Solving common marketing challenges with Machine Learning and AI

How can marketing and CRM professionals advance their programs without decimating their budgets or burning out their teams? One solution is to embrace technology built on machine learning and AI that enables cutting edge campaigns and automated insights at scale.

Amanda Groth, 

23 May 2023



Customer marketing teams have a lot on their plates right now. Marketing pundits (ourselves included) have reiterated these challenges time and time again: trading conditions are uncertain, consumer behaviour is increasingly difficult to interpret and predict, and cost per acquisition is getting higher while budgets are becoming more strained.

There is so much pressure to contend with, so it’s no surprise many marketers are feeling overworked, undervalued and under-resourced.

With all this in mind, how can marketing and CRM professionals advance their programs without decimating their budgets or burning out their teams?

One solution is to embrace technology built on machine learning and AI that enables cutting-edge campaigns and automated insights at scale.

Here are 7 problems we’ve been hearing from marketers lately and how they can be solved using Future Value Modelling, a methodology from Plinc (formerly called Planning-inc) that predicts the future value of each customer, then uses AI to identify the behaviours that will increase that value over time.

Problem: My budget is under pressure, so I need to optimise spend to achieve the best possible ROI.

Solution: Focus spend on the customers who are most likely to be influenced by it.

Future Value Modelling helps you optimise channel and promotional spend by creating a new framework for testing, targeting and optimisation. Using this approach, you can determine a customer’s current value, purchase likelihood and projected future value. These projections and insights enable marketing teams to differentiate their campaigns for different segments and understand the value a given marketing action could drive to the business.

In other words, by using Future Value segmentation strategies, you’ll be able to determine which cohort is most likely to be influenced by a given message or offer, thereby avoiding unnecessary spend and driving incremental value to the business.

Problem: Our customer data is siloed and disjointed, so we’re struggling to activate it.

Solution: Identify an accessible use case to kick start the process.

As part of the implementation process for Future Value Modelling, Planning-inc’s data engineers and programmers will evaluate your sources of customer data and join them together to inform the model. While Future Value Modelling doesn’t necessitate a fully actionable real-time Single Customer View, this process will inherently help organise your customer data and can act as a first step or proof of concept for a fuller data transformation project.

Problem: Senior leadership doesn’t understand the value we bring to the business.

Solution: Use more advanced metrics and KPIs to show them the impact of your program.

Demonstrate the true value of your work beyond standard campaign performance metrics. Using Future Value Modelling, you can report on incremental spend and incremental increases in future value trajectories, showing not only your current effect on revenue, but the effect your actions can have moving forward.

Problem: We send tons of emails, but we don’t have a sophisticated segmentation strategy to manage volume and prevent opt-outs.

Solution: Prioritise target audiences based on their likelihood to convert and the value they can bring to your business.

Future Value Modelling enables you to create more meaningful strategic segments. Rather than segmenting based on past behaviours,  Future Value enables you to target individuals by determining where you’re most likely to increase value trajectories, thereby improving the relevancy of your campaigns.

Problem: Between all of our channels and touchpoints, it’s becoming more difficult to anticipate and prevent churn.

Solution: Clarify what constitutes churn for your business, then let AI help you understand what actions are most likely to get a customer back on track.

Before you can prevent churn, you need to identify it; and before you can identify it, you need to define what it is. By bringing together different types of cross-channel customer data, Future Value Modelling can predict the trajectory of an individual customer’s value. Then, machine learning and AI are used to analyse the data and detect downward trajectory signals that are indicative of potential churn. Finally, Future Value enables you to identify the Next Best Actions most likely to reroute that trajectory and put customers back on a positive upward slope.

Problem: Senior leadership wants us to increase customer loyalty, but the marketing team isn’t sure an overt loyalty program is right for our business. Not to mention, it might be too much of a drain on resource.

Solution: Inspire loyalty through a surprise-and-delight model, rewarding the customers with the highest potential to bring value to the business in the long-term.

Every business is different. Some will benefit from overt loyalty programs while others will achieve an optimal ROI through more covert surprise-and-delight tactics. Rather than starting with a formalised, overt loyalty program, test the concept by rewarding customers with the highest future value potential to see how it impacts retention rates.

Problem: We know which customers have spent the most money with us to-date, but we don’t know if they’ll continue to be valuable.

Solution: Don’t measure a customer’s value by what they’ve done in the past…look to what they’re capable of in the future.

Even if you’re tracking individual spend, marketers need to understand how acquisition and promotional costs affect a customer’s value to the business. Once you’ve considered those costs, are they still as valuable as you thought?

The traditional Lifetime Value (LTV) metric has its role to play in campaign and customer analysis, but it’s outdated. It’s retrospective and only takes transactional data into account, ignoring other valuable behavioural data such as campaign engagement, browsing history and more. Just because a customer made one big purchase last year doesn’t mean he is more valuable to your brand than a customer who makes more frequent smaller purchases, engages with marketing, leaves positive reviews and more. With Future Value Modelling, you can define what value means to your brand and ensure you’re nurturing those relationships, keeping customers on an upward value trajectory.

Learn more about Plinc’s Future Value Model here, or get in touch to schedule a learning call. As a result of the call, our solutions experts will be able to tailor recommendations specifically for your brand.

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