AI for customer analytics
Achieve better conversion rates,
lower marketing costs and increase profits
Achieve better conversion rates,
lower marketing costs and increase profits
Only 1 out of 26 unhappy customers actually complains. The rest just go away without giving any sign they’re displeased. Even companies that have embraced the power of big data may benefit from further automating the data processing done by data analysts.
Applying AI models to predict customer lifetime value, enhancing cross-selling and fraud detection may increase a company’s performance and help it gain competitive advantage.
Our extensive training track gives your team the know-how it will need to build advanced, state-of-the-practice machine learning solutions for customer analytics.
The track is designed for teams of data analysts who work regularly with data sets and seek a hands-on approach for common areas of business.
The examples and projects used during the track are run in Python or R, so knowing at least the basics of the syntax is recommended. We can also provide additional training in the Python or R programming environment the team prefers to use for machine learning. No previous experience in machine learning is required.
Our training program contains six day-long units divided into two levels. Depending on your focus, you either choose specific areas that interest you or go through all the units.
Let us know which training units you need to help your team make the most of customer analytics.
Customer view 360°
Customer 360° offers access to all of the information that can be gathered about clients. The aim is to integrate knowledge on customer behavior across any channel, system, device or product.
Having access to the full range of the customer’s journey makes it possible to increase desirable patterns of behavior, reduce customer churn and facilitate personalized marketing offers.
Customer segmentation, Churn prediction
Part 1: Business requirements of Customer view 360°
Part 2: Data preparation
Part 3: Hands-on coding: Customer view 360° development
Customer segmentation
Customer segmentation helps you understand different types of customer behavior and quantify how many of each you’re dealing with. Marketing automation based on customer treatment according to their behavior or product usage is the best way to put forward appropriate offers.
Machine learning delivers algorithms to generate customer clusters based on transactions or other activity. The clusters enable a straightforward definition of different customer types, which can then be addressed with well-targeted marketing actions.
Customer view 360°, Cross-selling & up-selling
Part 1: Business needs and expectations in customer segmentation
Part 2: Data preparation
Part 3: Hands-on coding: Modeling customer segments
Customer Lifetime Value
Customer Lifetime Value (CLV) is employed to determine how to invest time, money and resources for your most valuable customers or whether customers are profitable. It measures all of the profits a customer will generate for a company.
CLV helps determine the amount that should reasonably be invested in each stage of the customer life cycle as well as which customer segment should be focused upon to maximize profit. It also helps you define customer churn.
Customer segmentation, Cross-selling & up-selling, Churn prediction
Part 1: Business context of Customer Lifetime Value methodology
Part 2: Data preparation
Part 3: Hands-on coding: Modeling Customer Lifetime Value
The propensity to buy: cross-selling & up-selling
Boosting customer engagement can be achieved by offering new products for customers, which can be reached by proposing higher-priced products (up-selling) or complementary ones (cross-selling).
Machine learning techniques support the automation of offers prepared for customers. This may include planning the optimal size of target groups, selecting the most interested customers only or proposing the optimal channel for marketing communications.
Customer segmentation, Churn prediction
Part 1: How analytics supports business needs
Part 2: Data preparation
Part 3: Hands-on coding: Modeling the propensity to buy
Fraud detection
Customer fraud usually boils down to methods used to deceive. Identifying suspicious operations takes time, labor and no small number of mistakes. Regardless of how it is carried out, however, fraud can be costly.
Machine learning algorithms can be used to both improve the detection of fraudulent behavior and obtain fewer false alarms.
Part 1: Business aspects of fraud
Part 2: Data preprocessing for fraud detection
Part 3: Hands-on coding: Fraud detection
Churn prediction
Customer churn prediction tells us how likely a customer is to discontinue a contract or stop using a service. Identifying such customers and preventing attrition is a challenging task simple business heuristics often fall short on.
During the workshop we lay out a complete path–from setting the assumptions of the definition of churn to be employed to data wrangling and modelling.
Customer segmentation, Customer Lifetime Value
Part 1: Business aspects of defining churn
Part 2: Feature engineering for churn modeling
Part 3: Hands-on coding: Churn modeling
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