--- Retail
WGSN Future Key Takeaways
An experiential and innovative environment
Experimentation is key for retailers to be successful in the future, and needs to be built into business models. Over the course of a short period, the business was able to experiment with a number of emerging technologies, and was able to show in a live situation what worked and what didn’t.
Jeun Ho Tsang, the co-founder of London-based experimental store laboratory The Dandy Lab, explained 83% of retailers are failing to innovate. Jeun Ho Tsang said RFID loyalty cards proved successful as it meant the business knew what its customers had seen previously and their colour preferences. This enabled it to create a better relationship with them. Mobile payments also had a significant uptake, with 42% of shoppers signing up. Customers initially use the app to pay, and then continued to do so on repeat visits to scan and find out more about items.
The Role of the Store
Shumacher said that by 2020, 39% of purchases will be influenced by omnichannel, and the move to an experience economy means changing how we view the store. Many stores won’t sell product in 20 years’ time, but they will remain one of the most important components of the brand experience. Where today, physical retail’s success is down to sales, success metrics in the store of the future will be things like customer experience per square metre, active participation, social interaction, and how well retailers have staged the product. Retailers need to understand what a good brand relationship is, how what that involves is changing, and how consumer expectations are rising. Brands that are doing well are those with a “really strong sense of purpose” which aligns with that of the customer base. “A brand purpose is useful, and a short cut to an emotional relationship,” said Betmead. “What matters is that you care about something that they care about.”
Instagram Stories: Brand Narratives
Instagram gives users across the globe an insider's look into celebrity, fashion, luxury and more.
A new brand created a story to announce a new product offering.The first clip was to draw viewers in, provoking curiosity and enticing them to keep watching. Next, it featured images of the product (with clever use of emoji). This type of announcement allows audiences to feel as if they were let in on a secret of sorts, likely increasing the audience's receptiveness of the brand.
Retail Analytics Models
- Data mining for customer relationship management (CRM)
1 Matching campaigns to customers
a. cross sell campaigns
b. up sell campaigns
c. usage stimulation campaigns
d. loyalty program
2 Segmenting the customer base
a. finding behavioral segments
b. typing market research segments to behavioral data
3 Reducing exposure to risk
a. predicting who will default
b. improving collection
4 Detecting customer lifetime value
a. Using current customer to learn about prospects
b. Start tracking customers before they become customers
c. Gather information from new customers
d. Acquisition time variables can predict future outcomes
5 Cross-selling, up-selling, and making recommendations
a. what to offer it
b. when to offer it
c. finding the right time for an offer
d. making recommendations
6 Detection and churn
a. recognizing churn
b. why churn matters
c. different kinds of churn including voluntary/forced, involuntary, expected
7 Different kinds of churn model
a. predicting who will leave
b. predicting how long customer will stay
Retail Analysis
Successful retailers are shifting away from omnichannel towards serving the customer as point of sale, and are looking at ways to best link up all the customer touchpoints to create a personalised and convenient shopping experience.
1 Demand-Driven Forecasting
This analysis involves gathering demographic data and economic indicators to build a picture of spending habits across the targeted market. For example, it would be more valuable to discover patterns due to the hurricane that were not obvious. To do this, analysts might examine the huge volume of retailer data from prior, similar situations to identify unusual local demand for products. From such patterns, the company might be able to anticipate unusual demand for products and rush stock to the stores ahead of the hurricane’s landfall.
One major problem for fashion companies is placing their production orders without having the actual knowledge of demand. Moreover, the demand is influenced by additional factors such as the economic situation, public holidays or changing weather conditions. Furthermore, items of a fashion collection are mostly replaced by following seasons collections, and therefore, companies often face a lack of historical sales data.
Use case: Weather based Recommendation
Use case: Ad-word optimization and ad buying. Calculating the right price for different keywords/ad slots.
Use case: Inventory Management (how many units)
In particular, perishable goods
2 Merchandising and Planning
With 1bn monthly active users on WhatsApp, 900m on Facebook Messenger, and the number of Snapchat users nearing 200m, retailers can no longer limit their communications to traditional channels such as email and phone for customer service. Trend forecasting algorithms comb brand perception surveys, social media posts and web browsing habits to work out what’s causing a buzz, and ad-buying data is analyzed to see what marketing departments will be pushing.
