The cost of acquiring a new customer can be higher than retaining a customer by as much as 700%. Increasing customer retention rates by a mere 5% could increase profits by 25%.

These are significant numbers. Especially when you consider that retention can be misleading in the short-term because it takes time, often years, to see its impact on your business’s growth. Taking a long-term view on growth can be challenging, as most businesses operate with short-term goals that must be met (e.g., quarterly sales targets, etc.).

As a result, brands and teams tend to deprioritize retention initiatives, as it doesn’t initially present itself as the burning problem or impactful opportunity that it truly is.

Historically, retention was typically only relegated to just being a metric of interest, with brands being able to do little to affect it. However, things are very different today.  Every relevant brand is capturing a deep set of data points across multiple attributes of customer engagement. The emergence of sophisticated artificial intelligence and data analytics techniques are further helping to leverage this rich data to address churn in a much more effective manner.

 

How can AI help in managing retention?

The heart of retention management lies in being able to identify the early warning signs from potential interactions your customers have with your brand.

If you know early enough that a specific customer is likely to leave or dis-engage, you can take proactive steps to prevent it, or at least minimize the risk. This is where analytics can play a transformational role. Let’s have a look at a few aspects within this.

 

  • Identifying relationships between known negative triggers and their effects.
    Studying historical data around negative customer experiences and how customers of different types have responded to them can help develop a robust model for predicting withdrawal and dis-engagement with your brand.
  • Understanding fading behaviors based on the past.
    Historical data can also be used to predict non-trigger driven attrition. Mapping current customers, based on a 360-degree assessment of them, with historical customers that have been lost, can help determine the high-risk customers.
  • Determining the next best action.
    Historical data can also be used to build recommendation systems. Understanding the impact actions have on consumers can allow you to determine the most appropriate course of action for individual customers.

 

What data is required?

As is with any use case dealing with customer behavior, the more data that can be leveraged the better it would be. Having a 360-degree view of all facets of a customer’s engagement with your business would enable you to have a holistic approach. Key data points include:

  • Demographics
  • Product – share of wallet
  • Product and service options
  • Past performance
  • Enquiries (in-bound and out-bound)
  • Community conversations (e.g., owned social channels)

Not all your data sources will be ready to be consumed. In fact, we have found that some of the richest sources can be found in organizations’ unstructured collection of service requests.

 

The all-important question of ROI.

It is not enough to just identify the customers that are at a high risk of leaving your brand. Comprehensive retention management requires following this up with determining the best course of action to address the attrition risk at an individual customer level. Fortunately, AI and data analytics can be of immense value here.

While it’s important to determine your most valuable at-risk customers to deliver an intervention strategy, it is critical to have a nimble operational process that lets you quickly learn from offers you give them and how they respond. This feedback loop continuously tunes the interventions that you are delivering to your customers.

There are a number of off-the-shelf retention prediction tools to enable businesses to operate in a self-serve manner. The challenge with these is usually the efficacy of these models as they rely on a one-size-fits-all approach. At the other end of the spectrum, businesses could opt for building in-house, dedicated data science capability to be used across multiple use cases.  This would typically require a large investment and elongated time-to-market.

Our team of experts at Infosys Consulting have developed a proprietary approach that helps you realize tangible benefits in less than 90 days.  Watch this video to learn more about our RetAIn solution and contact us today. 

 

 

Alec Boere

Alec Boere

Senior Principal, Infosys Consulting

Alec is a seasoned digital and AI expert with over 16 years of experience within the digital space, working with leading agencies and top global brands. He advises firms on the strategic development and delivery of revenue generating or category differentiating digital products and services. He has worked across the digital ecosystem, including, platform builds, apps, social as well as proposition development. His areas of expertise include AI, innovation management, product management, delivery (certified Scrum master), digital platforms, mobile, customer experience and strategy. 

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