This analytics project analyzed a company's sales and marketing dataset to determine which channel has the most potential to maximize growth (Retail, Catalog, or Website). This project answered the following questions:
What channel should strategic development be focused on to maximize growth?
Do customer segments correlate to a channel?
How do we determine synergies between the various sales channels?
Does demographic data correlate to a channel and push revenues into the other channels?
Are demographics and synergies important to growth of the company?
Forecast uncertain demand products
Choose the suitable place for production
Better forecast
Use both quantitative + qualitative method
Analyze historical data => forecast future trend
Use weighted factors (weighted average independent forecasts)
Shorten forecasting duration
This document presents a case study analysis of Alpine Ski House and Sport Obermeyer. It discusses Sport Obermeyer's history and product segmentation. It describes OberSport Ltd, a joint venture between Sport Obermeyer and Alpine Ltd to coordinate production in Asia. The document analyzes challenges around forecasting, manufacturing location, and supply chain. It provides recommendations to improve forecasting using historical data, enhance production quality and decrease lead times, and expand through new distribution centers and markets.
Sport Obermeyer is a high-end skiwear company that produces around 800 stock keeping units each year through a global supply chain. It works with a joint venture partner Obersport in Hong Kong and China to source materials and manufacture products. Sport Obermeyer is looking for ways to improve its forecasting accuracy and production flexibility to better meet demand. Some options it is considering include adjusting production allocation between Hong Kong and China factories and revising minimum order quantities.
Sport Obermeyer is deciding how to allocate production of women's parkas between manufacturing in Hong Kong and China. Factors that influence this decision include minimum order quantities, production costs, quality differences, workforce skills, and lead times. Based on an analysis of forecast accuracy and risk for each style, Sport Obermeyer determines how many units of each style to produce in Hong Kong versus China in their initial production run while meeting their total quantity needs.
The document provides an overview of the concepts and practices of marketing research. It discusses how marketing research helps improve decision making by providing relevant, accurate and timely information. It outlines the marketing research process and different types of marketing research including programmatic, selective and evaluative research. It also discusses the growing importance of international marketing research given increased globalization. Finally, it discusses how marketing research data can be organized and stored in databases and retrieved using a decision support system to provide useful information to managers.
Sport Obermeyer produces skiwear and uses a combination of qualitative forecasting techniques to estimate demand for initial production. They forecast demand for 10 styles and determine the optimal order quantity for each by balancing the risks and costs of under- and over-estimating demand. The summary recommends operational changes like reducing style complexity, shortening lead times, developing supplier relationships, expanding distribution, and collecting historical data to improve forecasting. For sourcing, Hong Kong is best for flexibility and quality on smaller orders and higher risk styles, while China is better for larger volumes and lower risk styles, but long term quality improvements in China could leverage both countries' advantages.
A mini case study on Walmart,including its Marketing strategies and SWOT Analysis,created by Siddharth Suman,ISM Dhanbad,under a Marketing Internship by Prof.Sameer Mathur,IIM Lucknow.
Sport Obermeyer produces ski parkas and must decide production quantities months in advance during the "speculative production" phase based on forecasts of demand. There are risks of either overstock or stockouts. The document discusses guidelines for choosing which parkas to produce speculatively based on characteristics like standard deviation and mean demand. It also provides recommendations to Wally, such as improving demand forecasts, obtaining earlier market feedback, decreasing lead times, and increasing production capacity, to better manage speculative production.
Forecast uncertain demand products
Choose the suitable place for production
Better forecast
Use both quantitative + qualitative method
Analyze historical data => forecast future trend
Use weighted factors (weighted average independent forecasts)
Shorten forecasting duration
This document presents a case study analysis of Alpine Ski House and Sport Obermeyer. It discusses Sport Obermeyer's history and product segmentation. It describes OberSport Ltd, a joint venture between Sport Obermeyer and Alpine Ltd to coordinate production in Asia. The document analyzes challenges around forecasting, manufacturing location, and supply chain. It provides recommendations to improve forecasting using historical data, enhance production quality and decrease lead times, and expand through new distribution centers and markets.
Sport Obermeyer is a high-end skiwear company that produces around 800 stock keeping units each year through a global supply chain. It works with a joint venture partner Obersport in Hong Kong and China to source materials and manufacture products. Sport Obermeyer is looking for ways to improve its forecasting accuracy and production flexibility to better meet demand. Some options it is considering include adjusting production allocation between Hong Kong and China factories and revising minimum order quantities.
Sport Obermeyer is deciding how to allocate production of women's parkas between manufacturing in Hong Kong and China. Factors that influence this decision include minimum order quantities, production costs, quality differences, workforce skills, and lead times. Based on an analysis of forecast accuracy and risk for each style, Sport Obermeyer determines how many units of each style to produce in Hong Kong versus China in their initial production run while meeting their total quantity needs.
The document provides an overview of the concepts and practices of marketing research. It discusses how marketing research helps improve decision making by providing relevant, accurate and timely information. It outlines the marketing research process and different types of marketing research including programmatic, selective and evaluative research. It also discusses the growing importance of international marketing research given increased globalization. Finally, it discusses how marketing research data can be organized and stored in databases and retrieved using a decision support system to provide useful information to managers.
Sport Obermeyer produces skiwear and uses a combination of qualitative forecasting techniques to estimate demand for initial production. They forecast demand for 10 styles and determine the optimal order quantity for each by balancing the risks and costs of under- and over-estimating demand. The summary recommends operational changes like reducing style complexity, shortening lead times, developing supplier relationships, expanding distribution, and collecting historical data to improve forecasting. For sourcing, Hong Kong is best for flexibility and quality on smaller orders and higher risk styles, while China is better for larger volumes and lower risk styles, but long term quality improvements in China could leverage both countries' advantages.
A mini case study on Walmart,including its Marketing strategies and SWOT Analysis,created by Siddharth Suman,ISM Dhanbad,under a Marketing Internship by Prof.Sameer Mathur,IIM Lucknow.
Sport Obermeyer produces ski parkas and must decide production quantities months in advance during the "speculative production" phase based on forecasts of demand. There are risks of either overstock or stockouts. The document discusses guidelines for choosing which parkas to produce speculatively based on characteristics like standard deviation and mean demand. It also provides recommendations to Wally, such as improving demand forecasts, obtaining earlier market feedback, decreasing lead times, and increasing production capacity, to better manage speculative production.
The document discusses Sports Obermeyer, a skiwear manufacturer founded in 1947. It provides details on the company's structure, sales figures, market share, and a joint venture called Obersport.
It then presents a sample problem asking how many units of each style Wally Obermeyer should order from its Hong Kong production. Using forecasting data and calculations, it recommends production quantities for 10 styles that meet the minimum order of 10,000 units.
