Teradata putting the “terror” in kickass data analysis
Teradata, a leading data and analytics company, had announced several enhancements to its Teradata Customer Journey solution in Kuala Lumpur recently. The solution will provide companies with easier access to analytics, dynamic visualizations, machine learning and predictive simulations to help Malaysian companies march towards digital transformation.
“We focus on turning the most complex and challenging data sets into high-value assets that produce high-impact insights and tangible value for the business.”
– Teradata website –
With that identity in hand, Teradata’s Customer Journey solution aims to help companies understand and optimize each customer’s experience over time, across all channels and touch points, in real time. Providing marketers with this holistic view, the right analytical insights and built-in automation enables them to execute thousands of concurrent, individualized, multi-channel campaigns without having to add manpower to the tasks.
The solution, which combines Teradata’s expertise in data integration, advanced analytics and multi-channel interaction management, is targeted towards boosting marketers’ ability to treat every customer as an individual, increasing response rates and reducing churn.
“In this release of Teradata’s Customer Journey solution, we are putting more analytics into the hands of marketing so they can build a deeper understanding of the customer experiences and then proactively optimize related journeys,” said Saqib Sabah, Country Manager of Teradata Malaysia.
In short, the new Teradata Customer Journey solution is intended to help businesses visualise customer paths, simulate the impact of new campaigns in advance, and therefore engage customers with the most relevant content.
A study by Gartner indicates that “by 2018, companies that have fully invested in online personalization will outsell companies that have not by more than 30 percent.” However companies are finding it increasingly difficult to understand and optimize customer journeys as this often involve billions of interactions for millions of customers. Customers are also interacting more through multiple devices and channels, and expecting a personalized and relevant experience in every channel.
“In the new world of digitally-enabled customers, it is crucial that companies have data-driven business models to transform from traditional customer relationship management to customer experience management. We will continue to work with partners like Teradata to help companies in Malaysia achieve this by leveraging Big Data Analytics (BDA) as we transition to a fully developed digital economy,” said Ir. Dr Karl Ng, Director of Data Economy, Malaysia Digital Economy Corporation (MDEC). Teradata is part of MDEC’s National BDA Innovation Network to accelerate BDA adoption in Malaysia.
In addition to industry-leading technology, Teradata also offers consulting resources to ensure companies realize these capabilities quickly and at the right price, to achieve higher business value. “Our solution brings together all the required technology, plus the consulting expertise to achieve faster time to market. With Teradata, organizations can have a complete customer journey hub, without the implementation challenges of having to cobble together a solution from multiple vendors,” added Sabah.
Business in the 21st Century
Driving home the need for true transformational change for any company to succeed in the 21st century, Stephen Brobst, CTO of Teradata Corporation, gave an engaging presentation. He expounded, “There are three choices in the new economy: All companies are (1) already data companies, (2) will become data companies, or (3) will cease to be relevant. The future belongs to the company which converts data into products.”
After all, being able to monetise that data requires advanced analytics to decipher user behaviour and preferences, and one can only harbour a guess that under the stoic guidance of Brobst, Teradata is well-placed to unleash that potential in companies. It already counts as its customers, bigwigs like eBay, PayPal, Vodafone as well as Digi and Maybank in Malaysia.
Offering deep insight in impactful campaigns, Brobst further shared case studies of great recommendation engines such as Amazon, Netflix and LinkedIn. Amazon recommendations account for up to 30% of their sales. Netflix recommended shows account for 75% of viewings while recommendations for “People you Know” make up 50% of the connections made on LinkedIn.
In finishing his presentation, Brobst shared Teradata’s five stages to the sentient enterprise which shows a roadmap for a journey that organizations will need to take to stay relevant in the era of automation and self-service.
About Teradata Customer Journey Solution
New features include:
- Integrated customer path analytics offer better understanding of the customer journeys, as well as ideal points of entry to engage with them. Marketers can use this capability to target customers on a specific path, such as churn, with personalized offers to influence decisions for desired business outcomes.
- Communication journey visualizations show how customers actually flow through a multi-step campaign, so marketers can evaluate factors driving offer acceptance and decline decisions. Parameters can then be refined for improved marketing performance.
- Visualizations for self-learning models show the relationship between customer attributes (age, income, life stage, life event, etc.) and response rates. This helps marketers understand the profile of customers most likely to respond to an offer and plan communications. Exposing the model to the marketer also enables confidence, thus increasing adoption.
- Real-time offer simulation gives marketers a predictive ability to see the impact of a new message, offer or strategy on existing campaigns. By understanding the impact on the number of targeted customers, and ultimately response potential, marketers can run more effective campaigns, and optimize their offer strategy.
- “Bring your own model score” allows marketers to inject third party or internally generated model scores into the arbitration logic of self-learning models to optimize the message for any given customer, ensuring no previous work goes to waste.