As I discussed in my Apparel Magazine article “How Social Media is Changing How Retailers Predict Demand and Provide Customer Service,” retailers are experimenting with ways to use social media data to better serve customers online and in stores. Experimenting is the key word, because in the absence of mature analytical solutions, retailers need to implement pilots that can be used to prove business value quickly and inexpensively.
The worst thing retailers can do is to sit back and do nothing. Those who wait too long to meet their customers’ needs will find those needs have been met elsewhere. In the past, these shifts happened over long periods of time. In the digital age, these shifts occur in the blink of an eye and retailers who don’t keep up can be irrevocably left behind.
Next best offer is a prime example of how retailers are using analytics in new ways to create personalized experiences for customers that are seamless across channels. Loosely defined as suggesting complementary items while a customer is online based on a market basket analysis of past sales or current shopping cart, this process has been used for years. What’s different now is that the rise of available data has made the practice more complex, and here are a few ways that retailers can incorporate new types of data in the process:
1. On-Hand Data
To avoid recommending items that are not available in a customer’s size, retailers are adding “on-hand” data into their calculations. Next best offer engines now include on-hand data at a stock keeping unit level so if a highly correlated item is not on hand at the store in the right size, the engine will find substitutable items. If no item exists, the engine will extend the search to other stores and channels. Also, promotions have been added to the engines to help close add-on sales. They can be made up of already existing promotions, conditional promotions based on total basket size being purchased, or promotions such as free shipping to save a sale when an item in the required size is not available at the store.
2. Consumer Segmentation/Shopping Occasion Data
Most market basket analytics are based exclusively on the relationship and correlation between products. However, different customer segments and different purchasing occasions will often result in different product affinities when added into the calculations. For instance, one consumer who shops at a retailer primarily for work may be more enticed by the recommendation of a suit with the purchase of a white blouse. However, another customer who tends to shop for more casual wear may be more influenced by the suggestion of a pair of jeans when purchasing that same white blouse. These types of distinctions between customers will only be visible by including consumer segmentation and/or shopping occasion data in the algorithms used to calculate affinity. These distinctions are necessary for creating truly personalized recommendations.
3. Targeted Recommendations
Most consumers are familiar with making a purchase and then receiving the same product recommendations over and over again. At best, consumers begin to tune out and ignore the recommendations completely. At worst, consumers become frustrated and annoyed by receiving the same recommendations, and take their business elsewhere. Next best offer engines can track previous recommendations to ensure that the same offer is not repeated ad nauseam and can track those recommendations that led to add-on sales as a potential guide for future recommendations.
4. Customer Service Issues
Customer service interactions, returns, and social media commentary can also be used to hone the next best offer. Sometimes, the next best offer is not necessarily a product or service, but is instead being able to identify and resolve an issue, a lesson learned by the retail banking industry. The last thing a consumer wants after having a bad experience is for the retailer to turn a blind eye to their issue and just try to shove more products at them. Consumers, wanting a seamless experience across all channels are increasingly expecting highly personalized suggestions via store associates or retailer apps. In addition, the capabilities of these next best offer engines have become more sophisticated as more information is available to be used to target these offers and better predict consumer needs. The line between being helpful and being annoying or intrusive for consumer is sometimes grey, and retailers risk hurting their relationships by utilizing older capabilities that can suggest unwanted items over and over again.
By creating or evolving next best offer engines and making them available to consumers and store associates, savvy retailers can create a more personalized and seamless experience for the customers which will lead to increased conversions and higher units per transaction.