Earlier we have discussed how to build intelligent Slack workflows. In this post, we take a more practical perspective and look into how to use Aito in high-speed lead qualification through Slack. For a quick look at the use case check out our lead qualification case study.

Background

According to the lead response management study the key to getting in contact with a lead is speed. The leads were collected through a web form and at least one attempt was made to call the lead. They found that, if you wait for 30 minutes vs. under 5 minutes until trying to reach the lead the odds of contacting them go down 100 times and odds of lead qualification drop 21 times.

To hit this 5-minute window, Intwixt built the DealFlow Slack app. Intwixt DealFlow empowers companies to do real-time lead qualification and assignment while not leaving their favorite communication environment. Dealflow also embeds NLP (Natural Language Processing) to do analysis and get additional information through natural language input directly in the respective team communication app (Slack, etc.).

Aito was integrated into DealFlow's recommendation engine to help with making intelligent recommendations on who to assign to an incoming lead. The engine assists the sales manager to make faster decisions and shortens the time to contact a lead, which is critical for sales.

Aito works in the background of selecting the correct sales rep to take the incoming lead.

Data collection

Intwixt DealFlow collects data from Slack, Hubspot, and Intercom. From the sources, it creates a company information table and adds the information of which sales rep was assigned to the lead. DealFlow customers can also request their own proprietary information to be added to the data sources. In Aito, the table could look like the following.

Sales reps table (example)

Derived Aito schema (example)

{
  "schema": {
    "sales_reps": {
      "columns": {
        "company_size": {
          "nullable": false,
          "type": "Numeric"
        },
        "country_region": {
          "nullable": false,
          "type": "String"
        },
        "city": {
          "nullable": false,
          "type": "String"
        },
        "annual_revenue": {
          "nullable": false,
          "type": "Numeric"
        },
        "total_revenue": {
          "nullable": false,
          "type": "Numeric"
        },
        "sales_rep_id": {
          "nullable": false,
          "type": "String"
        }
      }
    }
  }
}

Assigning a sales rep to a lead

The predict endpoint can be used to find the most suitable sales rep for an incoming lead.

Aito example query

{
  "from": "sales_reps",
  "where": {
    "country_region": "APAC",
    "city": "Singapore",
    "company_size": {"$numeric": 1000},
    "annual_revenue": {"$numeric": 50000000},
    "total_revenue": {"$numeric": 200000000}
  },
  "predict": "sales_rep_id",
  "select": ["field", "feature", "$p", "$why"]
}

When selecting $why we can also get the reason why would a certain sales rep be the best for the lead.

Aito example result

{
  "offset": 0,
  "total": 14,
  "hits": [
    {
      "field": "sales_rep_id",
      "feature": "435",
      "$p": 0.5951631844009917,
      "$why": {
        "type": "product",
        "factors": [
          {
            "type": "baseP",
            "value": 0.25689819219790677
          },
          {
            "type": "normalizer",
            "name": "exclusiveness",
            "value": 1.0
          },
          {
            "type": "relatedVariableLift",
            "variable": "company_size:1000",
            "value": 1.63686605014792
          },
          {
            "type": "relatedVariableLift",
            "variable": "total_revenue:200000000",
            "value": 1.6071305852268476
          }
        ]
      }
    },
    ...

In this example the sales rep with the ID 435 would the best for the case with the probability of ~60%. The incoming lead's company size and total revenue improve the odds (value is over 1) of 435 being the correct sales rep by a factor of 1.6.

When a sales rep has been selected for the lead, the information is appended to the company information table and used in the future predictions.

If we would have the definition of success of lead qualification for a sales rep, e.g. lead turned into a customer, the information could be used to further improve the model. With the added success information we could use the value as a goal and start using the recommend endpoint.

Creating your own prediction pipelines

If you're interested in making your own predictive pipelines using Aito, contact us at hello@aito.ai and tell us about your use case and we can see how we can help to bring your ideas to life! Or request for an early access through our landing page.