So how does it work?<\/em><\/h2>\n\n\n\nThere are four key steps that work together in a highly automated and scalable fashion! The outcome is a drastic increase in the confidence that an executive or merchant can have in his or her choices related to future product planning and assortment.<\/p>\n\n\n\n
\n- Analysis of history<\/li>\n\n\n\n
- Tracking of established and emerging product attributes<\/li>\n\n\n\n
- Evaluation of the performance of key metrics<\/li>\n\n\n\n
- Analysis of the probability of future success <\/li>\n<\/ol>\n\n\n\n
1. Analysis of history<\/h2>\n\n\n\n![\"Analysis](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643700490505-1024x576.png\")
<\/figure>\n\n\n\n<\/p>\n\n\n\n
As our data science team says, the evolution of a trend or attribute (a style) can have multiple possible outcomes. This means that on the extreme ends of the scale certain product trends and attributes will make that product a hit or a flop with shoppers. Our machine pinpoints where a product is in its lifecycle, and how it is resonating with the shopper audience. This reveals insight into a product\u2019s likelihood of future success. The machine can also gain insight into the likelihood of future success of similar products that are still in earlier and emerging phases of their lifecycle.<\/p>\n\n\n\n
2. Tracking established and emerging product attributes <\/h2>\n\n\n\n
The algorithm is able to identify and understand themes and trends around a given product through analyzing unstructured data from the bottom up. This is important because it allows the algorithm to differentiate between exactly what is winning and what is losing<\/em> in the marketplace. <\/p>\n\n\n\nLet\u2019s take the example of recycled jeans. <\/p>\n\n\n\n
First, the algorithm mines the product copy for keywords capturing recurring instances from the product name, description, and stated attributes across all relevant competitor websites of our clients. It uses our own comprehensive attribute libraries to marry and merge attribute synonyms that are informed by over one billion products! This is further enhanced with a cutting-edge technique called computer vision. Computer vision is a branch of Artificial Intelligence (AI) that enables our system to interpret and act on important information from visual inputs such as images and videos.<\/p>\n\n\n\n
Secondly, we hone in on the keywords, such as \u201crecycled and upcycled\u201d that relate to the attribute of \u201crecycled jeans\u201d.<\/p>\n\n\n\n![\"product](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643701880766-1024x576.png\")
<\/figure>\n\n\n\n<\/p>\n\n\n\n
Our methodology then creates logical clusters of these keywords that derive a theme, in this case, environmental consciousness.<\/p>\n\n\n\n![\"Recycled](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643702302616-1024x576.png\")
<\/figure>\n\n\n\nThe model then goes one step further to look for context similarity. It looks for similar keywords such as \u201corganic jeans\u201d that revolve around the identified theme of environmental consciousness.<\/p>\n\n\n\n![\"Organic](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643703801269-1024x576.png\")
<\/figure>\n\n\n\n3. Evaluation of the performance of key metrics<\/h2>\n\n\n\n
Our machine analyzes metrics such as share of shelf<\/a>, power rank and time rank to evaluate the performance of the identified trends, attributes and products to infer their probability of future success. <\/p>\n\n\n\na. Share of digital shelf<\/a> <\/em>is the percentage visibility a product gets from a keyword or attribute inquiry, for example, \u201cMom Jeans\u201d.<\/p>\n\n\n\n![\"Digital](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643704006303-1024x576.png\")
<\/figure>\n\n\n\nb. Power rank <\/em>pulls in a joint score on KPIs related to shopper interest:<\/p>\n\n\n\n- Customer reaction: The number and velocity of product reviews and ratings. Our algorithm counts all customer reviews across websites along with the velocity of these reviews to analyze customer sentiment for an attribute such as \u201cMom Jeans\u201d<\/li><\/ol>\n\n\n\n
![\"Tracking](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643704206944-1024x576.png\")
<\/figure>\n\n\n\n- Customer action: Tracking of sales velocity and product visibility across domains is done by continuously collecting inventory counts from multiple sources including the product page, shopping cart, the Amazon buy box, and more. The machine looks at the frequency at which the inventory levels are depleting over a time period. Faster depletion equates to greater sales velocity and higher resonance with shoppers.<\/li><\/ol>\n\n\n\n
![\"Shopper](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643712554122-1024x576.png\")
<\/figure>\n\n\n\n- Customer discoverability: Tracking product searchability and popularity by using the search algorithm of your competitor\u2019s website. The algorithm tracks the first 100 products on competitor domains and analyzes the share of every attribute. The algorithm also tracks weekly and monthly popularity of attributes, such as \u201cMom Jeans\u201d on Google trends. This ongoing search data is a direct reflection of interest in the trend, attribute or product across the defined regions.<\/li><\/ol>\n\n\n\n