Recommendations are made to improve Obermeyer's forecasting accuracy, reduce lead times, increase Chinese worker efficiency, and source non-standard zippers closer to reduce lead times. Long-term, shifting more production to Hong Kong is suggested due to lead
American Tool Works is looking to increase inventory levels and sales at small and mid-sized dealers. To increase inventory, they propose offering buy-back contracts that allow dealers to return unsold inventory, and revenue sharing contracts that provide discounts for bulk orders and allow dealers to share revenue. To increase sales, they suggest threshold contracts providing commissions up to sales levels, and sales incentive programs to motivate dealers' salespeople. Implementing these contracts could take 2-3 months. American Tool Works believes combining buy-back contracts and sales incentives will offer flexibility to dealers and lead to higher inventory and sales.
Klaus Obermeyer founded Obermeyer in 1947 in Aspen, Colorado. In 1985, Obermeyer formed a joint venture called Obersport in Hong Kong to increase production capacity. Obermeyer's supply chain stretches from Asia to Aspen, with textile and accessory suppliers in Asia manufacturing garments that are then shipped through Obersport and Sport Obermeyer to retailers in the US. Obermeyer faces challenges in uncertain demand forecasting for its seasonal ski fashion products and long lead times in its Asian manufacturing process.
Understanding Merchandise Mix of Marks & Spencers storeSrishti Raut
This document provides an overview of Marks & Spencer's fashion merchandising strategy in India. It discusses M&S's introduction to the Indian market in 2001 through a joint venture with Reliance Retail. It then analyzes M&S's merchandising mix of variety, breadth, and depth. Specifically, it examines the brand's product assortments in women's, men's, and kids' wear as well as beauty. The document also provides sales figures and discusses M&S's future plans to expand its store network in India.
Commerce Bank implemented a revolutionary business model in the US banking industry by focusing on deposits rather than loans and providing an excellent customer experience through retail-inspired branches and services. This allowed Commerce Bank to grow much faster than the industry, with deposit growth averaging over 30% from 1998-2001 compared to 6% for the industry. However, competitors began adopting some of Commerce Bank's service offerings, threatening its position, and it faced pressure to continue innovating.
Sport Obermeyer was founded in 1947 in Aspen, Colorado to design and produce skiwear. It sources materials from around the world and produces garments in factories in Hong Kong and China. It faces challenges in forecasting demand accuracy and allocating production given long lead times and little market feedback until trade shows in March. Production in China has lower costs but higher minimums and risks from trade relationships, while Hong Kong has higher costs but more flexibility.
Forecasting recommendation
Forecast Methodology
Obermeyer used combination of the “panel consensus” and “Delphi method” of qualitative forecasting for sales forecasts
We used a single period inventory model to estimate the financial risk of underestimating and overestimating demand
Single point forecast data provided is limiting. More complicated forecasting techniques require actual data collected over time
Recommend next sales forecast results are summarize and redistribute to the team. Given results new questions should be asked of the team in regards to what assumptions to apply in the decision making process
Forecast Assumptions
Initial 10,000 unit order is riskier due to lack of demand information. Second 10,000 unit order is less risky because of better demand information on each style.
The expected lose from liquidating inventory due to overestimating demand is assumed to be 8% of the wholesale price
The cost of lost profit from underestimating demand is assumed to be 24% of the wholesale price
The second order will allow us to adjust for quantities of each style based on better demand information
Reduce the number of styles handled to lower complexity of planning and risk profiles
Study fashion in Europe rather than waiting for Las Vegas shows
Reduce production lead times, as the preparation of raw materials takes a long time. For example:
To improve efficiencies, dye basic colors early in the year and fashion colors later in the season
Dyers could be offered a long-term contract regarding Greige goods
Develop relationships with big-time suppliers that are able to meet tight times and requested demand
Increase distribution channels and service level requirements
Collect and utilize historic data from previous years to better determine future trends
Where possible, obtain feedback from retailers prior to Vegas
Harley-Davidson saw decreased revenue, net income, and stock prices in 2008. The company continues to manage its balance sheet effectively with a return on invested capital of 17%. To increase growth, Harley-Davidson recommends expanding into the European and Asian markets, increasing sales of Buell and women's motorcycles, and developing environmentally friendly models. The company will implement these strategies through marketing research, product development, public relations, advertising, and distribution channels.
E-commerce is growing in Costa Rica, which has a highly developed telecommunications network and 99% literacy rate. Online shoppers in Costa Rica prioritize price, in-stock items, and free/fast delivery. Global companies like Amazon have expanded operations in Costa Rica, now employing over 7,500 workers. Direct-to-consumer brands are now seen as a bigger competitive threat than Amazon. Successful direct-to-consumer brands focus on serving unmet customer needs, clear value propositions, purposeful branding, and a personalized, data-driven customer experience from acquisition to fulfillment. Building an effective direct-to-consumer brand requires more than just e-commerce - it demands attention to customer data, targeted marketing,
How Leading Brands Drive Sales With Omni-Channel CampaignsDaniel Caridi
eMarketer moderates a presentation with Arpita Neelmegh, product marketing manager at Iterable, who breaks down one of the fundamental tactics that leading companies are employing to truly boost sales and cultivate customer relationships—sending personalized messages across multiple channels.
The document discusses using customer insight to drive performance for a large wireless communication company. It describes implementing a phased approach including developing tactical targeting tools, identifying growth opportunities, and establishing an infrastructure to capture value. Case studies demonstrate segmenting the customer base to understand needs, prioritize initiatives, and maximize revenue and retention through targeted campaigns.
Simon rowles conference presentation september 2010Simon Rowles
Loyalty programs have the data to drive ROI in any channel through a targeted and relevant communications mix. Case studies included : eliminating print from a bank loyalty program reduces the program's performance.
A Point of View for PIM in Retail, CPG and Distribution CompaniesShamanth Shankar
Gain a common understanding of PIM and its value to your organization. Understand why managing product information is critical to your Ecommerce / ERP initiative (upgrade or rip & replace)
Riversand's Enterprise PIM Solution manages the continuous flow of data throughout the entire Product Information Lifecycle, this product information management helps manage the information required to market and sell products through distribution channels. Know more on http://www.riversand.com/
The document discusses customer segmentation and provides examples of how to segment customers based on their needs, behaviors, and value to the company. It outlines four main types of segmentation methods - sociodemographic/firmographic, needs-based, usage-based, and value-based - that can be used alone or together based on a company's business objectives. The document also stresses that segmentation is important for improving marketing and sales efforts but that companies often rely too heavily on basic sociodemographic segmentation.
Alterian June 2009 Webinar Addressing Retail Trends Through An Integrated A...Alterian
Featuring Naj Uddin, Yoram Greener and Scott Cone, Merkle
Retailers face an increasingly difficult business environment with decreased store traffic and weak sales. An analytically-based approach that links marketing strategy, customer insight, predictive analytics, and leading-edge technology can assist retailers in maximizing the return on their marketing investments. In this webinar, Merkle discussed today's retail challenges and their unique solutions.
How to Align Demand Gen and Inside Sales (to Close More Deals)Sales Hacker
What You'll Learn:
- RingCentral success path to revenue
- High impact buying signals, lead routing and outreach cadences
- Importance of data accuracy and optimization
- Benchmark metrics for entire sales funnel
Thinking Outside the Mode. Improving Accuracy by Choosing a Multi-Mode ApproachRay Poynter
Presented by Allen Porter and Malgorzata Mleczko as part of the NewMR 'Collecting Better Data' webinar event.
