October 19, 2009 – 8:00 am
Products have so many choices. Customers hate the experience of having to answer 50 questions to get to selection. Asking them 50 questions translates to poor customer experience and it is actually bad for you too. Here is why. Every sale begins with a customer calling out the top [...]
By John Maller
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Posted in Uncategorized
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Also tagged amr, analytics, choice combinations, CRM, cross sell, customer buying patterns, customer experience, herd buying patterns, product alternatives, product selection, recommendation engine, sales cycle, sales productivity, suggestive selling, upsell
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October 15, 2009 – 6:37 pm
Buying patterns are real, and they manifest themselves in how customers buy combinations of options. With the computing power we have available today we can detect and capture them. These patterns can then be used to design “house specials”, forecast future sales, and guide customers to what we want to sell them.
By John Maller
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Posted in Uncategorized
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Also tagged analytics, business analytics, buying patterns, choice combinations, crosselling, customer fulfillment, customers, online marketing analytics, patterns, POS data, product affinity, profit, sales, sales analytics, upsell
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September 22, 2009 – 5:29 pm
Knowing who is buying what, where and why is “True Demand Intelligence”.
By John Maller
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Posted in Uncategorized
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Also tagged analytics, attributes, attribution, buying patterns, choice combinations, crosselling, demand sensing, Intelligence, patterns, POS data, predictive analytics, sales, SKU, up-selling, variety
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September 8, 2009 – 2:21 pm
The Amazon recommendation engine has received a lot of attention and imitation. It has been successful at increasing sales by pointing out that people who bought book x also bought book y. This simulates a helpful book store employee who has an extensive mental map of how books relate to each other. Recommendations have been [...]
By Roy Marsten
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Posted in Uncategorized
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Also tagged Amazon, business analytics, buying, buying patterns, configurable product, online marketing analytics, predictive analytics, product complexity, recommendation, recommendation engine, sales analytics
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September 3, 2009 – 7:17 am
Reporting, Business Intelligence and Data mining cause data overload. We need to provide business users with actionable tasks based on analytics.
By John Maller
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Posted in Uncategorized
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Also tagged analytics, business, business intelligence, CRM, data mining, demand driven, online marketing analytics, POS data, product mix, sales analytics, sales data, sales history
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August 26, 2009 – 10:41 am
Product complexity is driven by large number of options. Companies struggle to determine which feature choices are driving complexity. They typically “randomly cut choices” to streamline and rationalize SKUs. The cost of product complexity is tremendous on engineering. The current PLM systems do not have a method to measure this and provide intelligent feedback to engineers on how to standardize platforms to reduce engineering and maintenance costs.
This article clearly details the metrics around product complexity and how to solve this issue.
August 25, 2009 – 1:55 pm
The number of build combinations depends on which features are included. The build combinations is the product mix or the marketing mix. Understanding this is important as product complexity is a key driver of process complexity.
August 23, 2009 – 9:56 am
There exists a great opportunity for companies to streamline their sales process which will deliver immediate relief from the current economic reality. Emcien provides a “Recommendation Engine” for configurable products, empowering the sales process. In spite of tremendous technology advances, we see a selling process within companies that is very archaic, people intensive and time consuming. It involves sales people, sales engineers, quotes managers, configuration specialist, ……..
August 21, 2009 – 3:39 pm
A product is a collection of features, and each feature has mutually exclusive options. If a feature has only two options, then the choice is like a coin toss. The information contained in that choice is measured by entropy.
Entropy is a concept from classical thermodynamics that deals with the amount of disorder in a [...]
In today’s post, John Sloan talks about challenges dealers face in ordering inventory that best matches customer demand.
Emcien: Describe the Chrysler-Emcien initiative that examined dealers’ struggles with complexity in the ordering process.
JS: In a soft “push” market where volume is driven by heavy incentives versus the merits of the brand / model, managing cost is [...]
By Loraine Fick
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Posted in Uncategorized
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Also tagged analytics, b2b ecommerce, best match, cost management, customer fulfillment, demand, forecasting, inventory, manufacturing, margin, market, marketing, marketplace, model mix, popular choice combinations, predictive analytics, product inventory, product management, product pipeline, recommendation engine, sales, supply chain
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