Curious to know more about who your customers are and where they’re at? They’re online, telling you all about themselves.
Now more than ever, consumers want a unique experience as part of their customer journey. They expect personalized content and offers, providing organizations with more customer data. As your number of customers scales, a massive question arises: How are you executing your strategy to target these customers? The answer should include a robust analytics team, back-end model management and tracking, and a predictive modeling platform.
Model for the future
It is one thing to build a model, but it's another thing entirely to deploy it. Building a model captures the relationship between past behavior and future expectations at a point in time. The difficulty becomes frequently changing customer behavior dependent upon a variety of things, including the season, market, or business changes within a company.
The best models learn by continuously tracking who is targeted with what offers. They proactively track responses to these offers and then create a feedback loop to keep informing and improving the model. This process keeps insights fresh and relevant. Having this dynamic learning process, combined with a robust back-end architecture of model management and performance tracking, however, is where complexity sets in.
Algorithms aren’t just for social media
A marketer has a good understanding of what impacts customer behavior, which offers increase engagement, and what types of customers should receive those offers. But a model can hyper-refine thresholds to generate microsegments within an audience that are simply too granular for a person to create. What are the exact pinpoints on a certain behavior that you know to be a tipping point? A marketer can come up with an approach that generally works well, but predictive modeling takes it to the next level. If we look at a behavior—say the propensity to buy a product or respond to an offer—the model can determine the exact breaking point at which you want to start treating customers differently, sometimes even down to the cent.
How do you take this insight and turn it into a repeatable product for clients while also improving the work and discovering more insights?
With properly structured data, technology excels at analyzing it in a repeatable fashion. That's where leveraging a partner with a strong analytics team and the right tools, powered by a comprehensive understanding of these processes, can be extremely powerful. A committed partner with the right areas of expertise will have tools that enable the creation of previously unfound microsegments, allowing for a deeper understanding of your customers.
Predict your business through the entire customer journey
Harnessing the power of predictive modeling can also help with customer lifecycle management. Every single customer is at a different point in their relationship with a brand—from pre-customers and new customers to single-transaction, repeat, loyal, and lapsed customers. Work towards curating your customer approach to manage these lifecycle stages differently and customize your analytics to each one so that the analytic strategy is geared towards answering questions such as: Who is a typical single-transaction customer? What are the precise things that we want them to do differently than a loyalist or somebody who's about to go inactive? Aim to reach people where they’re at and guide them to where your brand wants them to be rather than having a blanket approach to all customers.
The customization of each customer’s touchpoints plays a strong role, but a robust marketing strategy is necessary to truly bring it to life. You may want to send offers to those who are most likely to take action so as to spend marketing dollars on quality leads over quantity. Or you may want to skip over those customers who are extremely likely to respond regardless of the offer in order to focus on the “movable middle”—those most likely to change behavior because of an offer or communication.
Embracing the future of personalization
Success requires a forward-thinking analytics strategy with both predictive and analytic expertise, combined with back-end architecture experience. Without all three of those areas, capitalizing on customer behavior will be a difficult process.
Leveraging the right partner with a deep bench of capabilities across these areas will elevate a brand's ability to deliver the personalized experiences consumers are craving. A partner with a rich history of customer relationship management (CRM) initiatives can help to understand your customer or member base, identify their lifecycle stages, develop the strategy within each of those stages, and implement predictive modeling with tracking so that you can track the value of this new method of interacting with members and customers.