Case study: merging sales & marketing for faster growth with ActiveCampaign, scoring & Watson NLU API

A B2B client approached us with a myriad of problems stopping them from faster growth: the disintegration and lack of collaboration of sales and marketing teams, which resulted in high cost per lead, inability to personalize the marketing messages per various stages of lead sales funnel, small lead conversion rate, and suboptimal ad bidding due to the lack of north star lead value metric.

We tackled these challenges using the merge of sales and marketing, ActiveCampaign automations, event-level tracking, custom lead scoring engine, and NLP-based sentiment analysis of deal notes.

The key challenges

Let’s start with an in-depth look at the challenges. The key problem was that once the lead entered the sales funnels, they struggled to direct a personalized, bottom-of-the-funnel message to him. The communication wasn’t personalized to how hot the lead was either. Everyone in the sales funnel was seeing the same ads everywhere, which resulted in low CTR’s and almost no impact of marketing activities on the time to close and lead conversion rate.

Another problem was that not only didn’t they have access to sales statuses, but also specific user activities in the underlying SaaS product (which has a pretty complex onboarding and needs a lot of information to be put down by the users), which means that they couldn’t really apply low-level marketing messages to fuel and smoothen product adoption, and hence make it an easier job for the sales team to close the leads faster.

Due to the lack of the proper measurement of the lead’s sales quality and the prospective MRR they can generate (as well as the probability of close), they weren’t able to optimize bidding, in particular in the Google Ads campaigns. The ideal scenario they were expecting was that the more engaged and lucrative the lead was, the higher they should bid for them. They were looking for a way to correlate the bidding with the lead’s potential value measured in MRR.

Finally, the problem in the discrepancy between sales and marketing was that marketing didn’t make any use of a lot of valuable notes assigned to the leads by the sales team. Since these notes expressed a lot of subjective information about leads that weren’t accessible anywhere else otherwise, they wanted to make maximum use of the knowledge contained in these notes. They wanted to use them to optimize campaigns for higher performance and show some specific messages based on their sentiment across the omnichannel communication.

On the sales side, the company had a serious problem with lead distribution among the various salespeople. Typically, the distribution would happen manually, even though it was clear that different salespeople have exhibited varying aptitudes in their ability to close companies or a certain type. Some salespeople had a higher success rate with larger companies, while others preferred smaller brands. It was one of the many manual things that the client was expecting to automate.

Automations, scoring, and efficiency

First, we’ve implemented ActiveCampaign for the client and made use of the majority of features of this platform. We integrated the ActiveCampaign with the client’s SaaS tool signup forms, while making use of many custom fields, and populating them automatically using our data enrichment API. The process would enrich lead data related to things like company size or industry at the moment of signup.

We used ActiveCampaign automations a lot. The first thing we tackled was lead distribution. We said farewell to the manual lead distribution and instead we automatically directed leads to the sales pipelines of specific salespeople based on the values of several custom fields of the new signup lead. We’ve also automated the task assignment. This worked wonderfully and helped the client cut tens of hours monthly.

By making extensive use of web tracking of leads, we were able to understand how leads behave after the signup, including which features of the client’s SaaS product they use. This information was then added to the view of the specific contact in the ActiveCampaign’s contact view, which provided the sales team with a lot of much-needed context of the background of the lead.

The next step was the custom scoring engine. The engine would score every activity, be it on the website or outside it (e.g. e-mail automation, product) that was performed by the leads. Scores became the north star that both teams were looking for. The scoring engine also revealed another problem with the marketing engine, namely, that leads don’t have a high-level organic engagement in the SaaS product and that they need to be nudged and directed to the key features to perform the key activities and reduce the risk of churn.

Further optimizations: Watson to the rescue

Due to scoring, the sales team saw how their deals are progressing through the product and which information they’re consuming, which helped them build more context on the sales calls, while the marketing team could design communication strategies conditional on the score values in a way that helped smoothen the lead’s journey through the sales funnel once they converted on the signup form.

We helped the company design e-mail and text automations for various company profiles and journey stages, depending on the values of the custom fields that a given lead had, and how they progress through the product. By employing custom scoring to e-mail automation, we’ve aligned the communication with how engaged the leads were, and increased personalization.

The custom-trained scoring engine assigned and updated scores for every lead that was active in the pipeline. The entire scoring engine was syncing score values to the company data warehouse, and then with Custom Audiences API in Facebook, Google Ads, and LinkedIn Ads. The marketing team used the audiences to design better marketing campaigns that targeted the key product adoption activities. We’ve also automated the audience flow between scoring-based audiences so that they always see the message aligned with their scoring.

Finally, we’ve made great use of the custom deal notes. Using webhooks, Google Cloud Translation API (and several other translators), as well as Watson Natural Language Understanding (NLU) API, we were able to post every new deal note added through ActiveCampaign for custom NLP assessment and hence not only update the lead scoring but also get an insight into the direction that given a lead is evolving. This meant that the marketing team could design the communication for the hottest of the hot leads appearing in the engine.

All in all, this integration helped reduce the unnecessary marketing spend on low-quality leads, the average cost per lead went down, and lead conversion rate increased, and work got more efficient. We’ve also broken down the silos between sales and marketing teams and made them collaborate to achieve great results for the company. Together. In an automated, smooth way.

Work and learn with us

If you like our thinking and would like to turn your manual marketing engine into a data-driven, semi-autonomous growth machine – get in touch with us and book your spot on the scoping call in the widget below or jump on the comprehensive, 90-minute, private workshop on building data-driven growth engines hosted by Datomni experts!