Brands and marketers engage in “sentiment analysis”, using sophisticated machine learning-based algorithms to determine the context when a product is discussed, and this data can be used to accurately predict what the top selling products in a category are likely to be. Meanwhile understanding customer’s enthusiasm, that is, what draws a customer to shop at the brand, the marketers can better craft messages and personalized visuals via direct mail, email, and mobile channels.
Can we identify fashion related discussion on Twitter?
- brand related tweets
Can we identify certain features of products such as colors, material or fashion styles?
- product type related tweets
Can we identify certain features of brands?
- event related tweets: fashion week
Can we identify associations between the mentioned attributes products/brands?
Use case: Survey study of First Insight on online product testing
Use case: Merchandizing to start stocking & discontinuing product lines
3 Pricing Strategy
Retailers spend millions on their real time merchandising systems, allowing action to be taken based on insights in a matter of minutes. Big Data also plays a part in helping to determine when prices should be dropped – known as “mark down optimization”. Prior to the age of analytics most retailers would just reduce prices at the end of a buying season for a particular product line, when demand has almost gone. However analytics has shown that a more gradual reduction in price, from the moment demand starts to sag, generally leads to increased revenues. Experiments by US retailer Stage Stores found that this approach, backed by a predictive approach to determine the rise and fall of demand for a product, beat a traditional “end of season sale” approach 90% of the time.
Use case: Everlane cost breakdown
What for: Optimize per time period, per item, per store
Was dominated by Retek, but got purchased by Oracle in 2005. Now Oracle Retail. JDA is also a player. (supply chain software).
4 Targeted marketing
Targeted advertising platforms of the type pushed by retailers offer businesses of all sizes the chance to benefit from big data-driven segmented marketing strategies. And also, there is undoubtedly still a great deal of untapped potential in social media, customer feedback comments, video footage, recorded telephone conversations and locational GPS data to benefits those who put it to best work, offering social analytics to help anyone work out where their customers are waiting for them on social media. Finding behavioral segments and linking market segments to behavioral data are crucial to marketing and advertising
Use case: RFM Segmentation Method
How often are customers shopping with retailers (loyal or infrequent customers).
Total Shopping – all shopping in any channel is used to score the customer/address.
On Coupon Shopping – Only on coupon transactions are used to score the customer/address (previously the only segmentation available to the company for direct mail targeting).
ECOM Shopping – Only ECOM transactions (currently web and TBS) are used to score the customer/address.
In-Store Shopping – Only In-store transactions are used to score the customer/address.
Use case: Customer segmentation based recommendation
5 Cross-Selling and Up-selling Marketing Campaigns
Up-selling - Given a customer’s characteristics, what is the likelihood that they‘ll upgrade in the future.
Given a customer’s past browsing history, purchase history and other characteristics, what are they likely to want to purchase in the future. Identifying creditable associations between two or more different items can help a business stakeholders make decisions such as what discount to offer, when to distribute coupons, when to put a product on sale, or how to present items in store displays. Employ targeted cross-selling tactics to increase sales among existing customers. And also, given a customer’s characteristics, what is the likelihood that they‘ll upgrade in the future. Market Basket analyses is able to discover and visualize item associations and purchase sequence, seamless integration of rules with inputs for enriched predictive modeling to predict the average basket size per store and how that changes regionally.
Use Case: Market Basket
Key Features of E-Miner Market Basket Analysis
1) Associations and sequence discovery / visualization:
2) Interactively subset rules based on lift, confidence, support, chain length, etc
3) Seamless integration of rules with other inputs for enriched predictive modeling
4) Hierarchical associations: Derive rules at multiple levels. Specify parent and child mappings for the dimensional input table.
5) Discount targeting: What is the probability of inducing the desired behavior with a discount?
6) The Average basket size per store and how that changes regionally.
Use case: Product mix
What for: What mix of products offers the lowest churn? Eg. Giving a combined policy discount for home+auto = low churn
Usage: online algorithm and static report
Use case: Wallet share estimation
What for: the proportion of a customer’s spending in a category accrues to a company allows that company to identify upsell and cross-sell opportunities
Usage: Can be both an online algorithm and a static report showing the characteristics of low wallet share customers
How much of customers wallet do offline and online stores garner.
