Table of contents for Marketing Decision Management Solutions
- Decision Management and Marketing – a Blog Series
- Marketing Decision Management: Critical Elements in a Solution
- First Look: IBM Real-time Interaction Management
- First Look: FICO Marketing Products
- First Look: SAP Real-Time Offer Management
- First Look: SAS Real-time Decision Manager
- First Look – Pitney Bowes Software update
- First Look: Idio
- First Look: Experian Marketing Solutions
- Listening, learning and acting in multi-channel marketing
- First Look: 7
- First Look: Teradata Integrated Marketing Management
- First Look: Lattice Engines
- First Look: NICE
- First Look: Provenir Big Data Listening and Engagement Platform
- First Look: Granata Decision Systems
I got an update on Teradata’s Integrated Marketing Management (IMM) solutions recently as part of my ongoing series on Marketing Decision Management. These solutions have evolved from internally developed Teradata solutions as well as their acquisitions of Aprimo, eCircle and Market Helm Interactive (reviewed by me here).
Several things are driving Teradata’s approach to integrated marketing solutions. First Marketers want an integrated suite (85% want one) yet 58% believe existing systems are too disparate to integrate different channels and 57% believe a lack of budget is a barrier to integrating channels. No matter how well marketers are doing at integrating their solutions they still see challenges with multiple systems, measuring effectiveness etc. The perspective of the marketer matters a lot because by 2017 CMOs will spend more money on technology than CIOs – IBM sees 7-8% increase in marketing budgets 2-3x IT, Gartner sees $148B in IT spending influenced by or owned by CMOs etc. Second Big Data, especially data from channels that are not relationally organized such as weblogs or call center logs, is increasingly seen as driving innovation in marketing particularly in its role as providing a time series of customer behavior. Social, mobile and sensor data all reflect on the customer journey. In fact 45% of big data deployments are about marketing. Teradata therefore see both their Market Helm acquisition (with its support for interactive marketing across channels) and their integration with Teradata Aster and its support for Big Data as critical advantages going forward.
Teradata IMM handles everything from marketing operations to campaign management, customer engagement through real-time interactions across channels and driving relevant marketing analytics for improving customer engagement and operations. The marketing operations piece involves plan and spend management, workflow and project management and asset management. Campaign management has more of a focus on what I would call decision management in its support for real-time marketing and outbound marketing. Teradata IMM also has links to digital marketing platforms focused on web analytics, to Salesforce automation and CRM systems and to email marketing providers
Campaign management with Teradata IMM is evolving with the transition occurring in the market. The traditional view originally involved identifying segments, targeting segments and selecting channels before developing and executing campaigns to deliver this. As companies have transitioned to more digital channels, and more real-time digital channels, Teradata has acquired both Market Helm for interaction management and eCircle for digital messaging. The new approach focuses on segmentation, dialogue design, digital messaging and real-time interaction. This new approach is managed from a Customer Interaction Management hub that pulls together all the various elements. From analytics (BI, web analytics and predictive analytics) as well as attribution and channel management for inbound and outbound channels to a focus on creating dialogs with customers and on responding to events with event-based marketing.
The Customer Interaction Manager hub lays out customer journeys and interaction or campaign flows. Data can be pulled in from Teradata and Teradata Aster but also Oracle and SQL Server. Social channels, like Facebook, are integrated so customer data from these channels can be added. This integrated view allows segmentation to be developed across all these different sources of customer information. Campaign flows can include emails, obviously, but the content of these emails can be customized based on the real-time state-of-the-customer interaction. Advanced and predictive analytics can be used in the segment definitions and real-time responses can be injected into the flow to ensure it responds to real-time responses or other actions. The idea is to provide a “traditional” campaign view that integrates inbound events and social channels – users can immediately respond to customer interactions but can also use those interactions to change what happens on a scheduled campaign.
Real-Time Interaction Management has grown out of the Market Helm technology and handles inbound marketing and real-time offer management that takes all the customer interactions and determines the best offer or message to respond with. It manages customer messages in a central repository, delivers them on-demand to customer touch point applications and, as a customer interacts with a company, determines the best one to display. Business users configure how to deliver this content to websites or channel-specific applications in contexts such as cross-sell and up-sell, customer retention and partner marketing.
Different groups can “own” the messages (corporate, web, marketing etc). Each message has a basic definition and this can be extended using client-specific data like margin, fulfillment method, product codes etc. Messages belong to different classes and can be flagged as appropriate for different channels. The core of a message is a business ruleset for the message. The various data elements (some pre-defined, others identified by the user as being available in a specific channel or system) are available in a tree-like view. A set of conditions can be defined based on these elements and ANDed or ORed together in various combinations using a point and click interface. The message history and current interaction can be used to build rules that understand things like “displayed in the last 10 days in any channel”. Client-specific attributes can be defined with allowed values and this too drives the UI to display the allowed value in the context of a condition. This end result is a rule that defines eligibility for each message and prioritization of these messages is defined.
A central concept is that of a message strategy. Message strategies describe how messages are selected and prioritized. The user can select from various business objectives defined in the system and give them a relative weight. Messages are associated with the objectives and an arbitration metric to specify how messages would be prioritized. Once defined, the user can identify where within the customer interaction to use the message strategy.
Analytics can be passed in as scores that are then treated just like other attributes, assuming these have been calculated using analytic models defined elsewhere, but Aprimo also develops some predictions on messages. Two self learning models are built automatically – the likelihood of a customer accepting (by channel) and the likely impact of the message on interaction length (longer or shorter). Each shows how different attributes contribute and can be used to prioritize the messages displayed.
There is also a complete set of campaign reports and some visual interactive analysis. Teradata is bringing predictive analytics to the Customer Interaction Manager product in order to solve marketing problems and are also using some of the new Teradata Aster capabilities such as the path to purchase analysis (see my recent post on Teradata Aster).
You can get more information on the Teradata IMM solutions here.