Customer Data Management Overview
SessionM’s customer data management solution creates a unified view of the customer, which activates smarter targeting, personalization and real-time interactions. This view enables a truly one-to-one, efficient customer experience.
There are three types of data that are essential to the SessionM Platform:
- Declared – Data provided to SessionM by the customer during account creation or account modification (e.g., name, address, email), along with direct input data from additional sources (e.g., completing a survey).
- Observed – Customer behaviors across engagement channels, such as making a purchase, opening an email, or interacting with an app, website, or piece of content.
- Calculated – Values established through observing a customer’s historical behavior, suggesting the customer’s overall value to a client’s program, such as Customer Lifetime Value (CLV) or RFM metrics.
The platform ingests, filters, and enriches data from multiple data streams in order to create a near real-time view of customer interactions. This view can power insightful understandings of a client’s overall customer base and, as a result, ensure more successful customer engagements.
Ingest Data From Multiple Sources
One of the most important steps in the customer data management process is the accurate and efficient ingest of customer data. The SessionM Platform is able to prepare for and ingest the initial data set as well as any subsequent data from ongoing transactions using a variety of methods. The level of preparation and configuration applied is based on the format and complexity of the data.
Initial Data Ingestion
SessionM Sync, an Extract, Translate, and Load layer (ETL) is provided for the high-speed ingestion of data sources, such as customer data, product catalog/menu data, and venue data. This ETL layer is a general purpose tool that enables the creation or update of multiple records quickly (>100/s). SessionM Sync closely monitors ETL processing and is able to generate reports/alerts based on status changes or failures.
Data Quality Assessment
The SessionM Solutions Team first analyzes the data for quality and consistency. This process dictates the amount of data hygiene that needs to be applied to the initial ingest and the rules that must be applied to any ongoing ingest.
Support for Ongoing Data Streams
The SessionM Platform is able to assemble customer data and behaviors from disparate systems including in-store activities and purchases, web-based browsing journeys, and mobile app engagements, appending them all to a single record.
There are a variety of methods available to integrate data streams with the platform, covering all potential touchpoints with your customers. Options include:
POS Adapter – The POS adapter is a lightweight, cloud-enabled agent that installs on both front-of-store and back-of-store systems. Once installed, menu and SKU items are automatically synced with the cloud, and all ongoing transactions are liberated for use in customer data management, engagement, and loyalty components of the platform.
Ecommerce Adapter – The Ecommerce Adapter processes purchase transactions submitted via the platform’s server-to-server API, and can execute campaign orchestration against that information. The adapter also transmits offer and discount data as part of the transaction processing.
SDKs – Software Development Kits are made available for a variety of platforms, including but not limited to:
Using these SDKs, event streams can easily be integrated with the SessionM Platform from one client or server to specific SessionM endpoints. The SDKs simplify all aspects of the API lifecycle, making integration easy for many different channels and platforms.
APIs – Application Programming Interfaces are provided to enable the ingest of data.
These cover data representing customers, catalogs/menus, stores/venues and other data streams or entities. APIs are provided for the following data models:
- Customers – Supports foundational types of customer data with operations that manage customer profile data with standard and custom profile APIs.
- Purchase Transactions – Supports the ingestion, filtering, translation, and SKU mapping of transactions that occur via point of sale and ecommerce channels.
- Stores/Venues – Supports store and venue data for each of a brand’s locations, including unique store IDs, addresses, and location coordinates.
- SKU Hierarchy – Supports the identifiers for items within a brand’s product catalog, including product SKU, name, and category.
- Offers – Supports the management of offers and any associated restrictions, such as catalog, acquisition, usage, and/or store location restrictions.
- Analytics – Supports the ingestion, aggregation, and analysis of normalized event data across mobile and web properties to provide funnel activity and engagement reporting.
Create a Single Customer View
SessionM bridges data from different channels and systems through cleansing and matching processes to create a single customer view that is instantly accessible and operationalized for customer engagement.
