EmcienMatch

An analytics application that uses customer buying patterns to guide new orders, quotes, and requests to the best configurations for both the customer and the company.

Product Features

Automate the selling process

Your sales history is a gold mine of product knowledge that can be utilized to drive higher sales, increase margins, and reduce the overall number of product configurations. EmcienMatch leverages the existing buying patterns in your sales history to complete partial specifications and guide customers based on what they want to what you know they will buy, and is good for both of you.

Search for the closest match

With configurable products, being out of one configuration doesn’t have to be a deal killer. If you can offer up the “next closest” configuration, there is an opportunity to satisfy the customer and complete the sale. EmcienMatch helps you identify the best choice by taking into account the features the customer cares about and looking for alternatives that differ on the features they haven’t specified.

Reduce quote to order time

The time it takes to convert a customer quote into an order can be hours, days or even weeks. This time is usually spent in Engineering determining if the product the customer wants is valid. EmcienMatch solves this problem by matching on what the customer cares about, staying within their budget and trying to offer existing configurations that satisfy their needs and make the manufacturer more money.

Sell competitively

EmcienMatch allows alternative names for every feature and option of your product. This means you can enter all of your competitor’s terms from their price sheets or website that map to your terminology. So now instead of just making your sales force fluent in the best configuration choices from your catalog, they will also be the best at selling using your competitor’s terminology as well!

The Technology

At the heart of every Emcien analytics application is our patented Pattern Detection Engine. The pattern engine identifies when elements (or tokens) are found together, always apart, “distance” apart, time-dependent, etc. This network of connections is then leveraged by our unique optimizers and user interface to “automatically reveal” the strongest (and weakest) patterns found, by category but also by item. Read more about the technology >>