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 recently got an introduction to the Provenir Big Data Listening and Engagement platform. Provenir is a cloud-based platform that serves as an omni-channel engagement hub, allowing brands to proactively listen for real-time events and comments across Big Data and leverage these moments to orchestrate real-time customer journeys for individual customers. It accomplishes this by orchestrating a series of complex events, planned customer processes, real-time decisions and immediate closed-loop actions.
The Provenir Platform is also designed to address challenges in customer treatment arising from multiple systems with latent (or completely missing) connectivity. Databases, campaign management, content management, CRM and analytics systems are all semi-detached while customers are using more devices, more channels and demanding a more integrated response. The Provenir platform is designed to wrap around a company’s existing systems and get them to work together in a new real-time and customer-centric way.
The platform is focused on taking action in real time:
- Allow companies to simultaneously listen across multiple channels, at individual level
- Identify what the company is listening for – their brand, competitor, products
- Obtain and connect data from multiple sources in real-time
- Do real-time analysis, matching people to customer databases and inventory for example or assessing a customer’s eligibility or ability to pay for a product
- Identify suitable content in real-time, offers or information for example
- Take an action in real-time by replying across channels, initiating a process, alerting someone etc
The platform uses a white board metaphor for designing. Listening nodes can be added and logic can be applied like the content of the inbound content or matching them to a customer. A decision can be made, like next best action, and then action taken using a preferred channel.
The designer has a fairly clean interface that allows users to configure customer journeys on a desktop designer before pushing them into the cloud for execution. Users can drag on various nodes such as adaptors that connect to specific internal or external environments and configure it to listen for new transactions, run procedures, listen for changes etc. Decision logic can be added using decision trees, decision tables, rule sheets, PMML, R, SAS, Scorecards, scripts or visual rules for instance. Building the journey is a simply drag and drop exercise. Once the journey is drawn, nodes can be dropped onto it from the repository.
Nodes can be configured, data can be graphically mapped from inbound sources to internal ones (storing off tweets etc). As data is accumulated it can be processed, used for lookups, enhanced with external data etc. Decision trees and other decision nodes can be used to label a transaction with additional data – an offer or a score for instance. Champion-challenger or A/B testing nodes can be added similarly and configured using a simple interface.
All this allows a journey or process to be defined centered around a series of customer-centric decisions – decisions about offers, about content, about timing etc. The product supports a range of social channels like Facebook or Twitter, websites, email or SMS. A wide range of databases and NoSQL sources can be accessed and analytic models executed in R, SAS, SPSS or PMML. Messages can be generated as emails or SMS and conversations can link to existing applications etc.
The solution can be implemented in the Amazon cloud. This allows customers to get complete solutions up and integrated in a few months. Real-time interaction is the key and the results can be immediate – one project generated incremental revenue that matched its cost in the first week alone.
Additional information on the offering may be found at www.provenir.com/listening.