6 Customer Acquisition and Retention
It is the simplest setting where one encounters the exploration-exploitation dilemma.A good way to find good prospects are to look in the same places that today’s best customers came from. That means having some of way of determining who the best customers are today. It also means keeping a record of how current customers were acquired and what they looked like at the time of acquisition. The danger of relying on current customers to learn where to look for prospects is that the current customers reflect past marketing decisions. Studying current customers will not suggest looking for new prospects any place that hasn’t already been tried. Nevertheless, the performance of current customers is a great way to evaluate the existing acquisition channels. Ideally you should start tracking customers before they become customers. Gather information form new customers at the time they are acquired. Model the relationship between acquisition-time data and future outcomes of interest.
Usage: can be both an online algorithm and a statistic report showing the characteristics of likely engaged customers
New: Customer/Address made the first identified transaction in a selected fiscal period (fiscal month).
Existing: ‘Base customer or address’ or ‘Existing <= 24 months’ – may or may not have made a transaction in the selected fiscal period (fiscal month) and their last identified transaction in the last 2-24 months.
Reactivated: Customer/Address mades transaction in the selected fiscal period (fiscal month) in the selected fiscal period (fiscal month) and their last transaction > 24 fiscal months ago.
Lapsed: Customer/Address existing in last 2-48 months – made their last identified last transaction in the last 25-48 fiscal months.
Attrited: Customer/Address made their last identified transaction is more than 48 months ago.
https://news.greylock.com/why-onboarding-is-the-most-crucial-part-of-your-growth-strategy-8f9ad3ec8d5e
What is your frequency target? (How often should we expect the user to receive value?)
What is your key action? (The action signifies the user is receiving enough value to remain engaged)
- Data mining for customer relationship management (CRM)
1 Matching campaigns to customers
a. cross sell campaigns
b. up sell campaigns
c. usage stimulation campaigns
d. loyalty program
2 Segmenting the customer base
a. finding behavioral segments
b. typing market research segments to behavioral data
3 Reducing exposure to risk
a. predicting who will default
b. improving collection
4 Detecting customer lifetime value
a. Using current customer to learn about prospects
b. Start tracking customers before they become customers
c. Gather information from new customers
d. Acquisition time variables can predict future outcomes
5 Cross-selling, up-selling, and making recommendations
a. what to offer it
b. when to offer it
c. finding the right time for an offer
d. making recommendations
6 Detection and churn
a. recognizing churn
b. why churn matters
c. different kinds of churn including voluntary/forced, involuntary, expected
7 Different kinds of churn model
a. predicting who will leave
b. predicting how long customer will stay
Retail Analysis
Successful retailers are shifting away from omnichannel towards serving the customer as point of sale, and are looking at ways to best link up all the customer touchpoints to create a personalised and convenient shopping experience.
1 Demand-Driven Forecasting
This analysis involves gathering demographic data and economic indicators to build a picture of spending habits across the targeted market. For example, it would be more valuable to discover patterns due to the hurricane that were not obvious. To do this, analysts might examine the huge volume of retailer data from prior, similar situations to identify unusual local demand for products. From such patterns, the company might be able to anticipate unusual demand for products and rush stock to the stores ahead of the hurricane’s landfall.
One major problem for fashion companies is placing their production orders without having the actual knowledge of demand. Moreover, the demand is influenced by additional factors such as the economic situation, public holidays or changing weather conditions. Furthermore, items of a fashion collection are mostly replaced by following seasons collections, and therefore, companies often face a lack of historical sales data.
Use case: Weather based Recommendation
Use case: Ad-word optimization and ad buying. Calculating the right price for different keywords/ad slots.
Use case: Inventory Management (how many units)
In particular, perishable goods
2 Merchandising and Planning
With 1bn monthly active users on WhatsApp, 900m on Facebook Messenger, and the number of Snapchat users nearing 200m, retailers can no longer limit their communications to traditional channels such as email and phone for customer service. Trend forecasting algorithms comb brand perception surveys, social media posts and web browsing habits to work out what’s causing a buzz, and ad-buying data is analyzed to see what marketing departments will be pushing.
