Srividya Sridharan

VP, Research Director Serving Customer Insights Professionals at Forrester Research

Srividya Sridharan has over ten years’ experience in marketing and customer analytics, product management, marketing management, and business analysis both on the client and agency side. At Forrester, she leads a team of analysts focusing on helping businesses collect, manage, analyze, and apply customer data to win, serve, and retain customers. Specifically, her team focuses on advanced analytics, business intelligence, Big Data, and the role of privacy in marketing. Sridharan holds an M.S. in Integrated Marketing Communications with a specialization in customer and marketing analytics from Northwestern University, and an MBA from India.

The Path To Understanding People And Patterns

What is the most interesting part about customer analytics for you?

Customer analytics allow firms to analyze data to optimize customer decisions and use the analytical insight to design customer-focused programs and initiatives that drive acquisition, retention, cross-sell/upsell, loyalty, personalization, and contextual marketing. The most interesting part about customer analytics from my perspective is that it is not just an analytical approach to make marketers smarter about their customers, but also an enterprise capability that can help in other areas – such as improving operational excellence, customer experience, and satisfaction – and help the enterprise as a whole get smarter about their customers.

There is a lot of hype around Big Data. Can you explain how much information is actually useful to marketers to understand the important customer needs?

Enterprises are barely scratching the surface with the data they do have at their disposal, let alone tapping into the potential of Big Data. While we see many companies have made significant investments in building data lakes and investing in Hadoop (an open-source software framework used for distributed storage and processing of Big Data through programming models), the ROI from these investments is still hard to prove and the general level of satisfaction with analytics is not high. So it’s not about Big Data, but the “right” data that gets converted into insights that feed business action. Marketers still need  basic information about customer interactions via various channels to match cross-device behavior – which are some building blocks of customer insights.

Which part of Big Data is the most intriguing?

The reason the promise of Big Data is talked about a lot is because of all the signals in unstructured data. Marketers have chosen to focus on patterns in Big Data versus understanding people behind patterns. We now want to quantify, measure, and predict everything in marketing because we have the data. Here’s the caveat: while Big Data produces more patterns, it also produces much more noise than signal. More correlations and patterns without causation will lead to an illusion of reality. Marketers are focusing on tangible things like purchase history, channel preferences, propensities, and campaign responses and not abstract things like values, perceptions, motivations, emotions, and context – what we used to do when marketers were once Mad Men and not Math Men.

In your opinion, what type of data is more useful?

No one type of data is more useful than the other. Companies must rely on mixed methods, multi-sourced data, to triangulate customer insights, i.e. using Big Data in combination with small data. Combine both human and machine-generated insights so that you are focusing on both the tangible and the intangible dimensions of customer behavior. For instance, a very large insurance company combines experience design and UX research with Big Data analysis of call center transcripts to get to the customer context during call center conversations, which helped them redesign their IVR process. An online retailer combines the expertise of fashion consultants with their recommendation engines to validate the recommendation they make for their customers.

How can marketers strike a balance to achieve a proper customer understanding?

A couple of tools can help break down barriers between quantitative and qualitative methods to achieve a deeper understanding of customers. While none of these tools can stand in isolation, they start to force the convergence of pattern understanding with the people understanding.

Customer journey analytics is an emerging analytics practice that combines quantitative and qualitative data to analyze customer behaviors and motivations across touchpoints. This quantifies qualitative journeys you may have already mapped out with your customer journey mapping efforts.

Text analytics can unlock emotional cues in unstructured data. You can use very structured, advanced natural language processing techniques to understand language in textual data from social media or call center transcripts of voice or customer feedback surveys.

Emotion measurement quantifies and reveals context of customer behavior by pinpointing their emotions. It involves speech analytics, text analytics, and facial emotion analysis.

How can the gap between the “available insight and action to be taken” be minimized?

You can’t just buy a bunch of marketing technologies and analytics software and hope it will be used effectively. It is important that organizational, process, and cultural transformation embed customer insights into the fabric of decision-making. This “insights-to-execution” loop should consist of learning and optimization processes that feed back the learnings from how the insight was applied. Customer insights centers of excellence should be responsible for executing this process to help their companies become insights-driven.

Interview by Nupoor Desai, Medill IMC Class Of 2017

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