Use Upgini for Marketing and CRM

Look-alike is used to identify new users that are similar to a given set of user. It is commonly used in applications such as recommendation systems, fraud detection, and customer segmentation. The goal is to find patterns or similarities in the data that can be used to make predictions or gain insights. Look-alike is also often used in CRM and direct marketing to find an audience that is similar to the target one.

1. A look-alike may increase the effect from your CRM and Direct Marketing campaigns two to three times.
2. Look-alike may be used for marketing research. Don't spend money to get insights from interviews, use external data directly.

1. Obtain a train dataset with customer ID and label. Upgini supports the following customer IDs: phone number, IP address, and email. Label is a segment ID or any target variable.
2. Search for new relevant features for your task: launch FIT method of Upgini and get a list of relevant features from connected data sources.
3. Make a test enrichment: launch TRANSFORM method of Upgini and enrich your dataset with only new relevant features. This dataset could consists any phone number or any IP-address.
4. Use Upgini in production: Upgini moves relevant features for the all Phone numbers, Emails and IP-addresses to your DWH. You may use this data directly in your DWH.

Ad fraud is any attempt to defraud advertisers for financial gain. Scammers often use bots to carry out ad fraud. Scammers could generate fake leads for you that could never be contacted.

Upgini can help you arbitrage leads from external systems. It can score leads and detect which lead is a fake, so you don't waste time and money contacting it.

Search for new relevant features that could help you determine whether a lead is fake:

1. Obtain a training dataset for determining fake leads that includes customer ID and label. Upgini supports the following customer IDs: phone number, IP address, and email. The label indicates whether a lead if fake or not.
2. Search for new relevant features for your training dataset: launch the FIT method of Upgini and receive a list of relevant features from connected data sources.
3. Make a test enrichment: launch the TRANSFORM method of Upgini and enrich your dataset with only the new relevant features.
4. Use Upgini in production: Upgini transfers relevant features for all phone numbers, emails, and IP addresses to your DWH. You can use this data directly in your DWH.

If you don't have your own Fake leads detection system or you don't have a training dataset, you may use the ready-made Upgini Fraud Score. The Upgini Fraud Score is an informational tool that helps you gauge the risk involved with leads from ads before processing them. This is done by identifying traits and historical trends associated with suspicious behavior and fraudulent contacts that you receive from ads.

Companies that interact with their current customers via phone or chat for customer retention often face issues with low retention. These challenges stem from the unique characteristics of the call center used for customer interaction. Not all call center employees may be qualified to accurately document the outcome of every interaction or remember to schedule call reminders at a convenient time for the customer. Additionally, some operators may make mistakes during calls.

Upgini has a function that extracts knowledge from conversation texts between your call center and a customer, leveraging the extensive knowledge base of Large Language Models. Attempt to utilize additional knowledge from conversation texts.

1. This can be used to identify customers that indeed have a high likelihood of churn. Avoid wasting time and promotions on clients who aren't genuinely on the brink of churn.
2. Also, it can identify customers that require a callback, even if the operator has categorized them under "do not call back".
3. Lastly, it can be used to assess the quality of work carried out by the call center operators.

1. Obtain a train dataset with customer ID, conversation text, and target label. The customer IDs that Upgini supports for this task are phone numbers or emails. Use manually labeled events such as "client have churned"as target labels.
2. Launch GPT feature generation in Upgini for your task: launch FIT method with parameter generate_features and you'll get a list of relevant features from connected data sources and relevant features extracted from conversation texts.
3. Make a test enrichment: launch TRANSFORM method of Upgini and enrich your dataset with only new relevant features.
4. Use Upgini in production: Upgini provides relevant GPT features through online API