Access the recording of this presentation via NewMR.org/Play-Again
Presentation Description
To collect better data. What does it mean?
Everybody has an opinion. We do too.
In this presentation, we focus on the multi-mode aspect. Listen to this session to get insights in adopting an authentic mode-independent approach to designing your studies and how it can significantly improve data accuracy across your organization.
Why think outside of the mode? A multi-mode strategy enables researchers to meet their target demographics and control data collection operations to minimize costs and/or project schedules.
The mix of technology allows phone interviewing to be added at any point in the study without compromising the performance of either collection mode. Field operations have access to the right tool for each job at any time. Researchers gain the flexibility to design data collection strategies that suit each job rather than suit the technologies at hand.
There are five main quantitative methods used today: in-person interview, phone interview, IVR interview, online questionnaire, and paper questionnaire mailed to the recipient.
We will focus on how the right combination of these methods can significantly impact the accuracy of your result.
We will touch upon aspects like:
Enhancing the customer experience
Improving your Return On Investment
Increasing the reach of your audience
Enriching your data with in-depth insights
Looking for questionnaire- and mode-specific ambiguities
Improving Customer Service
Researchers have tried abandoning or reducing phone-based data collection because spanning technologies and managing the mix of technologies and organizations has been proven difficult to manage. However, technology barriers are coming down and researchers should consider the strategic value of phone research to reach seniors, minorities and other vital segments.
Without the phone in the mix (CATI or IVR), specific demographics have suffered under-representation.
Data collection strategies that embrace a multi-mode approach can enable researchers to break out of any boxes, silos or myopic policies and allow them to field research any way they want.
Customer journey driving business growth for large and small companiesMartin Wright
The document discusses the importance of understanding the customer journey and using customer insights to drive business growth. It provides examples of how Halifax General Insurance and McCarthy & Stone improved conversion rates and sales by mapping customer journeys and addressing pain points. The document also stresses the importance of putting customers at the heart of business decisions and embracing organizational change to align with customers' emotional experiences. Failing to understand customers can lead companies to make incorrect assumptions. Understanding the customer journey across all channels is vital as customer expectations increase around convenience and service.
This document introduces MarketGEM, a data fusion process that combines internal customer data with external data sources to develop a 360-degree view of customers. It summarizes how MarketGEM can be used to better understand customer segments, identify best customers and potential clones, and uncover new business opportunities through enhanced data analysis and targeted market research. Case studies show how MarketGEM has helped organizations increase sales, reduce churn, and improve marketing effectiveness.
2016 Data Contest Marketing-3-Day-Golden-Thumb-RuleDhanashree Arole
The document discusses analyzing marketing email campaign response data over a 3 day period to validate the "80-20 rule", where 80% of responses are received within the first 3 days. The analysis looked at campaign data from various business units like tickets, annual passes, lead generation, and accommodation. It was found that waiting more than 3 days captured very little additional response data. This validated that most response occurs within 3 days, allowing marketing to more quickly analyze campaign performance and determine if campaigns should be modified.
The document discusses Sports Obermeyer, a skiwear manufacturer founded in 1947. It provides details on the company's structure, sales figures, market share, and a joint venture called Obersport.
It then presents a sample problem asking how many units of each style Wally Obermeyer should order from its Hong Kong production. Using forecasting data and calculations, it recommends production quantities for 10 styles that meet the minimum order of 10,000 units.
Recommendations are made to improve Obermeyer's forecasting accuracy, reduce lead times, increase Chinese worker efficiency, and source non-standard zippers closer to reduce lead times. Long-term, shifting more production to Hong Kong is suggested due to lead
American Tool Works is looking to increase inventory levels and sales at small and mid-sized dealers. To increase inventory, they propose offering buy-back contracts that allow dealers to return unsold inventory, and revenue sharing contracts that provide discounts for bulk orders and allow dealers to share revenue. To increase sales, they suggest threshold contracts providing commissions up to sales levels, and sales incentive programs to motivate dealers' salespeople. Implementing these contracts could take 2-3 months. American Tool Works believes combining buy-back contracts and sales incentives will offer flexibility to dealers and lead to higher inventory and sales.
Klaus Obermeyer founded Obermeyer in 1947 in Aspen, Colorado. In 1985, Obermeyer formed a joint venture called Obersport in Hong Kong to increase production capacity. Obermeyer's supply chain stretches from Asia to Aspen, with textile and accessory suppliers in Asia manufacturing garments that are then shipped through Obersport and Sport Obermeyer to retailers in the US. Obermeyer faces challenges in uncertain demand forecasting for its seasonal ski fashion products and long lead times in its Asian manufacturing process.
Understanding Merchandise Mix of Marks & Spencers storeSrishti Raut
This document provides an overview of Marks & Spencer's fashion merchandising strategy in India. It discusses M&S's introduction to the Indian market in 2001 through a joint venture with Reliance Retail. It then analyzes M&S's merchandising mix of variety, breadth, and depth. Specifically, it examines the brand's product assortments in women's, men's, and kids' wear as well as beauty. The document also provides sales figures and discusses M&S's future plans to expand its store network in India.
Commerce Bank implemented a revolutionary business model in the US banking industry by focusing on deposits rather than loans and providing an excellent customer experience through retail-inspired branches and services. This allowed Commerce Bank to grow much faster than the industry, with deposit growth averaging over 30% from 1998-2001 compared to 6% for the industry. However, competitors began adopting some of Commerce Bank's service offerings, threatening its position, and it faced pressure to continue innovating.
Sport Obermeyer was founded in 1947 in Aspen, Colorado to design and produce skiwear. It sources materials from around the world and produces garments in factories in Hong Kong and China. It faces challenges in forecasting demand accuracy and allocating production given long lead times and little market feedback until trade shows in March. Production in China has lower costs but higher minimums and risks from trade relationships, while Hong Kong has higher costs but more flexibility.
Forecasting recommendation
Forecast Methodology
Obermeyer used combination of the “panel consensus” and “Delphi method” of qualitative forecasting for sales forecasts
We used a single period inventory model to estimate the financial risk of underestimating and overestimating demand
Single point forecast data provided is limiting. More complicated forecasting techniques require actual data collected over time
Recommend next sales forecast results are summarize and redistribute to the team. Given results new questions should be asked of the team in regards to what assumptions to apply in the decision making process
Forecast Assumptions
Initial 10,000 unit order is riskier due to lack of demand information. Second 10,000 unit order is less risky because of better demand information on each style.
