All companies aspire to deliver superlative personalization and customer experience. Brands like Disney, Amazon and Netflix deliver amazing CX in our personal lives, but why haven’t more brands achieved these heady heights?
Consider what CX leaders have in common. First, their culture revolves—implicitly and explicitly—around customer experience. Jeff Bezos is quoted as saying that Amazon’s success is due to an “obsessive compulsive focus on the customer.” Second, they are all data-driven businesses. Analytical capabilities are helping brands accurately parse the multiple factors driving what customers say satisfies them and the design of actual interactions that creates economic value.
Done right, predictive analytics can enhance customers’ lives, increase brand engagement and deepen loyalty by delivering interactions that are tuned to and even anticipate customers’ desires. Of course, this requires reliable data. Ensuring data quality via automated input controls helps to verify and correct data quality issues before it leads to rework or worse, inaccurate analysis and predictive results.
But, reliable data doesn’t mean all data. Feeding superfluous data into a predictive analytics toolset risks clogging essential processes. The key is understanding and automating the appropriate data sets that are potentially useful for predictive analytics, while disregarding the rest.
Usability and transparency are also key when it comes to data and analytics. Brands suffer from one of two extremes: Either insights are so impenetrable that only data scientists can decipher them, or interpretations are so superficial that they provide no value to stakeholders. User interfaces that are purpose-built for audiences.
Here’s 5 tips for using data to create superlative customer experiences.
1- Start with the end goal in mind.
Have a clear, measurable business goal: improve customer acquisition, test marketing mix, manage customer defections or improve average order value of SKUs. Don’t get fixated on the “what,” but rather the “how” and “why.”
Ensure you’ve developed and secured agreement on KPIs and interpretations that are aligned between various business functions (marketing, service, operations, etc.) to illustrate the value of predictive analytics to business results.
2- Map the right data sources
Most importantly, map your analytics to specific stages of the customer journey. It’s about getting to know your customers and developing specific initiatives to put that knowledge into action.
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For example, during the buying phase, understanding how and when customers will purchase can help with targeting. Predictive techniques such as propensity models help marketers predict the likelihood that a customer will respond to a specific offer or message and convert. Expand wallet share with cross-sell and affinity models or understand future buying behavior through propensity models.
Or, during the post-purchase phase, use predictive analytics to uncover patterns of usage behavior and further drive customer engagement. For example, a retail site may tell you the status of your recent order the moment you land on the home page. Churn models such as uplift modeling and survival analysis can provide early warning signs of defection. Preempt customer churn with corrective actions, such as special offers or free upgrades.
3- Don’t skimp on data preparation and acquisition
Gather as much as possible, whether it’s historical data from CRM systems, real-time data from digital interactions, or streaming data from sensors. A robust data pipeline will help evaluate and enhance the performance of predictive models over time.
4- Don’t make data privacy an afterthought
There’s something spooky about a brand “knowing” something about a consumer’s innermost motivations and preferences. Yet consumers expect relevant, well-timed personalization. Surround your marketing with thoughtful data stewardship efforts (such as governance, security, protection) and transparent communications with consumers on how their data is being used.
5- Embrace the power of AI
New AI-powered technologies such as machine learning, computer vision, natural language processing and deep learning are uncovering new patterns, subtle correlations, and empower more real-time decision making in organizations.
Embrace AI as the next step on the journey to providing the best possible customer experience.
Wilson Raj is director of customer intelligence at SAS.
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