab marketing and CRM team also uses Sailthru for email marketing, email personalization, and are testing their website personalization capabilities. For JustFab, personalization is core to every aspect of their customer experience and Sailthru gives them the ability to individualize engagement using a variety of algorithms.
The results include a 50 percent increase in email conversion rate, a 46 percent decrease in customer churn and a 12 percent increase in purchasing customers. Find out more about strategies JustFab prioritizes to generate these results in this guide to increasing repeat purchase rates.
David Glueck is VP of Data Science at Sailthru.
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