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Growing Capabilities of ML Techniques in HubSpot

By Martech Outlook | Wednesday, August 28, 2019

HubSpotML is an inherent fit for pattern recognition and learning from vast data sets and is playing a key role in enhancing the capabilities of the HubSpot Software

FREMONT, CA: Artificial intelligence (AI) is changing the work dynamics of the industries. It is creating a better user experience and simplifying the customer’s journey. On its way to transform various verticals, AI is finding its applications in HubSpot software too. Here are the ways in which machine learning (ML) can be leveraged in HubSpot software for marketing and sales.

ML is an inherent fit for pattern recognition and learning from vast data sets. A HubSpot portal generates extremely large sets of data such as contacts, lifecycle stage, company info, timeline actions, social messages, and others. HubSpot is actively implementing AI-driven features into the portal and is also utilizing ML techniques inside the platform. Here are the ways in which AI and ML are being used in the HubSpot platform and how it can deal with some of the most critical marketing and sales challenges:

New Predictive Lead Scoring

While working on inbound strategies, lead scoring has always been a problematic marketing resource, and companies have struggled to leverage it. The new Predictive lead scoring version 2 enables the system to actually look into the database, including website visits, contact interactions, emails, and so on. It will make predictive lead scoring much more accurate and useful.

Content Strategy

Using the ML feature, new topics can be created with the help of existing blog posts and HubSpot pages. The feature will generate contents when a person wants to work with topic clusters. It also allows organizing and optimizing the current content. ML assists in finding broader topics and identifying success and failures.

Assistance with the Blog Post

Automatic promotion of the next recommended blog read for the visitors can power the content marketing strategies. HubSpot assists in recommending the posts based on what the leads have already been reading. Moreover, ML will enhance the relevance of suggested reads over a period of time.

Constant Optimization

While setting up a new lead flow, it is essential to know what pop-up type will be the most appropriate for a specific situation, where should it appear, and the time lag while it shows up. HubSpot’s ML-based solution enables a lead flow to make its own best choices. Later the system tests out various lead flow types automatically and zeroes down to the best possible solution. The method is much better than a standard A/B split testing, which involves a risk of sending much traffic to a lead flow that may not be the best-fit solution. In the case of the HubSpot system, various variants are tested and is followed by the continuous optimization process. The optimization can also consider other elements into account, such as the accounts in the CRM and Lead Status.

ML within the Sales Hub

ML has great potential to power the sales activities too. A sales representative is required to perform similar tasks, such as producing documents and having calls, sending emails, and so on. ML can assist in lending the representatives with assistants that will be adept in dealing with everyday affairs. Here are the ways in which ML is helping the sales/CRM part of HubSpot:

Offering Time Recommendation

The HubSpot helps the representatives with timings. For instance, it guides the representatives on when is the most appropriate time to reply an important lead. It does so with the help of ML capability. The functionality can also be customized for marketing emails, which will significantly improve the open-rate efficiency of those emails.

Call Transcription

It is possible to have a call transcript made based on the recorded call when Hubspot’s own calling feature is deployed inside the CRM. ML algorithms eliminate human involvement. Moreover, the feature also detects portions of the text and recommends task suggestions.

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