1 Customer - 1 Unique Offer Set

Retailers, whether online or brick and mortar, need three things to successfully achieve true 1-to-1 marketing:

A comprehensive transaction-level database, like that found with most loyalty card programs and credit card databases.
The data mining means to match the offer with the specific needs of the consumer in that database.
The data mining means to match the offer with the specific business goals of the retailer.

We call this "individualizing the offer" - for both the individual consumer and the individual retailer. In other words, true 1-to-1 marketing. If just one of these is not present, then the retailer may as well close his eyes and throw darts at a database printout.

Sagarmatha is the first company to offer Individualization Automation(SM) to retailers, true 1-to-1 personalization.

Finally, retailers have a tool that allows them to unlock the hidden profit potential within each individual customer's transaction history.

The millions of dollars spent annually by retailers for the collection and retention of shopper data can now actually yield business-building results - increased loyalty and sales. Best of all, Sagarmatha's PPB system is fully automatic - eliminating the need for human intervention and expensive IT skill sets.

Many less sophisticated tools claim 1-to-1 marketing capabilities even though most lump similar customers into buckets and give them the same set of offers - referred to as clustering. Up until now, the problem with most data mining solutions is that they lack the algorithmic horsepower to look at more than a few dozen criteria simultaneously. The result - most "clustered" consumers end up receiving offers they don't want or need, and the retailer's goals go unrealized.

Sagarmatha uses the most powerful data mining algorithms ever used in a promotion application. The PPB system evaluates several thousand purchase criteria simultaneously in order to optimize a distinct, often unique, set of offers for each individual consumer in a manner to best satisfy that shopper's unique needs and tastes. Further, these optimized consumer offers are specifically designed to maximize the goals as defined by the retailer.

Sagarmatha's PPB solution is one of those rare technology breakthroughs that come along which enable fundamental shifts in thinking. Where previous promotional solutions were content to measure success based on redemption rates, Sagarmatha is able to channel individual consumer behavior in a way that increases frequency and average purchase size - success measured in real sales-building terms.


		1 Customer - 1 Unique Offer Set Retailers, whether online or brick and mortar, need three things to successfully achieve true 1-to-1 marketing: A comprehensive transaction-level database, like that found with most loyalty card programs and credit card databases. The data mining means to match the offer with the specific needs of the consumer in that database. The data mining means to match the offer with the specific business goals of the retailer. We call this "individualizing the offer" - for both the individual consumer and the individual retailer. In other words, true 1-to-1 marketing. If just one of these is not present, then the retailer may as well close his eyes and throw darts at a database printout. Sagarmatha is the first company to offer Individualization Automation(SM) to retailers, true 1-to-1 personalization. Finally, retailers have a tool that allows them to unlock the hidden profit potential within each individual customer's transaction history. The millions of dollars spent annually by retailers for the collection and retention of shopper data can now actually yield business-building results - increased loyalty and sales. Best of all, Sagarmatha's PPB system is fully automatic - eliminating the need for human intervention and expensive IT skill sets. Many less sophisticated tools claim 1-to-1 marketing capabilities even though most lump similar customers into buckets and give them the same set of offers - referred to as clustering. Up until now, the problem with most data mining solutions is that they lack the algorithmic horsepower to look at more than a few dozen criteria simultaneously. The result - most "clustered" consumers end up receiving offers they don't want or need, and the retailer's goals go unrealized. Sagarmatha uses the most powerful data mining algorithms ever used in a promotion application. The PPB system evaluates several thousand purchase criteria simultaneously in order to optimize a distinct, often unique, set of offers for each individual consumer in a manner to best satisfy that shopper's unique needs and tastes. Further, these optimized consumer offers are specifically designed to maximize the goals as defined by the retailer. Sagarmatha's PPB solution is one of those rare technology breakthroughs that come along which enable fundamental shifts in thinking. Where previous promotional solutions were content to measure success based on redemption rates, Sagarmatha is able to channel individual consumer behavior in a way that increases frequency and average purchase size - success measured in real sales-building terms.