![\"Women's](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643707098031-1024x576.png\")
<\/figure>\n\n\n\n\u201cMom jeans\u201d accounts for 26% of the first 100 products listed on H&M.<\/em><\/p>\n\n\n\nAlso read : 8 Ways Retailers Can Leverage Customer Reviews Using Machine Learning<\/a><\/pre>\n\n\n\n- Product Lifecycle: The algorithm analyzes product lifecycle dynamics by tracking key product activities at an attribute level. This includes things such as product promotions, discount ratio, product affinity, product availability, rate of replenishment, average span of product stock outs, and more. <\/li><\/ol>\n\n\n\n
![\"Product](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643707217002-1024x576.png\")
<\/figure>\n\n\n\nc. Time rank<\/em>: This involves tracking the age of an attribute which helps to identify new trends at an early stage and also sheds light on declining trends across the marketplace.<\/p>\n\n\n\n![\"Time](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643707957027-1024x576.png\")
<\/figure>\n\n\n\n4. Analyzing the probability of future success<\/h2>\n\n\n\n
Based on the performance of the metrics covered in step 3, the machine then identifies which trends and attributes are likely to make a product a hit or a flop in the future. <\/p>\n\n\n\n![\"Performance](\"https:\/\/www.intelligencenode.com\/blog\/wp-content\/uploads\/2022\/03\/1643707455480-1024x576.png\")
<\/figure>\n\n\n\nIn the above example, we looked at the performance of key metrics for \u2018straight fit jeans\u2019 over the past 24 months. By applying this algorithm-led approach, we can predict the probability of success for straight fit jeans months into the future. <\/p>\n\n\n\n
This methodology is a new market-driven approach to identifying future trends and informing product strategies that lead to greater resonance with shoppers, and in turn, more efficient and profitable outcomes for the brand and retailer. Executives and merchants gain a great deal of confidence in the trend and product decisions they are able to drive for their businesses. Furthermore, the solution is able to absorb a level of insight and analytics work that no team of hundreds of humans possibly could. This in turn frees up the executive to focus on the art of her go-to-market strategy that can then be married with the data to produce the most powerful business result outcome. Digital Trend Foresights complements any existing trend forecasting methodologies and practices that retailers and brands have in place with data that reflects real-time shopper behavior<\/a>. <\/p>\n\n\n\nFinal word<\/strong><\/h2>\n\n\n\nThe pace at which shopper preferences are evolving today calls for brands and retailers that are able to harness the power of machine-driven data intelligence<\/a> in their go-to-market strategies. Do art and intuition still matter? Of course they do. A business would be lost without them but no longer are they enough. Leveraging advanced analytics will help retailers derive insights that identify future trends and shopper preferences at an otherwise impossible level of precision and pace. This is exactly what Intelligence Node\u2019s solution – Digital Trend Foresights – is delivering for the retail ecosystem. The solution is currently being put to work by brands and retailers in mid-market apparel & fashion categories. <\/p>\n\n\n\nAbout Intelligence Node<\/strong><\/em><\/p>\n\n\n\n
1. Analysis of history<\/h2>\n\n\n\n<\/figure>\n\n\n\n<\/p>\n\n\n\n
As our data science team says, the evolution of a trend or attribute (a style) can have multiple possible outcomes. This means that on the extreme ends of the scale certain product trends and attributes will make that product a hit or a flop with shoppers. Our machine pinpoints where a product is in its lifecycle, and how it is resonating with the shopper audience. This reveals insight into a product\u2019s likelihood of future success. The machine can also gain insight into the likelihood of future success of similar products that are still in earlier and emerging phases of their lifecycle.<\/p>\n\n\n\n
2. Tracking established and emerging product attributes <\/h2>\n\n\n\n
The algorithm is able to identify and understand themes and trends around a given product through analyzing unstructured data from the bottom up. This is important because it allows the algorithm to differentiate between exactly what is winning and what is losing<\/em> in the marketplace. <\/p>\n\n\n\nLet\u2019s take the example of recycled jeans. <\/p>\n\n\n\n
First, the algorithm mines the product copy for keywords capturing recurring instances from the product name, description, and stated attributes across all relevant competitor websites of our clients. It uses our own comprehensive attribute libraries to marry and merge attribute synonyms that are informed by over one billion products! This is further enhanced with a cutting-edge technique called computer vision. Computer vision is a branch of Artificial Intelligence (AI) that enables our system to interpret and act on important information from visual inputs such as images and videos.<\/p>\n\n\n\n
Secondly, we hone in on the keywords, such as \u201crecycled and upcycled\u201d that relate to the attribute of \u201crecycled jeans\u201d.<\/p>\n\n\n\n<\/figure>\n\n\n\n<\/p>\n\n\n\n