Brands and marketers engage in “sentiment analysis”, using sophisticated machine learning-based algorithms to determine the context when a product is discussed, and this data can be used to accurately predict what the top selling products in a category are likely to be. Meanwhile understanding customer’s enthusiasm, that is, what draws a customer to shop at the brand, the marketers can better craft messages and personalized visuals via direct mail, email, and mobile channels.
Can we identify fashion related discussion on Twitter?
- brand related tweets
Can we identify certain features of products such as colors, material or fashion styles?
- product type related tweets
Can we identify certain features of brands?
- event related tweets: fashion week
Can we identify associations between the mentioned attributes products/brands?
Use case: Survey study of First Insight on online product testing
Use case: Merchandizing to start stocking & discontinuing product lines
3 Pricing Strategy
Retailers spend millions on their real time merchandising systems, allowing action to be taken based on insights in a matter of minutes. Big Data also plays a part in helping to determine when prices should be dropped – known as “mark down optimization”. Prior to the age of analytics most retailers would just reduce prices at the end of a buying season for a particular product line, when demand has almost gone. However analytics has shown that a more gradual reduction in price, from the moment demand starts to sag, generally leads to increased revenues. Experiments by US retailer Stage Stores found that this approach, backed by a predictive approach to determine the rise and fall of demand for a product, beat a traditional “end of season sale” approach 90% of the time.
Use case: Everlane cost breakdown
What for: Optimize per time period, per item, per store
Was dominated by Retek, but got purchased by Oracle in 2005. Now Oracle Retail. JDA is also a player. (supply chain software).
4 Targeted marketing
Targeted advertising platforms of the type pushed by retailers offer businesses of all sizes the chance to benefit from big data-driven segmented marketing strategies. And also, there is undoubtedly still a great deal of untapped potential in social media, customer feedback comments, video footage, recorded telephone conversations and locational GPS data to benefits those who put it to best work, offering social analytics to help anyone work out where their customers are waiting for them on social media. Finding behavioral segments and linking market segments to behavioral data are crucial to marketing and advertising
Use case: RFM Segmentation Method
How often are customers shopping with retailers (loyal or infrequent customers).
Total Shopping – all shopping in any channel is used to score the customer/address.
On Coupon Shopping – Only on coupon transactions are used to score the customer/address (previously the only segmentation available to the company for direct mail targeting).
ECOM Shopping – Only ECOM transactions (currently web and TBS) are used to score the customer/address.
In-Store Shopping – Only In-store transactions are used to score the customer/address.
Use case: Customer segmentation based recommendation
5 Cross-Selling and Up-selling Marketing Campaigns
Up-selling - Given a customer’s characteristics, what is the likelihood that they‘ll upgrade in the future.
Given a customer’s past browsing history, purchase history and other characteristics, what are they likely to want to purchase in the future. Identifying creditable associations between two or more different items can help a business stakeholders make decisions such as what discount to offer, when to distribute coupons, when to put a product on sale, or how to present items in store displays. Employ targeted cross-selling tactics to increase sales among existing customers. And also, given a customer’s characteristics, what is the likelihood that they‘ll upgrade in the future. Market Basket analyses is able to discover and visualize item associations and purchase sequence, seamless integration of rules with inputs for enriched predictive modeling to predict the average basket size per store and how that changes regionally.
Use Case: Market Basket
Key Features of E-Miner Market Basket Analysis
1) Associations and sequence discovery / visualization:
2) Interactively subset rules based on lift, confidence, support, chain length, etc
3) Seamless integration of rules with other inputs for enriched predictive modeling
4) Hierarchical associations: Derive rules at multiple levels. Specify parent and child mappings for the dimensional input table.
5) Discount targeting: What is the probability of inducing the desired behavior with a discount?
6) The Average basket size per store and how that changes regionally.
Use case: Product mix
What for: What mix of products offers the lowest churn? Eg. Giving a combined policy discount for home+auto = low churn
Usage: online algorithm and static report
Use case: Wallet share estimation
What for: the proportion of a customer’s spending in a category accrues to a company allows that company to identify upsell and cross-sell opportunities
Usage: Can be both an online algorithm and a static report showing the characteristics of low wallet share customers
How much of customers wallet do offline and online stores garner.
