This article will explain how marketers can use artificial intelligence (AI) and machine learning (ML) to improve their digital marketing efforts’ effectiveness using Uplift Modeling. With uplift models, B2C and even B2B marketers can move beyond simply predicting a customer’s future activity to discovering the associated activities that will positively influence them.

Uplift Modeling explained.

Uplift Modeling improves return on ad spend (ROAS) and drives greater personalization. An uplift model will suggest the treatment needing to be applied to an activity so that the marketing motion can influence the recipient to respond in a way that they previously may not have done. 

How is Uplift Modeling different than predictive analytics or A/B testing?

It starts with the idea that not all customers are motivated equally. Let’s say that your business model is subscription-based such as is the case with a SaaS, annual software license, or PaaS product. 

Uplift Modeling as a predictive model can predict the buyer’s behavior that will result from applying one treatment over another. For each individual that you are marketing to, Uplift Modeling answers, “How much more likely is this treatment to generate the desired outcome than the alternative treatment?” 

For a B2B buyer, possible treatments could be:

• Schedule a demo activity,
• Download a whitepaper offer,
• Watch a video activity,
• Talk to a sales rep request, or
• Attend an event.

It’s an uplift model that will inform the specific marketing motions (activity) so that the individual is more likely to respond positively and move closer to a purchase or associated activity/action such as ‘get a demo.’

The value of Uplift Modeling.

Marketers want to know how we can nudge the buyer’s behavior to get the desired result with the available treatments (possible marketing motions) at hand. It’s Uplift Modeling that predicts the influence or effect of an action. Uplift models are distinct from standard predictive models that can only predict an outcome, such as whether a customer will take the desired action. It’s helpful to know this information, but wouldn’t it be nicer to discern the specific activity that would cause the target to take the desired action?

The most powerful outreach tool for a marketer is email. But email is a noisy channel, and the email channel is not “free” as it will take marketing resources to create the text and generate the creative. Before embarking on this activity, wouldn’t it be nice to verify whether it’s worth the effort? Uplift modeling is one way to validate the value of the action.

Customer and prospect behavior groups into a 2×2 matrix, as shown in Figure 1.

The 2×2 matrix above contains four categories of people, classified as:

1. Will Take Action (upper left)
2. Persuadable (upper right)
3. Do Not Disturb (lower left)
4. Never Will Respond (lower right)

For the marketer wishing to optimize the ROI of their marketing investment, this framework will be beneficial since it allows us to target activities (marketing motions or treatments) aimed at the ‘Persuadables.’ 

Thankfully, uplift modeling can fill in the gaps so that we can answer the question, “If I send the email, how much MORE likely will each user be to take the action that I intended?” 

Because very few marketing teams have the in-house resources needed to build an uplift model and apply the data science required, many marketing teams struggle to adopt this powerful tool. Still, Vidora has an ideal solution that is ready-made for marketers looking to add uplift models to their workflow.

The Vidora machine learning platform called Cortex enables marketers and product managers to explore the benefits of techniques like Uplift Modeling without any coding or machine learning experience. 

See Cortex in action below.

Supercharging ABM with Uplift Modeling.

For B2B marketers coping with fragmented buying journeys, ABM (account-based marketing) is an essential tool. Though ABM is centered on tactics designed to reach specific decision-makers and influencers, the term account-based engagement (ABE) most effectively captures the coordination needed to deliver highly personalized and integrated marketing experiences. 

B2B buyers have embraced the internet as their first step to research options, evaluate vendors, and even bypass the enterprise sales process by directly buying goods and services without contacting a sales rep. This digital shift helps B2B marketers engage with buyers more effectively through email, conversational messaging, social media, display advertising, and website landing pages. It has also resulted in an explosion of data that helps companies understand customer engagement and surface buying intent so that B2B marketers can optimize buyer journeys. 

According to BCG, the data suggests that B2B marketing practices can be improved as more than four out of five visitors to a website are unlikely to become customers. And we know that half of a marketing budget gets wasted generating leads that companies do not follow up. Furthermore, 60% of companies admit that generating the desired customer response is challenging, while 40% say that getting a response from prospects has become more difficult. Uplift Modeling can be applied with great effect to address the buyers’ specific needs by personalizing the engagement to have the best chance of causing them to take the action that we want them to take.

Account Base Engagement (ABE) will prove to be a new go-to-market strategy for most companies. This represents a shift in how they deal with their most valuable accounts. ABE drives value by shifting the focus from the performance of campaigns (driven by predictive models), the volume of leads generated, the quality of engagement, growth in opportunities, increase in revenue, and the enablement of success — all made possible by Uplift Modeling. 

Because Uplift Modeling does more than predict whether a customer will take action, an ABE program powered by Uplift Modeling and deployed across the customer journey will drive increased customer satisfaction, retention, and greater customer lifetime value. Without the benefit of Uplift Modeling, that level of integration isn’t easy to accomplish. Using marketing automation and CRM systems in conjunction with the Machine Learning solution from Vidora, companies can use technology and data for real-time personalization to coordinate marketing activities across the online and offline touchpoints of the buying journey.

Practical examples of Uplift Modeling.

1. Uplift Modeling for churn reduction. With Uplift Modeling, a marketer can calculate each marketing technique’s impact on a particular customer’s churn probability. (Read the full blog post here) 
2. Uplift Modeling for increased purchases. Uplifting modeling can tell us whether an email, a phone call, or direct mail will increase the likelihood of converting the most.
3. Select the best offer to send. Marketers frequently send customers and prospects offers to incent them to buy a new product or service. Naturally, we would like to send users offers that will incentivize them to purchase while at the same time not discount over aggressively. 

Uplift Modeling is an exciting technique for marketers that can result in a significant ROI improvement. Vidora is committed to providing a machine learning platform (Cortex) accessible to marketers so that they can experience the benefit of techniques like Uplift Modeling without needing any coding or machine learning/data science experience.

Read Vidora’s press release on Uplift Modeling in marketing here.