3 Key strategies for applying predictive models to improve ROI across your organization
With today’s competitive markets, businesses increasingly need highly tailored approaches to win and retain customers. This introduces a host of challenges: How do you offer the right message, to the right person, at the right time? Or determine which services will best suit specific customers? Or plan in advance which offers deliver the most value to certain individuals, and benefit the business as well? What are key strategies for applying predictive models to your business?
Predictive models can deliver the answers. We’ll discuss strategies for developing and using predictive models in an AI context, and then show predictive models use cases from the worlds of financial services, media, telecom, and retail that can boost the value of your business.
1. Start with strategy and collaboration
To get the most out of AI and predictive models, it’s best to start with a clear strategic plan of what you want to achieve, why it’s important to your business, and where you can have the greatest impact on your bottom line.
To set the stage for successfully implementing predictive models across an organization, it’s also critically important to involve all relevant stakeholders early. You need to build buy-in and collaboration, and determine if you can augment existing resources or need to build new systems. By laying this foundation, you’ll have a clearer path for putting your data-driven models to work.
2. Stay focused value and impact
There are always more projects than time and money to do them. The key is to focus your efforts on places where you get the best impact. Make a list of the opportunities, recording expected impact and cost. Predictive model and analytics projects that end up having the biggest financial impact are ones that scale by accumulating value across many predictions.
3. Integrate the data flow
Predictive models use data describing your customers and their past and present interactions. Since new data is always coming in and new interactions are always happening, the models need frequent or even continuous data flows.
Your first models will use custom datasets drawn from business systems. As work progresses, your data team, business experts, and data science teams will learn which elements best support the predictive models and machine learning processes. Working together, your teams will build automated business and data flows.
4. Integrate the results
Using predictive models means that you are taking personalized actions based on model predictions. Systems need to be designed to keep the input data, predictions, recommended actions, and final outcomes. Now the integrated data flow, model results, and customer responses are combined to create an overarching AI system that learns based on the past inputs and how well the predictions performed.
When these strategies are in place, your processes are aligned for success.
1. Targeting the most favorable prospects for an email campaign using predictive ranking
Goal of the model: Increase efficiency by targeting customers who are most likely to respond.
How the model works: Your business could send email to customers every week, such as offers related to recent purchases or incentives to boost momentum throughout the customer lifecycle. However, you may run the risk of being labeled as a company that generates spam. To avoid alienating customers with “email fatigue,” it can be more effective to only send email to those most likely to respond favorably to a given offer. In other words, send the right message, to the right person, at the right time. But how do you do it?
We can help you figure this out by examining the past behavior of your customers and map that behavior to your current situation to find those customers most likely to act now (the right time). Our predictive modeling usually reveals common characteristics for each customer segment. These characteristics help you craft the right message, and you can send unique messages to each segment.
For a major newspaper, we were able to determine which of the news sections customers read mattered in terms of subscription behavior. Our client could then customize offerings focused on specific subject areas, matching text and visuals to appeal to the right customers.
Best practice tip: Measure results to check for campaign effectiveness and to assist in improving the predictive model for future campaigns.
2. Improving customer retention with enhanced call center solutions based on behavioral segmentation
Goal of the model: Enhance call center profitability by identifying a caller’s behavioral segment and likely reasons for dissatisfaction. This allows the business to plan response strategies in advance, such as targeted service opportunities and incentives that help retain and strengthen the customer relationship.
How the model works: Along with resolving issues, call centers can increase the value of customers who call in by offering up-sell or cross-sell opportunities. But how does a company know in advance what to offer to ensure the most benefit to the customer and the business? You can use predictive analytics to make models that predict the cross-sell and retention opportunities based on customer behavior, and then assess offerings in advance for the potential to meet specific customer needs.
As an example, we worked with a telecom client to build predictive models targeting which customers were likely to call to cancel in the next 28 days, by highlighting specific behaviors for different groups of customers. Marketing, sales, and finance staff could then evaluate those behaviors, craft offers to address each situation, and prioritize by customer segment, based on predicted likelihood to respond. They then fed this information into the call center, so service reps could make offers that met customer needs and aligned with business objectives. Customer responses to the offers were then stored in the data warehouse so the predictive models team could use that data to further refine the next cycle of models.
Best practice tip: To make best use of this solution, ensure you have effective communication and collaboration between all relevant lines of business across the organization. This helps you build clarity and consistency into the customer experience, and ensure you can deliver on promises made –a key to brand loyalty.
3. Increasing Sales and Marketing effectiveness with targeted messages based on customer segments
How the model works: Businesses create a range of products to align with different customer constituencies. But how does a company choose which product to offer to each person? Or which features will best suit specific customers? To help answer these questions, we’ve used predictive models to segment customers into groups by similar behaviors, and then look at how those behaviors imply different types of customers. We could then map product and service offerings to each customer segment, and even assign preferred products or messages to specific customers. In this way, the models helped the business know how to provide the most relevant offerings to each customer segment.
Best practice tip: Build consistent, fluid communication between analytics teams and customer-facing teams to ensure understanding of customer insights is complete, relevant, and timely.
Back to strategy: innovating the future of your business
Once you get rolling, you can innovate numerous ways to leverage predictive models for more profitable outcomes across your organization: Identify strategies to reduce attrition; target improvements at key touch points to accelerate issue resolution; increase cross-sell rates with sophisticated customer segmentation; boost the value of your Voice of the Customer program. And you can use AI to tie all these together.
Whatever your objectives, predictive modeling can help you transform mountains of customer data into valuable insight that can make a powerful difference for both your customers and your business.
Johnson Controls is using AI to reduce churn and identify over $100M a year of protectable revenue
Johnson Controls had no usable data sets, no data science team or data engineers.
How could they rapidly build a global data team with new AI/ML capabilities to improve business outcomes on a major scale?
Is your company struggling with how to implement predictive analytics?