Our methodology then creates logical clusters of these keywords that derive a theme, in this case, environmental consciousness.<\/p>\n\n\n\n<\/figure>\n\n\n\nThe model then goes one step further to look for context similarity. It looks for similar keywords such as \u201corganic jeans\u201d that revolve around the identified theme of environmental consciousness.<\/p>\n\n\n\n<\/figure>\n\n\n\n3. Evaluation of the performance of key metrics<\/h2>\n\n\n\n
Our machine analyzes metrics such as share of shelf<\/a>, power rank and time rank to evaluate the performance of the identified trends, attributes and products to infer their probability of future success. <\/p>\n\n\n\na. Share of digital shelf<\/a> <\/em>is the percentage visibility a product gets from a keyword or attribute inquiry, for example, \u201cMom Jeans\u201d.<\/p>\n\n\n\n<\/figure>\n\n\n\nb. Power rank <\/em>pulls in a joint score on KPIs related to shopper interest:<\/p>\n\n\n\n- Customer reaction: The number and velocity of product reviews and ratings. Our algorithm counts all customer reviews across websites along with the velocity of these reviews to analyze customer sentiment for an attribute such as \u201cMom Jeans\u201d<\/li><\/ol>\n\n\n\n
<\/figure>\n\n\n\n- Customer action: Tracking of sales velocity and product visibility across domains is done by continuously collecting inventory counts from multiple sources including the product page, shopping cart, the Amazon buy box, and more. The machine looks at the frequency at which the inventory levels are depleting over a time period. Faster depletion equates to greater sales velocity and higher resonance with shoppers.<\/li><\/ol>\n\n\n\n
<\/figure>\n\n\n\n- Customer discoverability: Tracking product searchability and popularity by using the search algorithm of your competitor\u2019s website. The algorithm tracks the first 100 products on competitor domains and analyzes the share of every attribute. The algorithm also tracks weekly and monthly popularity of attributes, such as \u201cMom Jeans\u201d on Google trends. This ongoing search data is a direct reflection of interest in the trend, attribute or product across the defined regions.<\/li><\/ol>\n\n\n\n
<\/figure>\n\n\n\n\u201cMom jeans\u201d accounts for 26% of the first 100 products listed on H&M.<\/em><\/p>\n\n\n\nAlso read : 8 Ways Retailers Can Leverage Customer Reviews Using Machine Learning<\/a><\/pre>\n\n\n\n- Product Lifecycle: The algorithm analyzes product lifecycle dynamics by tracking key product activities at an attribute level. This includes things such as product promotions, discount ratio, product affinity, product availability, rate of replenishment, average span of product stock outs, and more. <\/li><\/ol>\n\n\n\n
<\/figure>\n\n\n\nc. Time rank<\/em>: This involves tracking the age of an attribute which helps to identify new trends at an early stage and also sheds light on declining trends across the marketplace.<\/p>\n\n\n\n<\/figure>\n\n\n\n4. Analyzing the probability of future success<\/h2>\n\n\n\n
Based on the performance of the metrics covered in step 3, the machine then identifies which trends and attributes are likely to make a product a hit or a flop in the future. <\/p>\n\n\n\n<\/figure>\n\n\n\nIn the above example, we looked at the performance of key metrics for \u2018straight fit jeans\u2019 over the past 24 months. By applying this algorithm-led approach, we can predict the probability of success for straight fit jeans months into the future. <\/p>\n\n\n\n
This methodology is a new market-driven approach to identifying future trends and informing product strategies that lead to greater resonance with shoppers, and in turn, more efficient and profitable outcomes for the brand and retailer. Executives and merchants gain a great deal of confidence in the trend and product decisions they are able to drive for their businesses. Furthermore, the solution is able to absorb a level of insight and analytics work that no team of hundreds of humans possibly could. This in turn frees up the executive to focus on the art of her go-to-market strategy that can then be married with the data to produce the most powerful business result outcome. Digital Trend Foresights complements any existing trend forecasting methodologies and practices that retailers and brands have in place with data that reflects real-time shopper behavior<\/a>. <\/p>\n\n\n\nFinal word<\/strong><\/h2>\n\n\n\nThe pace at which shopper preferences are evolving today calls for brands and retailers that are able to harness the power of machine-driven data intelligence<\/a> in their go-to-market strategies. Do art and intuition still matter? Of course they do. A business would be lost without them but no longer are they enough. Leveraging advanced analytics will help retailers derive insights that identify future trends and shopper preferences at an otherwise impossible level of precision and pace. This is exactly what Intelligence Node\u2019s solution – Digital Trend Foresights – is delivering for the retail ecosystem. The solution is currently being put to work by brands and retailers in mid-market apparel & fashion categories. <\/p>\n\n\n\nAbout Intelligence Node<\/strong><\/em><\/p>\n\n\n\n
<\/figure>\n\n\n\n<\/p>\n\n\n\n
