Posts tagged Analytics
Early Warning System for DTC brands - a look back at MVMT, a fashion watch disrupter

Direct to consumer brands (DTC) are known for their unique and individual products that disrupt traditional markets. For traditional brands, it is a real struggle to track every challenger that comes their way. New DTC companies spring out of nowhere with a lot of Instagram attention. What all brands need is an early warning system that alerts them to compelling challengers.

 We looked at Amazon review data for fashion watches ending in Q3 of 2015 to see what customers were talking about with two challenger brands, Daniel Wellington and MVMT. Seiko is the leader of value watches, with indestructible cases, mechanical movements, and precision craftsmanship. The company has been selling watches on amazon since at least 2004. From 2004 to 2015, over 45,000 people left a review on a Seiko watch they purchased. The average star rating during those 11 years was 4.35 out of 5 stars.

 In 2015 two new brands were hitting the market as well, Daniel Wellington and MVMT. Daniel Wellington had 1,025 reviews and an average star rating of 4.65, and MVMT had 277 ratings for an average of 4.39 stars. So based on average star rating, there is nothing that significantly differentiates either of these challengers from Seiko.

 Looking at all three brands together we created a dictionary that encompassed the watches themselves (accuracy, crowns, bezels, bracelets…etc ) as well as the buying experience (packaging, returns, descriptions…etc) and owning experience (Strong Positive Superlatives, Weak Superlatives, Gifting, Compliments…etc). Based on over 21 topics, our regression model showed Strong Positive Superlatives and Compliments as the two most important predictors of buyer satisfaction.

 Digging deeper on Strong Positive Superlatives did not yield meaningful insights, as all three brands achieved high ratings and similar frequencies of occurrence.  Compliments, however, told an exciting story that could have served as an early warning for Seiko.

 The average rating for a customer mentioning a Strong Positive Superlative was a 4.67, which exactly matched Seiko’s score for the same topic, MVMT was a 4.66, and Daniel Wellington was a 4.80, which looks like it would give them a slight edge. The average rating score for someone that received a compliment was noticeably higher on average across the three brands(4.81), with Seiko coming in at 4.80, MVMT coming in at 4.79, and Daniel Wellington coming in at a 4.95. Based on raw averages, Daniel Wellington looks more likely to be the disrupter of Seiko than MVMT. However, the real insight lies when we look at the frequency analysis.

 For Seiko, a respectable 2.95% of customers mentioned a compliment in their review. For Daniel Wellington, this percentage rose to 3.61%. A 22.4% improvement is impressive.  MVMT, however, had a full 12.27% of their customer reviews mention being complimented, a 415.93% improvement over Seiko.  And over triple the number of reviews on a percentage basis than Daniel Wellington produced.

This insight could have warned Seiko back in 2015 if they had an early warning system in place. For MVMT, it speaks highly - when you wear a MVMT watch, people compliment your choice. 

— Data available from Amazon reviews

Learn How to Smash Data Silos at the Marketing Metrics and Analytics Summit This Week

I’m Going on a “Data Raid” with Three Client-Side Practitioners in NYC on Thursday! Consumer decisions are not made in a vacuum, so why do we continue to expect to get the whole truth from siloed data? These are puzzle pieces, not the whole puzzle!

The biggest insights opportunity for just about any organization today lies in bringing disparate data sources together to develop an entirely new, holistic and infinitely more useful picture of what’s driving the results we’re seeing (or not seeing, as the sad case may too often be).

This used to be a pipe dream, but today it is feasible. Unfortunately, one of the greatest barriers to achieving holistic insights is the organization, itself!

At the 2017 Marketing Metrics and Analytics Summit in NYC this week, I’ll be joining three savvy insights jocks on a roundtable to tackle “Breaking Down Black Boxes,” or, more specifically, How to Address Communication Barriers When It Comes to Big Data.”

It’s a critically important topic, and I’m eager to unpack it along with Heineken’s Director of Data Science & Analytics, Shivanku Misra, Pandora’s Consumer Insights  Director, Andrea Lopus Cardozo, and Matthew Koppel, Senior Manager of Digital Marketing at Next Issue Media.

There are several challenges with intra-organizational information-sharing. Frankly, data and insights are power, and so there are reasons that aren’t necessarily healthy, let alone optimal, behind why data-siloing persists in organizations today.

The good news is that it’s far harder than it used to be to hoard data. As software becomes more powerful in terms of handling merged data and analyzing different kinds of data, everyone’s data is more exposed.

This presents a tremendous opportunity for individuals or departments who are equipped to analyze these data themselves. I know I may not sound like a team player here, but in my experience when a committee is entrusted with mixed data, the process is slow, wasteful, and overly political/bureaucratic. The most effective mixed data initiatives I’ve seen have involved smaller, more agile actors within the organization who take on skunkworks projects that often incorporate other departments’ data to really outshine their internal competition.

I’m sure we’ll be discussing ways to circumvent miscommunication and “forging harmonious and mutually-beneficial working relationships,” but let’s face it: what’s work without a bit of friendly competition? 😉

I’m really looking forward to this event and hearing from some of the other very interesting speakers like Wharton Professor of Marketing Peter Fader.  If you can get into NYC on Wednesday or Thursday, you should really consider attending. I’d love to see you there!

Feel free to use my speaker code [ODIN] for a $200 discount!



PS. If you’re not able to attend, or for some reason we don’t get a chance to speak at the event, please don’t hesitate to reach out. I love discussing how mixed data analysis can be an insights game-changer, and an important first step is getting access to the right data.

About Tom H. C. Anderson

Tom H. C. Anderson is the founder and managing partner of OdinText, a venture-backed firm based in Stamford, CT whose eponymous, patented SAS platform is used by Fortune 500 companies like Disney, Coca-Cola and Shell Oil to mine insights from complex, unstructured and mixed data. A recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research, Anderson is the recipient of numerous awards for innovation from industry associations such as CASRO, ESOMAR and the ARF. He was named one of the “Four under 40” market research leaders by the American Marketing Association in 2010. He tweets under the handle @tomhcanderson.

What I Would Analyze if I Could Analyze All I Can

The Analytics Quote for the Day Since we’re a Text Analytics company we’ve been wanting to start a quote of the week or month. Here's one by Sun Tzu for today:

“Can you imagine what I would do if I could do all I can?”

Inspiring.  How it might translate into an analytics quote? Perhaps something like:

"Can you imagine what I would analyze if I could analyze all I can?"

Don't underestimate yourself.  Now with good text analytics software no data is off limits, anything can be analyzed, and we're here to help.

Happy Labor Day!



PS. A few quotes are worth reading. Most are best left to OdinText