There are several possible reasons for not acquiring new leads (prospects).
What is a prospective client?
From this point, the sales, design, and web staffs may have different opinions.
First of all, it is necessary to unify the decision-making process.
At that time, it is important not to rely on completion or past experience.
We must talk by the numbers.
To do this, we analyze past data and create sample sites for test marketing.
A “prospective customer” is a person or company that is expected to have an interest in or desire to purchase a particular product or service. Specifically, a prospective customer has the following characteristics
Interested in a product or service, or taking actions that suggest such interest (e.g., viewing a product information page, signing up for a free trial, etc.).
has sufficient funds or authority to purchase the product or service.
not just interested, but actually considering buying or likely to buy in the future.
the time is right to purchase the product or service. For example, in the case of B2B, when a company is considering implementing a new solution.
A typical marketing and sales process involves first obtaining a large number of leads (information about potential customers) and then narrowing down the list of “prospects” who are most likely to purchase. This narrowing process is also called “lead qualification.
For example, while many users visit a website through online marketing activities, “prospects” are often identified as users who actually make inquiries, view certain content in depth, or take other actions that seem to indicate a high willingness to purchase.
By deeply analyzing such pages, we can increase the resolution of prospective customers.
Recently, we have entered data into BigQuery, etc., and found that there are good customers in completely different places from the existing ones. There have been cases where this has been the case.
There are several reasons. First, BigQuery is Google Cloud’s fully managed, serverless data warehouse, which has different properties than a NoSQL database, but both can offer similar benefits.
Here are some of the reasons why
BigQuery and NoSQL databases are designed to process large amounts of data efficiently; AI analysis requires large amounts of data, and these systems can meet that need.
NoSQL databases are schema-less, making them suitable for storing data in a variety of formats. This is especially useful when dealing with diverse data sources for training and forecasting AI models.
Cloud-based data warehouses such as BigQuery are seamlessly integrated with AI services within the cloud platform. For example, BigQuery ML can train and predict AI models using only SQL queries.
NoSQL databases excel at real-time data insertion and querying, enabling real-time AI analysis and prediction.
Services like BigQuery, in particular, are charged only for queries, making it possible to analyze large amounts of data while keeping costs down.
BigQuery and many NoSQL databases can be easily integrated with popular AI-related open source tools such as TensorFlow and Jupyter Notebook.
We can take your existing data and help you analyze AI in a new way using BigQuery and other tools.
Please feel free to contact us for more information.
