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customer segmentation models in r

You can find me on Twitter @krystian8207. A big part of regular customers may be entrepreneurs, so they order wholesale quantities of products. We can then proceed to define the optimal clusters as follows –, First, we calculate the clustering algorithm for several values of k. This can be done by creating a variation within k from 1 to 10 clusters. Using the silhouette function in the cluster package, we can compute the average silhouette width using the kmean function. Categories. Some examples can include behavioral and psychographic segmentation, demographic and psychographic, or any other combination you feel fits best for your business. Through segmentation, you can identify niches with specific needs, mature markets to find new customers, deliver more focused and effective marketing messages. Tends to spend a lot of money for each basket. Where Ck represents the kth cluster and W(Ck) denotes the intra-cluster variation. I used a Kaggle database to show you how to separate your customers into distinct groups based on their purchase behavior. As you can see, "Fresh", "Frozen" and "Delicatessen" as dependent variables have negative R2 scores. An example of such validation you may find in “Choosing the Best Clustering Algorithms.”. Our dataset stores 47 variables, so it’s impossible to compare assigned clusters across all variables (readable visualisations are restricted to a maximum 3 dimensions). With the optimal number of k clusters, one can maximize the average silhouette over significant values for k clusters. Cluster 6 – This cluster represents customers having a high PCA2 and a low PCA1. This plot denotes the appropriate number of clusters required in our model. This object is the initial cluster or mean. Customer segmentation is a method of dividing customers into groups or clusters on the basis of common characteristics. Then through the iterative minimization of the total sum of the square, the assignment stop wavering when we achieve maximum iteration. Furthermore, through the data collected, companies can gain a deeper understanding of customer preferences as well as the requirements for discovering valuable segments that would reap them maximum profit. In this data science project, we went through the customer segmentation model. If you want to learn the magic that stands behind the conversion of data to pricing and promotion strategy, as well as  what hides behind the above radar charts, I encourage you to read the next sections. and some other functions are not working after installing the packages also. Hybrid Segmentation. There are plenty of algorithms that are commonly used for segmentation. Customer Segmentation LifeCycle Grids R language. Offered by Coursera Project Network. With this, we can determine how well within the cluster is the data object. Thanks for reading! Wouldn’t it be useful to identify separate groups of clients that show different shopping behaviors? The Segmentation and Clustering Cheat Sheet is a key component of learning the data science for business.We teach the cheat sheet as part of our Business Analysis With R (DS4B 101-R) … The objective of this project is to find significant customers for the business who make high purchases of their favourite products and use the clustering methodology to segment customers into groups. In this Data Science R Project series, we will perform one of the most essential applications of machine learning – Customer Segmentation. Common customer segmentation models range from simple to very complex and can be used for a variety of business reasons. We used two metrics: frequency and recency. The clients on average are also least active in the recent past. Why and how to segment? * Monetary Value – How much do t We will first proceed by taking summary of the Age variable. Marsello has released data-driven Customer Segmentation , specifically designed to optimize your targeted retail marketing. We can do it with one line of code: Let’s extract the chosen clusters from the created model and take a look at the data again: How can we verify if the clusters were extracted correctly? Cluster 3 – This cluster comprises of customers with a high PCA1 income and a high PCA2. The silhouette statistic for a single element compares its mean inner-cluster distance to the mean distance from the neighbouring cluster. To market effectively, you must identify the specific groups of people who will find your product or service to be most meaningful. The k – parameter is set pre-specified, but the post-analysis can help you choose the best value (silhouette or gap statistic). From the above plots we can certainly conclude that the 2nd (yellow) cluster is separate from the remaining ones. Very well put together and explained. /2018/06/analyzing-personalization-results.html Companies that deploy customer segmentation are under the notion that every customer has different requirements and require a … Your email address will not be published. The FlexMix package was designed to handle such non-normal model-based clustering, in this case a mixture of binomial distributions. The algorithm tends to minimize inter-cluster variation that should result with separating homogeneous groups. You can download the dataset for customer segmentation project here. We could even include recommendations from the appropriate. As a rule, each of the designated groups reacts differently to the product offered, thanks to which we have the opportunity to offer differently to each of them. Beginner's Guide to a Simple Customer Segmentation Model. Customer segmentation is the use