Questions: 1. What were the main challenges faced by Ingram Micro in developing a BIC? 2. List all the business intelligence solutions developed by Ingram to optimise the prices of their products and to profile their customers. 3. What benefits did Ingram receive after using the newly developed Bl applications? Application Case 6.2 Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions Ingram Micro is the world's largest two-tier distribu- vendor-customer segment level. It does so by tak- tor of technology products. In a two-tier distribution ing into account the trade-off between the demand system, a company purchases products from man- signal and pricing at that level. ufacturers and sells them to retailers who in turn The company also developed a digital market- sell these products to the end users. For example, ing platform known as Intelligence INGRAM. This one can purchase a Microsoft Office 365 package platform utilizes predictive lead scoring (PLS), which from Ingram rather than purchasing it directly from selects end users to target with specific marketing Microsoft . Ingram has partnerships with Best Buy, programs. PLS is their system to score predictive Buffalo, Google, Honeywell, Libratone, and Sharper leads for companies that have no direct relation Image. The company delivers its products to 200,000 with end users. Intelligence INGRAM is used to run solution providers across the world and thus has a white space programs, which encourage a reseller large volume of transaction data. Ingram wanted to to purchase related products by offering discounts use insights from this data to identify cross-selling For example, if a reseller purchases a server from opportunities and determine prices to offer to spe- INGRAM, then INGRAM offers a discount on disk cific customers in conjunction with product bun- storage units as both products are required to work dles. This required setting up a business intelligence together. Similarly, Intelligence INGRAM is used center (BIC) to compile and analyze the data. In to run growth incentive campaigns Coffering cash setting up the BIC, Ingram faced various issues rewards to resellers on exceeding quarterly spend goals) and cross-sell campaigns (e-mailing the end 1. Ingram faced several issues in their data- users about the products that are related to their capture process such as a lack of loss data recently purchased product). ensuring the accuracy of end-user information, and linking quotes to orders. Results/Benefits 2. Ingram faced technical issues in implementing Profit generated by using IMPRIME is measured a customer relationshin management (CRM) 1 A capture process such as a lack of loss data, recently purchased product), users about the products that are related to their ensuring the accuracy of end-user information, and linking quotes to orders. Results/Benefits 2. Ingram faced technical issues in implementing Profit generated by using IMPRIME is measured a customer relationship management (CRM) system capable enough to handle its opera- ology compares periods before and after changing using a lift measurement methodology. This method- tions around the world, 3. They faced resistance to the idea of demand groups. Lift measurement is done on average daily the prices and compares test groups versus control pricing (determining price based on demand sales, gross margin, and machine margin. The use of product). of IMPRIME led to a $757 million growth in revenue Methodology/Solution and a $18.8 million increase in gross profits. Ingram explored communicating directly with its QUESTIONS FOR DISCUSSION customers (resellers) using e-mail and offered them 1. What were the main challenges faced by Ingram discounts on the purchase of supporting technolo- Micro in developing a BIC? gies related to the products being ordered. They identified these opportunities through segmented 2. List all the business infelligence solutions devel- oped by Ingram to optimize the prices of their market-basket analysis and developed the follow- products and to profile their customers. ing business intelligence applications that helped 3. What benefits did Ingram receive after using the in determining optimized prices. Ingram devel- newly developed BI applications? oped a new price optimization tool known as IMPRIME, which is capable of setting data-driven What Can We Learn from This Application prices and providing data-driven negotiation guid- Case? ance. IMPRIME sets an optimized price for each level of the product hierarchy (i.e., customer level. By first building a BIC, a company begins to bet- vendor-customer level, customer-segment level, and ter understand its product lines, its customers, and (Continued Application Case 6.2 (Continued) their purchasing patterns. This insight is derived Sources Mookherjec, R., Martineau, J. Xu, L., Gullo, M., Zhou, from what we call descriptive and predictive ana- K. Hazlewood, A., Zhang, X., Griarte, F. & Li, N. (2016) lytics. Further value from this is derived through End-to-end predictive analytics and optimization in Ingram Micro's two-tier distribution business Interfaces, 16(1), 1973 price optimization, a purview of prescriptive ingrammicrocommerce.com "CUSTOMERS https://www analytics ingrammicrocommerce.com/customers/ (accessed July 2016), uncertainty, or risk. To expedite model construction, we can use special decision analysis systems that have modeling languages and capabilities embedded in them. These include spreadsheets, data mining systems, online analytic processing (OLAP) systems, and mod- eling languages that help an analyst build a model. We will introduce one of these systems later in the chapter MODEL MANAGEMENT Models, like data, must be managed to maintain their integrity, and thus their applicability. Such management is done with the aid of model-based man agement systems, which are analogous to database management systems (DBMS). KNOWLEDGE-BASED MODELING DSS uses mostly quantitative models, whereas expert systems use qualitative, knowledge-based models in their applications. Some knowledge is necessary to construct solvable and therefore usable) models. Many of the predic tive analytics techniques, such as classification and clustering, can be used in building knowledge-based models. 11:43 AI