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Predictions may help a sales organization limit resources to focus on growth is when the company is looking to expand into new markets.

The sales team may be struggling to identify which potential customers are most likely to be interested in their products or services, and as a result, they are spending a lot of resources reaching out to the wrong prospects.

By using predictive analytics, the sales team can analyze data on past sales, customer demographics, and buying behavior to identify patterns that indicate which customers are most likely to be interested in their products or services. This can help the sales team to focus their efforts on the most promising prospects, rather than wasting resources on less promising leads.

The predictive model can also help the sales team to prioritize their efforts by identifying which leads are most likely to convert into customers. This allows the sales team to focus their resources on the most promising prospects, rather than spending time and money on leads that are unlikely to result in a sale.

In addition, the model can also help the sales team to identify which products or services are most likely to be successful in the new market, allowing the company to focus their resources on the most promising products or services.

Using predictive analytics can help a sales organization to limit resources by focusing on the most promising prospects and products, allowing the company to grow its sales and expand into new markets more effectively.

Predictions may help a service organization focus on increasing NPS (Net Promoter Score) and customer satisfaction is when the company is experiencing high levels of customer churn or complaints. The service team may be struggling to identify which customers are most likely to be dissatisfied with their service, and as a result, they are spending a lot of resources trying to retain customers who are unlikely to return.

By using predictive analytics, the service team can analyze data on past customer interactions, demographics, and buying behavior to identify patterns that indicate which customers are most likely to be dissatisfied with their service. This can help the service team to focus their efforts on the most critical customers, rather than wasting resources on customers who are unlikely to return.

The predictive model can also help the service team to prioritize their efforts by identifying which customers are most likely to give a low NPS score or to file a complaint. This allows the service team to focus their resources on the most critical customers, rather than spending time and money on customers who are unlikely to file a complaint.

In addition, the model can also help the service team to identify which service interactions are most likely to result in dissatisfaction, allowing the company to focus their resources on improving those interactions.

Overall, using predictive analytics can help a service organization to focus on increasing NPS and customer satisfaction by identifying and prioritizing the most critical customers and service interactions, allowing the company to improve its service and retain customers more effectively.

Predictions may help a marketing organization focus on increasing qualified lead flow is when the company is experiencing low conversion rates from its marketing campaigns. The marketing team may be struggling to identify which potential customers are most likely to convert into paying customers, and as a result, they are spending a lot of resources on marketing campaigns that are not resulting in sales.

By using predictive analytics, the marketing team can analyze data on past customer interactions, demographics, and buying behavior to identify patterns that indicate which customers are most likely to convert into paying customers. This can help the marketing team to focus their efforts on the most promising prospects, rather than wasting resources on less promising leads.

The predictive model can also help the marketing team to prioritize their efforts by identifying which leads are most likely to convert into paying customers. This allows the marketing team to focus their resources on the most promising prospects, rather than spending time and money on leads that are unlikely to result in a sale.

In addition, the model can also help the marketing team to identify which marketing channels or campaigns are most likely to result in conversions, allowing the company to focus their resources on the most effective marketing channels.

Overall, using predictive analytics can help a marketing organization to focus on increasing qualified lead flow by identifying and prioritizing the most promising prospects and marketing channels, allowing the company to generate more sales and increase revenue more effectively.

First you need to decide what you want to predict, what is the question you want to ask?

Will this person buy under these circumstances? Will this customer payback their loan on the time?

You are looking to ask a question around a specific outcome.

The predictions will answer questions like

  • Will this customer Buy?
  • Will this customer churn?
  • Is this a good spend of Marketing Money?

The answer is in the form of a % likelihood of that event happening for that particular situation. Its what you do with that information that really maters.

If someone has a 89% chance of buying then why don’t you spend the time to get them over the line?

If someone has a 40% chance of buying is it worth the effort to push them along? 

Its all about focus and allocation of resources. Only you can decide.

As a SaaS (Software as a Service) platform we charge monthly based on how many predictions you think you will be making.

