Digital MarketingData-Driven MarketingInteresting Ways Businesses Use Big Data to Improve Personalization

Interesting Ways Businesses Use Big Data to Improve Personalization

If you listen properly, your data will tell you vital information about your customers and clients.

Big data has careened onto the scene in a major way, promising huge rewards: McKinsey Global Institute estimates that employing big data knowledge could net the U.S. healthcare system somewhere in the neighborhood of $300 billion in value on an annual basis. By the same estimate, industries that rely on personal location data (such as cellphone triangulation information) can expect to see consumer surplus rise to the tune of $600 billion when they look to big data for results.

Consider for a moment both the enormous scale of these estimates and IBM’s claim that nearly 90 percent of the world’s data has been created in the last two years, and at least one thing is clear: big data is huge. Given the enormity of the data sets that comprise the field and the logistical (and sometimes touchy) issues that accompany its use, is it any wonder that big data has come to be regarded as a resource best utilized by only the largest companies, the Googles and Microsofts of the world?

It is no secret that these big data pros are whizzes at utilizing big data results to create opportunities. However, smaller businesses miss an opportunity to experience their own share of success in this emerging field when they decide that big data is too big for them to tackle. Here’s why this constitutes a missed opportunity:

Using Big Data Results to Improve Personalization

One of the great ironies of big data is that it can be used to make commerce experiences feel smaller and more intimate to customers. This, in turn, may positively impact purchasing experiences and deepen customer relationships and brand loyalty. Mining the treasure trove that is big data has tremendous potential for smaller companies to improve customer experience through personalization.

Consider for a moment Amazon’s personalization strategy, which many consider to be the go-to example of personalization strategies that work. As you are no doubt aware, Amazon relies on wish lists, browsing histories, and purchasing history to create individualized product suggestions. “The world’s largest marketplace” feels smaller when the online store remembers your name, and that the last time you were shopping you were in the market for leather gardening gloves (size medium, please).

Here we take a look at several of the most interesting (and effective) strategies that organizations (businesses and government organizations alike) employ to improve personalization:

Improve User Experience With Personalized Ads, Products, and Services

Anaconda Sports, a successful sporting goods retailer, found itself stuck with an inefficient and costly e-commerce platform that had a lot of issues:

  • It could not store customer information, which meant that customers had to reenter information each time they visited the site.
  • Their platform didn’t provide a unique experience for their customers based on that customer’s individual preferences and buying history.
  • Customers who had questions about an order’s status had to call customer service rather than looking the status up online.

In order to remain relevant in today’s competitive online retail market, Anaconda knew that it needed to make a change (as cited in the above case study).

The solution came to the retailer in the form of Amazon’s Webstore. Previously, the company was providing even repeat customers with a clunky and anonymous online buying experience. Opening an Amazon Webstore (which is an independent store outside of allowed Anaconda to develop the type of efficient online store it needed.

Perhaps most importantly, Anaconda can now place Amazon Product Ads, which target sports shoppers searching However, when someone clicks on an Anaconda ad on, they are taken directly to the Anaconda Webstore. In doing this, Anaconda is able to use Amazon’s big data by targeting customers searching specifically for sports gear, while still maintaining its identity as a small online store.

As reported in the above case study, since making the move to the much more personalized Amazon Webstore, Anaconda is experiencing a higher ROI. It reports that the conversion rate from Amazon Product Ads is three times higher than the conversion rates from other online advertising streams that they use. Director of e-commerce at Anaconda, Rob Meyer, reports that the company is “reaching more shoppers, generating more revenue at a lower cost-per-click, and earning a much higher ROI.”

In the long run, this is huge for small businesses, as it puts the big data from corporations like Amazon at their disposal. The overwhelming amount of Amazon’s big data isn’t a problem here either, because Anaconda is able to tap into only the niche market it needs: sports gear consumers. In a situation like this, small businesses are able to maintain the best of both worlds: big data from big corporations and a small business feel.

Crowdsourcing Leads to Smart Business Decisions

ModCloth began as a small fashion venture in an even smaller dorm room. Since then, the company has expanded – seriously, seriously expanded. The online clothing retailer, which is known for its eclectic vintage and indie fashion, currently attracts over five million unique viewers each month. However, even though it is one of the fastest growing online clothing retailers, the shopping experience it provides feels anything but anonymous. And that’s the whole point.

While ModCloth’s selection of trendy and unique women’s clothes certainly has buyers impressed, it is the company’s innovative personalization strategies that turn heads in the marketing world. ModCloth’s brand of personalization relies on involving its loyal customers at nearly every level of the company through crowdsourcing strategies that get customers talking, both to one another and to the company.

At its core, ModCloth’s personalization strategy is to knock out the barriers between customer and company, thereby creating an intimate online retailing experience. Take, for example, the “Be the Buyer” program, which asks shoppers to vote on a given product and decide if they would “pick it or skip it.” Items with high pick rates are tagged as “Be the Buyer Picks.”

Not only is this an innovative and fun way to introduce visitors to new items, but it also yields mountains of valuable information (an average of 6,786 customers vote in each “Be the Buyer” poll) about buyer preferences and helps the company make valuable predictions about what will sell and what should be cut. If everyone is suddenly “skipping” all of the peplum products, that’s information that ModCloth wants to know and act on, before it is stuck with mounds of peplum dresses languishing in the warehouse.

ModCloth is a great example of a company that has integrated the collection of big data into its marketing strategy seamlessly. Big data is part and parcel of what makes the ModCloth buying experience feel unique. The dialogue that develops between brand and customer is valuable to both parties: ModCloth knows what its customers want and its customers can easily see that their input informs the brand’s inventory. It’s a win-win.

Prediction Leads to Improvement

Big data allowed the struggling school system in Mobile County, Alabama to make some big changes in the way that they reach out to at-risk students. Like many struggling school systems, Mobile had a problem with students dropping out of high school at an alarming rate: prior to 2011, the dropout rate was 45 percent. This wasn’t good for the students and it wasn’t good for the community.

In order to find a solution to the complex problem of attrition rates in Mobile, the school system looked to big data to tell the story of their students in a unique way (as outlined in this case study). What they found was surprising: when they looked at the data sets they noticed distinct trends and patterns. For example, they found that suspensions and serial absences frequently preceded a student’s decision to drop out. This knowledge allowed the school to flag students displaying these risk signs and make sure they made targeted efforts to keep the student in school.

Using data from across the entire school system, including attendance records, test scores, and disciplinary histories allowed the school system to gain a unique insight into their students. Looking carefully at the whole student body over time actually allowed the school system to create a much more personalized strategy to keep kids in school.

And they have some serious results: since beginning their big data experiment, Mobile now has a 70 percent graduation rate, as well as across-the-board test score improvements.

The Takeaway

Marketers are always looking for ways to deepen customer loyalty and improve user experience, especially in the face of fierce competition. Big data is perhaps the most important way to create a user experience that treats customers in the way that they want to be treated – like individuals. Big data pros have known that data tells a story for a long time, and if you listen properly, your data will tell you vital information about your customers and clients.

Big Data image on home page via Shutterstock.


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