The expected lose from liquidating inventory due to overestimating demand is assumed to be 8% of the wholesale price
The cost of lost profit from underestimating demand is assumed to be 24% of the wholesale price
The second order will allow us to adjust for quantities of each style based on better demand information
Reduce the number of styles handled to lower complexity of planning and risk profiles
Study fashion in Europe rather than waiting for Las Vegas shows
Reduce production lead times, as the preparation of raw materials takes a long time. For example:
To improve efficiencies, dye basic colors early in the year and fashion colors later in the season
Dyers could be offered a long-term contract regarding Greige goods
Develop relationships with big-time suppliers that are able to meet tight times and requested demand
Increase distribution channels and service level requirements
Collect and utilize historic data from previous years to better determine future trends
Where possible, obtain feedback from retailers prior to Vegas
Harley-Davidson saw decreased revenue, net income, and stock prices in 2008. The company continues to manage its balance sheet effectively with a return on invested capital of 17%. To increase growth, Harley-Davidson recommends expanding into the European and Asian markets, increasing sales of Buell and women's motorcycles, and developing environmentally friendly models. The company will implement these strategies through marketing research, product development, public relations, advertising, and distribution channels.
E-commerce is growing in Costa Rica, which has a highly developed telecommunications network and 99% literacy rate. Online shoppers in Costa Rica prioritize price, in-stock items, and free/fast delivery. Global companies like Amazon have expanded operations in Costa Rica, now employing over 7,500 workers. Direct-to-consumer brands are now seen as a bigger competitive threat than Amazon. Successful direct-to-consumer brands focus on serving unmet customer needs, clear value propositions, purposeful branding, and a personalized, data-driven customer experience from acquisition to fulfillment. Building an effective direct-to-consumer brand requires more than just e-commerce - it demands attention to customer data, targeted marketing,
How Leading Brands Drive Sales With Omni-Channel CampaignsDaniel Caridi
eMarketer moderates a presentation with Arpita Neelmegh, product marketing manager at Iterable, who breaks down one of the fundamental tactics that leading companies are employing to truly boost sales and cultivate customer relationships—sending personalized messages across multiple channels.
The document discusses using customer insight to drive performance for a large wireless communication company. It describes implementing a phased approach including developing tactical targeting tools, identifying growth opportunities, and establishing an infrastructure to capture value. Case studies demonstrate segmenting the customer base to understand needs, prioritize initiatives, and maximize revenue and retention through targeted campaigns.
Simon rowles conference presentation september 2010Simon Rowles
Loyalty programs have the data to drive ROI in any channel through a targeted and relevant communications mix. Case studies included : eliminating print from a bank loyalty program reduces the program's performance.
A Point of View for PIM in Retail, CPG and Distribution CompaniesShamanth Shankar
Gain a common understanding of PIM and its value to your organization. Understand why managing product information is critical to your Ecommerce / ERP initiative (upgrade or rip & replace)
Riversand's Enterprise PIM Solution manages the continuous flow of data throughout the entire Product Information Lifecycle, this product information management helps manage the information required to market and sell products through distribution channels. Know more on http://www.riversand.com/
The document discusses customer segmentation and provides examples of how to segment customers based on their needs, behaviors, and value to the company. It outlines four main types of segmentation methods - sociodemographic/firmographic, needs-based, usage-based, and value-based - that can be used alone or together based on a company's business objectives. The document also stresses that segmentation is important for improving marketing and sales efforts but that companies often rely too heavily on basic sociodemographic segmentation.
Alterian June 2009 Webinar Addressing Retail Trends Through An Integrated A...Alterian
Featuring Naj Uddin, Yoram Greener and Scott Cone, Merkle
Retailers face an increasingly difficult business environment with decreased store traffic and weak sales. An analytically-based approach that links marketing strategy, customer insight, predictive analytics, and leading-edge technology can assist retailers in maximizing the return on their marketing investments. In this webinar, Merkle discussed today's retail challenges and their unique solutions.
How to Align Demand Gen and Inside Sales (to Close More Deals)Sales Hacker
What You'll Learn:
- RingCentral success path to revenue
- High impact buying signals, lead routing and outreach cadences
- Importance of data accuracy and optimization
- Benchmark metrics for entire sales funnel
Thinking Outside the Mode. Improving Accuracy by Choosing a Multi-Mode ApproachRay Poynter
Presented by Allen Porter and Malgorzata Mleczko as part of the NewMR 'Collecting Better Data' webinar event.
Access the recording of this presentation via NewMR.org/Play-Again
Presentation Description
To collect better data. What does it mean?
Everybody has an opinion. We do too.
In this presentation, we focus on the multi-mode aspect. Listen to this session to get insights in adopting an authentic mode-independent approach to designing your studies and how it can significantly improve data accuracy across your organization.
Why think outside of the mode? A multi-mode strategy enables researchers to meet their target demographics and control data collection operations to minimize costs and/or project schedules.
The mix of technology allows phone interviewing to be added at any point in the study without compromising the performance of either collection mode. Field operations have access to the right tool for each job at any time. Researchers gain the flexibility to design data collection strategies that suit each job rather than suit the technologies at hand.
There are five main quantitative methods used today: in-person interview, phone interview, IVR interview, online questionnaire, and paper questionnaire mailed to the recipient.
We will focus on how the right combination of these methods can significantly impact the accuracy of your result.
We will touch upon aspects like:
Enhancing the customer experience
Improving your Return On Investment
Increasing the reach of your audience
Enriching your data with in-depth insights
Looking for questionnaire- and mode-specific ambiguities
Improving Customer Service
Researchers have tried abandoning or reducing phone-based data collection because spanning technologies and managing the mix of technologies and organizations has been proven difficult to manage. However, technology barriers are coming down and researchers should consider the strategic value of phone research to reach seniors, minorities and other vital segments.
Without the phone in the mix (CATI or IVR), specific demographics have suffered under-representation.
Data collection strategies that embrace a multi-mode approach can enable researchers to break out of any boxes, silos or myopic policies and allow them to field research any way they want.
Customer journey driving business growth for large and small companiesMartin Wright
The document discusses the importance of understanding the customer journey and using customer insights to drive business growth. It provides examples of how Halifax General Insurance and McCarthy & Stone improved conversion rates and sales by mapping customer journeys and addressing pain points. The document also stresses the importance of putting customers at the heart of business decisions and embracing organizational change to align with customers' emotional experiences. Failing to understand customers can lead companies to make incorrect assumptions. Understanding the customer journey across all channels is vital as customer expectations increase around convenience and service.
This document introduces MarketGEM, a data fusion process that combines internal customer data with external data sources to develop a 360-degree view of customers. It summarizes how MarketGEM can be used to better understand customer segments, identify best customers and potential clones, and uncover new business opportunities through enhanced data analysis and targeted market research. Case studies show how MarketGEM has helped organizations increase sales, reduce churn, and improve marketing effectiveness.
2016 Data Contest Marketing-3-Day-Golden-Thumb-RuleDhanashree Arole
The document discusses analyzing marketing email campaign response data over a 3 day period to validate the "80-20 rule", where 80% of responses are received within the first 3 days. The analysis looked at campaign data from various business units like tickets, annual passes, lead generation, and accommodation. It was found that waiting more than 3 days captured very little additional response data. This validated that most response occurs within 3 days, allowing marketing to more quickly analyze campaign performance and determine if campaigns should be modified.
This presentation talks about the Retail industry inside out and focusses on the IT strategy being followed in the industry. A business case for Carrefour is built up for various candidate projects analysed using a 10 lens method.
