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How Retailers Use Big Data for Product Customization

October 03, 2017

How Retailers Use Big Data for Product Customization

Big data analytics are proving ideal for retailers. Any company offering consumer goods can harness big data to improve operations, streamline the supply chain, manage inventory, adjust pricing, and build better marketing programs. And increasingly retailers are using big data for product customization.

Big data is the perfect tool to develop a 360-degree perspective of customers. Using structured data from sales figures, stocking numbers, and transaction records, combined with unstructured data such as social media traffic, online queries, and online shopping data, retailers can get a detailed picture of what customers want. And they can develop better custom offerings by using big data for product customization.

According to a Bain survey of more than 1,000 online shoppers, 25 to 30 percent are interested in product customization. For example, 25 percent of all footwear sales are for customized products – just look at Nike or Tom’s Shoes. That’s the equivalent of $2 billion a year. And the same Bain study shows that shoppers who customize their purchases are more engaged with the retailer and spend more money.

Using Big Data for Custom Goods

Big data can play a big role in improving customer design and appeal. For existing products, big data can be used to predict consumer behavior. For example, some fitness brands are using smartphone apps to help consumers with their workouts, and gather data to help with product design and marketing.

For e-commerce, smart retailers are offering real-time shopping options based on past purchases. Some are customizing purchases based on how visitors are shopping, such as from their cell phone. Some e-commerce sites are using big data to use up to 80 options – location, gender, past shopping experience, etc. – to deliver customized product offers.

Using big data for product customization, retailers also are creating passive micro-categories for goods. Netflix started the trend with the “micro-genre” that lets you find specific types of movies. Analytics can be used to create micro categories such as black sweaters within a specific price range.

Amazon’s Custom Catalog

Amazon has been using big data for product customization for years, and continues to rank as the best in customer service, largely because of personalization. They have a rich big data resource of 152 million customers to draw from. Using clickstream data and historical purchases, Amazon is able to use big data for product customization by offering up purchasing suggestions for each customer.

Of course, Amazon has its own big data resources with Amazon Web Services (AWS), which offers tools to collect, store, and collate data for big data analytics. In addition to using big data for product customization, Amazon also uses big data to monitor and secure 1.5 billion inventory items scattered across 200 fulfillment centers.

And Amazon isn’t just using big data for product customization of its own catalog. Working with MIT, Amazon is packaging its customer data for use by marketers who can use it to advertise their own products. Amazon knows what people want to buy, and so big data analytics will help increase their advertising revenue.

Customization Offers a Competitive Advantage

Taking a lesson from Amazon, other retailers are adopting mass customization. Product customization is proving to be a valuable differentiator in an online market where shoppers can conduct global pricing comparisons. Using big data for product customization also lets retailers mine social media and other online consumer hangouts to determine what’s hot and follow the latest styles and trends.

Through their research, Bain discovered there are five rules to customization success:

  1. Know your objective. Are you offering product customization as a means to build customer loyalty, or do you want to make customization a profit center? Know why you want to customize.
  2. Know how much customization you really need. Some brands let you build everything to order. Others see great success with a simple monogram on catalog goods. Others like the convenience of online customization and local pickup at a retail outlet. Determine what works best for your brand.
  3. Start with design options. Don’t offer consumers a blank canvas; they will become frozen by too much choice. Instead offer a template with design options such as colors to make it easy to mix and match.
  4. Make returning goods easy. Give consumers the option to return goods within a reasonable timeframe, such as 30 days. Most consumers won’t return orders, but they want the comfort of knowing that they aren’t stuck with a custom purchase they don’t like.
  5. Help customers share their creativity. Make it easy for customers to share their purchases with friends and family online. When someone buys something cool or gets a good deal they like to tell others.

The more retailers use big data to learn about their customers, the more customized their offerings and the greater the revenue and customer loyalty.