Customer Case Study

How Dealtale Helped
Convert 22% More Leads

The account executives at have a big ask for their prospects. They’re not just persuading them to take an online demo or try a free trial. No. They’re asking them to pack up their expensive jewelry and ship it to’s headquarters…

We’re talking jewelry that’s worth tens of thousands of dollars!

It’s a tall order, but a necessary one.

It’s a tall order, but a necessary one.

See, is an online marketplace for people to sell their expensive jewelry. To ensure the value, every seller must send their items into for evaluation. The AE’s job is to help prospective sellers kick off the process by completing the shipping phase.

The shipping phase is the hardest because it requires a very high level of commitment from the seller. If the jewelry is sent in, chances are high it will be sold.


Could use Dealtale to help more jewelry sellers follow through on the shipping process?



Dealtale's Scientific Method


As most organizations are well aware, staffing resources are limited and expensive, so it’s very important that AEs focus their attention effectively on accounts that would benefit from contact.

Many companies use predictive technology to help with this. The problem is that predictive technology can’t tell which leads:

Will convert regardless of rep contact

Could be persuaded to convert if contacted

Will actually disengage if contacted

Have no hope of converting at all!

These four missing insights lead to inaccurate data AND do nothing to help optimization. On the other hand, Dealtale’s Causal Inference can glean insights about each lead by how they interact with online marketing assets. Bottom line? needed to use Causal Inference to optimize their conversions.


Dealtale set out to not only improve the lead conversion in the immediate term but also to set the policy for future outreach. So, a subset of’s business was chosen for this test and run through these specific steps.

Problem Framing

The team had to agree on which leads to test in this study.

Collection of Observational Data

Dealtale’s revenue scientists studied past decisions made by AEs and what the outcomes were.

Making Educated Assumptions

After capturing the data on past decisions, Dealtale surveyed the AEs to understand why they made decisions to contact or not to contact.

Finding the Formula for Impact

Everyone decided to focus on the start of the shipping stage within the sales process because it had the greatest opportunity for value and impact.

Building Recommendations

Dealtale built a recommendation system based on how each lead would be affected by contact.

Based on this data, Dealtale’s proprietary AI was applied to specific prospects in a subset of’s book of business. Dealtale made recommendations about which prospects to engage with. This was then A/B tested to measure improvement.


Three months later… had a 22% increase in conversions for the subset using Dealtale.

Conclusion? By understanding the cause and effect of marketing activities, Dealtale can determine which leads are more likely to convert, as a result of AE contact.