How Retailers Can Use Data Analytics to Succeed in 2018

How Retailers Can Use Data Analytics to Succeed in 2018

The first day of trading at Wall Street has kicked 2018 off to a much stronger start than was expected, but not a terribly sturdy one. This is largely due to everybody having a better Christmas shopping season than had been expected

This doesn’t mean that everyone is in the clear. Over 20 different retail chains have either been liquidated or have filed for bankruptcy. This includes companies like Sears, HHGregg, Radio Shack, and Toys R Us. Many of these are some of the slowest to adapt to the digital marketplace. When counting how many individual brick and mortar locations are planned to be closed throughout 2018, the number reaches over 3,600 closures.

The Difference

The difference between who stays and who goes is plain. One group (those who are surviving and even thriving) has adapted and continued to provide appeal to customers throughout a quickly changing economic landscape, while the other group has not, or has done too little, too late.

The specifics we will discuss outline some of what the latter group could have done. These are some of the same actions propelling the first group ahead of the pack. This can be summarized as a deliberate effort to create a seamless integration of digital and brick and mortar that all works to build an excellent customer experience (CX).

The most powerful tool in gathering the information to build this CX focused infrastructure? Data Analytics; the “qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.”

Using Big Data and Data Analytics

Reason One: Analyzing the Customer Behavior.

If you want to appeal to a customer, you need to understand what motivates and appeals to your customer. Where this once depended on educated guess work, studying big data allows for this to be based on measurable statistics.

Identify and measure the patterns found in your store’s customers. Once you have collected this information you can put it to action. Do this by entering the aggregate data into a POS (point of sale) system. From here you can clearly see the aggregate behavior of your customers and adapt your practices to better meet the customer’s indicated preferences.

For example, If you find that your store’s ‘10-Item-or-Less’ aisle is usually backed up with customers who are only buying one or two items, then you can install self checkout stations to speed up check out for your customers.

Reason Two: Customizing the In-Store Experience.

Merchandising and advertising used to be more art than science. With online sales growing, however, shoppers make a habit of doing research on site in store to decide if they really want the product before going online to find the lowest price, or just holding off to buy at a later time.

Being able to track individual customers allows us to gather information that was never available before. Personalizing loyalty rewards and applications and optimizing marketing and merchandising strategies becomes possible when we see what our customers are looking at. It allows for providing greater incentive to buy at your location or through your application.

By analyzing the data provided from their in-store sensors and POS systems, omni-channel retailers are able to:

  • Personalize in-store experiences and service for customer based on browsing and purchase history.
  • Test and analyze the effectiveness of different merchandising and marketing strategies. The results of the different strategies can be directly measured based on customer behavior and sales.
  • Keep track of your in-store customers to provide timely offers and incentives for buying on the spot or online later on. In doing this, you can keep the sale for your retailer.

Reason Three: Increasing Conversion Rates.

Increase the conversion rate to acquire more sales, and spend less money by not advertising to those who are not interested in your products. This means more revenue from sales, and less in overhead costs from advertising.

Customer information before was only collected at the point of sale. Loyalty programs allowed for collection of demographic data during the sale. This is good information to have, but to use only this is crippling when you consider how much more in depth the competition’s information is.

Customer’s interact with your brand and with one another over different channels on social media. Generate customer information based on these interactions to understand what is in demand both in terms of services and customer experience, as well as what products are trending.

Data engineering is able to make the connections between customer profile information, browsing history, purchase history, and social media use. These connections are a huge help for revealing what a customer’s likes are. If they “liked” a food post from Shingletown Saloon’s Facebook page, for instance, it says what types of foods they are more likely to buy when presented the chance, as well as what aesthetics appeal to that customer.

Specifically, big data analytics allows omni-channel retailers to:

  • Gauge the effectiveness of different marketing and merchandising efforts on customer behavior and conversions.
  • Provide personalized advertising and promotions based upon a customer’s established purchasing and browsing history.
  • Observe customer social media activity and purchasing behavior together in order to provide timely advertising.

Reason Four: Improved Supply Chain Efficiency.

Supply chain management is a massive expense that is often riddled with inefficiencies. Not only must the kinks in the outbound supply chain be worked out to improve efficiency and by that lower overhead, but reverse logistics for damaged products must be established to run just as smoothly to reduce the cost of operations.

The keystone to efficiency in supply chains is the aggregate data compounded from log, sensor, and machine data. This will highlight the outliers in performance that can be improved on and/or modeled after.

This is an immense amount of data that can quickly double or triple in size, but the insights it provides over small details that are repeated thousands of a day can improve efficiency enough to save millions in otherwise wasted overhead costs.

Concluding

Purely brick and mortar retail is a thing of the past, and those that clung to that model for too long are providing a prime example of what not to do as they drift to the wayside. Mobile payments are becoming the norm as they are adapted by Walmart and Starbucks employing similar ideas to what Alibaba is with it’s smartphone app, company and customer interaction over social media is increasing, and omni-channel retailers are working harder than ever to create a seamless experience that has their physical and digital efforts support one another.

Big data and big data analytics are here to stay as permanently as the internet itself. To stay competitive is to know the landscape, and the collected and applied aggregate data is the key to maximizing efficiency in all aspects of business from keeping stock, supply chain management, customer research, and intelligent advertising.