The Account Likelihood to Buy model (or "Account LTB") model is designed to help you identify the most active accounts or accounts showing the highest engagement. This product is most suited for Enterprise sales teams (teams going after enterprise accounts) or PLG account-based motions, to help them prioritize accounts based on their level of activity.

Pre-requisites

Subscription to MadKudu Enterprise plan
Use Salesforce as a CRM
- Currently, MadKudu can only push an Account Likelihood to Buy to Salesforce Account object
- 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, 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

Sales

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

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 users explicitly attached to the account and orphan* users with the same domain as the account, where an account is defined by its account ID.

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 they convert, in order to separate accounts that are on the path to conversion from cold accounts.

*orphan users = emails from any of your systems (Salesforce, Hubspot, Marketo, Segment, Data Warehouse...) that are not linked to a Salesforce account ID but have a matching domain

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 usually available at the domain level, not at an individual level (email). Learn more about how to integrate Intent data into your Account LTB model.

What is the output of the Account LTB model?

Score

The Account LTB score is based on the activity of all the users explicitly attached to the account via the account ID and the users not attached but with a domain matching the account domain.
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.

Segment

The Account LTB segments represent 4 buckets of 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

Signals

Similar to the Lead LTB model, MadKudu surfaces the recent event stream and some aggregations you may want to display, such as:

Number of active user(s) in the last 90 days
Number of occurrences of events in the last 90 days (within a character limit of 512 in Salesforce)

MadKudu fields

MadKudu pushes to the following fields

Field Label: MK Likelihood to Buy Segment
- Field API Name: mk_likelihood_to_buy_segment__c
Field Label: MK Likelihood to Buy Score
- Field API Name: mk_likelihood_to_buy_score__c
Field Label: MK Likelihood to Buy Signals
- Field API Name: mk_likelihood_to_buy_signals__c

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 12h 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.

F.A.Q

What is the pricing like?

This will be an add-on to your MadKudu plan that includes a variable component according to your volume of leads. Contact your Customer Success Manager for more details.

How to enable this?

Contact your Customer Success Manager at support@madkudu.com to enable this use case.

What fields do we need to create in Salesforce?

In the Salesforce account object:

mk_likelihood_to_buy_segment__c: Type: "Text", Length: 255
mk_likelihood_to_buy_score__c: Type: Number, Precision: 18, Scale: 0
mk_likelihood_to_buy_signals__c: Type: LongTextArea, Length: 1024, VisibleLines: 10
mk_likelihood_to_buy__c: Type: Formula (then select Text at the next step)

IMAGE("https://cdn.madkudu.com/images/fire.png","",0,0) + CASE(mk_likelihood_to_buy_segment__c ,
'very high', IMAGE("https://cdn.madkudu.com/images/fire.png", "Fire", 20, 20) + IMAGE("https://cdn.madkudu.com/images/fire.png", "Fire", 20, 20) + IMAGE("https://cdn.madkudu.com/images/fire.png", "Fire", 20, 20),
'high', IMAGE("https://cdn.madkudu.com/images/fire.png", "Fire", 20, 20) + IMAGE("https://cdn.madkudu.com/images/fire.png", "Fire", 20, 20),
'medium', IMAGE("https://cdn.madkudu.com/images/fire.png", "Fire", 20, 20),
IMAGE("https://cdn.madkudu.com/images/snowflake.png", "Snowflake", 20, 20))