In today’s dynamic business environment, predictive analytics is a must if you want to transform your customer experience. In this blog, we bring you information about the first key steps that you need to take to successfully implement predictive analytics to transform your CX (Customer Experience). These steps have been excerpted from a Mckinsey study titled as ‘Prediction: The future of CX (February 24, 2021).
But before that let’s quickly define predictive analytics and broadly know about its benefits that organisations can accrue. Predictive analytics is advanced analytics in action that analyses historical data to predict future outcomes and deliver personalised experience to customers. It is a processing system that combines the historical data with statistical models and also uses machine learning and data mining techniques.
In terms of benefits, predictive analytics helps organisations to accurately estimate what a customer might need, thereby identifying risks and opportunities. It is an automated system and there is no need for manual effort to investigate a customer’s profile and purchase history every single time. Traditional investigation methods like customer surveys does not always find the root cause of CX issues while predictive analytics can provide actionable insights to improve CX.
now, let’s get cracking on the first key steps to get you going. Before that a word of caution, it will be a challenging journey, so you need to be prepared to put some amount of effort! Here are 4 key steps to start your journey as per the Mckinsey report –
1. Work on changing mindsets: The transition will inevitably involve challenges, not least of which will be a mindset shift for both teams and CX executives. Leaders may feel that predictive systems are outside their purview, the domain of the IT department or a data-science team. But times are changing, and today’s CX leaders need to focus on data as they once zeroed in on a single CX score.
The role of the CX leader is evolving, which means that executives will need to reposition themselves within their organizations. When asked about the biggest challenge with the current system, one chief experience officer responded: “People associate CX with marketing, not technology.” That is changing as more and more companies take up predictive analytics, and it’s up to CX leaders to help encourage the change in perception.
2. Break down silos and build cross-functional teams: CX functions often fall into the trap of creating their own silos within a company. To begin the transition, CX leaders need to better integrate with the rest of the organization.
Data owners will inevitably span operations, marketing, finance, and technology functions, so convening across senior leadership will be vital to ensure efficient data access and management. (And, of course, data scientists—not CX professionals—will be the ones writing the algorithms.) The CX team should define direction and strategy, but ensuring buy-in and excitement among the affected stakeholders will be key to scaling impact.
Even in the case of smaller-scale initiatives—for example, where an organization hires contractors rather than standing up an in-house data-science team—these strong, cross-functional relationships at both the development and steering-committee level will be vital to creating and scaling the CX insight engines of the future.
3. Start with a core journey data set and build to improve accuracy: Most organizations face challenges with data quality and availability—and without data, this transition is a nonstarter. The good news is that organizations can get started with basic customer-level data, even if the data are not perfect. The first step is to collect individual customer-level operational and financial data. A combination of customer profiles, along with digital and analog interactions, is usually a solid jumping-off point.
Ultimately, companies can look to integrate data from sources across the customer journey, including chat, calls, emails, social media, apps, and IoT devices. Regardless of the source, all data collection, storage, and use should follow privacy and cybersecurity best practices.
4. Focus first on the use cases that can drive quick value: Data-driven, predictive systems offer CX organizations a unique opportunity to tie CX strategies to tangible business value. In the early days, it is important to have a clear view for how the insights will be applied and to focus on a few specific use cases that will create immediate return. As a simple framework, organizations can review major sources of opportunity, pain points, or both across existing customer journeys and think through how a predictive system might create new solutions or enhance existing ones that may have a direct impact on loyalty, cost to serve, cross-sell, and up-sell behaviours.
In conclusion, predictive analytics can enhance the customer lifetime value (CLV). Delivering personalised service (enabled by predictive analytics) to the right set of customers enhances the CX across stages of the customer lifecycle and ultimately drives up the CLV.
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author – Worxpertise CX Lab
