Delivering effective customer analytics

Lyndsay Wise, president and founder, WiseAnalytics
Lyndsay Wise, president and founder, WiseAnalytics

Businesses that leverage analytics for internal use understand the value their data can bring. In many cases, organizations are also collecting data on a continual basis that could benefit their customers. More and more, these companies are using the information they store, and applying analytics to provide additional services to their customers. Some are providing regular reports or portal access to a pre-defined set of dashboards. Others are selling data back to customers or developing value-added services that complement existing products and services. Achieving success in this area entails consideration of the following practices:

  1. Speaking to customers directly to identify requirements: Organizations sometimes hold on to the fallacy that they know best when it comes to their customers. The reality, however, might be something completely different. Customers need to be included in the requirements gathering process because they have very specific viewpoints regarding the information they need and how they want to interact with it. Organizations run the risk of developing services that won’t be used or customers may feel they aren’t getting value because they don’t have access to what they want. Customer facing applications need to address what customers need and not what the organization thinks they do. There is always the potential for organizations to get it right the first time, but there is also the possibility that customers are currently consuming data in the way they are because no alternatives exist.
  2. Identifying the platform/delivery method that will be most effective: This requirement is important for the organization. From the perspective of the customer, any solution will appear to be hosted or cloud based. An organization can choose to store data within their firewall or externally. The platform and delivery method should complement the BI tools being used and the current infrastructure. If the analytics platform will be the same as what is already being used internally, then customer delivery might be as simple as developing a unique interface. Otherwise, it might involve dedicated servers or cloud based storage. If this is the case, organizations should evaluate integration requirements with the current platform and what will be required to maintain the customer offering over time.
  3. Looking at the toolsets and design: Most organizations considering customer facing analytics are using BI internally. Some of these companies decide to leverage the same products that will be packaged as customer applications. Although these offerings appear to be an extension of internal analytics, customers might require something totally different. Some of these differences include data access, interactivity, user interface, and latency. These differentiations may require more than one solution being used. Selecting the right offering requires a proper software evaluation and that if a secondary solution is selected for this purpose that it is designed to meet the needs of customers. For instance, customers might not be as analytics savvy as internal BI users and will require more guided analytics or ready-made reports.
  4. Understanding security and privacy — both to protect the organization and to protect user information: The collection and provision of customer information means that organizations may be required by law to protect customer privacy. In the case of healthcare this is obvious. Irrespective of vertical market, however, organizations need to have processes in place to ensure customer trust and the security of their data. The way in which data is stored will determine how secure access is provided. Some organizations store customer data within individual databases, while others will be stored within unique records. How data is stored will affect how it is accessed. Organizations need to ensure that storing data centrally does not interfere with customer access or that customers can have access to a dedicated server where their information is stored if desired. Ensuring security efficiently requires many considerations and organizations require strong IT involvement to ensure that security can be guaranteed. Additionally, external security parameters may be required from software vendors that will need to be integrated with the solution developed.
  5. Analyzing current customer use (clickstream, etc.) to define usage patterns: Although it is important to speak directly to customers to understand their needs and how they interact with data, for new solutions or redesigned applications it might be difficult the right requirements without conducting more quantifiable intelligence. Looking at clickstream and usage patterns can provide organizations with additional insight into what information is important for customers. Information being accessed on a continual basis can be used as a guiding point to drive application development, delivered metrics, and the level of self-service needed. Usage patterns also help identify what data is essential and how services can be packaged.
  6. Evaluating scalability: Organizations tend to provide customer facing analytics applications based on potential opportunities. As new productized analytics take off, organizations need to make sure they can support more data storage, number of users, and general complexities as adoption grows. This requires making sure that the infrastructure can scale and that software contracts take into account user licenses. After all, organizations do not want to get into a situation where they are not profiting from new offerings because they have to support infrastructure expansion.

These are only some of the considerations that are important as businesses look to develop customer-focused analytics. The bottom line is that developing new data-oriented services for customers can enhance the value proposition of products and services that are already provided. Doing so effectively means not cutting any corners, doing due diligence, and going about the requirements gathering phase in the same way as a full scale internal BI implementation.

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