Like many industries today, the world of retail has accumulated an array of computer applications over time from the warehouse to the point of sale and beyond to improve efficiency and competitiveness. In recent years, the number of data inputs has grown to include electronic social media, Web site clicks, mobile applications, and video systems that record and correlate individual and mass consumer behavior in the store. This increased volume of data available to businesses from both internal systems and external channels has led Big Data. For retailers, the benefits of using analytical applications that tap into the Big Data stream are considerable. These applications can provide information to significantly enhance sales, marketing, and customer relationships; introduce operational and merchandising efficiencies that reduce costs; and lead to strategies to better understand what customers want, even before they realize it.
Retail companies that can establish closer and more direct relationships with consumers will be the ones that come out on top. To grow and compete, Retailers need to leverage their data analytics capabilities to identify and micro-target niche opportunities. Underneath broad market segments are smaller, more profitable segments, and these groups of consumers are who should be targeted.
We can help Retailers
- Predict potential customer actions
- Identify the best campaigns for individual customers
- Develop user personas and archetypes
- Segment users based on patterns and behaviors
- Build a behavior-driven customer experience
In today’s data-rich world, retail enterprises need to rethink traditional approaches to knowing the customer. Traditionally, retailers analyze customer interactions on a channel-by-channel basis: store sales are tallied from cash register receipts; web data is analyzed to determine site effectiveness; call center data is used to report phone sales and common consumer requests or complaints. Few retailers have visibility into sales and service interactions that span channels, and so they don’t understand the full breadth of an individual customer’s experience.
We help modern retailers with single, 360° view of their consumers, cutting across data silos and fragmentation to provide that single view. This helps retailers calculating lifetime customer value (LCV) and can create effective promotions and offers. This intimate understanding of their customers, provides retailers upsell and cross-sell opportunities.
Though, many retailers are collecting and analyzing data, it is often only used in a reactionary capacity, such as correcting previous mistakes or acting based on historical information which could be months or even years old. We help take next step in this analysis and use it in a predictive manner: one that can be used to optimize and streamline every step of the retail supply chain. This involves not just reacting to previous problems but tackling potential issues before they occur. For example, predictive data analysis can inform retailers of the exact supply and demand for certain products, which can then be used to inform stock purchases and even internal store marketing in real-time. This can be tailored based on behavior within individual outlets, stores, and customer demographics. For example, if a clothing retailer can see that certain types, sizes, and styles of clothing are popular in certain store locations, they can ensure that there is enough of that stock in those stores to meet demand, while reducing the number of other products ordered. This enables the retailer to operate a leaner operation – ordering only what they need and reducing what they don’t need.