At A.I. Optify, we run B2B marketing campaigns for various clients to generate qualified leads for their products. Over time, we have developed internal mechanims to approach the problem computationally by measuring the campaigns performance and feed the performance data back to the ad buying system for optimizing quality of generated leads. This approach allows us to predict the outcome of the campaign over time and identify what audience segments work and what don't.
Our predictive modeling approach for generating qualified leads has adopted some tactics from Account Based Marketing (i.e. ABM) for B2B space. The goal here is to optimize the economy for marketing efforts to generate qualified leads and engage with them down the funnel. In this article, we describe some of the high level processes we use at A.I. Optify for leads generation for our B2B clients.
B2B vs B2C
While designing B2B marketing strategies, it's crucial to understand why and how B2B space is different from B2C a few listed below:
The B2B market size is smaller than B2C.
B2B Economy is usually an order of magnitude higher than B2C.
B2B buying happnes by a group (i.e. several decision makers).
The cost of irrelavance is high. For instance, putting business message in front of a teenager is irrelevant.
B2B buying cycle is long (i.e. from weeks to months).
Due to higher buying cost for B2B products/services, the buying cycle is longer. Companies spend several months doing research to find out what product answers to their needs/pains. This pushes larger companies to make their buying decisions as a group. Thus, we need to execute targeted and focued marketing startegies for B2B.
A B2B Account is a qualified company that we would like to target and show them our messsage pitch and turn them to a prospect. Here, we describe the mechanics of our marketing strategies for targeting a set of qualified accounts for leads generation for our clients. Our computational approach is data-driven, predictable and systematic as we measure the performance of every step and optimize our budget spend to reach the client campaign goal.
Let's assume that we are serving a technology client "A" with a SaaS product. They want to run a campaign to target a set of B2B accounts, grab their attentions, and drive them to their products landing pages.
Below is the list of steps we take for generating qualified leads:
Account Modeling & Segmentations:
In the first step, we need to talk to the client "A"'s CEO, Sales and Marketing teams to learn about their product, makerket and sales mechanism and find out the campaign goal. Next, we need to formulate the definition of campaign success and how we should measure it, and also the metrics we need to optimize to reach the goal.
Next step is to discuss with Sales and Marketing teams to identify the list of companies that the client "A" wants to target as ideal customers. This step is qualitative and the goal is to get human insights from the company to define the target accounts.
Export company "A"'s CRM database including the list of their existing customers and the decision makers inside those companies, marketing qualified leads, and sales qualified/generated leads. Next, we need to build models for the exported CRM data. By analyzing the company A's first party data we want to characterize the properties of positive and negative leads.
Extract Firmographics of positive leads generated by the previous model such as industry, company size, company location, and annual revenue. These features will be used for accounts segmentation on positive leads that the client "A" wants to target.
Develop a ranking algorithm to rank positive leads based on a predefined quality score.
Demo the result of the predictive ranking algorithm to the client "A" Sales/Marketing teams for quality approval and human evaluation. Prioritize segments that are most valuable to the client "A" and import those B2B segments to the ad buying platform. These B2B segments can be characterized by accounts industry, size, location and demographics of the decision makers in those target accounts that we want to show our ads message to.
Work with the design team to create personalized Ad copies, landing pages, and call to action (CTA) based on the final list of B2B segments we want to target.
Take a crawl/walk/run approach by testing a few number of B2B segments first. Buy traffic for those segments and test your initial assumptions. Validate or invalidate your assumptions about the right market, product prop value, Ad/LP messages etc by collecting and analyzing the advertising performance data.
Constantly measure the performance of the campaign and revisit your assumptions around ideal target companies and ad messaging and iterate until you find the right Segments/Ad Copies/CTA/LPs which resonate to the buyers.
Targeting ideal accounts is in the core of programmatic ad buying step. For targeting, we use a combination of techniques including using first-party data, 3rd party B2B data providers, contextual targeting, IP targeting, retargeting strategies. You can learn more on targeting techniques from here: B2B Audience Targeting.
After finding the right combination of parameters (B2B segments, Ad Copy, LP) responding to your message, scale up your ad spend and heavily target the audience who are responding to your ad.
Nurturing Leads/Followup Startegies:
After bringing the right target B2B audience to the client website, the company "A" needs to follow up with the prospects and continue the conversations through different tactics such as Sales Call and Email Marketing. The goal here is to nurture the leads, educate them and push them lower down the Sales funnel.
Please contact us at info@AIOptify.com if you want to learn more about our Computational B2B Leads Generation services.
We have run Leads Generation Campaigns for a number of clients including technology startup companies.