Data Cleansing and Matching
Common to many e-commerce or retail environments, customers often have multiple accounts/identities with different email addresses, but their contact information, such as cell phone number and home address, remains the same. SessionM applies the following models to merge known duplicate accounts and create a single customer profile:
Deterministic Match – This match method finds accounts with matching attributes (first/last name, phone number, email, home address, etc.) and applies rules configured during setup to merge or link these accounts to one master account.
Customer Profile Merge – This merge method migrates a configured set of customer data to a specific source account, indicated by applying the deterministic match process.
Orchestrate Data and Uncover Insights
Once data has been ingested and cleansed, it is enriched through a series of flows (shown below), enabling you to score customers dynamically based on key metrics like recency, frequency, spend, LTV, churn propensity, or product specific tagging.
The first flow, shown in blue, is characterized by event enrichment, the process that adds useful information to the event data being processed. Once enriched, the data resides in a platform store for enriched data and is ready for utilization in the Customer Profiles Module.
The second flow, shown in green, runs through the data pipeline, where event data can be enhanced with calculated metrics such as customer recency, frequency, and monetary spend (RFM), customer lifetime value (CLV), churn, and recommendation scores. From there, this calculated data can then join the augmented data in the platform’s store for enriched data, where it too can be utilized in the Customer Profiles Module.
The diagram also includes an ancillary flow, shown in orange, that is available after the event processing stage occurs and features a proprietary rules engine that can be configured by platform users to trigger the orchestration of data to other entities such as messaging platforms, DMPs, ESBs, or other queuing platforms.
View Surfaced Data Within The Platform
Once the SessionM Platform has ingested, synchronized, and enriched various forms of customer data, it is ready to be surfaced and leveraged for analysis and customer engagement.
Single-View Customer Profiles
Data presented through the lens of a single customer is surfaced within the SessionM Platform through the Customer Profiles Module. The module displays first-party profile data at an individual level, along with capturing the many ways a customer interacts with your brand.
Here are a few ways the module can enable you to make the most of the customer data:
Customer Profile Tab – An individual customer view of basic and custom profile attributes synced from touchpoints that customers have utilized to interact with your brand.
Event Stream – A real-time log listing all of the ingested data gathered across channels, showcasing the customer’s engagement throughout the history of their account. Event Stream data is listed with date/time and can include purchase, offer, loyalty, and other custom activities.
Customer Analytics – Customer scoring and raw data associated with the customer insights that have been revealed through the data, such as recency, frequency, and monetary spend metrics, customer lifetime value, and risk of churn. These metrics provide a view of where the individual customer stands in comparison to the rest of the customers interacting with your brand
Audiences Through Unified Data
Along with single-view profiles, customer data can also be utilized through advanced segmentation within the Audiences Module.
Build and adjust audiences with the variety of data streams flowing into the platform, with the following capabilities:
Variety of Targeting Attributes – Create audiences across both standard demographic attributes (age, gender, address, etc.) and custom behavioral attributes (appended data, tags, RFM metrics).
Real-time Audience Sizing – Obtain an instant view of audience size during creation, viewing the number of customers targeted as attributes are added and removed.
Audience Insights Over Time – The platform is able to process audiences and report changes that have taken place since inception, including size, composition, and attribute summaries.
Act on Customer Data in Real Time
The final step within the customer data management process is leveraging the ingested data to generate action, driving high value customer behaviors as a result. The SessionM Platform features a proprietary rules engine that can be configured to trigger the orchestration of data both internally through the platform, or externally through various systems.
Within the platform, make your data actionable through a number of options:
Campaigns – Deliver contextually relevant and real-time campaigns with omnichannel marketing automation through the Campaigns Module.
Offers – Automatically deliver a unique customer offer that seamlessly discounts at the point-of-sale through the Offers Module.
Rewards – Award points or currency in real time for associated behaviors such as purchase recognition or visit frequency through the Loyalty Rules Module.