This means this means that you can change usage tiers, stop or pause billing at any time. There are no other costs involved.

We aim to keep things simple and easy so you are in control and we look after the technology whilst you understand the costs and concentrate on running your business.

Check the pricing page for more info.

The free trial is fully functional 7 Day access to the MOBJAI platform limited only by amount of datasets you can create predictions against.

No credit card details are required and nothing will be charged after the 7 days.

You have complete control.

You need no coding skills whatsoever – this is drag and drop at its finest. MOBJAI does all the hard work and delivers world class results direct to your inbox so you can concentrate on working on your business.

MOBJAI is the missionobjective AutoML engine designed to make accurate and valuable predictions un less than 60 seconds. Think of MOBJAI as your very own Data Scientist, just at a fraction of the cost!

MOBJAI is your business strategy consultant and Mo is your tactical business consultant.

MO is your very own Automated Virtual Business Analyst. Mo can understand and interpret your data then make suggestions based on the great work performed by MOBJAI to guide you, rather like a management consultant, to think about exciting opportunities to grow revenue or save money.

Mo is your tactical business consultant and MOBJAI is your business strategy consultant.

Questions about Data, Ai & Analytics

To start working with MOBJAI, you will need two CSV files.

The first, called the “MISSION CSV,” should contain historical data with known outcomes. This will be used as the training dataset.

The second, called the “OBJECTIVE CSV,” should be structured the same as the MISSION dataset, but should include transactions with unknown outcomes. This will be used as the prediction dataset.

You may already have this information in a CRM, marketing, or sales system, or even just a spreadsheet. Simply export the data in CSV format.

For optimal results, a minimum of 1000 rows (individual transactions) and at least 10 columns are required. The more columns included, the better.

To make predictions, there must be at least one binary outcome column in the data. Examples of binary outcomes include “Yes/No,” “1/0,” “Red/Green,” “Top/Bottom,” “In/Out,” and “Closed Won/Closed Lost.”

Everyone thinks their data is messy, small or dirty – the fact is that Ai is very good at seeing through the problems and working with difficult data sets.

As part of the MOBJAI platform we preprocess the data to check the value of the dataset and its suitability for training Ai models. If there are issues then we fix them where we can for you.

As long as the dataset meets the criteria of:

  • 1000+ transactions with known outcomes
  • 10+ Columns
  • An Outcome Column

Then you should be good to go – if in doubt then contact our team for some help.

Creating a CSV (Comma Separated Values) file from a spreadsheet is a relatively simple process that can be done using a spreadsheet software such as Microsoft Excel, Google Sheets, or LibreOffice Calc. The steps to create a CSV file from a spreadsheet will vary slightly depending on which spreadsheet software you are using.

Here are the general steps to create a CSV file from a spreadsheet:

Open your spreadsheet software and open the file you want to convert to CSV.

Click on “File” in the top menu bar, then select “Save As” or “Export.”

In the “Save As” or “Export” dialog box, select “CSV” or “Comma Separated Values” as the file type.

Choose a location to save the file and give it a name.

Click “Save” or “Export” to create the CSV file.

You can now open the CSV file in any text editor or spreadsheet software and the data will be separated by commas, making it easy to import the data into other applications.

Note: Some spreadsheet software may have additional options when exporting to CSV, such as the ability to specify the character encoding or to include column headers. Be sure to review these options before exporting the file.

Additionally, if you’re using Google sheets, you can also use the “File” menu and select “Download” and choose “Comma-separated values” to export the spreadsheet as a CSV file.”

Exporting a CSV (Comma Separated Values) file from a sales, marketing, or customer service system will vary depending on the specific system you are using, but the general steps are similar.

Here are the general steps to export a CSV file from a sales, marketing, or customer service system:

Log in to the system and navigate to the data you want to export. This could be customer data, sales data, or marketing data, depending on the system.

Look for an “Export” or “Download” button or option in the top menu or on the data page.

Select “CSV” or “Comma Separated Values” as the file type to export.