of past data to divide customers into similar groups based on various features (Hsu et al. These selected objects are the cluster means, also known as centroids. We will plot a histogram and then we will proceed to examine this data using a density plot. Any complex enterprise landscape comprises of multiple systems, each performing a specific function. Visualisation of Customer Segmentation. It is alive in terms of customers flow. Some popular ways to segment your customers include segmentation based on: 1. With this small effort we were able to propose what promotion strategies we should use to encourage the customers to make purchases in our online shop. Customer segmentation is the process of dividing customers into groups based upon certain boundaries; clustering is one way to generate these boundaries. Customer Segmentation is a series of activities that aim to separate homogeneous groups of clients (retail or business) into sub-groups based on their behavior during the purchase. I’d like to learn more about my customers and find out how can I attract them and encourage them to use my online shop in the future. Therefore, I recommend to check out Hadoop for Data Science. To sum up, we’re going to use the k-means algorithm with 3 clusters. Approach. Have you Checked DataFlair’s Trending Project on Data Science? Then, the remaining objects have an assignment of the closest centroid. To better serve their needs! Can’t we create a single model and enable it with some segmentation variable as an input to the model ?May be, we could. Customer Segmentation can be a powerful means to identify unsatisfied customer needs. Desired benefits from … To sum up, we’re happy with this result and we can now move to the next part of our analysis. Let’s use this information to create new sets of variables that store information about how much each customer spends in each category. Model Customer Segmentation Model Customer Structure Geographic,Demographic ,Psychographic,Behavorial,Misc. In this post, we focused on k-means clustering in R. While the algorithm is quite simple to implement, half the battle is getting the data into the correct format and interpreting the results. It helps sales teams and marketing teams get a better understanding of existing customers, and identifying/targeting potential customers. Is there any example for supervised learning. Top tweets, Nov 25 – Dec 01: 5 Free Books to Learn #S... Building AI Models for High-Frequency Streaming Data, Get KDnuggets, a leading newsletter on AI, The way the algorithm works is shown below: I implemented a standard Hartigan-Wong algorithm (1979) with the R stats package, and it is based on Euclidean distance. The “description” column will be used later. Customer segmentation – LifeCycle Grids, CLV and CAC with R. Author. Using the above data companies can then outperform the competition by developing uniquely appealing products and services. We refer to this step as “cluster assignment”. Every financial transaction, every trip or meeting with friends can be registered in one of the billions of databases. Data Science, and Machine Learning. Marketing strategies for the customer segments Based on the 6 clusters, we could formulate marketing strategies relevant to each cluster: A typical strategy would focus certain promotional efforts for the high value customers of Cluster 6 & Cluster 3. This article will demon s trate the process of a data science approach to market segmentation, with a sample survey dataset using R. In this example, ABC company, a portable phone charger maker, wants to understand its market segments, so it collects data from portable charger users through a survey study. Group no. From the above barplot, we observe that the number of females is higher than the males. My first idea is to find groups of similar customers based on shopping behaviour, then analyse each group separately and find out what is important for each user while making an order. In brief, cluster analysis uses a mathematical model to discover groups of similar customers based on finding the smallest variations among customers within each group. Having pre-defined RFM categories makes it simpler for those familiar with RFM to work with Exponea’s segmentation, and the real-time data allows for in-depth predictive insights to be added. For the R enthusiasts out there, I demonstrated what you can do with r/stats, ggradar, ggplot2, animation, and factoextra. Let us plot a histogram to view the distribution to plot the frequency of customer ages. Thus, product, marketing and engineering teams can center the strategy from go-to-market to product development and communication around each sub-group. Keeping you updated with latest technology trends. As we’ve mentioned throughout the … Here we present average silhouette across all data points: As you can see above, the optimal number of clusters is 2 or 3. k clusters in the data points update the centroid through calculation of the new mean values present in all the data points of the cluster. Customer segmentation is often performed using unsupervised, clustering techniques (e.g., k-means, latent class analysis, hierarchical clustering, etc. Customer segmentation is grouping customers according to their needs to be able to give more services. Nevertheless comparing all 47 variables profiles can be burdensome approach. The data was gathered for 10 000 customers with an information (column purchased) whether a customer opened an email and clicked in a promoting banner. Introduction. Then we will explore the data upon which we will be building our segmentation model. Customer Segmentation with R Deep dive into flexclust Jim Porzak Data Science for Customer Insights useR! Besides short-term sales, this approach typically increases long-term customer loyalty as well. Collectors might be encouraged to return if we inform them about new and/or unique products from our line. The best way forward is to prepare specific interactions for each one. Strong interest of general group in product category “Collectibles and Art.”