Consumer-oriented firms need big data solutions to gain actionable insights into customer behavior. In banking, media, and retail, startup NGDATA is part of the conversation.
Big data is a disruptive technology that is changing how enterprises gain insights into their most valuable revenue generator: the customer. Knowing big data's potential and its importance in customer analytics is one thing — productively managing and leveraging it is another.
Luc Burgelman, CEO of consumer intelligence firm NGDATA, proposed a three-step approach for effectively using big data in customer analytics: listen to individual consumer interactions, learn from his or her behavior, and execute personalized marketing campaigns. What enterprises need, said Burgelman, is an "actionable" big data solution that can predict consumer behavior.
NGDATA was founded in 2012 (a pivotal year in the growth of big data), and in 2013 was named one of 7 Big Data Players to Watch by Bank Systems & Technology magazine. The company released in May 2014 version 3.0 of Lily, its flagship customer experience management product used in the banking, media, retail, and telecoms industries.
TechRepublic recently had a Q&A exchange by email with Burgelman about leveraging big data in B2C marketing.
TechRepublic: What is the most unusual or surprising thing you have seen in the big data analytics space?
Luc Burgelman: It's interesting — so many businesses have been so focused on big data and how they can best leverage it to derive business value that many find themselves overwhelmed. They recognize the importance of big data and how it can improve their customer experiences and optimize the expansion of their product portfolio, but when it comes to analyzing, managing, and actually acting upon this information, many businesses feel paralyzed.
That's normal, though, and we're already starting to see a massive uptick in big data project spending. The companies we're working with, too, prove that productively and effectively leveraging big data to derive true business value is possible, and it doesn't have to be overwhelming! Our customers are able to witness real and consistent traction as a result of productively leveraging their big data, and with that traction, they're realizing that there's additional potential for their business — potential they didn't even realize existed until they began uncovering all of the customer, product, and operational insights made possible by effectively leveraging big data analytics.
We expect to witness even more companies investing in big data technology within the next 24 months.
TechRepublic: What is the THE most important thing that you want potential customers to know about big data capabilities?
Luc Burgelman: "More data is better than a better model, and trends are more important than values."
This is why Lily 3.0 accumulates all data sources involving customer interactions in order to produce individual Customer DNA (a term our firm uses), and why our product is continuously monitoring all metrics and identifying trends in real time. It's crucial to create an actionable big data solution that can predict specific, customer behaviors.
Effectively leveraging big data enables businesses to "listen" to customer interactions across web, mobile, internal systems (CRM, call centers, transactions, etc.) and social channels. It allows businesses to "learn" from their customers' behavior by continuously building intelligent, individual customer profiles. And, it allows businesses to "execute" personalized campaigns through highly targeted product offers and content.
TechRepublic: What business opportunity did you and your cofounders see in the market when you launched NGDATA?
Luc Burgelman: My cofounders and I come from backgrounds in data analytics, content management, publishing, technology, and marketing in the financial services, telecoms, and media industries. While working with data warehouses and running models on flat files to manage the segmentation needs, we quickly observed the massive expansion of useful consumer data. It didn't take long for us to realize that consumer-oriented companies would need more automated solutions to store, organize, sift, and gain customer insights with what would become known as big data.
We knew that there would be value in analyzing and structuring data in a more efficient, actionable, and real-time way. This is how we decided to bring our customer experience management product to market.
TechRepublic: How can companies best gain from the marketing opportunities that big data offers?
Luc Burgelman: Productively leveraging big data analytics through a solution like Lily 3.0 allows and encourages marketing departments to develop and execute marketing plans based on truly knowing their customers' preferences at the individual level — something that, for many, has never been done before. As a result, marketing teams are able to improve their customers' satisfaction and acquisition rates with more timely, relevant offerings and interactions that make for better customer experiences. Big data solutions like ours also help marketing teams build more effective and frequent customer channel campaigns by leveraging customer data in real-time and producing relevant, timely, and useful offers for end-users at the individual level.
TechRepublic: What are the key questions that marketers need to ask to improve the customer experience? How do they answer those questions using big data analytics tools?
Luc Burgelman: Key questions that marketers must be able to answer in order to improve customer experiences include:
- Which top customers are likely to buy my product today?
- What are the top 3 products any given customer is likely to buy?
- What is the best channel to connect with my customers, and when should I do so?
- How can I save my most valuable, potential churners well before they (potentially) churn?
Lily 3.0 makes answering these crucial questions possible by continually analyzing massive amounts of customer data via the following methods:
- Listening across many different digital channels, collecting interaction related with behavioral, operational, and socio-demographic observations.
- Learning based on individual customer behavior (such as offer responses) to generate individual customer preferences.
- Executing upon customer activity based on simple instructions for how to find, optimize, and engage targets.
TechRepublic: Rather than becoming overwhelmed by growing and changing customer data, how can enterprises best govern and manage their big data?
Luc Burgelman: You want to avoid a data science nightmare, and mining tons of data without knowing what you are looking for is often a waste of time. Creating Customer DNA is key here. With all of the data feeding in to deliver a consistent and dynamic view of the customer, all areas of the organization can take advantage of this intelligence.
With Customer DNA, everyone in the organization can access well-organized, up-to-date, and easily accessible profiles on which to base more informed decisions. All types of groups and people, like data scientists, marketing analysts, CRM groups, BI teams, and Customer Intelligence groups, should have access to, and an understanding of, an organization's data.
The following 3 points can help make this possible:
- Consistent Analytics. All of the data should be made available for analysis across an entire organization. This way, everyone is looking at the same data, and there's no risk of partial data views creating inconsistencies and contradictions during analysis.
- Consistent KPIs. Correctly establish and define relevant modeling and reporting metrics and KPIs. Everyone across the entire organization should be well-equipped to make cohesive decisions and analyze data.
- Consistent Data. Big data should be translated in a way so that everyone can understand it. Overly complicated data sources and data organization can quickly become unusable.
TechRepublic: Let's use the banking industry as an example. What kind of individual customer insights can financial firms derive with your consumer intelligence solution?
Luc Burgelman: Banks are sitting on a gold mine (literally) of big data, with billions and billions of data points on their customers that can help them better understand customer needs, preferences, and purchasing behaviors. Unfortunately, though, banks hold a lot of "dark data," meaning underutilized data (different for every organization), due to the complexity of integration and aggregation of big data.
Lily 3.0 provides banks with real-time, cumulative, and trending views of their customers and their personal preferences. It breaks down all types of data, including the consumer's credit card purchase histories, investments, frequency of transfers, recent interactions, and behavioral, contextual, and loyalty information. These insights allow banks to turn existing, big data sources into a competitive advantage, and it enables them to deliver personalized product offers and targeted content.