Using Big Data to Perfect Your Customer Engagement Strategy
This article originally appeared in Volume 2, Issue 1 of <theScript> Quarterly digital magazine.
How can you differentiate from your competitors in a world where the internet provides price transparency in seconds? Provide unparalleled customer engagement. Put your customers at the center of your business and engage them in a way no one else can. But how do you do that?
When it comes to apps and technology, people tend to think about the customer experience as being only about the User Interface (UI). And although it’s important to present the customer with a clean and intuitive UI, the secret sauce to a great user experience is personalization. Tailor the experience to give customers exactly what they need when they need it, and you gain them for life. How exactly do you do it? Believe it or not, it’s all about the data.
Changing user expectations
Unlike people who grew up when the FBI was the only organization profiling people’s demographics, geolocation, internet history, etc., millennials accept that companies collect mass quantities of data about them. They expect the businesses they frequent to know their preferences and buying patterns. They don’t want to be bothered with ads for things they’ll never buy. They don’t want to be bothered with ads at all. They want relevant and useful information that helps them simplify their lives.
Research shows that, for the most part, people are not interested in perusing the aisles at various stores to see what’s available. SiriusDecisions says as much as 67% of the buyer’s journey is now done digitally. Buyers look at product reviews and research their purchases online, and then shop for the best value. They only enter a store if it’s cheaper and more convenient than online shopping, or if the experience is worth the effort.
How do you make it more convenient? Know what your customers are going to buy and keep a smart inventory. How do you make the experience better? Anticipate the buyer’s purchase. Use a combination of engagement metrics, customer data and ambient data sources to do predictive modeling of what customers are likely to buy. Ambient data sources may include anything from social to economic to environmental data. The data will determine the appropriate customer journey mapping and help you prevent wasted advertising and operational dollars.
For example, we helped a motorcycle and apparel company develop a predictive marketing engine to recommend which online ads to run to specific markets based on upcoming weather forecasts. Why try to sell a motorcycle on a cold, rainy day when your advertising money would be better spent hyping that heated waterproof riding jacket?
It’s not just commerce.
This is more than a retail or hospitality story. Think about how applicable this approach is to major industries, such as financial services or healthcare. Imagine being able to tap into the power of the cloud to give individual investors the same type of analytical and compute power previously only available to major investment firms. Envision being able to improve patient care by predicting what tests and treatments may be required before patients even enter the hospital.
We helped a major health system improve patient outcomes and significantly reduce costs by creating big data tools that cross-reference internal hospital data with external data sources to predict the flow of patients and their probable conditions. This allowed the hospitals to staff the right people at the right times, thus improving patient care and reducing overall staffing costs.
Making it real
Now think about your customer. Is it someone who enters your store? A patient walking through the doors of the emergency room? A citizen attending a public event? Each engages your organization with a specific goal in mind. Plenty of organizations can help them achieve that goal, but providing a modern solution that leverages data to deliver a personalized experience can make the difference between a one-time interaction and a customer for life.