Some systems may have additional options when exporting, such as the ability to specify the character encoding or to include column headers. Be sure to review these options before exporting the file.

Click “Export” or “Download” and the system will export the data as a CSV file.

You will then be prompted to select a location to save the file on your computer.

Note: Some systems may require additional steps or permissions to export data. Be sure to check the system documentation or contact customer support if you are unsure how to export a CSV file.

Additionally, some systems may allow you to export data by using an API, in this case, you will need to use a programming language such as python or R to extract the data and save it as a CSV file.

Once you have followed the steps in MOBJAI you will be asked if you would like the results emailed to you, saved or presented with a download option.

You will then receive a CSV which you can use to load the predictions into a Spreadsheet tool, CRM, Marketing System or in fact any application that you use. These predictions will enrich the data you already have and give you the opportunity to see where to focus resources.

Importing a CSV (Comma Separated Values) file into a spreadsheet is a relatively simple process that can be done using a spreadsheet software such as Microsoft Excel, Google Sheets, or LibreOffice Calc. The steps to import a CSV file into a spreadsheet will vary slightly depending on which spreadsheet software you are using.

Here are the general steps to import a CSV file into a spreadsheet:

Open your spreadsheet software and click on “File” in the top menu bar.

Select “Open” or “Import” and then choose the CSV file you want to import.

In the “Open” or “Import” dialog box, choose “Comma Separated Values” or “CSV” as the file type.

Some software may have additional options when importing, such as the ability to specify the character encoding or to include column headers. Be sure to review these options before importing the file.

Click “Open” or “Import” and the data from the CSV file will be imported into the spreadsheet.

You can now edit and analyze the data within the spreadsheet.

Note: Some spreadsheet software, such as Google Sheets, have the option to import a CSV by simply dragging and dropping the file into the spreadsheet.

Additionally, if you’re using Google sheets, you can also use the “File” menu and select “Import” and then choose “Upload” to import the CSV file into the sheet.

The predictions will answer questions like

  • Will this customer Buy?
  • Will this customer churn?
  • Is this a good spend of Marketing Money?

The answer is in the form of a % likelihood of that event happening for that particular situation. Its what you do with that information that really maters.

If someone has a 89% chance of buying then why don’t you spend the time to get them over the line?

If someone has a 40% chance of buying is it worth the effort to push them along? 

Its all about focus and allocation of resources. Only you can decide.

AI models are trained using a process called supervised learning. This process involves providing the model with a large amount of labeled data, which is data that has been labeled with the correct output or answer. The model is then able to learn from the data and make predictions based on the patterns it has identified.

The process of training an AI model typically involves the following steps:

Data collection: Collect a large amount of labeled data that will be used to train the model.

Data preprocessing: Clean and prepare the data for use in the model. This may include removing any missing data, handling outliers, and normalizing the data.

Model selection: Choose an appropriate model architecture for the task at hand. This may include selecting a neural network, decision tree, or other machine learning algorithm.

Model training: Use the labeled data to train the model by providing it with input data and the corresponding correct output. The model will adjust its parameters to minimize the difference between its predictions and the correct output.

Model evaluation: Evaluate the performance of the model using a set of data that was not used during the training process. This is typically done by comparing the model’s predictions to the actual output.

Model tuning: Based on the evaluation, fine-tune the model by adjusting its parameters, such as the number of layers or the learning rate, to improve its performance.

Model deployment: Once the model has been trained and fine-tuned, it can be deployed to a production environment where it can be used to make predictions on new data.

The process of training an AI model can be computationally intensive and requires large amounts of data, but with the help of powerful computing resources and specialized software, the process can be streamlined.

We do not store any data unless agreed by you. We are fully GDPR compliant and use K-Anonymity to ensure we do not see or store transactional data or pass to third parties.

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What can I use MOBJAI™ for?
What Kind of Data do I need?
What is AutoML?
Why does MOBJAI™ Exist?
What is Machine Learning?
Learning to code for Machine Learning
WHat is Overfitting?
What is Feature Selection?

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