By Nick Antoniades (IPSY) and Meghan Mori (Amperity)
The ability to provide personalized and relevant experience hinges on capturing, managing and utilizing customer data. A previous article covered the entirety of the customer data journey. This article serves to further hone in on the first and critical component, what customer data needs to be captured and how. Included is a framework for organizations to identify not only the right customer data to collect but also how to measure the effectiveness of their experiences with the customers as they work to personalize engagements in a meaningful and trusted way.
Most of us share an extraordinary amount of personal information online, including places we frequent, our tastes, and our digital journeys, without really recognizing the amount of data we are creating. Depending on sources, the volume of data currently generated is estimated at around 90 zetabytes, growing at roughly 30% YoY and expected to reach over 150 zetabytes (that’s trillions of GB) by the end of this year. This is an incredible amount of data to collect, understand, and steward.
Despite the growing amount of data created, consumer sentiment on sharing data is changing, and people are beginning to limit the information that they consent to share with sites, mostly driven by concerns about privacy and the use of that data without consent. Depending on which research you read, 90+% of consumers do not want companies to capture, store, and use their data, reflected by privacy-related legislation, which is on the rise.
To further complicate things, some research has found that 80-90% of consumers expect companies to provide them with personalized experiences and cater to their specific needs.
So, how do we balance the consumer’s demand for personalization with the rise in data proliferation, the increasing regulatory landscape, and the customer’s desire to reduce or control data collection?
We must be intentional about data capture with a focus on what data is critical to delivering a personalized experience. How do we uncover what data is most critical to collect and how? Where does it get generated and how will it be used to support great engagements between consumer and brand?
The answers to these important questions begins as far away from data as possible. It resides within the mission and strategic positioning of an organization. And that is the first step in determining what data should be captured within the Customer Data Journey. The next step is to translate the mission and positioning of an organization into a value proposition for the customer. What value do customers get by interacting or buying from an organization compared to all other options? This may vary from product uniqueness (e.g. Tee Public, Etsy), the emotional value from owning the product (e.g. Rolex, Louis Vuitton) or buying from a brand (e.g. Target), “incremental” benefits (e.g. Amazon prime content), price and convenience (e.g. Walmart) and so on.
This value proposition will define what experiences customers will have in order to realize that value. Both emotional and physical. This may cover the experience of finding the product (e.g. unique Tee that reflects my likes and preferences in Tee Public), the experience of purchasing it (e.g. ease and price for Walmart and Amazon), the value of using it (e.g. Hoka, Aldo, vitamins, cosmetics) or the emotional “high” of ownership (e.g. Tiffany, Ferrari, etc).
These experiences will then define the KPIs the company should track to understand if consumers realize that value and continue to do so. Some may be straight forward like customer tenure, repeat purchasing or even last mile delivery time. However, as the value proposition gets infused into the entire ecosystem of the organization, more and more KPIs will emerge that will neither be straight forward to identify nor measure.
The last step is to translate the KPIs into customer data that need to be measured, not only for KPI reporting but also to be used in personalized reach campaigns, retention content for CRM channels, SEO and of course, to measure the effectiveness as A/B testing happens across communication tactics and channels.
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