I appreciate you leave a comment on the slideshow. You are free to use to use the information as long as you mention the source although I would not be able to share the originals with you since it is not under my ownership alone.
1) The document proposes using advanced data analytics to build knowledge of customer behavior, preferences, and aspirations in order to maximize revenue.
2) A case study uses data from an online beauty/personal care subsidiary to demonstrate how clustering, classification, and regression analyses can provide insights.
3) The analyses identify customer subgroups, predict which customers will churn, and forecast spending amounts. This knowledge can then be used to target marketing and improve customer retention and spending.
This document discusses how The Data People helps companies identify their best customers and maximize profits through data-driven strategies and analytics. They analyze customer data to build detailed profiles, identify valuable customer segments, predict churn, and develop targeted marketing strategies. Case studies show how they helped companies like Alliance & Leicester increase website visits by 100% through improved targeting, and helped Nescafe launch a successful direct marketing campaign by creating an accurate customer profile model.
The Age Of New Reality Marketing V5.1 FinalTony Mooney
It\'s been a bug-bear of mine for many years that the average marketing skill set has not moved on very much from the 1960\'s model of 4 \'P\'s (Product, Price, Promotion, Place). Or that marketing is still largely synonomous with advertising - and spam advertising at that. This is a presentation I did to a marketing forum out in Singapore, where I\'ve tried to outline the new capabilities of the marketer of the 21st century. I also postulate the (controversial) perspective that a chunk of this new capability - especially around data and decisioning - might be better out sourced, leaving the internal marketing skills to be concentrated on strategy and proposition. See what you think. [Sorry you won\'t have my spoken narrative just yet but the slides are reasonably self explanatory]
An Advanced Analytics Approach to Resource Allocation Optimization & MCM Anal...Eric Levin
View our presentation from the PMSA 2017 European Summit in Basel.
Abstract: Pharmaceutical companies spend a significant portion of their sales revenue on promoting brands through various marketing channels even in emerging markets. While a major part of the promotional budget is spent on Field Force visits, other promotional activities are equally resource intensive. In the world of competitive spending, companies are likely to overspend on some brands and channels, and underspend on the rest if not guided by insights based on data. However, an optimization study conducted at a high level may not yield the optimal benefit for the business if such a study is not in sync with the local realities of the market space.
Objectives:
• How uncovering hidden opportunities within and across portfolios of brands for boosting sales growth & reach forecast goals
• Using econometric modelling approach, the impact (incremental sales) of channels are quantified at the most granular level (territory, brick, etc.)
• How to employ non-linear optimization techniques for Bottoms Up sales and profit maximization for optimizing resources at most granular level (territory, brick, etc.)
A summary of the presentations made during our SMART-Drop door drop seminar in which better targeting of leaflet distributions was demonstrated. This also reduces wastage and provides an improved ROI.
Similar to Marketing Analytics - Multi-Channel Retailing (20)
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
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We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
1. Chris Robinson
Mary Sauer
Adam Schackmuth
James Young
December 11, 2014
Marketing Analytics
Multi-Channel Retailing
1
Group 4
*Slides include notes and voiceover
3. Multi-Channel Retailing Organization
Analyzed data to determine which channel
has the most potential to maximize growth
› Channels
Retail
Catalog
Website
Analysis can be used to identify customer
segments to:
› Attract new customers
› Retain the best customers
› Avoid unprofitable customers
Overview-Business Case / Marketing
Data
3Group 4
4. Business Case – Questions we focused on:
› What channel should strategic development be
focused on to maximize growth?
› Do customer segments correlate to a channel?
› How do we determine synergies between the
various sales channels?
› Does demographic data correlate to a channel
and push revenues into the other channels?
› Are demographics and synergies important to
growth of the company?
Product
› Food products purchased during the Christmas
season as gifts
Overview-Business Case / Marketing
Data
4Group 4
5. Customers
› Loyal to brand
› Products purchased for gifts
› Wide variety of personal interests
External Market
› Mail-Order catalog market on decline
› Low cost of e-commerce makes it difficult for
brick-and-mortor stores to compete on price
› A multi-channel approach is necessary in
today’s economy
Overview-Business Case / Marketing
Data
5Group 4
7. Dataset 9 contains 4 separate files:
› DMEFExtractSummaryV01
› DMEFExtractContactsV01
› DMEFExtractLinesV01
› DMEFExtractOrdersV01
Description of Data
7Group 4
8. DMEFExtractSummaryV01 – Summary File
› 101,051 records
› Customer buying activity, demographic, psychographic
and distance to retail store information
› Data summarized by channel & season (Internet, catalog,
retail / Spring, Fall)
› This file contains all of the information used in regressions
and data analysis
› Demographic (10,929 cases)
Age – (45-54 years old)
Income - (over $50k, most over $100k)
Home – (homeowners)
Dwelling – (single-family home)
Length Residence – (over 20 years)
Occupation – (professional/technical,
administrative/management)
› Information can be used to segment & target
Description of Data
8Group 4
9. DMEFExtractSummaryV01 – Summary File
› Cleaned data of no responses, 10,929 cases
9Group 4
Description of Data
Statistics
AgeCode IncCode HomeCode Dwelling LengthRes OccupCd
N Valid 10929 10929 10929 10929 10929 10929
Missing 0 0 0 0 0 0
Mean 4.76 6.54 1.98 1.16 13.83 5.02
Median 5.00 7.00 2.00 1.00 15.00 5.00
Mode 4 9 2 1 20 1
Std. Deviation 1.240 2.198 .134 .588 6.010 4.743
10. DMEFExtractSummaryV01 – Summary File -
Continued
Sales Dollars – summarized by channel(retail,
internet, catalog) and season (Fall, Spring) for 2004
– 2007, and Pre-2004
Internet & Catalog purchases were categorized
into Gift/Non-Gift Purchases
› Retail - minimum ($1), maximum ($2,318)
› Internet - minimum ($18), maximum ($2,518)
› Catalog- minimum ($19), maximum ($2,106)
Description of Data
10Group 4
11. DMEFExtractContactsV01 – Marketing
contact records
3,389,239 records
Customer contact dates and contact types
(catalog or email)
Shows data for each month for 2005-2007
Data shows us:
› Contacts peak in November and December
› 70% of contacts are made via email
Description of Data
11Group 4
12. DMEFExtractLinesV01 – Line item detail
618,661 records
Order dates, dollar amount, items purchased as
gifts
Shows data for each month for 2001-2007
Data shows us:
› ~90% of items are purchased as gifts
Description of Data
12Group 4
Gift
Frequency Percent Valid Percent
Cumulative
Percent
Valid N 24098 11.2 11.2 11.2
Y 190774 88.8 88.8 100.0
Total 214872 100.0 100.0
13. DMEFExtractOrdersV01 – Order/trip
information
241,366 records
Order date, purchasing channel, payment
method
Shows data for each month for 2001-2007
Data shows us:
› Preferred purchasing channel is in-store; phone
second
› Preferred payment method is a bank card; cash
second
Description of Data
13Group 4
14. DMEFExtractOrdersV01
14Group 4
Description of Data
OrderMethod
Frequency Percent Valid Percent
Cumulative
Percent
Valid I 54484 22.6 22.6 22.6
M 5315 2.2 2.2 24.8
P 72483 30.0 30.0 54.8
ST 109084 45.2 45.2 100.0
Total 241366 100.0 100.0
PaymentType
Frequency Percent Valid Percent
Cumulative
Percent
Valid BC 187707 77.8 77.8 77.8
CA 41181 17.1 17.1 94.8
CK 7684 3.2 3.2 98.0
GC 422 .2 .2 98.2
HA 2229 .9 .9 99.1
NV 1687 .7 .7 99.8
PC 456 .2 .2 100.0
Total 241366 100.0 100.0
19. Discussion of Model Specification:
Dependent Variable
› First Channel
Independent Variables
› Store Distance
› Customer Age
› First Month of Contact
› Income Level
› Email
Model Statement
19Group 4
20. Data Transformations
› Recode FirstYYMM to get FirstMonth
› Experimented with creating interaction
variables, but none were significant
Hypotheses
1. Customers who came through the internet site
would be younger than those who came
through other channels.
