The top 3 sources for customer analytics

According to a recent report by IBM’s Marketing Cloud, 90% of the world’s data was created in the last 2 years!!! It means the world had grown 10X in data at a rate of 2.5 quintillion bytes of data a day! There are so many implications for this – where do you save all this data, how do you manage it, is there such a thing as too much data? What does it mean for the future? Will we have 10X data growth again in 2 years?

With so much data out there, its an opportunity to upgrade your customer analytics initiatives!

Customer analytics used to be about data sampling, surveys and focus groups and in general about estimating the overall market behavior and preferences based on a small group of individuals. Now we vast amounts of data you can actually analyze loads of opinions and not need a small sample.
But where are the best data sources for that?

Online reviews on brand and eCommerce sites

According to a recent research 97 percent of customers said they had read reviews in 2017. There are so many ways now for consumers to share data and information that online consumer reviews and feedback data is in fact become the world largest consumer panel. And because it is an anonymous one it’s easy to share loads and loads of data since no one is worried about saying the wrong thing.

In fact, its even better then just the world largest consumer panel, as consumers are not concerned that they are listened to and as such convey their opinions more freely…

This data source in our experience seems to be covering a wide range of areas. Examples are – customer service and QA (returns, complaints, failures, missing parts, packaging), product or service (popular features, negative reviews, competitive analysis) and market research (analyzing brands, products and sentiment and looking for white spaces for innovation).

Another benefit is that it is the most detailed in terms of industry coverage – it will cover the most brands in your industry and will also cover most SKUs.

Internal data – from stores, call center, open ended surveys or even emails

This is the real hidden gem. On your own servers you’ve to the most direct and brand specific feedback you can get. Your own customers are expressing their thoughts about you and your offerings, now all you have to do is pick it up.

Typical data sources include the call center, open ended surveys and emails. If you’re in retail, data from the stores around customer feedback or customer returns or even complaints can serve you well if you drill down on it.

The point is this is data about your brand, products and/or services and nothing else. Use it wisely.

Social media

Given that this year (2018), an estimated 3.2 billion people will be using social media worldwide there is no shortage of data to mine in social media. This is why you should make the most of it. Keep in mind several things that make it typically the least detailed oriented data source (in our experience):

• There’s lots of “noise” in social media, so you need solutions that can clean up the data easily
• Feedback is typically delivered at the brand level, you wont see mentions of SKUs
Still from a sheer volume perspective this is likely the source with the highest volume of feedback you can get today.

Conclusion

On a positive note we now have more data we ever dreamt about to better analyze our customers. Further to that its diversified as we can get it from industry sites like retailers or other brands, we can get it from our own call center or social media outlets and in each source the opinions and focus will be slightly different. So we will get a great 360 degree view of our industry if we can mine these opinions accurately and with high granularity.

Revuze is an innovative technology vendor that addresses just this with the first self service customer analytics solution. Without any involvement from your IT or Insights team, quick to setup and intuitive for use by anyone, Revuze typically delivers 5-10X the granularity of topics compared to anything else, and it does it without humans, in a self-training solution that can adapt to the moving target of consumer interests in the fast pace of today’s data growth rates

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