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Boosting Lead Qualification with Prediction
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Boosting Lead Qualification with Prediction

On average, the cost of one lead is $198 in 2020. The better the lead qualification, the higher the chances of closing with the customer and generating revenue from these $198 invested. TADA can help sales and marketing teams improve their lead qualification and increase the number of deals closed.

Industry

Marketing

Project Duration and Effort

One week

Type of Prediction

Binary Classification

Customer Benefits

  1. Identify with an 85% accuracy the chances of closing a deal based on the lead characteristics.
  2. Understand which macro-economic criteria improve deal closings.

Problem to solve

Lead generation necessitates anywhere between 30 and 600 euros per lead, according to popupsmart.com. The price varies depending on industry type, channel, and target company category. Marketers focus massively on lead generation, with 53% of them allocating half or more of their total budget on lead generation efforts, says BrightTALK. According to Ziff Davis, a 2019 research discovered that catching up with digital leads in fewer than 5 minutes gets them nine times more prone to convert into paying customers.

Interestingly, most more prominent companies appear to struggle to produce large numbers of qualified leads. A Hubspot report from 2017 hints that the large majority of companies generate less than 5,000 leads per month, with the average being 1,877 leads per month. With figures that small, it’s even more critical to concentrate on converting as many of those leads as possible.

Problem Statement of the Dataset

There has been a decline in revenues for a Portuguese bank and they would like to know what actions to take. After investigating, we found out that the root cause is that their clients are not depositing as frequently as they used to. Knowing that term deposits allow banks to hold onto a deposit for a specific amount of time, banks can invest in higher gain financial products to make a profit. In addition, banks also hold better chances to persuade term deposit clients into buying other products such as funds or insurance to further increase their revenues. As a result, this Portuguese bank would like to identify existing clients that have a higher chance to subscribe for a term deposit and focus marketing effort on such clients.

Based on a dataset containing 2884 records, including lead, conversion information, and macro-economic data:

  1. Age of prospect,
  2. Duration of last contact,
  3. Number of contacts during the campaign,
  4. Number of contacts before this campaign for this client,
  5. Quarterly average employment rate,
  6. Monthly consumer price index,
  7. Daily three month Euribor rate,
  8. Quarterly average of the total number of employed citizens,
  9. Type of job of citizens,
  10. Education of citizens,
  11. Month of last contact,
  12. Day of previous contact,
  13. Number of leads converted.


We have asked TADA to determine which leads were likely to convert.
We also seek to understand which criteria are essential in increasing lead conversion, i.e., deal closing, to better qualify upcoming contacts.

Objectives

  • Identify which leads are going to convert.
  • Find out what characteristics the converting leads have.
  • Improve the accuracy of sales prediction.

 

This challenge raises the following marketing question:

Can lead conversion be predicted with accuracy?

Solution

TADA managed to predict which leads would become signing customers with an 85% accuracy and a 92% AUC. As if we required more evidence of how valuable lead nurturing is, Marketing Sherpa found that a massive 73% of leads aren’t sales-ready. To prepare them, marketing teams need to engage with them and move them down the sales funnel, claims Marketing Sherpa. Not only does lead nurturing result in more sales, but it also encourages larger purchases. Annuitas determined that nurtured prospects make 47% bigger purchases than non-nurtured prospects. How do marketing and sales teams know which leads to nurture in priority?

TADA has selected the following two main criteria out of the seventeen available in the dataset. They approximately bear the same weight in its decisions:

  1. The daily three month Euribor rate.
  2. The duration of the last contact with this prospect.
According to TADA, the number one indicator representing the global economy’s state is the three-month Euribor rate, i.e., a percentage based on the averaged interest rates at which Eurozone banks offer to lend unsecured funds to other banks. The higher the Euribor rate, the less companies borrow money, the slower the economy. Interestingly, the last call’s duration is a powerful conversion signal, probably as a secondary metric. Most likely, when the call lasts, the conversation is going well, and the product offered matches the customer’s needs. As a result, there is a higher conversion rate.
We can analyze the impact of both these factors statically or dynamically through a live predict analysis. Noteworthily, our algorithm selected only objective criteria and has not selected any criteria which might provoke bias in the salesperson, i.e., customer age.

Live Predict

Customer Benefits

In one week, marketing and sales knew the criteria that contribute to qualify leads better and improve sales performances. They managed to:

  • Identify with an 85% accuracy the chances of closing a deal based on the lead characteristics.
  • Prove that deal closing is highly effective when a salesperson takes time for the customer.
  • Recognize the economic criteria which improve deal closings.