The Account LTB model is designed to help you identify the most active accounts or accounts showing the highest intent. This product is most suited for Enterprise sales teams (teams going after enterprise accounts), to help them prioritize accounts and reach out to the identified best contacts in the accounts.
- MadKudu Enterprise plan
- Use Salesforce as a CRM
- Currently, MadKudu can only push an Account Likelihood to Buy to Salesforce Account object
- The Account records must have a clean website or domain field
- Customers with HubSpot as a CRM cannot have an Account LTB model
- Have at least one source of behavioral data integrated
- Salesforce campaigns, Marketo, Eloqua, Segment, Amplitude, Kissmetrics, Mixpanel, HubSpot
What is the Account LTB model for?
Looking only at your most active user can hit limitations https://www.youtube.com/watch?v=r4BobnE5sX4
- Inbound account prioritization: identifying out of your best fit accounts which ones show the highest level of engagement ready to purchase.
- Outbound account prioritization (when external intent data included): identifying out of accounts visiting your website or other websites which ones are ready to buy
- Marketing campaign segmentation: identifying the accounts least active to increase engagement before sending to Sales
What is the Account LTB model?
The Account LTB model aims at scoring accounts based on the aggregated behavior of all the account users as well as account intent. MadKudu computes this score at the domain level.
The Account LTB model only scores Business accounts (accounts with a business valid domain).
The Account LTB model is only based on Behavioral data and does not include any firmographic data. For firmographic-based Account scoring, please refer to the Account Fit.
The model learns from the past activity of accounts before their converted to separate accounts that are on the path to conversion.
What data does the Account LTB model use?
The Account LTB model scores accounts based on behavioral and intent data
- inbound behavioral data (web visit, product usage, email activity, sales interaction)
- intent = behavioral data observed on other websites and provided by 3rd party (g2crowd, bombora, PredictLeads ...). This data is at the domain level, not at an individual level (email)
What is the output of the Account LTB model?
- The Account LTB score is based on the aggregation of the score of all the users with the same domain as the account (where user = email, account = domain if business).
- The Account LTB score is a number between 0 and 100, and is updated every 4 hours with new activities, and decays over time without new activity, based on a 90-day decay.
- The Account LTB model only scores accounts active in the last 90 days, and defaults to Low the others.
The Account LTB segment is derived from the Account LTB score.
- Very high segment : scores from 85 to 100
- High segment : scores from 70 to 84
- Medium segment : scores from 50 to 69
- Low segment : scores from to 0 to 49
Similar to the PQL model, MadKudu surfaces the recent event stream but we also start with a quick summary of the activity levels:
- Number of active user(s) in the last 90 days
- Number of instances of each event in the last 90 days with a time decay
We currently push the following fields
How often is the Likelihood to Buy score of an Account updated?
The Account scoring works in batch only (no realtime scoring), and would update scores of the accounts in the scoring audience at every Sync process (every 4h to 24h depending on the volume of data and numbers of models running on the MadKudu platform). This applies for newly created accounts as well: they will get scored at the next Sync process.