Tell Customers What They Want
Customers are signaling they need help, guidance, and suggestions for products and services that will enrich their lives, but the vast majority of businesses are failing to answer their flares. What marketers need right now is a lesson in how to tell customers what they want.
“Listen to your customers.” It’s a message that has been programmed into the minds of marketers since undergrad. But what marketers need right now is a lesson in how to tell customers what they want.
As Steve Jobs famously said, “A lot of times, people don’t know what they want until you show it to them.”
This controversial quote is probably truer today than ever as we transition from a culture of long-form content (i.e. newspapers) to a society of 140-character snippets. Customers are inundated with information that is changing too quickly. It’s increasingly difficult for customers to sift through the avalanche of options to determine what is worthy of their limited time or what will buy them more time.
Customers are signaling they need help, guidance, and suggestions for products and services that will enrich their lives, but the vast majority of businesses are failing to answer their flares. To determine and deliver what customers need and want, businesses must balance listening and predicting.
Tapping The Power Of Predictive Analytics
Companies have access to data that allows them to learn and understand what customers want. However, they face a challenge in decoding the data to truly discover the hidden desires of customers. To do this effectively, it requires companies to pivot from backward-looking to forward-thinking in their data management practices.
Companies that can get their arms around the data and deliver relevant suggestions will create and capitalize on new markets and earn the trust and loyalty of customers. For companies to accelerate down the path of predictive analytics, they need to overcome some barriers.
1. Too much data within a big company: Valuable customer data streaming into companies today is multifaceted, with social, experience, and digital media data often unstructured and scattered across the organization. Companies are struggling to make sense of it. The first problem is silos. A lack of data management consistency between business units makes it difficult to integrate data. Data disconnection can create customer blind spots. Secondly, companies should consider adopting more emerging technologies, such as data lakes, which enable organizations to break free of tagging every data tidbit. Rather, organizations dive into the data to decipher patterns as they trek through it. Chief data scientists are needed here.
2. Differing incentives and marketing programs, goals, and tactics: Technology is spreading across the enterprise and has become everyone’s responsibility. However, when everyone is in charge, no one is in charge. Enterprises need to designate a digital leader who can work with other connected leaders, such as the chief data scientist and chief customer officer, to chart a cohesive course for digital transformation and rally business units to paint a contiguous picture of the customer’s journey from researching products and buying to engaging customer service. The customer’s journey should serve as the unifying force that aligns the organization.
3. Companies simply don’t have enough inventory to make significant, valuable recommendations: In a sense, companies are suffering like their customers. They have too much information and technology to choose from. Companies must be more aggressive about exploring emerging technology and creating new markets. According to PwC’s 2014 Global Data & Analytics Survey, highly data-driven companies are three times more likely to report significant improvement in decision-making, yet only one in three executives say their organizations are highly data-driven. Harnessing data’s predictive power will enable organizations to create new revenue streams.
So What Do You Do?
- Build a roadmap to accelerate your enterprise down the predictive path that includes assigning an executive who is charged with leading the initiative;
- Establish a data framework that will create data management consistency across business units and a data management strategy that takes into account today’s diverse data environment;
- Align the business to enterprise segments and personas so that the customer’s journey serves to keep the organization on track;
- Collect enough benchmark data to inform your predictions;
- Experiment with emerging technology, including creating predictive prototypes;
- Validate your experiments with real-world testing; and
- Expand your predictive capabilities to generate revenue.
When you master predictive analytics, you channel the power of inventing the future. And the companies that invent the future will lead the way.