6 Customer Acquisition and Retention
It is the simplest setting where one encounters the exploration-exploitation dilemma.A good way to find good prospects are to look in the same places that today’s best customers came from. That means having some of way of determining who the best customers are today. It also means keeping a record of how current customers were acquired and what they looked like at the time of acquisition. The danger of relying on current customers to learn where to look for prospects is that the current customers reflect past marketing decisions. Studying current customers will not suggest looking for new prospects any place that hasn’t already been tried. Nevertheless, the performance of current customers is a great way to evaluate the existing acquisition channels. Ideally you should start tracking customers before they become customers. Gather information form new customers at the time they are acquired. Model the relationship between acquisition-time data and future outcomes of interest.
Usage: can be both an online algorithm and a statistic report showing the characteristics of likely engaged customers
New: Customer/Address made the first identified transaction in a selected fiscal period (fiscal month).
Existing: ‘Base customer or address’ or ‘Existing <= 24 months’ – may or may not have made a transaction in the selected fiscal period (fiscal month) and their last identified transaction in the last 2-24 months.
Reactivated: Customer/Address mades transaction in the selected fiscal period (fiscal month) in the selected fiscal period (fiscal month) and their last transaction > 24 fiscal months ago.
Lapsed: Customer/Address existing in last 2-48 months – made their last identified last transaction in the last 25-48 fiscal months.
Attrited: Customer/Address made their last identified transaction is more than 48 months ago.
https://news.greylock.com/why-onboarding-is-the-most-crucial-part-of-your-growth-strategy-8f9ad3ec8d5e
What is your frequency target? (How often should we expect the user to receive value?)
What is your key action? (The action signifies the user is receiving enough value to remain engaged)
The principles of successful onboarding
Principle #1: Get to product value as fast as possible — but not faster
A lot of companies have a “cold start problem” — that is, they start the user in an empty state where the product doesn’t work until the user does something. This frequently leaves users confused as to what to do. If we know a successful onboarding experience leads to the key action adopted at the target frequency, we can focus on best practices to maximize the number of people who reach that point
Principle #1: Get to product value as fast as possible — but not faster
A lot of companies have a “cold start problem” — that is, they start the user in an empty state where the product doesn’t work until the user does something. This frequently leaves users confused as to what to do. If we know a successful onboarding experience leads to the key action adopted at the target frequency, we can focus on best practices to maximize the number of people who reach that point
Principle #2: Remove all friction that distracts the user from experiencing product value
Retention is driven by a maniacal focus on the core product experience. That is more likely to mean reducing friction in the product than adding features to it. New users are not like existing users. They are trying to understand the basics of how to use a product and what to do next. You have built features for existing users that already understand the basics and now want more value. New users not only don’t need those yet; including them makes it harder to understand the basics. So, a key element of successful onboarding is removing everything but the basics of the product until those basics are understood. At Pinterest, this meant removing descriptions underneath Pins as well as who Pinned the item, because the core product value had to do with finding images you liked, and removing descriptions and social attribution allowed news users to see more images in the feed.
Retention is driven by a maniacal focus on the core product experience. That is more likely to mean reducing friction in the product than adding features to it. New users are not like existing users. They are trying to understand the basics of how to use a product and what to do next. You have built features for existing users that already understand the basics and now want more value. New users not only don’t need those yet; including them makes it harder to understand the basics. So, a key element of successful onboarding is removing everything but the basics of the product until those basics are understood. At Pinterest, this meant removing descriptions underneath Pins as well as who Pinned the item, because the core product value had to do with finding images you liked, and removing descriptions and social attribution allowed news users to see more images in the feed.
Principle #3: Don’t be afraid to educate contextually
There’s a quote popular in Silicon Valley that says if your design requires education, it’s a bad design. It sounds smart, but its actually dangerous. Product education frequently helps users understand how to get value out of a product and create long term engagement. While you should always be striving for a design that doesn’t need explanation, you should not be afraid to educate if it helps in this way.
Onboarding is both the most difficult and ultimately most rewarding part of the funnel to improve to increase a company’s growth. And it’s where most companies fall short. By focusing on your onboarding, you can delight users more often and be more confident exposing your product to more people. For more advice on onboarding, please read Scott Belsky’s excellent article on the first mile of product.
