Top Data Analytics Trends for Reatilers of 2017

It’s not easy for a retailer to face ongoing economic challenges. The power of the customers is rising. They have the right to choose the best, and they are not happy with anything less. The competition in every single industry is brutal. We’re not exaggerating when we say that every business battles for survival.

In this war of competitors, data analytics are the most effective weapon. In February 2017, JDA Software Group and PwC (PricewaterhouseCoopers) released the results of a survey of retail CEOs. 69% of CEOs said they planned to invest more resources in digital transformation over the year. They are mostly investing in big data (86%), mobile-enabled apps (85%), and use of social media data (85%).

They are doing this because they want to understand their consumers better. They want to connect with the audience and give them reasons to engage with the business across retail channels. Data plays a huge role in the battle among competitors. That’s why retailers are so focused on analyzing it. Let’s check out the top data analytics trends among retails in 2017.

People are getting so used to technology that they are demanding businesses to incorporate it in the stores. That’s why stores are implementing augmented reality and Internet of Things technologies. IKEA, for example, introduced a layer of augmented reality to their catalogues in 2013. The users could virtually place a piece of furniture in their homes before buying it.

Retailers are measuring how in-store technologies affect the buyer’s decisions. They collect information that enables them to improve this experience with more effective technologies.

Through technology, the businesses have an ability to monitor the movement and actions of the customers in their stores. The efficient data collection enables prompt analysis of supply and demand. Thus, the retailers can always provide the products the customers need.

According to a report provided by Forrester, location analytics can transform the customer’s experience. This data enables businesses to attract a relevant audience by sending geo-targeted mobile push notifications. They can also use this data to understand people’s in-store behavior and improve their experience.

Nordstrom, a US fashion retailer, introduced in-store sensors and Wi-Fi signals that indicate where the customers go and at what point they decide to pick up an item. Thanks to this information, the retailer can position promotions and products more effectively in the store.

Predictive analytics is important, but it’s never 100% certain. It gives the business a likely outcome if all factors remain the same. As we know, the factors never remain the same when the competition is huge. That’s why explanatory modeling is getting more important than ever. Predictive modeling is useful when the store needs to estimate which items are going out of stock. However, it doesn’t explain why those particular items are required.

Explanatory analytics identifies the factors that affect buying decision. If the weather is getting hot, people will want to buy more shower gels and soaps with fresh scents. If the weather is getting colder, they will need to change the stock levels and bring warmer scents in the focus.

Before a customer makes a purchase, they engage with it on different channels. They check the information on the company’s website, they search for reviews (on Amazon or other platforms), and they check out social media pages related to that product. Finally, they check it out in the store before buying it.

A retailer’s ability to create a seamless customer experience across channels is crucial for the success of the business. They have to track and analyze data as it moves across different platforms. Thus, they will understand where the customers get their information and where they buy the products.

Julie Petersen, the founder of askpetersen.com, says that retailers need to understand what kind of information the customers are looking for through all available channels. Then, they will make better target offers to attract the customers their way.

Cross-platform analytics also helps the retails to provide effective support through every stage of the customer’s journey.

There’s no question about it: proper data analytics are an essential part of growth. When you use the right methods, you’ll understand the customer’s behavior and improve their experience. Hopefully, the explanations of the trends above will set a good foundation for your analytics strategy.