A journey from big data to small data. How to use customer behaviors to drive an omnichannel experience.

“Big Data” is a marketing term that has been used extensively over the past decade to emphasize the need to collect, process, and activate the vast, ever-changing, amounts of data available to improve the customer journey and offer a more personalized experience. This caused brands to invest heavily in data-lake technologies to help brands collect, catalog, and process their data to create actionable insights. The vast majority of these implementations resulted in outdated customer insights as the solutions were mainly batch-oriented and did not quickly respond to fast-moving customer intent signals due to the technology constraints of the time.

In today’s hyper-connected world, people interact in real-time with brands through multiple offline and online touchpoints. People now expect highly personalized, connected experiences with brands that are aligned with their intent, interests, and values. These interactions create a treasure trove of data that can be leveraged to gain valuable insights and provide a deeply personalized customer journey. The resulting data signals, often referred to as “small data,” provide a wealth of information about customer preferences, behaviors, and interests.

By collecting and analyzing these data signals, companies can gain a competitive edge by using these insights to personalize their marketing efforts and improve customer experiences, resulting in increased brand loyalty and revenue. Small data signals can come from multiple channels within the client’s ecosystem, both in the owned (physical spaces, email, app, website) and paid space (social media, video, connected TV, etc). These signals need to be collected, aggregated, and attached to customer portraits in real-time to deliver on the client expectations that in turn deliver stellar business results. (See our Heathrow case study.)

Organizations need to embark on a radical rethinking of their data and technology operating models if they are to deliver on customer needs and future-proof the overall experience to acquire, grow, and retain more customers. Like any transformation initiative, several focus areas need to take center stage in the process to ensure the changes deliver on the business vision and objectives.

Setting a clear roadmap helps deliver speed-to-value for the business. Personalization and channel enablement must be prioritized for high-value and low-complexity use cases that are relevant to the organization as opposed to strictly a value or complexity-based approach. Delivering on a crawl-walk-run roadmap is far more impactful for the organization as opposed to standing still for the extended period required for a “Big Bang” approach. For example, depending on the industry, web personalization use cases using behavioral data directly from the website can help deliver the most value with the least amount of integration effort and as such are a very good first step for organizations to start on their personalization maturity journey.

Following the definition of the personalization roadmap, businesses need to evaluate their data landscape and identify the key data systems that will deliver the data to fuel the roadmap. Typically, this process will help uncover organizational gaps around the data availability, recency, accuracy, and documentation. Early mitigation of these gaps will help in the seamless delivery of the strategic objectives and help provide an additional dimension in the use case delivery prioritization process.

A “data fabric” approach is recommended for the data strategy definition. Systems should connect to other platforms and freely exchange data without a middleware layer to broker the exchange which adds latency and complexity to the landscape. This will ensure the delivery of the required data to the right systems to enable data democratization and foster a data-driven approach.

After the data strategy is developed, next is assessing the technology systems that host, process, analyze, and activate data across the organization. This thorough technology assessment ensures lackluster systems are identified, and a roadmap to replace them is established. Most businesses prefer a vendor-based approach to capitalize on efficiencies and benefit from a product roadmap. However, this approach requires robust due diligence with clearly outlined scoring criteria aligned to organizational objectives.

Privacy is a key consideration with a key focus on the regulatory framework that applies to the jurisdictions companies operate. Organizations have focused on regulatory requirements (e.g., GDPR, CCPA) and have delivered key improvements in areas around data privacy, accessibility, and portability. A data governance framework that adheres to local requirements is key to ensuring compliance. In the context of personalization, an additional dimension comes into play – customer consent.

The legacy approach to gathering customer consent at the broadest possible level (e.g., marketing opt-in) is no longer considered best practice and might hamper the ability to execute use cases that require data analysis, and segmentation. A full audit of the consent data and associated terms and conditions is important so issues that prevent use case activation can be addressed.

Customer data platforms (CDPs) provide the ability to process data at scale by joining offline data with online and behavioral signals. CDPs unify customer data across all channels, including email, social media, website, and mobile app, creating a 360-degree view of the customer. By leveraging the power of CDPs, companies can deliver a seamless omnichannel personalization experience that helps drive brand loyalty and value for the customer. That being said, not all CDPs are created equal. Organizations need to ensure they invest in a platform that offers end-to-end real-time capability including the ingestion, unification, processing, and finally activation of the customer data. The utility of a CDP platform is diminished if customer personas are unified and small data signals are aggregated only once a day, which results in “stale” customer details and insights. Adopting a CDP also ensures organizations can reap the rewards from product innovations that can deliver additional value to the business. The advent of AI capabilities in leading CDPs over the last year demonstrates the advantages. Innovation is delivered at breakneck speed with capabilities in the ML and AI space spanning analytics, audience discovery, segment creation and activation, and, of course, real-time journey optimization.

Investing in the right platform that delivers on organizational requirements but also has a robust roadmap is the cornerstone of today’s marketing ecosystem.