How to Implement a Data-Driven Customer Experience: Looking Beyond CX Software
Customer Personalization As the Future of e-commerce
Businesses are moving towards data-driven decision-making to create more personalized customer experiences. The customer-centric motivation pushes enterprises to harvest customer data and use them to predict their behaviors, but how?
Customer personalization is an emerging trend that most brands are moving with speed to implement because of its business value. While this might sound simple, it is an uphill task for most businesses. Considering this emerging technology, it is important to look beyond vendor customer experience software.
Customer personalization starts with creating a data-driven culture, understanding your audience’s needs, and leveraging data analytics to meet these needs.
Why Data-Driven Customer Experience?
Delivering stellar Customer Experience (CX) requires an in-depth understanding of your customers; this process calls for exploiting all the customer-related data.
Customer experience analytics is a market best practice that puts customers at the center of the business’s marketing operations. The basis of data-driven CX is collecting relevant customer experience data and conducting a sentimental analysis to understand these customers’ interactions with your business. Gaining such insights helps a brand deliver exactly what customers need at the right time, increasing satisfaction.
The data collected from customers are used to predict their behaviors, buying patterns, emotions, and needs; this allows business to customize their services and better serve their customers.
Its Begins With A Single Data Point
Software vendors dupe most enterprises to purchase CX software without making the right preparations to adopt this technology. Most businesses lack consolidated customer data, which such software use for personalization; therefore, getting a CX software will be of no good.
Most companies harvest, store, and process huge amounts of data. These data are used by various departments, leading to duplication. The redundancy is even worse for businesses that have not moved to the cloud.
Therefore, achieving a data-driven consumer experience starts with data availability. All enterprise data must be collected and stored in a shared database to allow all departments to access each other’s data. This allows, let’s say, the marketing department to access sales statistics and design promotional materials based on various consumer needs.
Creating a single data point allows companies to have a more accurate understanding of various customer characteristics. For instance, when the customer’s identity is extracted from the CRM system, all the other attributes are researched within the enterprise database and other systems like e-commerce. A single view of the customer is then created based on these data. When we have individual customer use cases, it is easier to use various AI tools to conduct analysis and help give businesses gain more insights about their customers.
Experience Data: The Missing Puzzle Piece
Traditionally, brands equated data-driven CX with enterprise data and AI tools. However, this gave a limited scope, forcing enterprises to engage other conventional and analog tools like surveys to understand their customer experiences. The survey approach proved inaccurate and time-consuming, creating a need to improve the CX technologies. Experience data came in at this point.
Brands started capturing data on customers’ behavior when browsing or using mobile apps. These browing experiences give businesses insights into their customers’ interests, product preferences, and emotional inclinations. This information is then used to give these customers exactly what they want.
Although harvesting and using consumers’ experience data sounds invasive, it is conducted with consent and high levels of privacy.
How To Go About It
You are doing it wrong if you are looking to outsource a vendor CX software before creating a data-driven organization. I am not against outsourcing these software solutions in any way; I am just concerned with the prerequisites before outsourcing them.
Therefore, I recommend creating an organizational data-driven framework with these three elements:
1. Customer-level data lake
Creating a data-driven experience starts with data. Therefore companies must collect and store customer-related data in a cloud environment for decentralized access. The data lake is a set of dynamic and comprehensive customer-level data sets which allows businesses to track their customers’ transactions, operations, interactions, and other behavioral patterns useful for the brand.
Having well-connected customer data is the foundation of understanding customer experiences. This framework should be consistent and reliable throughout the organization. It should have unique identifiers for customers, their needs, and the brand’s products. A customer-level data lake is drawn from sources like:
- Purchase history
- Web footprint
- Reviews and feedback data
- Customer emails
- Social media interaction
2. Predictive Customer Scores
The brand should then develop analytics using Machine Learning algorithms to understand and track customer satisfaction, behaviors, and needs. This is where predictive analytics, a core function of enterprise AI, comes in.
Since shoppers need personalized experiences, leveraging this technology will have them hooked on your brand. Generating predictive customer scores enables your business to predict customer satisfaction, forecast their needs, and evaluate the outcomes of its customer experience initiatives.
3. Decentralized Action and Insight Engine
Since creating a data-driven customer experience starts with a single data point, all the suggestions, actions, and insights generated by the predictive algorithm should be shared with all teams.
Various peripheral APIs (application programming interface) and tools like CRMs can be used to make this data available to all departments. For instance, the design team should be able to access call center voice logs to listen to the customer’s concerns and preferences and customize their designs to meet these needs.
These action engines are critical service frameworks that provide timely insights based on predictive customer scores and customer data lakes.
Let’s Recap
We have seen that tools and technology alone cannot guarantee a data-driven customer experience. Businesses interested in creating a customer-centric model should consider starting with a singular view of their customers across all departments.
The singular view creates focus and a well-connected picture of the customer’s needs and interactions with the business. Creating a superior data-driven experience entails creating accessible data points, consolidating and cleaning data, and leveraging action engines. This is the secrete to a data-driven organization that a brand should explore before blindly outsourcing Customer Experience software.
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