Improving customer retention with machine learning\u00a0<\/span><\/h2>\nAccumulating a massive volume of data, only a few companies analyze it. But in today’s data-driven world, retailers shouldn’t underestimate the impact of even a tiny percentage of information. Even a small but actionable insight may increase customer satisfaction or help optimize ROI. Here comes machine learning (ML) powered churn analysis in retail.<\/span><\/p>\n![\"Customer](\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/07\/customer_satisfaction_770_450-01.jpg\")
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No matter what size or operational model is your business, you have to keep customers satisfied all the time, knowing their needs and foreseeing wants. You may know customers who already left your business, but it’s a tough challenge to identify customers planning to leave soon. The best way to tackle this problem is to analyze clients that don’t buy from you anymore.\u00a0<\/span><\/p>\nMachine learning algorithms and applied statistics methods can help build a business intelligence reporting system<\/a>\u00a0that allows revealing clients at churn risk. Using the ML power to historical data, you’ll make it work to predict future churn as accurately as possible. The deployment model will show you valuable figures daily.\u00a0<\/span><\/p>\nGiven that customer churn analysis is an essential part of complex customer relation management, you need to integrate it with your overall marketing plan. When you know churn probability for each client, you can apply an actionable strategy for their retention and restructure marketing activities accordingly:\u00a0<\/span><\/p>\n\n- focus on valuable customers and let go jumpers;<\/span><\/li>\n
- develop customer loyalty campaigns;<\/span><\/li>\n
- use more flexible marketing and sales campaigns;<\/span><\/li>\n
- change products to cover customer needs.<\/span><\/li>\n<\/ul>\n
Clothes stores might even consider new assortment based not only on the fashion demand forecasting\u00a0that defines upcoming trends but based on consumer demand forecasting.<\/span><\/p>\nThe most common reasons for customer attrition may even surprise you. They include such factors as the company’s service level, pricing terms, delivery policy, competitors’ strategies, economic climate, seasonality, and general industry trends. But do retailers need to keep <\/span>all the customers<\/span><\/i> at any cost?\u00a0<\/span><\/p>\n\n \n Learn how we've helped a global automotive player ranked among the 100 Best Global Brands by Financial Times enhance their customer journey with the all-in-one retail service platform<\/p>\n
\n <\/div>\n <\/div>\n <\/div>\n Read more<\/span>\n\t\t <\/a><\/div>\nKeeping only valuable clients in focus<\/span><\/h2>\n![\"Customer](\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/07\/shutterstock_548384146.jpg\")
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According to the CodeBroker Loyalty Program Customer Survey, an average customer reports being a member of more than six loyalty programs, while 65% actively engage with fewer than half of them. While there are always clients who switch between you and your competitors, retail companies should offer a differentiated client experience and tailor their loyalty rewards individually.\u00a0<\/span><\/p>\nThere are always migrating clients, and a certain level of customer churn is quite reasonable. One of your main tasks is to differentiate customers who belong to this group. Defining figures that describe this client outflow and observing it is the key to successful customer relationship management.<\/span><\/p>\nEven if there were no jumper-customers, the natural attrition would always exist. For example, children’s products cease to be relevant when the child grows up. The client can switch segments \u2014 from an economy to a premium sector \u2014 or move to another area, region, or even country.\u00a0<\/span><\/p>\nGlobal trends also affect churn. When electronic books substituted paper ones, bookstores experienced terrible losses of customers.\u00a0<\/span><\/p>\nWe suggest creating the criteria for a clear understanding of which customer is considered to be lost. Plus, you should understand the customer value: always define the ratio between the price of customer acquisition and the profitability they bring or will bring to the company. And if talking about churn, you should always distinguish between the customer turnover and the cash one.\u00a0<\/span><\/p>\nCustomer churn prediction model and machine learning in retail analytics\u00a0<\/span><\/h2>\n![\"Customer](\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/07\/tubl_770_2.jpg\")
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During the churn analysis, it’s vital to conduct an assessment of the acceptable churn level. It will allow adjusting the churn model according to the company’s current conditions. Different customer segments vary in behavior, so one or several specific customer groups form the most significant turnover.\u00a0<\/span><\/p>\nNow, let’s define critical factors for a successful churn analysis.\u00a0<\/span><\/p>\nData<\/span><\/h3>\nThe prerequisite to building an actionable machine learning model is that you collect and analyze customer data. Here are the characteristics of the data relevant to create an ML algorithm:<\/span><\/p>\n\n- Quality<\/b><\/li>\n<\/ul>\n