2. Customers who lived farther from retail
locations would be more likely to choose the
catalog or internet channels.
Model Statement
20Group 4
22. Set the stage:
› Dependent Variable:
FirstChannel – First time users preference for order
RET – Retail store order
CAT – Catalog order
INT – Internet Order
› Independent Variables:
StoreDist = Distance to nearest retail location
AgeCode = Codes (1-7) for grouped ages
IncCode = Codes (1-9) for group income brackets
Email = Y (yes) or N (no)
FirstMonth = derived from FirstYYMM the MM part (Jan (01) – Dec (12))
Goals - Understanding the customers’ first purchase may lead to:
› Understanding how to market to these customers allowing the
company to increase profits and market growth.
› Additionally, building customer loyalty by segmenting these
customers and their buying channels,
Section 4: The Findings…
Group 4
22
23. Independent variable(s) impact on the dependent
› StoreDist
Further away distance more likely to use Internet as first order
purchase
Shorter distance to retail location increases chance of first time
purchase as Retail channel.
Segmenting these customers within retail locations and marketing/advertising with
store coupons and flyers;
using Internet marketing/advertising to those not within reach of the retail
locations; and
further segmenting non-internet using customers by use of catalog would make
the most sense
› AgeCodes
All fell within the 5% significance level.
Age groups from 18-24 years old and 65-74 years old have less
effect on the dependent variable.
The younger aged most likely do not have the income to spend
The elderly have less impact because they probably do not spend much time on or perhaps
never use the Internet.
Section 4: The Findings… (continued)
Group 4 23
24. Independent variable(s) impact on the dependent
› IncCode
Those in the low incomes levels (under 20K), and those in the
higher income level (100K and above), both are above the 5%
tolerance level
It appears the income range of 30K to 99K has a likely effect
of making a first time purchase on the Internet. You have to
have money to spend money.
› Email:
For the Catalog, analysis does not show an impact and falls
out of the 5% significance level.
It is significant for the Internet customer where most likely email
is a way of communication for billing, order receipt, etc.
With technology advances, many more customers have the ability to
order on the Internet and long as the customer remains receptive to
this channel, it may push down catalog orders.
Exceptional customer service drives catalog orders, which usually
means the multi-channel company invests in such practice and keeps it
as part of the business model.
Section 4: The Findings… (continued)
Group 4 24
25. Independent variable(s) impact on the dependent
› FirstMonth
A few of the months fall out of the 5% significance level for both
Internet and Catalog.
Summer months of Jun, Jul, and Aug and the month of Oct for Internet
Creates opportunity for first time purchasers in the holiday months to
use the channel of their preference.
Catalog has the months of Jan, Nov, and Dec as solid months of
first time purchases.
Internet has the months most effecting first time purchases as:
Jan, May, Nov, Dec.
These key months should provide the multi-channel company an
opportunity to build brand loyalty efforts and encourage return
purchases by marketing/advertising to those first time purchasers.
Section 4: The Findings… (continued)
Group 4 25
29. Multi-Channel Retailing Organization
› Overall Highly Seasonal
› Mail Order/Catalog Holiday Peak 6-8X Higher
› Retail has Smaller Holiday Peak – More Consistent
Throughout Year
Most Successful Segment
› Middle-aged
› High Income
› Home Owners
Overall Market
› Explosive Internet Growth
› Stagnating Mail Order & Retail Storefront
Summary
29Group 4
30. Multinomial Regression
› Consumer Choice Model
› Heavy Reliance on IBM’s SPSS Tool
› Two Models Developed
First Time Mail Order Purchases
First time E-Commerce Purchases
Two Hypotheses
› Effect of Distance from Retail Store
› Younger Demographics Prefer Internet?
Summary
30Group 4
31. Distance from Retail Store
› The Farther Away – Mail Order & Internet Increase
› Conveniently Place Retail Pulls Sales
› Use Model to Locate Retail Stores
Age & Income Significance – E-Commerce
› Younger has Less Disposable Income
› Older Not Heavy Internet Purchasers
› Model Good for Middle Aged & Middle Income
Mail Order
› Don’t Like or Don’t Want to Use Internet Channel
Conclusions
31Group 4
32. November & December Sales Peak
› Huge for Internet & Mail Order
› Smaller but Still Significant for Retail Storefront
› Use Retail Storefront to Smooth-Out Revenue Flow
Mail Order & Retail – Down but Not Out
› First Time Buyers – Mail Order Preferred Channel
› Internet Close Behind
› Some Always Prefer Brick-and-Mortar Experience
Mail Order Preference
› Don’t Like or Don’t Want to Use Internet Channel
Conclusions
32Group 4
33. Use Model to Calibrate Retail Presence
› Distance to Store Pulls Revenue
Use Model to Fine-Tune Going Down-
market
› Higher-Income, Middle-Aged, Homeowners
› Opportunity to go Down-market
Continue Growing Internet
› Catalog not Going Away – YET
› Convert Mail-Order Buyers to Internet
Opportunities
33Group 4
Hello! Welcome to Group 4’s presentation of our final project. Group 4 members are: Chris Robinson, Mary Sauer, Adam Schackmuth and James Young. We decided to complete our final paper and project using Dataset 9. We chose this dataset because we felt that it had a lot of rich data that we could derive our model statements and hypotheses from, and we felt it would challenge us throughout the semester. Dataset 9 is from a multichannel gift company with sales of several hundred million dollars per year. The company is a well-known organization that has a network of retail stores, a well-established catalog channel and a website as well.
Section 1 of our project discusses the overview of the business case and an overview of the marketing data.
Again, the dataset is from a Multichannel Retailing organization.
Throughout this presentation, Group4 Consulting will analyze and model Dataset 9 to determine which channel has the most potential to maximize growth. Our audience can use this analysis to help target marketing efforts and potentially identify customer segments for:
Attracting new customers
Retaining the best customers
Avoiding unprofitable customers
The business case are questions that we wanted to determine answers for.