7 Customer Churn Analysis and Reactivation Likelihood
Churn prediction is one of the most popular use cases for people who want to leverage machine learning. It has a large business value and benefit attached to itself especially in industries like the telecom and banking. Several challenges such as the skewed nature of the data set available and the ability to decide which models to use are going to be under a lot of debate. Working out the characteristics of churners allows a company to product adjustments and an online algorithm allows them to reach out to churners
Usage: can be both an online algorithm and a statistic report showing the characteristics of likely churners
Reactivation likelihood
What is the reactivation likelihood for a given customer?
8 Fault detection
There is a lot said online these days and it is hard to determine what is true and what is fake. We have bots smart enough to publish content like human beings and there are social aspects attached to the ratings of various entities online. Machine learning will be leveraged as a big challenge to determine the veracity/truth of information online.
Usage: use shrinkage analysis on theft analytics/prevention
Usage: can be both an online algorithm and a statistic report showing the characteristics of likely fault alerts
9 Customer Behavior/ Customer Segmentation
Deciding which customers are likely to want a particular product, and the best way to go about putting it in front of them, is key here. At a basic level, it means that knowing important factors about the customers such as gender, demographic, location, website browsing habits, search habits, and where they shop in store. And also, measure the influence of all touch points on a customer’s journey to purchase – online, offline and across devices – using sophisticated measurement systems. Track the customer’s journey through each channel – TV, display, search, email, and direct mail – providing a holistic view of how a valuable customer makes a purchase. To this end retailers rely heavily on recommendation engine technology online, and data collected through transactional records and loyalty programs off and online. Increase customer loyalty will create better retention programs to increase loyalty and customer lifetime value among existing customers.
What for: understand qualitatively different customer groups, then we can give them different treatments (perhaps even by different groups in the company). Questions be addressed like what makes people buy, stop buying, etc
Where else do customers spend money, in and out of the category
Different income levels of consumers across the board and where Walmart is losing or gaining market share amongst specific income levels.
Analyze regional markets, climate patterns and consumer spending when planning promotions and pricing strategies.
10 Customer Lifetime Value / Predicting Lifetime Value (LTV)
Case study: Customer lifetime value propensity application based on marketing customer type
What for: if you can predict the characteristics of high LTV customers, this supports customer segmentation, identifies upsell opportunities and supports other marketing initiatives.
Increase customer loyalty will create better retention programs to increase loyalty and customer lifetime value among existing customers.
Usage: can be both an online algorithm and a static report showing the characteristics of high LTV customers
11 Campaign Effectiveness
Over time how are campaigns affecting market share among the competitive set across region, DMA and certain zip codes.
It provides an insider's look into the best practices, challenges and examples of brands taking advantage of the closed-off social platform.
12 SKU lifetime Value - Inventory Management (how many units)
In particular, perishable goods
13 Channel Optimization
What is the optimal way to reach a customer with certain characteristics?
14 Location of New Stores / Store Placement
For a given trade area, is there any significant relationship between sales volume of stores and customer’ sentiments towards the brand? Are there significant correlations? How does the above relationship change with the presence of location based demographics and socioeconomic factors such as ‘Total number of households’, ‘Average home value’ and ‘Total retail spending’ in the trade area of the store location. The association of customer sentiments and retail sales improves.
Pioneerd by Tesco. Dominated by Buxton. Site selection in the restaurant industry is widely performed via Pitney Bowes. Where do I open my next store based on similar competitor success in certain DMAs and ZIP codes.
15 Warranty analytics
Rates of failure of different components
What types of customers buying what types of products are likely to actually redeem a warranty
Churn prediction is one of the most popular use cases for people who want to leverage machine learning. It has a large business value and benefit attached to itself especially in industries like the telecom and banking. Several challenges such as the skewed nature of the data set available and the ability to decide which models to use are going to be under a lot of debate. Working out the characteristics of churners allows a company to product adjustments and an online algorithm allows them to reach out to churners
Usage: can be both an online algorithm and a statistic report showing the characteristics of likely churners
Reactivation likelihood
What is the reactivation likelihood for a given customer?
8 Fault detection
There is a lot said online these days and it is hard to determine what is true and what is fake. We have bots smart enough to publish content like human beings and there are social aspects attached to the ratings of various entities online. Machine learning will be leveraged as a big challenge to determine the veracity/truth of information online.
