Customers interact far more with brands anonymously than they do signed in. Enterprises are yet to effectively leverage insights from identifiable customer interactions. Enterprises that do not have a strategy to connect anonymous and identified data are missing out on truly significant business benefits. The more you know about your customers, the better you can serve their needs. The foundational aspect of personalization is therefore a question of how good is your data strategy.
Drawing a clear boundary between anonymous data and identified data, what you can share and what you can’t is important. Also known as 1×1 matching, this is absolutely key to an effective cross channel personalization strategy.
A good balance is needed – each extreme will be detrimental in the long run. Marrying every bit of anonymous clickstream data with a customer record will result in false positives and bad experiences. On the other hand, by keeping them forever separate enterprises will simply forgo significant opportunity for customer delight.
Don’t be afraid to cross the streams but do so on a case to case basis.
A flexible data model that allows for such variations (for example pipelines, which can send different sets of data variables without reworking the IT behind it) as well as effective data security and governance (encryption, retention, obfuscation) is essential.
Essential Systems of Data
Following systems should be part of your long term personalization and data strategy:
- Click Stream Store: The prime location for storing your clickstream or behavioral data – typically it is a part of the offering of whichever analytics cloud you choose to deploy
- Transactional Data Store: Your customer masters or your CRM
- Data Warehouse: Highly structured and formatted data to draw insights from complex sets of data. A data warehouse is NOT for serendipitous discovery of insights and relationship. Enterprise reporting generally runs off Data Warehouses
- Data Lake: Ability to store large amounts of unstructured data and structured data in native format and allow visualization of desired data relationship
- Big Data Systems / Machine learning: To mine undiscovered insights from various sources of data including 3rd party data
An Approach to Data – 10 Industry Best Practices
An enterprise data strategy has many aspects to consider and things can get quite complex very quickly. It can be a daunting experience even for the best of us. Here are the top 10 industry best practices to help smooth out that ride.
- Don’t simply measure everything; all you will get is an avalanche of noise with little practical value.
- Set clear KPIs; unambiguous and aligned to business goals to drive data measurements
- Click-through-rates is rarely enough. If your main goal is increased conversion for sales, then other behaviors, not on your radar, may lead to that. Ensure you’re tracking the right metric with the right context.
- Make sense of your KPIs on an enterprise level. Ensure success of a department is not at the loss of another. Your conversion should not be just a shift from another division.
- Capture the necessary context in all your channels such that they can co-exist with each other for correlation and reporting. Typically this means a foundational data layer that captures context in a uniform way or can be easily translated across channels.
- Track the complete lifecycle for a customer data from prospect to customer when deciding your data strategy.
- Allow your data model to be flexible. Allow for varying set of traits and attributes that can grow / reduce over time. The systems and pipelines that manage and transmit said data must be capable of accommodating this variation as well.
- Employ data scientists and analysts to make sense of what you collect and remember to pivot on new insights or requirements. Often what’s missing in the data can speak volumes.
- Plan a clear strategy for your sources of data such as own, partner, third party data, or even data marketplaces. Be clear on governance required for each source.
- Understand the roles and responsibilities of various systems managing and transmitting data as outlined in Essential Systems of Data earlier.
- Respect your customer’s privacy. If they give you their data, that’s an extension of trust. Be responsible with it.
- Don’t be afraid to ask for more data but ensure it leads to better service in a clear recognizable way.
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