eCommerce Marketing Analyst
At Dalstrong we’re hiring for an all-star eCommerce Marketing Analyst to join our remote team! Whether you’re a hobbyist or a pro, our aim is to enhance the experience of our clients’ culinary creations.
This is a full-time engagement to oversee our data analytics on a variety of platforms, as well as strategize eCom conversion techniques. If you’re a detail-oriented, organized and self-motivated Marketing Analyst, with a passion for e-Commerce, we’d love to hear from you!
Who You Are (Requirements)
- A data-ninja! You love data, and get excited by finding tweaks to increase sales
- Fully fluent in English
- Available for 40hrs/week (preferred hours: EST schedule)
- Experienced with analyzing data for eCom teams, and strategizing impactful solutions based off of your findings
- Hard-working, resourceful, self-motivated, detailed and analytical.
- Experience using Amazon + Shopify and is a plus.
- Min. 3 years experience in a similar Data Analyst / Marketing Analyst role.
- Passionate about using data to make informed, strategic eCom decisions
- Wired for success, and eager to show off how you can help improve our product conversion rates, among many other metrics
What You’ll Do!
- Split-testing product content: Titles, Bullet Points, Copy, Prices
- Analyze results, additional testings, and brainstorm new conversion methods etc
- Analyze sales fluctuations, creating strategies to improve sales based on analysis
- Optimize website conversion rate, CTR, bounce-rate, etc.
- Optimize email conversions, and other metrics you’re assigned (or find!)
- Analyze keywords, search results…Working with paid ads team on data
- Asses a holistic view of data across our marketing initiatives, what is working, what is not
- Brainstorm new solutions and tests, keep our optimization techniques and data analysis efforts moving forward!
- Full-time USD salary ($2,500 - $2,700 USD /mo) based on experience, profile, and location
- Flexible work-life-balance and a fun remote culture with a booming eCom team!
- Free premium culinary products from our company.
**Ready to Meet Us? Send an email to firstname.lastname@example.org with the below:
1) Resumé / CV.
2) Cover Letter explaining your relevant background and why you’re the best candidate.
3) Please review our website, and provide a few strong ideas to optimize conversions. Kindly include detail, as well as your technical process to why you’ve selected these suggestions. We understand that you do not have access to our back-end data for this. Your process and findings will not be used without your paid consent.
4) Please select one product from our Amazon store, and provide suggestions on optimizing the product’s presentation. Please also discuss why you’ve chosen this particular product. Be sure to not just look at the product page, but anything else that you can measure and analyze. We understand that you do not have access to our back-end data for this. Your process and findings will not be used without your paid consent.
5) It’s your first month on the job, and you’re analyzing our product catalogue’s online performance. Please map out a short outline of what you’d like to assess (and how) during this first month’s process.
- Tell me about your background in Data and Analytics (they must have data and analytic roles in their CV)
- What are the main metrics that you look for when analyzing why a product that isn’t selling well, and what would you do to strategize solutions?
(They should mention CTR, Conversion Rates, Bounce Rate, Session %, and anything else that indicates they know about how to fix those problems (more/better keywords, better images, etc.)
What are the main reasons a product won’t convert well on Amazon? How would you prioritize optimizing a large product catalogue when your time is limited? (There are different answers, but ideally they will talk about the product’s keywords, its images, its traffic sources, etc. And they should mention that they will prioritize products that have the strongest sales, or -- the one with the lowest conversion rate. There is an argument for both sides).
- What tools, platforms and technologies do you use for data analysis? What would you use when you work on Amazon products (they should ideally have researched and know a little bit about Amazon’s reporting, or Helium10, JungleScout, etc).