March 07 2018
It’s a simple truth:
The more you know about your customers’ needs, actions, and desires, the more equipped you’ll be to get them engaged with your brand. It’s also true that it’s never been easier to dig up all of this information than it is today. Oddly enough, though, many of today’s marketers still haven’t jumped aboard the big data bandwagon:
- According to CMSWire, even among marketers who collect and utilize data to inform their campaigns, many of them aren’t using this data to their fullest capacity
- AMA.org explains that many of today’s marketers tend to use data in a reactive, rather than proactive, manner
- Emarketer found that 10% of marketers don’t use data to inform their creation of marketing campaigns at all
Of course, it’s not exactly possible to determine just how much potential business these companies are missing out on – but, chances are, the answer is somewhere between “a lot” and “a ton.” On the other hand, the organizations that have fully embraced big data as a means for creating effective and engaging marketing campaigns have a lot to show for it. In this article, we’ll take a look at five companies that have used data to spearhead innovative campaigns and initiatives that ended up having a major impact on the organizations’ sales and revenue numbers.
Let’s dig in.
Spotify Learns “How Students Listen”
In 2017, the people at Spotify decided to collect data revolving around the music tastes of college students across the country. The company analyzed trends regarding the most (and least) played songs, songs that were often listened to in full (or those that were often skipped), and which genres of music were frequently listened to on a specific campus.
After collecting this data, Spotify then created a microsite titled “How Students Listen,” which compared the results from dozens of the top schools in the US. Visitors to the site were presented with an interactive graph that ranked the propensity of students from each school to listen to a specific genre of music (or a type of music featuring a certain characteristic, such as acoustic guitar or “danceability”). The site also ranked the colleges in terms of students’ diverse musical tastes, as well as which campus’ students listened to the most music overall. Finally, the site lists a number of popular tracks, artists, and Spotify playlists students listened to – essentially acting as recommendations for individuals with similar interests.
Perhaps the main lesson to take away from Spotify’s “How Students Listen” campaign is that your data is 100% unique to your organization. The insight you glean from it is also guaranteed to be 100% unique, as well. This will enable you to generate original marketing campaigns and initiatives that will all but certainly stand out from your competitor’s campaigns created who may rely on readily-available data distributed by a third party.
Spotify’s campaign also exhibits the notion that you can use your customer-facing data to facilitate a sense of community and identity among your fanbase. In Spotify’s case, students from specific schools were certainly able to identify with the most listened-to music on their campuses. Essentially, you want to use your customer data to create content and offers that, when placed in front of your typical customer, makes them say “Hey, that’s me!”
Target Takes Personalization to a Whole New Level
All the data. Seriously.
If you’ve ever shopped at Target using a credit card or store account, the retail giant has a file detailing your past engagements with the company. This includes your purchase history, your online browsing history, your return and exchange history. Essentially, the only bit of information Target doesn’t keep track of is the actual path you take when walking through one of its physical locations…we hope.
Target’s ongoing initiative to collect as much data regarding their customers as possible enables the company to provide them with highly personalized offers through a variety of methods. As By analyzing each customer’s purchasing habits, Target is able to predict how often a given customer will stop into their local store, and for what purpose they’ll do so. In turn, Target can send personalized offers to each customer at almost the exact time their customer is preparing for a trip to their favorite store.
Learn as much as you possibly can about your customers’ needs and desires, and how they engage with your company. By digging into each of your customers’ purchasing habits, you’ll not only be prepared to provide the service they need right when they need it, but you can also be proactive in doing so. Think of it like this: Even if a customer shows a high propensity to make a purchase every two weeks, there’s no guarantee they’ll continue this pattern in the future. But, by serving them up a personalized offer just before they hit that two-week period, you increase the chances of them forming – and keeping – a buying habit. One thing to note, however, is that too much preemptive action on your part can actually do more harm than good. We’ve talked about this before, but let’s just say you should stop short of trying to predict when your customers are going to become pregnant.
Bona Uses Geographical Data to Send Timely Offers
In 2016, Bona Hardwood Floor Cleaner partnered with location marketing firm inMarket to analyze the shopping habits of nearly 50 million consumers. The main piece of data Bona’s marketing team looked at was the timeframe during which these individuals typically completed their weekly/monthly trip to the grocery store. Essentially, Bona used geolocation data collected by inMarket that showed when each customer was within the vicinity of a local supermarket.
(Note: inMarket didn’t collect this data by spying on consumers; they had been given permission by each individual to collect this information.)