Your data should correspond to reality. For example, if your sales assistants don’t validate customer questionnaires, the data collected is most likely useless.\u00a0<\/span><\/p>\n\n- Variety<\/b><\/li>\n<\/ul>\n
You need every piece of information: social data, financial data, data on transactions, purchases, and preferences.<\/span><\/p>\n\n- Amount<\/b><\/li>\n<\/ul>\n
The more, the better.\u00a0<\/span><\/p>\n\n- Relevance\u00a0<\/b><\/li>\n<\/ul>\n
Only up-to-date insights are valuable.\u00a0<\/span><\/p>\n\n- Origin and format<\/b><\/li>\n<\/ul>\n
The format of data is also precious \u2014 text, photos, videos, and so on. The higher the variety of relevant data used to build the model, the more accurate is its prediction.<\/span><\/p>\nYou may find inconsistencies and anomalies during the initial data review \u2014 it’s possible to eliminate them with each iteration, validating and selecting the most relevant pieces.\u00a0<\/span><\/p>\nPrediction window<\/span><\/h3>\nThe forecast period is called the prediction window. You should adjust this parameter along during the process of model validation. One of the most compelling cases is to create a stepped scheme that allows making predictions for several periods simultaneously.\u00a0<\/span><\/p>\nRelevance<\/span><\/h3>\nEvery model becomes outdated over some time \u2014 internal and external conditions change, company goals grow. You should update and calibrate the model frequently upon the results you get.\u00a0<\/span><\/p>\nResults interpretation<\/span><\/h3>\n![\"Customer](\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/07\/model_770_450.jpg\")
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The ML-powered model aims to tell you what is the probability of this or that client leaving your business within a specified period. Considering clients based on the profitability they bring will allow managing churn risk with the help of targeted marketing strategies \u2014 either loyalty campaigns or assortment changes. The acceptable churn rate is defined by industry peculiarities in general and company specifications in particular. Thus, for example, the 75% churn rate of a client is quite risky for one company, while another will accept the level of 85%. The same company may also have different numbers in various locations.\u00a0<\/span><\/p>\n\n \n Learn how we've helped one of the largest online stores in Eastern Europe create the roadmap for their retail marketplace platform migration<\/p>\n
\n <\/div>\n <\/div>\n <\/div>\n Read more<\/span>\n\t\t <\/a><\/div>\nStop customer churn before it even starts<\/span><\/h2>\nKnowing the probability of customer churn lets you create targeted loyalty campaigns aimed at the segment with the highest chances of attrition. Churn analysis provides valuable insights on the risk and level of outflow (both client and money) as well as the ability to manage these factors.\u00a0<\/span><\/p>\nAt Intellias, we help provide churn assessment in a tight cooperation with you. We provide a detailed report on key factors that make customers leave. We conduct the assessment based on both industry and company peculiarities, defining segments at the highest churn risk. Our experts validate data, eliminate anomalies, and help customers remove errors in data collection and storage processes with the help of retail software design<\/a> services.\u00a0<\/span><\/p>\n
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No matter what size or operational model is your business, you have to keep customers satisfied all the time, knowing their needs and foreseeing wants. You may know customers who already left your business, but it’s a tough challenge to identify customers planning to leave soon. The best way to tackle this problem is to analyze clients that don’t buy from you anymore.\u00a0<\/span><\/p>\n Machine learning algorithms and applied statistics methods can help build a business intelligence reporting system<\/a>\u00a0that allows revealing clients at churn risk. Using the ML power to historical data, you’ll make it work to predict future churn as accurately as possible. The deployment model will show you valuable figures daily.\u00a0<\/span><\/p>\n Given that customer churn analysis is an essential part of complex customer relation management, you need to integrate it with your overall marketing plan. When you know churn probability for each client, you can apply an actionable strategy for their retention and restructure marketing activities accordingly:\u00a0<\/span><\/p>\n Clothes stores might even consider new assortment based not only on the fashion demand forecasting\u00a0that defines upcoming trends but based on consumer demand forecasting.<\/span><\/p>\n The most common reasons for customer attrition may even surprise you. They include such factors as the company’s service level, pricing terms, delivery policy, competitors’ strategies, economic climate, seasonality, and general industry trends. But do retailers need to keep <\/span>all the customers<\/span><\/i> at any cost?\u00a0<\/span><\/p>\n Learn how we've helped a global automotive player ranked among the 100 Best Global Brands by Financial Times enhance their customer journey with the all-in-one retail service platform<\/p>\n According to the CodeBroker Loyalty Program Customer Survey, an average customer reports being a member of more than six loyalty programs, while 65% actively engage with fewer than half of them. While there are always clients who switch between you and your competitors, retail companies should offer a differentiated client experience and tailor their loyalty rewards individually.