Before SpearmintLOVE became a powerhouse brand offering children’s clothing and accessories, it was a blog with a devoted social media following. Founder Shari Lott launched the SpearmintLOVE brand in 2013, capitalizing on the audience she’d amassed (much in the same way Glossier founder Emily Weiss used her blog audience at Into the Gloss as a launching pad for a line of beauty products.)
Since then, we’ve seen brands across many different verticals taking a similar approach in pairing content with an e-commerce component, and that content and commerce trend is continuing to grow.
But in the unique case of SpearmintLOVE, the marketing genius didn’t stop there.
When Shari’s husband John came on board and brought along his expertise in finance, optimization and improved return on investment (ROI) quickly became a top priority for the business. John knew he wanted to put their customer data to work, so he started using a cohort analysis leveraging the brand’s advertising data to study customer behaviors, trends, and patterns over a set period of time.
What is a cohort analysis?
Cohort analysis is a subset of behavioral analytics that takes the data from a given data set (e.g. an e-commerce platform, web application, or online game) and rather than looking at all users as one unit, it breaks them into related groups for analysis. These related groups, or cohorts, usually share common characteristics or experiences within a defined time-span.
Cohort analysis allows a company to “see patterns clearly across the life-cycle of a customer (or user), rather than slicing across all customers blindly without accounting for the natural cycle that a customer undergoes.” By seeing these patterns of time, a company can adapt and tailor its service to those specific cohorts.
With this rich customer data, SpearmintLOVE was able to deliver better, more relevant marketing materials to their audience because they had a clearer idea of what different customer groups needed and when.
The approach paid off in a big way: They saw a 47% decrease in cost per purchase, lowered cost per conversion down to $.11, and started earning an average 34% return on their Instagram ad spending over a 60-day period. Their data-driven approach was so effective that Instagram even went on to feature it as a remarkable success story on the company website.
So what does this tell us?
In short: Rich customer data is what lives at the heart of any optimized and highly-functioning marketing strategy.
In this post, we’ll look at how you too can build a data driven marketing approach and ensure you’re leveraging testing and experimentation for an effective and fully-optimized strategy.
Building a data driven marketing approach
When it comes to building a data driven marketing approach, the first step should be planning and documentation. The surprising thing is: Many marketers skip this part completely.
Research shows that less than 50% of marketers have a documented marketing strategy—which means that even fewer have benchmarks or reporting metrics in place that help gauge their efforts. As a result, marketers often end up making decisions based on assumptions, estimations, and guesswork, rather than data and hard numbers. This leads to underperformance, poor ROI, and wasted resources in the marketing department.
But it doesn’t have to be that way.
Thanks to advances in technology that make it easier to collect, visualize, and leverage data, you can create a data driven marketing approach and make decisions based on real customer data. Pairing customer data with experimentation, you can generate actionable customer insights that lead to optimized conversion rates, more relevant, effective marketing materials, and an increased bottom line.
It works, too. Insights-driven organizations are seeing this approach pay off. E-commerce retailer and manufacturer weBoost, for example, saw a 100% lift in year-over-year conversion rate by optimizing their site through experimentation. What’s more: this process also produced a 41.4% increase in completed orders for homepage visitors.
Your marketing team can do this too, but it takes a willingness to experiment and break from the ‘this is how we’ve always done things’ mentality that so often stunts growth and limits opportunities for businesses.
Next, let’s talk more about experimentation and look at a few key elements of a strategy that takes a data-driven approach.
Key elements of a data driven marketing strategy
Not all data driven marketing strategies are created equal. In our experience, the most effective approaches tie in a few signature elements.
1. A process that promotes the Zen Marketing Mindset
Order of operations can be tricky when you’re working to make your customer data actionable. However, with experimentation, you can feed data into hypotheses and actually validate whether or not your ideas work.
To do this, we recommend leveraging a process that marries data and creative thinking with validation. At WiderFunnel, we use the Infinity Optimization Process™, which is a structured approach to experimentation strategy and execution. This multi-step process helps add structure and logic to testing efforts and minimizes false assumptions. That means more accurate experiment outcomes and more validated marketing messages that translate into bottom-line impact.
The approach is highly effective because it covers the two main sides of marketing: the intuitive, qualitative, exploratory side that imagines potential insights, as well as the quantitative, logical, data-driven, validating side that backs up outcomes with hard numbers. Paired together, they provide meaningful insights that boost marketing efficiency.
2. Exploration to power experimentation
Exploration in this context refers to information gathering and data collection. It is a major part of any successful marketing strategy, as it can help marketers find and develop the most impactful insights.
For exploration to be effective, it’s important to be sure you are considering both qualitative and quantitative data sources. Again, this is where the Infinity Optimization Process comes in handy.
The Explore phase focuses on gathering information through many different sources and then prioritizing this information for ideation. In this process, all information collection is centered around the LIFT Model®, which is a framework for understanding your customers’ barriers to conversion and potential opportunities.
In the case of SpearmintLOVE, the insights derived from their cohort analysis would be considered in Explore.
How to turn your customer insights into revenue driving experiments
Categorize, organize, and put your customer data into action with this 28-page workbook. Dig into the Explore phase and learn how to translate your data and insights into experiment hypotheses.
How SpearmintLOVE leverages data driven marketing
We see an example of data driven marketing in action when we look at SpearmintLOVE’s use of cohort analysis, wherein quantitative data is paired with qualitative data to inform experiments.
By testing different products, messaging, and imagery based on the customer lifecycles they’d uncovered via cohort analysis, SpearmintLOVE was able to improve the effectiveness of their efforts and lower costs associated with marketing. By constantly testing and improving their marketing strategy based on real customer data, they’ve found success with customer-centric marketing that both resonates and produces meaningful results.
Bonus: this approach also helped them solve a major problem.
John noticed that within customer cohorts, there was a recurring drop-off in advertising ROI that he couldn’t explain. After studying the data, the answer occurred to him: Their audience’s needs were changing, but the marketing was staying the same.
“Data showed us that our customers were changing,” John Lott told BigCommerce. “We learned that our customers were evolving into different life stages. It took us six months to figure that out. Insights are funny that way.”
So what was the issue?
The young parents SpearmintLOVE initially attracted had newborn babies…but eventually those babies grew into toddlers. Which meant shoppers needed to be shown products for older children with different needs. But because SpearmintLOVE was still promoting products and messages for new/first-time parents, they were seeing dramatic drop off in marketing effectiveness as babies got older and the parents’ needs changed.
What we can take from this lesson: Illustrative data and a structured approach helps brands build stronger emotional connections with customers and get a deep understanding of what they both want and need.
Making data driven marketing work for you
When we look at brands like SpearmintLOVE who are seeing incredible success, we see common themes around what’s happening behind the scenes: Customer obsession and data-centric decision-making.
And it’s working. By leaning on data and giving customers what they want, SpearmintLOVE was able to grow its revenue by a whopping 1,100% in just one year.
The question is: What’s keeping you from doing the same?
In future editions of this series, we’ll continue to explore how different brands are executing customer-centric experiences via feedback collection, customer support insights, analytics, and experimentation.
If you’re curious about how to step up your company’s customer experience strategy and get on the level of brands making waves (and money), stay tuned and sign up to get future editions in your inbox.
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