Bona used this data to make educated predictions regarding when a given individual’s next grocery shopping trip would be. They then sent out promotions via email and push notifications not en masse, but individually, to each consumer in the hours leading up to the time predicted. The company didn’t target consumes as they entered a supermarket or grocery store. Rather, the company sent out notifications and offers on the day in which it predicted an individual would be heading to the store. As reported by Geomarketing, the campaign resulted in a 25.3% increase in post-engagement purchase intent and a 55% increase in overall brand awareness among targeted consumers. Additionally, through the campaign, Bona achieved an ROI of about 3.2x its original investment.
While this campaign focused specifically on timing purchasing intent, the lesson to take away is to use as much data as you can to determine when your target consumers are most likely to engage with your brand in any way, shape or form. This means knowing when they’re most likely to be active on social media, when they typically check their email, and, yes, when they’re most likely to be in “buying mode.” You also want to take note of when they aren’t likely to engage with the promotional materials you send out. This will help you avoid instances in which you spend days, even weeks, creating the perfect piece of content…only for it to fall on deaf ears.
100% Pure Individualizes Its Prices
Cosmetics company 100% Pure began tracking data relating to the way in which website visitors engaged with the site’s content. Though not much information is available regarding the actual data tracked throughout this process, it likely included metrics such as the average amount of time users spent on a given page (and the website as a whole), which areas of the site users frequented the most, and the actions users typically took before making a purchase (or choosing not to do so).
After collecting this data, 100% Pure used machine learning algorithms to predict whether a specific visitor is likely to make a purchase, or likely to venture away from the company’s website without converting. When the algorithm determined a given visitor was not likely to make a purchase, it would automatically adjust the prices displayed on a given product page to entice the individual to convert. This same method was also applied to individuals who the algorithm predicted would only make a single purchase; for these customers, the price of complementary products was adjusted in order to increase the chances of a cross-sell. The results? After only three months, 100% Pure saw its online sales numbers increase by over 13%!
While fluctuating your products prices “on the fly” for any given consumer might not lead you to the amount of success 100% Pure experienced, but always keep in mind that each of your customers are individuals. No matter how typically they may fit the mold of a given persona, there will always be a unique quality to each of your customers; it’s up to you to figure out how to meet these individual needs best. Another lesson to keep in mind is that it’s important to look at what your customers aren’t doing just as much as you look at what they are. On that same token, you should also take a deeper look at what happens in the moments leading up to a conversion (or a non-conversion, as the case may be). The actions your target customers take – as well as the feedback they receive from your company – can provide a ton of insight into the reasons behind their decision to make a purchase.
Topshop Revamps Its Mobile Website to Improve the Customer Experience
Clothing retailer Topshop analyzed, assessed, and audited visitor data relating to its mobile website. Again, this data related mainly to the amount of time visitors spent browsing the website, how many pages they visited before bouncing, and whether or not these visitors ended up making a purchase.
The purpose for assessing this data was to get a better understanding of the way in which visitors navigated Topshop’s mobile site. In turn, the company was equipped with the information needed to redesign the site in a way that made navigating it much easier – improving the overall customer experience in the process. Rather than implement arbitrary changes and hope for the best, Topshop made intentional tweaks to the size and location of navigation tabs and buttons, and also added assistive “bubbles” to point new users in the right direction. Topshop then went even further, designing four different versions of the new mobile site and split-testing it among the company’s visitor base. The company used this data to determine the best course of action when creating the final version of the updated site.
After implementing the changes, Topshop saw a clear increase across the board:
- A 4% increase in cart additions
- A 5.8% increase in conversions when visitors utilized the search bar
- An increase in conversions by around 9-11% due to changes in the layout of product pages
Whether your company operates online, via brick-and-mortar storefront, or both, it’s essential that you provide your customers with a streamlined and intuitive experience that enables them to get what they need from you with relatively little friction. Not only do you need to ensure your store’s layout is navigable, but you also need to make sure that “bumps in the road” are minimal – if not nonexistent. As we’ve spoken about before, even the smallest hang-up in the shopping process could be reason enough for a potential customer to turn tail and run without making a purchase.
Wrapping It Up
Nowadays, it’s incredibly easy to find and collect data relating to your customers and the effectiveness of your organization’s campaigns and initiatives. But, there’s a huge difference between simply accessing this data, and knowing what to do with it. Simply put: collecting data without taking subsequent action ultimately means your data is useless.
Even the smallest bit of data or information when examined through the right lens, can provide an absolute treasure trove of insight that could help springboard your company to greatness.