Dynamic Pricing Analysis

1. Dynamic Pricing:

Online retailers have been experimenting with different types of dynamic pricing practices as analytics capabilities have increased in sophistication. Dynamic pricing enables companies to test various price levels and customer segmentations to find the optimal price point, and change strategies in real time as market conditions change. Sellers can essentially gather intelligence on their competitors’ pricing and slash or raise prices in seconds to capture consumer activity.

Traditional clothing retailers have been reluctant to implement dynamic pricing due to concerns about brand integrity and customer loyalty, but it is clear that for high-volume retailers the “discount early and often” strategy is no longer adequate to

Consumers are increasingly researching product pricing, features and discounts across multiple retail outlets, but may prefer to make big purchases through a Web site with a more detailed view, or in a store.

5. Basket/Affinity Analysis:

Businesses must capitalize on the time that a customer spends with them and introduce the shopper to relevant products. Basket analysis provides evidence-based models of customer purchase behavior so that a retailer can make the right product recommendations at the right time. Running affinity analytics on a customer purchase data set can reveal products that customers are more likely to purchase together, behavior patterns around coupon usage or effectiveness of sales promotions. Large retailers have been at the forefront of experimenting with basket analytics and recommendation engines, but any company with customer purchase data can benefit from analysis of the behavior patterns of their target consumers.

6. From Offline to Online and back

One of the most obvious reasons is the level of noise online - it’s much harder (and more expensive) to find new customers than it was a few years ago. With all the ways savvy customers can find new and better deals online, retailers need another way to attract new customers. These are the omnichannel customers; those who shop online and in stores and tend to spend several times more than traditional shoppers. In the case of many retailers, hopes are high that opening storefronts will offer that customized, personal experience that many have lost with only online shopping.

7. Offline Merchandise Assortment:

Retailers are taking localization as far as it will go by making smarter use of customer data. Initially, this data is leveraged for marketing and e-commerce purposes. The analytics derived from their data will cause a positive domino effect, helping retailers understand local customer demand and choice patterns, resulting in improved inventory allocations, and increased revenues and margins. This is a significant departure from the traditional means of setting merchandise assortments purely based on climate, demographics, or geography.

8. Omni-channel emerges as the business function with the highest potential opportunity for analytics