As our data science team says, the evolution of a trend or attribute (a style) can have multiple possible outcomes. This means that on the extreme ends of the scale certain product trends and attributes will make that product a hit or a flop with shoppers. Our machine pinpoints where a product is in its lifecycle, and how it is resonating with the shopper audience. This reveals insight into a product\u2019s likelihood of future success. The machine can also gain insight into the likelihood of future success of similar products that are still in earlier and emerging phases of their lifecycle.<\/p>\n\n\n\n
2. Tracking established and emerging product attributes <\/h2>\n\n\n\n
The algorithm is able to identify and understand themes and trends around a given product through analyzing unstructured data from the bottom up. This is important because it allows the algorithm to differentiate between exactly what is winning and what is losing<\/em> in the marketplace. <\/p>\n\n\n\n Let\u2019s take the example of recycled jeans. <\/p>\n\n\n\n First, the algorithm mines the product copy for keywords capturing recurring instances from the product name, description, and stated attributes across all relevant competitor websites of our clients. It uses our own comprehensive attribute libraries to marry and merge attribute synonyms that are informed by over one billion products! This is further enhanced with a cutting-edge technique called computer vision. Computer vision is a branch of Artificial Intelligence (AI) that enables our system to interpret and act on important information from visual inputs such as images and videos.<\/p>\n\n\n\n Secondly, we hone in on the keywords, such as \u201crecycled and upcycled\u201d that relate to the attribute of \u201crecycled jeans\u201d.<\/p>\n\n\n\n <\/p>\n\n\n\n Our methodology then creates logical clusters of these keywords that derive a theme, in this case, environmental consciousness.<\/p>\n\n\n\n The model then goes one step further to look for context similarity. It looks for similar keywords such as \u201corganic jeans\u201d that revolve around the identified theme of environmental consciousness.<\/p>\n\n\n\n Our machine analyzes metrics such as share of shelf<\/a>, power rank and time rank to evaluate the performance of the identified trends, attributes and products to infer their probability of future success. <\/p>\n\n\n\n a. Share of digital shelf<\/a> <\/em>is the percentage visibility a product gets from a keyword or attribute inquiry, for example, \u201cMom Jeans\u201d.<\/p>\n\n\n\n b. Power rank <\/em>pulls in a joint score on KPIs related to shopper interest:<\/p>\n\n\n\n \u201cMom jeans\u201d accounts for 26% of the first 100 products listed on H&M.<\/em><\/p>\n\n\n\n c. Time rank<\/em>: This involves tracking the age of an attribute which helps to identify new trends at an early stage and also sheds light on declining trends across the marketplace.<\/p>\n\n\n\n Based on the performance of the metrics covered in step 3, the machine then identifies which trends and attributes are likely to make a product a hit or a flop in the future. <\/p>\n\n\n\n In the above example, we looked at the performance of key metrics for \u2018straight fit jeans\u2019 over the past 24 months. By applying this algorithm-led approach, we can predict the probability of success for straight fit jeans months into the future. <\/p>\n\n\n\n This methodology is a new market-driven approach to identifying future trends and informing product strategies that lead to greater resonance with shoppers, and in turn, more efficient and profitable outcomes for the brand and retailer. Executives and merchants gain a great deal of confidence in the trend and product decisions they are able to drive for their businesses. Furthermore, the solution is able to absorb a level of insight and analytics work that no team of hundreds of humans possibly could. This in turn frees up the executive to focus on the art of her go-to-market strategy that can then be married with the data to produce the most powerful business result outcome. Digital Trend Foresights complements any existing trend forecasting methodologies and practices that retailers and brands have in place with data that reflects real-time shopper behavior<\/a>. <\/p>\n\n\n\n The pace at which shopper preferences are evolving today calls for brands and retailers that are able to harness the power of machine-driven data intelligence<\/a> in their go-to-market strategies. Do art and intuition still matter? Of course they do. A business would be lost without them but no longer are they enough. Leveraging advanced analytics will help retailers derive insights that identify future trends and shopper preferences at an otherwise impossible level of precision and pace. This is exactly what Intelligence Node\u2019s solution – Digital Trend Foresights – is delivering for the retail ecosystem. The solution is currently being put to work by brands and retailers in mid-market apparel & fashion categories. <\/p>\n\n\n\n About Intelligence Node<\/strong><\/em><\/p>\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n\n\n\n3. Evaluation of the performance of key metrics<\/h2>\n\n\n\n
<\/figure>\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n\n\n\nAlso read : 8 Ways Retailers Can Leverage Customer Reviews Using Machine Learning<\/a><\/pre>\n\n\n\n
<\/figure>\n\n\n\n<\/figure>\n\n\n\n4. Analyzing the probability of future success<\/h2>\n\n\n\n
<\/figure>\n\n\n\nFinal word<\/strong><\/h2>\n\n\n\n