Increasing ROI through categorising MQLs and SQLs

Overview

Our campaigns for LINBIT, our client in the block storage industry, have generated leads and raised brand awareness. But you still need to convert your leads into contracts and revenue. When your sales team is limited in size and resources, how do you sift through all of them? 

And sheer volume is not the sole issue here. As you all know from experience, some clients are ‘hot’ and have to be contacted as soon as possible. In addition, your sales team operates with people who all require attention and motivation. So the quality of the leads they get also determines their overall performance, inspiration, and satisfaction. 

Closed deals - Lead traffic source: Email

7.2%

Increase
(Q221 & Q321)

Subscription renewal target

+ $500k

Outperforming target
(Q221 & Q321)

Email open rate

8.7%

Increase
(Q221 & Q321)

Click-through rate

4.5%

Increase in CTR
(Q221 & Q321)

Challenge

Our challenge was to create a custom lead scoring system to classify the leads, including Marketing Qualified Leads (MQLs), those with medium-high marketing actions, and Sales Qualified Leads (SQLs).

The custom lead scoring system would inform the sales team of the best time to reach out to a contact in the company’s CRM.

Lead scoring is the process of assigning values, often in the form of numerical points, to each lead you generate for the business. You can score your leads based on multiple attributes they’ve submitted to you through form fields.

This process helps sales and marketing teams prioritize leads, respond to them appropriately, and increase the rate at which those leads become customers. 

For example, the higher the interest towards specific services and products of the company (demos and white papers) and the higher the intensity (requests and emails), the higher the points on a particular lead. And it will be delivered to the sales team ahead of another lead that may just have shown an interest in a generic industry topic, but nothing else.

Solution

First, we went with a predictive lead scoring mix but quickly switched to implementing a custom lead scoring algorithm aligned with LINBIT Sales. 

It included: positive + negative attributes split, disqualifying data (competitors, non-customers, former customers). In addition to the lead scoring, we connected the scored leads to lifecycle stages. This solution provides automated segmentation, qualification, and correct nurturement at the correct time.

With the installed custom scoring system, LINBIT has a more defined overview of its contacts’ lifecycle stages on a contact and database level. The point scoring system moves a lead up or down the funnel depending on its importance and value. Additionally, sales get notifications when a lead has reached a high enough level (SQL) to be contacted (Handoff). 

Results

The result of the new system allows marketing to deliver fresh and relevant leads and foresee potential bottlenecks. In addition, having better data like lead-to-customer rates and a more organized customer database also allows higher quality in forecasts for sales and marketing. 

On the marketing end, by successfully optimizing SQLs and MQLs through lead scoring, we nurtured these segments more efficiently, e.g., raised opening rates by 8.7% and click-through rates by 4.5%.

For the sales team, lead scoring has successfully sped up the sales cycle and has allowed them to focus entirely on closing deals rather than spending time on uncategorised leads. In addition, we’ve outperformed subscription renewal goals for Q221 & Q321 by $ 500k and have increased the number of deals closed from contacts coming from email by 7%.

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