Usage: use shrinkage analysis on theft analytics/prevention
Usage: can be both an online algorithm and a statistic report showing the characteristics of likely fault alerts
9 Customer Behavior/ Customer Segmentation
Deciding which customers are likely to want a particular product, and the best way to go about putting it in front of them, is key here. At a basic level, it means that knowing important factors about the customers such as gender, demographic, location, website browsing habits, search habits, and where they shop in store. And also, measure the influence of all touch points on a customer’s journey to purchase – online, offline and across devices – using sophisticated measurement systems. Track the customer’s journey through each channel – TV, display, search, email, and direct mail – providing a holistic view of how a valuable customer makes a purchase. To this end retailers rely heavily on recommendation engine technology online, and data collected through transactional records and loyalty programs off and online. Increase customer loyalty will create better retention programs to increase loyalty and customer lifetime value among existing customers.
What for: understand qualitatively different customer groups, then we can give them different treatments (perhaps even by different groups in the company). Questions be addressed like what makes people buy, stop buying, etc
Where else do customers spend money, in and out of the category
Different income levels of consumers across the board and where Walmart is losing or gaining market share amongst specific income levels.
Analyze regional markets, climate patterns and consumer spending when planning promotions and pricing strategies.
10 Customer Lifetime Value / Predicting Lifetime Value (LTV)
Case study: Customer lifetime value propensity application based on marketing customer type
What for: if you can predict the characteristics of high LTV customers, this supports customer segmentation, identifies upsell opportunities and supports other marketing initiatives.
Increase customer loyalty will create better retention programs to increase loyalty and customer lifetime value among existing customers.
Usage: can be both an online algorithm and a static report showing the characteristics of high LTV customers
11 Campaign Effectiveness
Over time how are campaigns affecting market share among the competitive set across region, DMA and certain zip codes.
It provides an insider's look into the best practices, challenges and examples of brands taking advantage of the closed-off social platform.
12 SKU lifetime Value - Inventory Management (how many units)
In particular, perishable goods
13 Channel Optimization
What is the optimal way to reach a customer with certain characteristics?
14 Location of New Stores / Store Placement
For a given trade area, is there any significant relationship between sales volume of stores and customer’ sentiments towards the brand? Are there significant correlations? How does the above relationship change with the presence of location based demographics and socioeconomic factors such as ‘Total number of households’, ‘Average home value’ and ‘Total retail spending’ in the trade area of the store location. The association of customer sentiments and retail sales improves.
Pioneerd by Tesco. Dominated by Buxton. Site selection in the restaurant industry is widely performed via Pitney Bowes. Where do I open my next store based on similar competitor success in certain DMAs and ZIP codes.
15 Warranty analytics
Rates of failure of different components
What types of customers buying what types of products are likely to actually redeem a warranty
18 Product layout in stores
Plan-o-gramming
Hierarchical taxonomy of interests
Commercial relevant categories including retail, travel, automotive, finance, CPG, Pharmaceuticals, Small business, Sports, etc.
18 top-level categories
Up to 6 levels deep, 90% of categories are at depth 4 or lower
Around 500 categories are being actively used worldwide
Assumptions on collected data:
1 Have to ignore the temporal factors that could potentially influence sentiments such as festivals, holidays and other economic factors including unemployment rate, poverty level etc.
2 Use 100 mile radius from the store location to collect tweets to maintain the sample of the order of 1000 tweets for each location.
3 A trade area for each store is assumed to be a circle of 10 mile radius from the store location. Demographic factors and sentiments of people residing in a trade area are characteristics of potential customers.
4 For a given store location, although the sample of tweets collected for the analysis represent data from a much larger trade area, the sentiments of people remain fairly uniform within the trade area including the studied 10 mile radius trade area.
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Marketing
● Predict Lifetime Value (LTV): Predict the characteristics of high LTV customers; this supports customer segmentation, identifies up-sell opportunities, and supports other marketing initiatives.
● Churn: Determine the characteristics of customers who churn (i.e., customer defection); this enables a company to develop adjustments to an online algorithm that allows them to reach out to churners.
● Customer segmentation: Allows you to understand qualitatively different customer groups to answer questions like what makes people buy, stop buying, etc.
● Product mix: What mix of products offers the lowest churn rate? For example, giving a combined insurance policy discount for home and auto yields a low churn rate.