Our business case was to determine what channel strategic development be focused on to maximize growth; if multiple channels, what the order of prioritization should be. This prioritization would be related to whether or not there is a correlation between a channel and a customer segment and to identify any synergies between the various sales channels. . Some channels may correlate strongly with certain demographics or may push revenue into the other channels.. If this is the case, it should be determined if those demographics and synergies are important to the growth of the company, or if the company would be better off withdrawing from the channel and investing those marketing dollars elsewhere.
Product: The Multichannel Gift Company sells food products which are usually purchased as gifts during the Christmas season. While the products are available year-round, the sales peak during the last few months of the year.
Customers: The customers of the Multichannel Gift Company are loyal to the brand and are very familiar with it. The brand is well-known and respected. Customers usually purchase goods from the Multichannel Gift Company around Christmas time and give the items as gifts. The customer can shop for the products in the retail locations, through catalogues and online. The customers have a variety of personal interests, ranging from travel to fine arts to fashion and camping.
External Market: The mail-order catalog market is a mature market that is projected to decline 7.8% annually from 2014 to 2019 due primarily to steadily increasing competition from e-commerce. The industry shows that internet sales offers “greater convenience” than mail order or traditional brick-and-mortar sales, which combined with projected sluggish consumer spending growth over the next five years, does not bode well for traditional mail-order or local retail stores. E-commerce sites also experience lower operating costs, thus making it difficult for brick-and-mortar stores to compete on price.
Globally, e-commerce sales now exceed $1.2 trillion, thus making a multi-channel approach necessary in today’s economy .
Hello – I am Mary Sauer, and I will be discussing Section 2 of our paper, which is the description of the data. We completed our project using Dataset 9.
Dataset 9 contains four separate files: DMEFExtractSummaryV01, DMEFExtractContactsV01, DMEFExtractLinesV01, and DMEFExtractOrdersV01. I will go into a little bit of detail regarding each file – what is contained in each file, what information we can derive, and what insights we can gain.
In total, the DMEFExtractSummaryV01 has 101,051 customer records, or cases, that show the customer buying activity along with demographic, psychographic and distance to retail store information. The buying activity is summarized by channel (internet, catalog or retail,) for eight seasons (Spring and Fall 2004 through 2007.) This Customer Summary file contains all of the information used in the regressions and data analysis, as it was the most comprehensive file in the dataset. This file shows us helpful demographic data that could be used to target our customers. After cleaning the data of no responses to AgeCode, IncCode, HomeCode, Dwelling, LenghtRes and OccupCd, we were left with 10,929 cases. This data give us insightful information regarding who our customer is. The statistics and frequencies can be found below, but the data tells us the profile of the customer. The customers are between 45 and 54 years old, the majority makes an annual income of greater than $50,000, with the most making greater than $105,000, and are home owners of a single-family home and have lived there more than 20 years. The customer’s occupation is usually professional or technical, while many are also in administrative management positions. This information can be used to segment the customers into groups and it gives the company the ability to better target market to the customers.
This slide shows what I just discussed, this is the SPSS output showing the mean, median, mode & standard deviation for each of the demographic variables.
In looking at sales dollars per purchasing channel, retail, catalog or internet, we are able to see the spending patterns across each season. For retail purchases, the minimum purchase is $1, and the maximum purchase is $2,318. The internet sales dollars were broken out into gift or non-gift purchases for each season of each year. After summing the gift and non-gift variables, we can see the minimum and maximum purchases for each season; the minimum purchase being around $18 and the maximum being $2,518. Similar to the internet sales, the catalog sales were broken out into gift and non-gift purchases. After summing the gift and non-gift purchases for each season, we can see that the minimum purchase is $19 while the maximum is $2,106.
The file DMEFExtractContactsV01 contains 3,389,239 marketing record contacts, and contains information regarding customer’s contact dates and contact types; catalog or email. This data shows information for each month for the years 2005 through 2007. Because the SPSS output was several pages long, a summary of the findings are included in the graph below. The data shows the months that the contacts peak. This file gives us insightful information regarding the time of year the organization makes the most contacts. The graph clearly shows that November and December are the busiest months. This file also shows us that the majority of the contacts, 70%, are made via email, rather than catalog.
The file DMEFExtractLinesV01 contains 618,661 line item records. The data shows the order date and dollar amount, as well as if the item was purchased as a gift. It shows data for each month from 2001 through 2007. After cleaning the data, it shows that the majority of the items are purchased for gifts, approximately 90% of the time.
The file DMEFExtractOrdersV01 contains 241,366 order or store trip records and shows the order date, channel for purchasing (store, internet, phone or mail) and payment method (bank card, cash, check, gift certificate or house account). The file contains information for each month from 2001 through 2007. This data shows us that the preferred purchasing channel is in-store, with phone and internet coming in second and third, respectively. The least preferred purchase channel is the mail. The majority of the customers pay for the items using a bank card, nearly 78%, whereas cash is the second most used payment method at 17.1%. All statistical and frequency information can be found in our written paper for each of the four files discussed.
Again, here are the tables from the SPSS output that shows the information I just discussed.
For our research project, we used a multinomial logistic regression because it was best suited to modeling consumer choice. Using this method, we developed two models for determining how consumers who first come into contact with a retailer via the company’s catalog and consumers who first come into contact via the company’s internet site differ from those who first come into contact with the company through their retail stores.
We also created a bar chart of the number of sales in each month for each channel in order to determine if there are any seasonal effects in any of the channels and what their magnitude might be.
Following are the equations for determining if a consumer will be more likely to choose the company’s catalog or internet site for their first channel instead of the brick and mortar storefront.
The McFadden R-square value for these models is .133.
For our dependent variable, we used the first channel through which the consumer came into contact with the company.
By knowing the differences consumers who first come into contact with the company through the store versus those who first go through the internet site or catalog, the company can tailor the marketing messages presented through those channels to be the most effective.
For our independent variables we analyzed the following:
Distance away from a store location because it is possible that the distance from a store makes the internet or catalog channels more convenient.
Customer Age because consumers of a certain age group may be more or less technologically savvy and comfortable with online shopping, and therefore more or less likely to first conact the company through their website.
The month that the customer made contact with the company because the time of year that a person is shopping may make convenience and selection a greater determining factor in choosing a channel, and thus drive up catalog and internet traffic.
A customer’s income level may make them place a greater value on the convenience of the internet site or catalog.
And we measured whether or not the customers gave the company permission to contact them via email. We believed consumers who chose the internet channel may prefer to have their interactions online and be more likely to give the store permission to contact them via email.
It was necessary to recode the FirstYYMM data in order to get the number of sales by month. Every data point ending in 01 was recoded as January, every data point ending in 02 was recoded as February, etc., until we had sorted 84 categories down to the 12 months.
We had two hypotheses to test:
Customers who came through the internet site would be younger than those who came through other channels.
Customers who lived farther from retail locations would be more likely to choose the catalog or internet channels.
