A food and beverage company produces multiple products in a product family. Multiple supermarket chains and stores handle distribution. Several million items are sold every day. The company is interested in identifying any correlations between products sold in these supermarket chains, in special stores, or in geographical regions.
If product A has on average a share of 30% in the product family, but only 10% within a certain store, analysts would like to drill into the data to identify how to increase the share. In other words, they would like to have the ability to analyze a huge amount of data to identify divergence to the average. Date latency is key to this scenario, as the data should be analyzed within the same day as it was collected. The scenario also requires flexibility and speed in the analysis because an answer typically leads into the next question.
The scenario was implemented with SAP HANA and enables the brand manager to analyze unexpected buying patterns within the billions of records delivered by the retail stores. The data is transferred on an hourly base allowing analysts to perform their analysis in SAP HANA within the same day. Management counter-action can now be taken to increase sales.
This case could also be extended as a service for the supermarket manager to benchmark themselves against other stores of the same size. They could look at changes and directly act by checking self-availability and effects of running promotions.
There was a very similar case at a large car manufacturer. This company uses SAP HANA to analyze sales figure from the last 10 years including car sold, model, region and city, all the way down to car-dealer who sold the car, reselling price, and discount level. The car manufacturer ran the exact same advertisement campaigns all across the US, but found in its sales data from the last 10 years, that there is clearly different buying behavior in the different regions of the US. Based on this information, the company adopted more regionalized campaigns as part of their marketing strategy. For the future, this customer will use SAP HANA to better validate the results of marketing activities, even adding unstructured data from social media to their analysis.
A telecom company even extended the scenario by adapting marketing campaigns within days based on the early results. This led to specific offers on the east coast, while a different phone with specific contract are offered at the same time within the same campaign on the west coast. It may not come as a surprise to learn that folks in New York City have different buying behaviors than those in the Midwest or California. But knowing it is just a start, SAP HANA helps identify these differences.
These scenarios are custom scenarios that take advantage of SAP HANA capabilities: high volume, reporting speed, reach and real-time data availability.