Improve business outcomes using big data for consumer products
For years, consumer products companies have relied on traditional data warehouse and business intelligence solutions, and data from internal and external sources to improve operations and profits. Supplementing these existing data sources by analyzing information from new sources (social media, blogs, etc.) will help consumer products companies to better determine what consumers really think about new products, promotions, advertising or pricing; how new offerings are being received by consumers and retailers; and where there are opportunities to make immediate improvements.
Consumer products companies that want to incorporate streaming data from new structured and unstructured sources, glean faster intelligence, and perform immediate, predictive analytics on data will need to deploy big data technologies. Successfully harnessing big data can help achieve three critical objectives for consumer products transformation: collaborate with channel partners to drive efficiency; build life-long relationships with consumers; and manage products from raw materials to finished goods.
IBM can help you achieve your business objectives by applying big data solutions to key business use cases. These include:
Optimized promotions effectiveness
Consumer products companies with access to greater amounts of data can leverage their promotional dollars more effectively by accurately predicting which types of promotions will result in the best outcomes (increased volumes, profits or customer goodwill) in a particular chain or cluster of stores within a chain.
IBM's big data solution enables organizations to optimize promotional effectiveness by reducing the cost of merchandising and improving profitability. Business benefits include:
Aperity uses IBM's PureData for Analytics to provide sales and merchandising forecasts for 18-plus months in the future with greater than 95% accuracy.
Beyerdynamic uses IBM big data analytics to increase global market share and growth rate – delivering a full return on investment within two years.
Micro-market campaign management
Micro-market campaigns target specific individuals or groups of consumers to improve the outcomes of marketing programs and create better customer relationships. By offering more personalized marketing messages to local markets, different consumer segments or individual consumers through micro-market campaigns, Consumer products companies can realize higher reach and frequency while spending less on campaigns.
Without big data technologies, consumer products companies could not effectively leverage social media platforms or branded sites to promote products to a large number of targeted consumers or change marketing campaigns midstream to improve outcomes. They also could not immediately process and analyze large volumes of high-velocity online feedback about campaigns and their effectiveness.
Nielsen uses IBM's PureData System for Analytics to deliver speed of-thought analytics to its customers, for a true competitive advantage.
Real-time demand forecast
Accurately predicting the quantity of products that consumers will purchase can be challenging and becomes even more complicated as additional promotions occur. Accurately projecting demand and closely managing inventory levels can significantly reduce inventory costs and increase profitability.
If streaming data from trucks, distribution centers, warehouses and in-store point-of-sale systems can be immediately analyzed, then inventory can be rerouted or loads shifted as needed. During promotions, companies can gather real-time shipment and point-of-sale pricing information to ensure that stores that have committed to running a promotion are actually participating. If stores have not implemented a promotion, field sales personnel or retail store managers can be contacted to fix the problem, which will help improve the overall profitability of a promotion.
Delivering new insights and value to consumer products companies through big data
Learn what to consider when deploying big data solutions in order to improve outcomes and increase customer loyalty and satisfaction.
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