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Does your team want better account qualification? Are you curious which accounts look like your best customers? Do you want to assess your current pipeline to determine which accounts are most likely to close? Then look no further, Predict is the answer!

With Datanyze’s new iteration of Predict, you can build out and customize a predictive analytics model to help you close your next deal! Here’s how it works:

  1. Upload List
    First, upload a list of domains to analyze. This could be a list of all the customers who have purchased a company’s solution, or a subset of them. The more domains you include, the better the analysis will be. We recommend including at least 100 domains. The list can be uploaded via CSV or Salesforce.   Predict_Article_2.png

    From CSV

    1. Name ModelPredict_Article_CSV_Name_Model.png

    2. Upload CustomersPredict_Article_CSV_Upload_Custys.png

    3. Upload Prospects (optional)

      From Salesforce 

      1. Name Mode Predict_Article_SFDC_Name_Model.png

      2. Sync CustomersPredict_sfdc_sync.png

        Choose in which Salesforce object (Lead, Account, Opportunity, Contact) the customers are found and which filters (if any) should be applied. There are no limitations to the number of conditions you can apply.

      3. Sync Prospects (optional)
        Please note: the Salesforce upload is enabled only when a customer has active Salesforce Integration
  2. List Analysis

    Predict starts creating a model once the list of companies has been received. When it analyzes the list of domains Predict looks at the shared attributes and assigns a weight based on how common that attribute is across your list of domains.Predict_Article_Attributes_2.png

    Predict analyzes all firmographic and technographic data points during this step to come up with the list of firmographic and technographic attributes that correlate well with the uploaded list of the companies. This process happens quickly (in about a minute).

  3. Customization

    You have the option under Actions to customize the attribute weights based on attributes you know represent your ideal customer or delete the attribute entirely.Predict_Article_Customization.png

    At the bottom of the Attributes page you will also see a button with the option to Add New Attribute, where you can add an attribute that is not already represented in the model, allowing you to customize the model even further.Predict_Article_Add_Attributes.png

  4. Account Scoring

    Once the list of attributes is finalized, Predict scores and assigns a grade to the domains in Datanyze’s database based on those attributes. Once Predict has a list of all attributes it can now score all the domains in Datanyze’s database.Predict_Article_Results_2.png

    This process takes about 30 minutes. You will receive an email from Datanyze once your model is ready for review.Predict_Article_Email_2.png


There are different ways you and your team can leverage Predict model results:

  • Find net-new accounts
    Predict will show the list of domains with the highest scores according to this model. This list will exclude the domains that were selected during Step 1: Upload List. You can immediately see the list of companies with which you have not closed a deal yet, but that look very similar to your current customers.
  • Analyze existing pipeline
    If you submitted a list of your prospects during Step 1, Predict will also score them so that you can see who is more or less likely to close. This can be used in combination with Datanyze’s enrichment products. Example: Inbound lead comes in > Datanyze enriches it with all the data that we have AND provides a score based on the model that was built.
  • Filter Targeting Reports
    For each model Datanyze will add a ‘Prediction Model’ filter in Targeting. You can then select accounts with only a certain grade (A, B, C, etc.).
  • Easily qualify accounts
    With Predict, you can quickly understand how good of a fit any company is when you look at it in one click. Each domain has a ‘Predict’ tab that shows the overall score and grade as well as firmographic and technographic attributes that are present that correlate well with your existing customer base.
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