Beyond these two hypotheses, this was a discovery search to find what else might affect channel choice.
The dependent variable FirstChannel in the nominal regression has the values Ret, Int and Cat indicating Retail, Internet, and Catalog channels respectively.
After several calibrations of using several different independent variables, we settled on the following variables causing impact on the dependent variable with Ret (retail as reference):
StoreDist = Distance to nearest retail location
AgeCode = Codes (1-7) for grouped ages
IncCode = Codes (1-9) for group income brackets
Email = Y (yes) or N (no)
FirstMonth = derived from FirstYYMM the MM part (Jan (01) – Dec (12))
The FirstChannel provides how the customer initially bought from the multichannel company.
Understanding the customers’ first purchase may lead to
understanding how to market to these customers allowing the company to increase profits and market growth.
Additionally, building customer loyalty by segmenting these customers and their buying channels, should encourage future purchases by these customers.
StoreDist:
Significance level indicates the further away or greater the distance the customer lives from a brick-n-mortar store, the more likely they are to make their first time purchase on the Internet. Therefore, Retail first time purchases increase with customers having a shorter distance to a Retail location.
Segmenting these customers within retail locations and marketing/advertising with store coupons and flyers;
using Internet marketing/advertising to those not within reach of the retail locations;
and further segmenting non-internet using customers by use of catalog would make the most sense.
AgeCodes:
All fell within the 5% significance level, however the age groups from 18-24 years old and 65-74 years old have less effect on the dependent variable.
The younger aged most likely do not have the income to spend while the elderly have less impact because they probably do not spend much time on or perhaps never use the Internet.
IncCode:
A couple of the income levels fall out of the 5% significance level.
Those in the low incomes levels (under 20K), and those in the higher income level (100K and above), both are above the 5% tolerance level, therefore not rejecting the null hypothesis.
It appears the income range of 30K to 99K has a likely effect of making a first time purchase on the Internet.
You have to have money to spend money.
Email:
For the Catalog, analysis does not show an impact and falls out of the 5% significance level.
It is significant for the Internet customer where most likely email is a way of communication for billing, order receipt, etc.
With technology advances, many more customers have the ability to order on the Internet and long as the customer remains receptive to this channel, it may push down catalog orders.
Exceptional customer service drives catalog orders, which usually means the multi-channel company invests in such practice and keeps it as part of the business model.
FirstMonth:
It appears a few of the months fall out of the 5% significance level for both Internet and Catalog.
These months include the summer months of Jun, Jul, and Aug and the month of Oct for Internet, which again
creates opportunity for first time purchasers in the holiday months to use the channel of their preference.
Catalog has the months of Jan, Nov, and Dec as solid months of first time purchases.
Internet has the months most effecting first time purchases as: Jan, May, Nov, Dec.
These key months should provide the multi-channel company an opportunity to build brand loyalty efforts and encourage return purchases by marketing/advertising to those first time purchasers.
Graph of Overall Population initial total dollars spent by FirstChannel by FirstMonth
This Graph indicates that around the holiday season – November and December – are the peak times of the year where new customers spend the most money. For Catalog and Internet, the lowest spending months are in summer – June, July and August.
Graph of Overall Population initial customers new by FirstChannel by FirstMonth
This graph indicates that around the holiday season, November and December are the peak purchasing months for new customers. For Catalog and Internet, the lowest is summer (June, July, and August).
Section 5, Summary and Conclusions
Group 4 found through its analysis of a multi-channel retailing organization dataset that its revenue of gift baskets is highly seasonal, as evidenced by its mail order and catalog holidays sales peaking at 6 to 8 times higher than any other time of the year. Their third channel, retail storefronts, also has a holiday sales peak in November and December, but the peak is much smaller than the other channels and sales that are much more consistent throughout the year.
This organization is most successful selling to middle aged, high income homeowners. With the explosive growth of Internet sales and the overall decline or stagnation of mail order and retail storefronts, this company needs help analyzing sales to maximize the growth and synergies of their three retail channels.
Group 4 used Multinomial Regression testing using a consumer choice model with heavy reliance upon IBM’s SPSS software tool. Two models were developed to assess first time mail order purchasers and first time E-Commerce purchasers. First Channel Catalog and First Channel Retail were the dependent variables with store distance, customer age, first month of contact, income level and email as independent variables. Two hypotheses were developed regarding affect of distance from retail store on Internet sales and a question as to whether younger demographics prefer the Internet channel.
Several conclusions were evidenced by the analysis. For instance, the farther away a customer lives from a store, the more likely they are to use the Internet and mail order channel for their first-time purchase. This indicates that physical retail storefronts pull sales away from other channels. It can also be said that the absence of a conveniently located storefront will push sales towards the Internet and mail order channel. Therefore, this model can be used to fine-tune their retail store rollout to properly balance the three channels, depending on their overall strategy. The data demonstrates that despite the overall stagnation of retail, a retail store helps to provide more consistent revenue and subsequent cash-flow throughout the year.
We find that the model is significant for middle-aged and middle-income consumers, but has weakness in the younger segment which has less disposable income and in the older demographic, which has lower use of the Internet.
We also find that some people just don’t want to use the Internet channel and prefer the traditional storefront experience.
We therefore conclude that while the November and December sales peak is huge for Internet and Mail Order, it still exists for the retail storefronts, but with a lower impact. We believe that the retail storefronts can be used to smooth-out revenue stream throughout the year.
Also, even though mail order and retail is down, the data shows that it cannot be considered out. First time buyers still prefer the company’s traditional mail-order channel, but the Internet is close behind. Retail is a distant third, but still significant. We also believe that some will always prefer the brick-and-mortar experience and simply don’t trust the Internet, especially for the older demographics.
The challenge will be slowly converting traditional mail-order clients to Internet consumers of their product, especially for those for which the retail store front is not an option. The Internet channel should be continually improved and heavily marketed to ensure it picks up the customers that migrate from the mail order channel. With more and more options available for consumers today, the company must not simply wait and see what happens with the mail-order versus Internet battle, but continue to market and support both channels to retain these customers, regardless of which channel they come in on.
Several opportunities were uncovered through our analysis. For instance, this model can be used specifically to calibrate where to locate retail stores to determine where a physical store best maximizes revenue without pulling too much revenue from mail order or catalog. However, another way to see this issue is this model can help to push revenue to the Internet and mail order channel simply through the unavailability of a retail store front. Therefore, the company an use this model to help develop the proper strategy and store rollout.
There appears to be an opportunity for this company to go down-market with its products, based upon its heavy reliance on older, higher income professionals. The younger demographic should be targeted with products and marketing methods, particularly through the Internet channel, to ensure a steady supply of new customers moving into the future.
We find that while the traditional mail-order channel is still the revenue leader for this company and is therefore not going away quickly, it is clearly a trend that the Internet channel will one day dominate sales, especially as the younger demographic ages with its heavy usage of the Internet. Slow conversion of mail-order consumers to the Internet along with heavy marketing of the Internet channel to bring in increasing numbers of Internet first time buyers appears to be the best way to ensure continued healthy revenue growth into the future.