Time-Series Analysis Lays Groundwork for Improved Customer Understanding

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Laks SrinivasanIn this special guest feature, Laks Srinivasan of Opera Solutions reflects on the power of time series analytics and how it can model non-linear behavior designed to develop deep, long-term customer relationships. Laks Srinivasan is Co-Chief Operating Officer of Operation Solutions, a technology and analytics company mainly focused on capturing profit growth opportunities emerging from big data. Laks brings over 20 years of experience to these roles, having held numerous senior positions in marketing, credit risk management, and customer portfolio management, with a focus on analytics and decision automation technologies. He has worked with a number of customers in the retail banking, capital markets, mortgage, and retail industries addressing and solving various business problems for growing the top line and the bottom line.

Have you ever walked into a restaurant and the server already knew your order? It’s a great feeling. You feel like you belong there — and more important, like they’d miss you if you didn’t show up. While this sometimes happens in our neighborhood shops we frequent regularly, it’s definitely not the norm. Big Data now provides businesses with the ability to truly anticipate their customers’ needs and effectively compete based on superior customer service.

There are two key elements to this scenario: time and behavior. While larger companies know this is good business, they’ve struggled to implement this level of analytic sophistication at scale. The data has been too dispersed, the analytic expertise spread too thin, and the legacy IT workflows too rigid.

New advances in analytic technologies are allowing enterprises to get to know their customers well enough to anticipate each one’s needs, regardless of scale. A “need state” for each customer can be inferred, which helps marketers interpret their current relationship with each customer while identifying the actions required to migrate toward an ideal relationship based on both current and future needs.

So how does time-series analytics work? Legacy modeling approaches are linear, whereas behavior is not. By contrast, next-generation analytics are nonlinear in nature and therefore much closer to the way things work in real life.

Instead of starting with a small number of predictive variables, they look inside massive databases (both structured and unstructured) for characteristics that correlate with certain behaviors. These approaches have no preconceived notions or ideas about what might be causing humans to do what they do; they simply have the capacity to look back and forth between behavior and characteristics until they find a set of characteristics that is highly correlated with a certain behavior. And then, once they have this set (which could number a hundred variables or more), these models have the capacity to find other people with similar characteristics.

New analytics models combine computers’ tireless computational power and vast capacity with the elastic, connective, intuitive, and learning capabilities of the human brain. And they are stunningly good at revealing patterns, correlations, and segmentations hidden deep within enormous data pools. They enable us to uncover a view of action‑centric customer understanding. This view expresses each customer’s psychology, relationship drivers, and behavior based on his or her relationship and interactions with all touch points of an organization over a period of time. It brings to light the hidden reasons behind each customer’s purchase and relationship patterns.

This deep understanding of customer relationships is revealed by analyzing, among other things, relationship length and individual customers’ purchase behavior in all its dimensions: quantity, amount, where, what, recency, frequency, and sensitivity to different types of offers. The full expression of a customer’s relationship with a company is captured in a single, predictive package that organizations can use to ensure that they are maximizing the value of each relationship and each interaction. And because it allows companies to deeply understand what their customers want, need, and respond to, it enables large enterprises to communicate with them through their preferred channels and frequency, as well as to target and price offers to them for the products and services they value most.

This ability to provide enterprises a way to develop deep, long-term customer relationships at great scale makes it the single most powerful tool in modern-day marketing.


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