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5 Ways Retailers Are Using Predictive Analytics

people-colors 730x280.pngOver the past few weeks, there were several articles published on how analytics is transforming retailers for the better.  We’ve boiled down the deluge into a few basic yet impactful points for you to take away. 

1. Data On the Runway

One of the sectors dominated by trends and change is the fashion industry. The importance of leading fashion weeks is to not only showcase new styles, but also build new customer connections.

burberry-prorsum-001-1366.jpgDespite all the glitz and glamour, the styles showcased on the Fashion Week catwalk are not guaranteed to translate into bestsellers, and so, fashion retailers must find ways to predict which fashion collection items will resonate with their in-store shoppers. And while attempts to predict consumer purchasing habits is as old as retail itself, in-store analytics solutions have proven to be extraordinarily successful in helping retailers analyze shopping behavior and customer preferences.

Essentially, these analytics solutions allow fashion retailers to create conversations with customers, which can then be used to inform the retailer of exactly what the customer is looking for, making shopping connected and simple.  

Want to know how pervasive analytics is in the fashion world? Burberry’s efforts were featured last month in Wired magazine.

2. When Big Data Gets Too Big

Here’s the thing about big data. Everyone talks about how it is revolutionizing “x” or forever altering “y” or solving “z,” and they have a point. It’s true.  Big data is a game changer. But all that massive amount of information is rather useless without a means to synthesis it, no? Retailers can collect all the data they want, but without a way to make sense of it, it’s kind of pointless.

Predictive analytics is the tool retailers can use to makes sense of all these disparate data points.  Retailers can get actually insights from consumer preferences, shopping behavior, and purchasing habits and then make actionable decisions. In short, it’s the piece that bridges big data to satisfied customers.

3. Find It Faster Online

The vast majority of product search functionality online for most retailers involves fairly rudimentary search functions. They mostly search for synonyms of the entered word or phrase and of those, look for the most popular results.  

But with predictive analytics, the search can go beyond looking for what the shopper said but instead what they meant. This is done by using a search function that considers many more data points including conversion rates, customer ratings, product relationships, and margin.

4. Intelligent Customer Service 

Quality customer service involves a conversation, and in an ideal world, that conversation would take place between two actual human beings—the customer and the customer service representative.  While that may be possible for small to mid-size retailers, larger retailers may struggle with staffing enough customer service reps to handle the massive amount of customer questions, complaints, and feedback. 

An analytics solution can learn to manage the workload, providing basic customer support and offering help to customers in unique, store-specific ways. They can handle on-site inquiries, as well as social media posts.

5. Setting the Right Price

Selecting the right merchandise for your customers is only half the battle. To truly maximize profits, you need to set the right price while avoiding heavy markdowns. Doing so, however, is a lot easier said than done.  Business Insider even referred to it as one of the most difficult things a business can do.  


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Topics: predictive analytics, retail analytics

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