Businesses across the globe are waking up to the need for putting their customers at the heart of every decision. The need to better channelize the innovation budget to address what customers really want, has never been more acute.
A PC giant with over $30BN in revenues, has been looking to incorporate customer-centricity deep into their product development and planning cycles. Traditionally very strong in the enterprise PC segment, they want to replicate and augment their success with the consumer PC segments – hence the need for customer-centricity and the call to CoreCompete.
Disclaimer: We don’t have Sherlock Holmes and Dr. Watson on our payroll…yet!
Dr. Watson: Sherlock, how do we get to know what customers want?
Sherlock Holmes: Well the old school would say – ask them. The traditional way has been to leverage market research campaigns to interview relevant candidates and categorize their responses.
In a typical campaign, you interview existing or potential customers, trying to understand their preferences, likes and dislikes to landscape potential innovation areas for the next product cycle. Towards the end of the survey, you go about identifying the behavioral type of the interviewee so as to slot their responses into relevant customer segments. A good example is Conjoint Analysis where the questions are designed to evoke customer responses to potential product profiles and feature trade-offs.
These campaigns also involve “tracking studies” that comprise of periodically conducted surveys. These are typically shorter and are good for maintenance of the preference models created earlier, just like you would service your car.
Dr. Watson: So, what’s the catch?
Sherlock Holmes: Nothing, it’s awesome but don’t you want to be more awesome. It’s got a few glaring limitations that have intensified in today’s fast paced world:
- Dependence on the sample quality: The preference scores thus generated from survey responses inherently contain the noise caused by low sample quality
- Lack of volume: In today’s world where even an abacus would generate gigabytes of data, a marketing research sample can rarely be large enough to be conclusive on all the questions you have
- Expensive: Scaling up the sample size of the research effort is expensive with costs increasing linearly for every additional response
Dr. Watson: So is this “old school” a passé then?
Sherlock Holmes: Not at all. I am apparently old school too, remember? Well, this technique has existed eternally for a reason and iterations over time have only improved it statistically and economically. Maybe there is a way to best use its most powerful offering, which is, getting exact responses for limited but targeted questions. Hold on to that thought and we will come back to it.
Dr. Watson: OK. So what’s next?
Sherlock Holmes: The new age my friend – the art of listening to customers. The mention of “Big Data” instantly strikes up an image of bits and bytes zooming around at supersonic speeds to the dreamier folks. To the realists, it comprises of high volumes of data, being tapped at even higher frequencies and from a wide variety of sources. Whichever side of the personality trait you might be, one bitter truth that does exist still with big data is that the more you think you got it under control, the closer you inch towards a data fusion bomb, ready to explode. Only few analytical initiatives have been able to successfully harness its power so far, and Social Media Analytics is among them. By the way, this now is the cue to link back to the thought you so patiently held on to a couple of minutes back. So how about making the best of both worlds collaborate with each other.
Dr. Watson: But how do we do that?
Sherlock Holmes: Convergence! Traditional market research helps you categorize customers into meaningful segments and consequently provide the precious dimensions that one needs to know to understand the DNA of a customer segment. And this is where you leverage social media analytics to scale up massively! Calibrate these dimensions to build models on big data that categorize the voice of current and potential customers, based on what they have been expressing online across various social channels. Tap into what they have been tweeting, posting on Facebook, expressing on various blogs or through product reviews on different e-tailer websites. The mission is convergence, of knowing what each customer persona has been expressing online, everywhere. Scanning for everything that a customer segment writes about, starts yielding unimaginable level of granular insights. You get an ocean of knowledge on offer because you are not restricted by the limited number of questions in a conventional market research anymore. Even the “tracking studies” conducted for model maintenance can become less frequent now since you are already validating on the fly.
To sum up, use a short but crisp Market Research to discover and categorize, and Social Media Analytics to amplify.
Dr. Watson: Excellent!
Sherlock Holmes: Elementary, my dear Watson.