December 1, 2009 – 10:30 pm
Track sales transactions to understand your customer’s buying patterns, establish a more relevant product mix, satisfy more people and sell more.
By John Maller
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Posted in Uncategorized
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Also tagged analytics, business analytics, buying pattern analytics, cross sell, crosselling, customer buying patterns, customers, demand, demand shaping, merchandising, multichannel ecommerce, online marketing analytics, order fulfillment, retail, substitution, suggestive selling, up-selling, upsell, upselling
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October 26, 2009 – 10:30 pm
The manufacturers should be giving the dealers guidance on what to stock and order based on their global visibility into sales and customer buying trends. Customers buy combinations of features, and they do this in predictable ways. Detecting and using the patterns can make inventory turn faster, even if, technically, it doesn’t exist.
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, customers, online marketing analytics, patterns, POS data, product affinity, product choices, profit, sales, sales analytics, upsell
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October 12, 2009 – 2:19 pm
First order take rates tell us about the relative popularity of different options. For example, consider a small set of possible pizza toppings.
Topping
Take Rate
Pepperoni
40%
Mushrooms
20%
Pineapple
3%
Canadian Bacon
3%
Green Peppers
10%
Customer buying patterns really start with second order take rates, which tell us about pairs of options, or toppings. Second order take rates tell us about relative [...]
By Roy Marsten
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Posted in Uncategorized
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Also tagged analytics, attach rate, business analytics, choice combinations, long tail, online marketing analytics, patterns, product complexity, product management, product mix, sales analytics
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August 27, 2009 – 3:25 pm
If a product is too complex, where is the complexity coming from? Which features are causing the explosion in the number of build combinations? The stairway to complexity tells us where to look.
The stairway to complexity shows how the number of unique configurations drops as features are removed. Here is another stairway for 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, demand, forecasting, inventory, manufacturing, margin, market, marketing, marketplace, model mix, popular choice combinations, predictive analytics, product choices, product inventory, product management, product pipeline, recommendation engine, sales, supply chain
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Advances in interconnection technologies are driving an increasingly demand-driven market. Customers are learning to expect to get what they want, when they want it, how they want it. And they tell you in each and every interaction they have with your company, or not. In a demand-driven world, increasing product variation and complexity in your [...]
By Kathy Chiang
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Posted in Uncategorized
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Also tagged algorithm, analytics, choice combinations, clusters, complexity, cross sell, customers, data mining, demand, demand driven, intelligent, long tail, margin, margins, markets, Pareto, POS data, predictive analytics, product mix, revenue, suggestive selling, upsell, variants, variation
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We often talk about the sales history of a product, so let’s explain exactly what it means. There is a raw sales history and a collapsed sales history. The sales history, raw or collapsed, is the starting point for all the analytics we will be introducing later.
Raw sales history
A product is a collection of features, [...]
By Roy Marsten
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Posted in Uncategorized
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Also tagged analytics, demand driven, demand fulfillment, features, lead time, POS data, predictive analytics, product, sales, sales history, volume
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This morning I was talking to the VP of business process improvement for a company that sells industrial machinery. Their products are highly configurable. She told me that every year they have 50% new configurations they have never seen before. The number of choices on their products has grown over time. ”A salesperson can’t know everything about the product,” [...]
By Radhika Subramanian
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Posted in Uncategorized
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Also tagged algorithm, complexity, configurable, configuration, cross sell, customers, demand driven, demand shaping, industrial, machinery, margin, order, order fulfillment, predictive analytics, product, product variety, sales funnel, salesperson, suggestive selling, upsell
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One of the most important components in choice complexity is the product configuration itself, the mixture of product options that give a product its unique signature. Obviously the typical product orderable options are needed to analyze the complexity of a product, but other more abstract options can offer surprising insights into product and customer behaviors.
A [...]
By Mike Merrill
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Posted in Uncategorized
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Also tagged algorithm, analytics, attribute, attribution, choice combinations, complexity, configuration, customers, demand driven, demand shaping, long tail, margin, options, product, product extension, product mix, quality, sales mix, trends
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