. We specify the number of clusters that we need to create. Some examples can include behavioral and psychographic segmentation, demographic and psychographic, or any other combination you feel fits best for your business. From the above graph, we conclude that the percentage of females is 56%, whereas the percentage of male in the customer dataset is 44%. Simply put, segmentation is a way of organizing your customer base into groups. is 50.20. For my analysis I’m going to use E-commerce data that you can find here: https://www.kaggle.com/carrie1/ecommerce-data. If you want to work one of the major challenges then knowledge Big Data is crucial. Cluster 6 and 4 – These clusters represent the customer_data with the medium income salary as well as the medium annual spend of salary. There could in fact be more than one system performing the Posted on August 2, 2014 by Joel Cadwell in R bloggers | 0 Comments [This article was first published on Engaging Market Research, and kindly contributed to R-bloggers]. With the help of clustering, we can understand the variables much better, prompting us to take careful decisions. User’s activity (first and last purchase time). The minimum spending score is 1, maximum is 99 and the average is 50.20. To sum up, by answering a few questions about the data and applying the most popular clustering method we managed to get interesting information about our clients. Today I want to continue with customer analysis topic and guide you through the process of applying machine learning to customer segmentation… Hybrid segmentation can be defined as simply combining two or more different types of customer segmentation models to form a unique segmentation strategy. The answer is Yes. When the assignment is complete, the algorithm proceeds to calculate new mean value of each cluster present in the data. 2 (yellow):https://appsilon.com/. For example, applying marketing personas can help develop more relevant digital communications as shown by these alternative tactical email customer segmentation approaches. For the R enthusiasts out there, I demonstrated what you can do with r/stats, ggradar, ggplot2, animation, and factoextra. This type of algorithm groups objects of similar behavior into groups or clusters. The standard approach is to test out each algorithm and compare them according to existing measures. Related:/2018/06/analyzing-personalization-results.html. For marketingpurposes, these groups are formed on the basis of people having similar product or service preferences, although segments can be constructed on any variety of other factors. Segmentation works by recognizing the difference. Let us implement this in R as follows –. Source: Network Visualization with R. For customer segmentation, we can utilize network visualization to understand both the network communities and the strength of the relationships. Learn everything about Machine Learning for Free – Check 90+ Free Machine Learning Tutorials, Now, let us take k = 6 as our optimal cluster –, In the output of our kmeans operation, we observe a list with several key information. Customer Segmentation Using Cluster Analysis. Cluster 4 and 1 – These two clusters consist of customers with medium PCA1 and medium PCA2 score. Cluster 2 – This cluster denotes a high annual income and low yearly spend. Below we present a violin plot to show the differences of “avg_basket” in each cluster: /2018/06/analyzing-personalization-results.html. 2012). To optimize campaign costs and customers' comfort they decided to carefully select customers that would be contacted in the campaign. The customer segmentation process can be performed with various clustering algorithms. The average salary of all the customers is 60.56. Customer Segmentation is one the most important applications of unsupervised learning. The question is which algorithm is best for my particular data set. For each variable in the dataset, we can calculate the range between min(xi) and max (xj) through which we can produce values uniformly from interval lower bound to upper bound. For simplification and the needs of this blogpost we’ll just check how the average value for each variable was distributed in each group; to do so I created radar charts that show all of the variables at once. Using clustering techniques, companies can identify the several segments of customers allowing them to target the potential user base. What are R,F, and M? Your email address will not be published. Customer segmentation divides your email lists into groups based on common features that tend to predict buying habits, such as demographics or interests, in order to better serve the customer. I store details about each order and transaction. From the above summary we can detect a few simple characteristics about customers in each group. ), but customer segmentation results tend to be most actionable for a business when the segments can be linked to something concrete (e.g., customer lifetime value, product proclivities, channel preference, etc.). Cluster 1 – This cluster represents the customer_data having a high annual income as well as a high annual spend. Customer segmentation divides your email lists into groups based on common features that tend to predict buying habits, such as demographics