Client Spotlight: Springbot Shares How Successful Businesses Use Data

Ten years ago, the term “data science” sounded about as relevant to marketing as quantum physics. Today, companies rely on sophisticated data to guide each and every strategy. While large businesses like Amazon and Google can afford more robust, predictive data-crunching endeavors, smaller businesses are challenged with finding a way to compete.

Springbot, a data, automated analytics and marketing optimization eCommerce platform, works with small to mid-size businesses to make the most of their data. This month, we sat down with Marketing Manager Kelly Schmalz to learn how the company’s most successful clients use data.

TD: There’s been a lot of changes in the industry in the past couple of years, especially when it comes to data. How has Springbot evolved to manage these growing needs?

KS: To start, we more than doubled our workforce over the past year. And we’re getting more and more sophisticated with the tools we use. Obviously, we’ve had rapid customer growth. We’ve evolved and changed our platform, based on feedback from our clients as well as trends within the industry.

TD: What do you think it is that makes Springbot so successful?

KS: Nobody else is doing exactly what we’re doing in this space. Some companies offer one or two features similar to ours, but we offer everything in one cohesive tool. We take our clients’ customer demographic data, social data and their product purchase data, and integrate it into our platform. Then, we analyze the data and make actionable recommendations. For example, “We noticed that women in California under the age of 25 spend 20% more money than anybody else in any other state, in any other age demographic.”

We take this information, analyze the revenue and calculate our clients’ ROI attribution by marketing channel. We help companies optimize their marketing by taking smarter, data-driven action. For example, we can tell you, “You launched all these social media campaigns, but did you know email actually generates the most revenue for your store, and therefore has the highest ROI?”

TD: Obviously, data science is a hot topic right now. Large businesses have long been using it to their advantage, but how do you think smaller businesses can compete?

KS: Amazon has been doing this for years. But, how do the mom-and-pop stores compete with Amazon? They can’t – or, at least, they couldn’t before because they didn’t have the workforce or the resources. We built a tool for small to medium-sized businesses so they don’t have to hire teams of specialized employees to analyze their data. We can do it for you, and help you determine what is and is not working.

TD: Let’s talk a little bit about your forte: automated analytics. How do you explain automated analytics to someone who is unfamiliar with the concept?

KS: Basically, automated analytics works by taking all your data, analyzing it, crunching some numbers and making recommendations. It’s really as simple as logging into your dashboard, and on the homepage of the dashboard are three recommendations based on data. Sometimes they are as simple as, “Hey, you haven’t sent out a Tweet in five days, and you said you wanted to Tweet once a day.” Sometimes we offer recommendations on SEO like, “Hey, we went through your product catalog. We noticed this product needs improvement on the short description. This can help your SEO.”

Or, “Your three-hour abandoned cart email is not generating as much revenue as your 24-hour email, and has a high unsubscribe rate.”

There’s all sorts of different things that could be happening and the reality is, particularly for clients in the small to medium business space, you don’t have time. Nobody has time to comb through all these things to find out what’s working and what’s not working. With Springbot, it’s in one convenient dashboard.

TD: How does automated analytics differ from predictive analytics?

KS: The automated part is more about automating multiple marketing strategies based on data. One automation feature is triggered emails. If somebody abandons a cart, a store can automatically send a reminder based on the client’s settings. For example, a client may want to send out reminders three hours after abandonment, or a day or maybe even three days. While there are industry best practices as to when to send this email, each store is different. This is why we always suggest testing to see what works best based on data. The predictive analytics part is we’re saying, “We noticed you have a really high conversion rate on your abandoned cart emails sent three hours after purchase. We suggest you also try sending a three-day reminder, as many of our customers have positive results with this additional email.”

TD: With big data growing every day, do you think more companies are looking to third-party data?

KS: Companies want to know more about their contacts. If somebody signs up from an email campaign, it’s natural to want to find more information about your contact so you can market better to him or her.

Companies today have realized marketing to people using the same message doesn’t work. Customers expect personalization. They don’t want to receive the same email that their spouses, co-workers or neighbors receive. Your lists are made up of people with different incomes, different ages, different purchases and different interests. If you’re not personalizing your message then you’re not competing. No one’s going to pay attention.

TD: What are some of the benefits of using purchase data in conjunction with demographic data?

KS: Let’s say you have two customers who are both women of the same age, but they might purchase different products. Although all of their other demographic and social data is the same, one purchases a red shirt and one purchases a blue shirt. So you might say, “OK, most women in this category are purchasing the blue shirt, but another small percentage of this same segment is purchasing a red shirt. Maybe we should recommend this red shirt. Maybe this part of the same segment didn’t know this red shirt existed.”

Purchase data tells a lot about a person because you could also say, “OK, well we know this customer is a woman and we know she usually purchases female clothing, but we noticed around the holidays she suddenly starts purchasing men’s clothing. We know she’s married, so we’re going to assume she’s buying for her husband. Maybe we’ll send her emails in November and December with recommendations for the latest men’s products as gifts.'”

TD: Do you have any client examples that you can share?

KS: First Aid & Safety Online is a client that wanted to better understand their data so they could improve their targeting, and increase sales and ROI. Like most small businesses, they just don’t have the bandwidth to be doing all this work on their own. They wanted a tool that could help them determine who exactly their customers are and where to allocate their resources. By understanding their data and segmenting their customer base, First Aid & Safety Online increased sales by three times within a six-month time period.

TD: What are some of the big trends that you anticipate as we look forward into 2016? What are you talking about with your customers?

KS: The two big trends are personalization in the sense of personalized content within a marketing campaign. While you and I might be in the same segmented email campaign, maybe we want different content within that email. Or maybe we are served different versions of the same website because I buy more when I see red and you buy more when you see purple. It’s just about getting more and more granular in your personalization. With data, the possibilities are endless.

The second big trend is the customer journey. Knowing more about the customers who buy from you, and using that data to make them happier, grows your sales and helps you attract new customers. We’re in the age of the customer, and the customer can easily ruin your reputation in five seconds by sending out a negative Tweet. If you watch your data and listen to your customers, however, you can avoid those negative interactions.

TD: What’s the No. 1 data challenge your clients are facing? How is Springbot working to solve that challenge?

KS: Several clients have been shocked with who their customers are, based on the data. Obviously, with our clients being in the eCommerce industry, they know what inventory has been moving in their store, but they may not know who’s purchasing what.

For example, one of our clients was running a campaign on Facebook. He realized that if he posted products more than $40, they wouldn’t convert. According to his data, he found most of the prospects who followed him on Facebook were part of a really young demographic. He realized that if an item was more than $40, the prospect may have to ask their parents to make that purchase. If it was below, they typically had the cash to purchase it themselves. Of course that makes sense but, without having the data right in front of him, he didn’t know 100%.

TD: When it comes to data, what has surprised you the most?

KS: There are two things. The first is the fast adoption. You’d think customers would find it weird to get an email from a company that knows your interests and which products you want to buy, but people are very accepting. For me personally, I’m like, “Oh, well that’s really easy because all I have to do is click this button and I can buy this shirt without having to go through the whole product category. That’s exactly the message I wanted, and that’s exactly the product I wanted.”

The second thing is the rapid growth. What is trending today isn’t trending six months from now, or even one month from now. The constant evolution and change is really exciting. I’m looking forward to seeing what happens next.

The more you know about your customers, the more targeted your marketing and the higher your ROI. Try InstantData for free to get the data you need to power more successful campaigns.

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