● Cross-selling/up-selling and recommendation algorithms: Given a customer’s past browsing history, purchase history, and other behavioral characteristics, what are they likely to want to purchase (or upgrade to) in the future?
● Discount targeting: What is the probability of encouraging the desired behavior with a discount offer?
● Reactivation likelihood: What is the likelihood of reactivation for a given customer?
● Google Adwords optimization and ad buying: Determine the optimal price for different search keywords and ad slots.
Sales
● Lead prioritization: Determine the likelihood that a given sales lead will close.
● Sales forecasting: Provide strategic planning and insight into the sales forecasting process.
Supply Chain
● Demand forecasting: Determine optimal inventory levels for different distribution centers, enabling a lean inventory and preventing out of stock situations.
Risk Management
● Fraud detection: Predict whether or not a transaction should be blocked because it involves some kind of fraud, e.g., credit card fraud or Medicare fraud.
● Accounts payable recovery: Predict the probably that a liability can be recovered, given the characteristics of the borrower and the loan.
Customer Support
● Call center management: Call center volume forecasting (i.e., predicting call volume for staffing purposes).
● Call routing: Determine wait times based on caller ID history, time of day, call volumes, products owned, churn risk, LTV, etc.
Human Resources
● Talent management: Establish objective measures of employee success.
● Employee churn: Predict which employees are most likely to leave.
● Resume screening: Score resumes based on the outcomes of past job interviews and hires.
● Training recommendation: Recommend a specific training program based on employee performance review data.
Ad products or features: multiple impressions, streaming fatigue, NSFW.
Brand Survey targeting algorithms
Performance bidding, auction simulator parameter tuning
Deep dive analysis for A/B experiments
Diagnosis analysis of CTR prediction model and logging diagnostic
4 For a given store location, although the sample of tweets collected for the analysis represent data from a much larger trade area, the sentiments of people remain fairly uniform within the trade area including the studied 10 mile radius trade area.
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Marketing
● Predict Lifetime Value (LTV): Predict the characteristics of high LTV customers; this supports customer segmentation, identifies up-sell opportunities, and supports other marketing initiatives.
● Churn: Determine the characteristics of customers who churn (i.e., customer defection); this enables a company to develop adjustments to an online algorithm that allows them to reach out to churners.
● Customer segmentation: Allows you to understand qualitatively different customer groups to answer questions like what makes people buy, stop buying, etc.
● Product mix: What mix of products offers the lowest churn rate? For example, giving a combined insurance policy discount for home and auto yields a low churn rate.
● Cross-selling/up-selling and recommendation algorithms: Given a customer’s past browsing history, purchase history, and other behavioral characteristics, what are they likely to want to purchase (or upgrade to) in the future?
● Discount targeting: What is the probability of encouraging the desired behavior with a discount offer?
● Reactivation likelihood: What is the likelihood of reactivation for a given customer?
● Google Adwords optimization and ad buying: Determine the optimal price for different search keywords and ad slots.
Sales
● Lead prioritization: Determine the likelihood that a given sales lead will close.
● Sales forecasting: Provide strategic planning and insight into the sales forecasting process.
Supply Chain
● Demand forecasting: Determine optimal inventory levels for different distribution centers, enabling a lean inventory and preventing out of stock situations.
Risk Management
● Fraud detection: Predict whether or not a transaction should be blocked because it involves some kind of fraud, e.g., credit card fraud or Medicare fraud.
● Accounts payable recovery: Predict the probably that a liability can be recovered, given the characteristics of the borrower and the loan.
Customer Support
● Call center management: Call center volume forecasting (i.e., predicting call volume for staffing purposes).
● Call routing: Determine wait times based on caller ID history, time of day, call volumes, products owned, churn risk, LTV, etc.
Human Resources
● Talent management: Establish objective measures of employee success.
● Employee churn: Predict which employees are most likely to leave.
● Resume screening: Score resumes based on the outcomes of past job interviews and hires.
● Training recommendation: Recommend a specific training program based on employee performance review data.
Ad products or features: multiple impressions, streaming fatigue, NSFW.
Brand Survey targeting algorithms
Performance bidding, auction simulator parameter tuning
Deep dive analysis for A/B experiments
Diagnosis analysis of CTR prediction model and logging diagnostic
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