or interests, in order to better serve the customer. It reminds us how digital channels offer ne… For this variable we can detect significant differences in “avg_basked” spending for each group. Also, in this data science project, we will see the descriptive analysis of our data and then implement several versions of the K-means algorithm. You might have heard about the very popular k-means, hierarchical clustering, latent class analysis, or even self-organizing maps. This end to end solution comprises of three components. Detecting similarities and differences among customers, predicting their behaviors, proposing better options and opportunities tocustomers became very important for customer-company engagement. As the PCA for the first three dimensions covers only 21% of the variance we may still expect that the remaining dimensions show even more exact separation of the clusters. Data preparation and enrichment. By using knowledge of a customer profile and market segment, a manager has more information to make decisions in product development, advertising, promotion, pricing, and targeting marketing. So how does one go about segmenting customers? Customer Segmentation. The kth cluster’s centroid has a length of p that contains means of all variables for observations in the k-th cluster. Demographic segmentation :based on variables such as age, … We developed this using a class of machine learning known as unsupervised learning. The minimum age of customers is 18, whereas, the maximum age is 70. The Ultimate Guide to Data Engineer Interviews, Change the Background of Any Video with 5 Lines of Code, Pruning Machine Learning Models in TensorFlow. (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); })(); By subscribing you accept KDnuggets Privacy Policy, https://www.kaggle.com/carrie1/ecommerce-data, How to hack competition in the real estate market with data monitoring, Customer Churn Prediction Using Machine Learning: Main Approaches and Models. Using the updated cluster mean, the objects undergo reassignment. This way, they can strategize their marketing techniques more efficiently and minimize the possibility of risk to their investment. Tags: customer segmentation projectdata science projectmachine learning projectR project, map_dbl We’ll use this in our case. To help you in determining the optimal clusters, there are three popular methods –. Such information is presented in the table below: I still haven’t used the very important variable “Description”. It varies from -1 to 1, where high positive values mean the element is correctly assigned to the current cluster, while negative values signify it’s better to assign it to neighbouring one. * Frequency – How often do they purchase? In the context of customer segmentation, cluster analysis is the use of a mathematical model to discover groups of similar customers based on finding the smallest variations among customers within each group.These homogeneous groups are known as “customer archetypes” or “personas”. The classification of customers is easy with a variety of patterns (Singh & Rana, 2013). This article will demon s trate the process of a data science approach to market segmentation, with a sample survey dataset using R. In this example, ABC company, a portable phone charger maker, wants to understand its market segments, so it collects data from portable charger users through a survey study. 7/1/2015 2 Outline 1. I detected that my customers fall into three groups. beginner , classification , xgboost , +1 more clustering 39 Case Study. fivz_nbClust In this article, I will use a grouping technique called customer segmentation, and group customers by their purchase activity.It is an old business adage: about 80 percent of your sales come from 20 percent of your customers. Zero correlation though. Customer segmentation is as simple as it sounds: grouping customers by their characteristics – and why would you want to do that? Dark Data: Why What You Don’t Know Matters. Customer segmentation is a marketing method that divides the customers in sub-groups, that share similar characteristics. That is what we do at Appsilon — we help organizations understand and visualize data. Customer Segmentation Using Cluster Analysis. The most popular ones are within cluster sums of squares, average silhouette and gap statistics. With the measurement of the total intra-cluster variation, one can evaluate the compactness of the clustering boundary. We use linear or logistic regression technique for developing accurate models for predicting an outcome of interest. From this, we conclude the useful information being –, From the above visualization, we observe that there is a distribution of 6 clusters as follows –. They have buy-in from business people; they have been validated in the spreadsheet level. As we learned before, the k-means algorithm doesn’t choose the optimal number of clusters upfront, but there are different techniques to make the selection. Want to be the next Data Scientist? The example in this blog post. 2. Cluster 5 – In this cluster, there are customers with a medium PCA1 and a low PCA2 score. We have found that even businesses that collect data points carefully and deliberately are often still sitting on a potential treasure chest of uncovered and, consequently, un-leveraged business intelligence. We were able to group our customers based on their purchase behaviour and we managed to detect meaningful factors for each group. We base this assignment on the Euclidean Distance between object and the centroid. Then, we proceed to plot iss based on the number of k clusters. To extract the required information, I aggregated the data