Storefront
Customer information exchanged during a storefront interaction is often captured but seldom leveraged for other purposes. This data may be captured by an online form, storefront kiosk or in an employee's notes entered into a CRM application. Much of this information contains customer preferences, product issues, history of past transactions or feedback on their in-store experience. This invaluable information can be captured during a variety of interaction scenarios and can be leveraged to develop marketing campaigns, product development or website design. Perhaps some of the following scenarios sound like something your organization would like to better learn from? A busy wireless carrier asked customers, upon entering the store, to enter the reason for their visit and then adds them to the appropriate queue. Many of the customers indicated that they visited the store to buy a new phone, because the store's website didn't have enough information for them to complete the transaction. This information was valuable since many customers prefer to buy phones and accessories online. The company was able to find out the most common questions asked before someone would buy a phone, and then add the related information to the website to help complete more transactions online. A customer goes into your home improvement warehouse after seeing an ad for kitchen cabinets that were on sale. The associate worked through two designs and the customer bought the cabinets with the lower profit margins. By analyzing similar interactions, its possible to identify common patterns and preferences to help design a marketing and sales strategy that will enable the retailer to better promote the higher margin items in the future. Product Availability and Inventory Management
The associate at a local department store did not have her customer's favorite shoes in stock so she checked the inventory at nearby locations. The shoes were not in stock and the customer left. Including the associates notes from this experience and other similar situations, the retailer can identify how many other customers were looking for the same shoes, if they tried on similar shoes and the brands which customers seeking. This valuable insight about customer preferences will help the retailer improve purchasing decision and maintain better inventory levels. The prevailing question remains...How does the rest of the organization use all of this valuable information as part of its customer experience management planning? What trends are we seeing and how can we change them for the better? What are customers saying over and above what we are asking (unstructured vs. structured responses)? Autonomy's Meaning Based Computing can help answer all of these questions by allowing organizations to manage their storefront information in-place rather than trying to move the data into a central repository. Autonomy Explore, powered by IDOL, automatically process the data from all storefront interactions and identifies the relevant concepts and patterns. Autonomy's industry leading workflow will ensure that the resulting insights are sent to the right person or system to take appropriate action.
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