\u00a0<\/span><\/p>\n There are always migrating clients, and a certain level of customer churn is quite reasonable. One of your main tasks is to differentiate customers who belong to this group. Defining figures that describe this client outflow and observing it is the key to successful customer relationship management.<\/span><\/p>\n Even if there were no jumper-customers, the natural attrition would always exist. For example, children’s products cease to be relevant when the child grows up. The client can switch segments \u2014 from an economy to a premium sector \u2014 or move to another area, region, or even country.\u00a0<\/span><\/p>\n Global trends also affect churn. When electronic books substituted paper ones, bookstores experienced terrible losses of customers.\u00a0<\/span><\/p>\n We suggest creating the criteria for a clear understanding of which customer is considered to be lost. Plus, you should understand the customer value: always define the ratio between the price of customer acquisition and the profitability they bring or will bring to the company. And if talking about churn, you should always distinguish between the customer turnover and the cash one.\u00a0<\/span><\/p>\n During the churn analysis, it’s vital to conduct an assessment of the acceptable churn level. It will allow adjusting the churn model according to the company’s current conditions. Different customer segments vary in behavior, so one or several specific customer groups form the most significant turnover.\u00a0<\/span><\/p>\n Now, let’s define critical factors for a successful churn analysis.\u00a0<\/span><\/p>\n The prerequisite to building an actionable machine learning model is that you collect and analyze customer data. Here are the characteristics of the data relevant to create an ML algorithm:<\/span><\/p>\n Your data should correspond to reality. For example, if your sales assistants don’t validate customer questionnaires, the data collected is most likely useless.\u00a0<\/span><\/p>\n You need every piece of information: social data, financial data, data on transactions, purchases, and preferences.<\/span><\/p>\n The more, the better.\u00a0<\/span><\/p>\n Only up-to-date insights are valuable.\u00a0<\/span><\/p>\n The format of data is also precious \u2014 text, photos, videos, and so on. The higher the variety of relevant data used to build the model, the more accurate is its prediction.<\/span><\/p>\n You may find inconsistencies and anomalies during the initial data review \u2014 it’s possible to eliminate them with each iteration, validating and selecting the most relevant pieces.\u00a0<\/span><\/p>\n The forecast period is called the prediction window. You should adjust this parameter along during the process of model validation. One of the most compelling cases is to create a stepped scheme that allows making predictions for several periods simultaneously.\u00a0<\/span><\/p>\n Every model becomes outdated over some time \u2014 internal and external conditions change, company goals grow. You should update and calibrate the model frequently upon the results you get.\u00a0<\/span><\/p>\n The ML-powered model aims to tell you what is the probability of this or that client leaving your business within a specified period. Considering clients based on the profitability they bring will allow managing churn risk with the help of targeted marketing strategies \u2014 either loyalty campaigns or assortment changes. The acceptable churn rate is defined by industry peculiarities in general and company specifications in particular. Thus, for example, the 75% churn rate of a client is quite risky for one company, while another will accept the level of 85%. The same company may also have different numbers in various locations.\u00a0<\/span><\/p>\n Learn how we've helped one of the largest online stores in Eastern Europe create the roadmap for their retail marketplace platform migration<\/p>\n Knowing the probability of customer churn lets you create targeted loyalty campaigns aimed at the segment with the highest chances of attrition. Churn analysis provides valuable insights on the risk and level of outflow (both client and money) as well as the ability to manage these factors.\u00a0<\/span><\/p>\n At Intellias, we help provide churn assessment in a tight cooperation with you. We provide a detailed report on key factors that make customers leave. We conduct the assessment based on both industry and company peculiarities, defining segments at the highest churn risk. Our experts validate data, eliminate anomalies, and help customers remove errors in data collection and storage processes with the help of retail software design<\/a> services.\u00a0<\/span><\/p>\n\n
Keeping only valuable clients in focus<\/span><\/h2>\n
<\/p>\nCustomer churn prediction model and machine learning in retail analytics\u00a0<\/span><\/h2>\n
<\/p>\nData<\/span><\/h3>\n
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Prediction window<\/span><\/h3>\n
Relevance<\/span><\/h3>\n
Results interpretation<\/span><\/h3>\n
<\/p>\nStop customer churn before it even starts<\/span><\/h2>\n
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