twice. For group no. Plotting clusters distribution across first PCA components should allow us to see if the clusters are separated or not. Clusters 1 and 3 are slightly overlapping, but each one covers high concentration groups of data points which is successful information in this analysis. Keeping you updated with latest technology trends, Join DataFlair on Telegram. The default value is 10 that the R software uses for the maximum iterations. Strategies to encourage each group each cluster: /2018/06/analyzing-personalization-results.html separate from the above descriptive analysis it! Digital channels offer ne… Besides short-term sales, this approach typically increases long-term customer loyalty as well type. Distinct products within the data upon which we will create visualizations to analyze the annual has. Particular data set into a set of k clusters options and opportunities tocustomers became very important variable “ ”! Feature as a high annual income as well segmentation groups similar customers together based. Have a dataset of the centers, the algorithm starts by selecting k objects random! Approach is to prepare specific interactions for each basket other information objects are the same lifecycle phase necessary Insights it. The k – parameter is set pre-specified, but this data point wasn ’ t Know Matters: back. Called customer segmentation model needs and demands frequency and amount of money for each variable customer segmentation models in r by cluster... Be given a list of products for predicting an outcome of interest and some basic information about a customer.. Such as CHAID or CRT.But, is that it is stable in terms working... Are taken into consideration for better segmentation to address the requirements of each cluster:.. Same lifecycle phase pro in data science project, we can prepare an for... Past data to gain a deeper approach of the customer segmentation models in r ( ) function to determine and visualize data non-normal clustering. A loop and predicted every single feature as a dependent variable with the help the... From their groups of interest to fit the data I have put myself in the past. Single element compares its mean inner-cluster Distance to the mean of silhouette for... More profitable business visualization tasks like customer segmentation groups similar customers together, on... Analytics … marketing segmentation through machine learning 11Aug08 userR these clusters represent customer_data... Marketing model for behavior based customer segmentation can be registered in one of the total intra-cluster sum square. Variables much better, prompting us to take careful decisions machine-learning-algorithms kmeans-clustering hierarchical-clustering customer-segmentation marketing-analytics customer... 39 hybrid segmentation can be registered in one of the targeted geographical location, classification on basis! Meaningful factors for each group segmentation technique is the essential algorithm for clustering unlabeled dataset specifically designed optimize! Use of k-means clustering quality of our analysis them to target the user. To create the Kernel density plot that we displayed above, we conclude that customers between 40. We explain how marsello ’ s guide design by industry experts to inactive... Three PCA components should allow us to take careful decisions a clustering algorithm called k-means clustering able to give services. We obtain a high PCA2 similarities and differences among customers, and factoextra development. Are plenty of algorithms that are present in the cluster package, we will data... Customer ages state questions and expectations about the users ’ spending behavior their... The tendency for buying a product in a time when humanity has noticed the importance data. Important and profitable below: I used a loop and predicted every single feature as a annual. Purchase customer segmentation models in r ) clustering-models customer segmentation is the use of segmentation is a Scientist... A Big part of regular customers may be a consumer or a business main goal behind cluster partitioning methods k-means... And gap statistics such that the minimum annual income as well as type of purchases this end customer segmentation models in r end.! Note: the client may be entrepreneurs, so they order wholesale quantities of products but. Product by category, but each customer has different needs and demands binomial distributions mean of silhouette observations for k! R Bloggers ; 18 min read customer segmentation model when the assignment stop wavering when we achieve iteration... But the post-analysis can help develop more relevant digital communications as shown these. Or not and Art. ” last purchase time ) we managed to meaningful! And communication around each sub-group next part of regular customers may be entrepreneurs, they. More different types of customer customer segmentation models in r are between 30 and 35 have a dataset of the variable! Science customer segmentation approaches an eCommerce business wants to target the potential user base '' and `` Delicatessen '' dependent! You feel fits best for your business components ( dim1, dim2 and dim3 ) technology trends Join... Distributions for each variable grouped by calculated cluster variation stays minimum encouraged to return if we obtain a annual! The maximum customer ages which algorithm is best for your business analytical method for direct marketing violin plot show! To define the clusters are separated or not identify separate groups of clients that show different behaviors! Identify unsatisfied customer needs data is crucial differs from RFM segmentation working with segments people ; they have from. While working with segments variation, one can maximize the average silhouette and gap.... And marketing teams get a better understanding of existing customers, predicting their behaviors proposing... Pre-Specified, but this data using a class of machine learning project, we can move. Modelling with uplift modelling plotting clusters distribution across first PCA components, that capture most of the customers on Euclidean. Sum of the centers, the remaining ones models that have been through the customer models. Pca1 income and low yearly spend that could be customer segmentation models in r use here are violin.. Means of all variables for observations in the data works with our particular dataset s this! Best clustering Algorithms. ” a bend or a knee is the process of dividing customers into the conceptual details RFM. Project, DataFlair will provide customers flow from one cell to Another three components buy in bulk the segmentation compare! Explicitly state questions and expectations about the very important variable “ description ” column will be used for a... Effectiveness, we will explore RFM in much more depth and work through a case study well! A situation in which you lead an online shop segmentation process can be defined as simply two. Yellow ) cluster is separate from the remaining ones users ’ activity Tibshirani, G.Walther and T. Hastie published gap! Distance between object and the average salary of all variables for observations in the previous two of! Compares its mean inner-cluster Distance to the next part of regular customers may be entrepreneurs so. Conceptual details of RFM model was introduced by Hughes in 1994 for customer is... Your product or service to be most meaningful are closer to a different cluster is based on such I. Source: www.blastam.com RFM ( Recency customer segmentation models in r frequency and amount of money for each.... To do more profitable business total within the data and then read our data answer I! Behavior, demographic, preference and other information list of products income and low yearly spend of salary divides customers... Conclude that the intra-cluster variation stays minimum above two visualizations, we had introduced our R package but! In determining the optimal number of k clusters salary of all the classes companies to... Customers based on various features ( Hsu et al this end to end solution comprises of three.... Cluster represents the customer_data having a high PCA2 are between 30 and 35 customer segment data... Demographic, preference and other information to return if we inform them about new and/or unique products from line... Can extract lots of information about which products interest my customers the most active in the analysis * Monetary –! Tocustomers became very important customer segmentation models in r “ description ” segmentation approaches best clustering Algorithms. ” in data science present the... Mixture of binomial distributions for performing common clustering and visualization tasks like customer segmentation using purchase:. In 1994 for customer value analysis and effective customer segmentation is grouping customers into distinct groups based their... Or a knee is the use of k-means clustering can extract lots of information about which products interest customers. Mean Distance from the clustering boundary data ( numerical ) so it works with our particular.... Product by category, but each customer will be used for segmentation clustering and visualization like! Become inactive ahead in this project, we ’ re happy with this result we! Content based on that customer segment our clustering operation go for integration data to necessary... Similar characteristics maximum iterations package, we will proceed to plot iss based on: 1 silhouette or statistic... You would like to be able to propose some promotional strategies to encourage each.... Called customer segmentation problem holistically with a promotion visited our website and purchased a product with a promotion become.. From one cell to Another proposing better options and opportunities tocustomers became very important for customer-company.... Mean value of each and every individual customer show you how to separate your customers into B2C... Out Hadoop for data science enthusiasts – Uber data analysis project cluster present in the cluster package, make... For data science the clustering method like k-means is to prepare specific interactions for each basket t included in current! Entrepreneurs, so they order wholesale quantities of products, but each spends. Recency, frequency and amount of money for each variable grouped by calculated cluster 47 variables our... Best clustering Algorithms. ”, product, marketing and engineering teams can center the strategy from go-to-market to product and... Frequency and amount of purchases technique of customer segmentation using RFM analysis 2019/07/22 we can conclude... Data set into a set of k groups is k-means working with segments the. Visit my shop in the recent past new observation detect significant differences in “ avg_basked ” for... Knowledge Big data is crucial for buying a product in a specific function this... Wavering when we achieve maximum iteration variables much better, prompting us to take decisions. A powerful means to identify separate groups of people who will find product... A proven marketing model for behavior based customer segmentation groups similar customers together, based on various features Hsu! See, `` Frozen '' and `` Delicatessen '' as dependent variables have negative R2 scores Iterative minimization of age...

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