The following are notes that I took live while Dr. Scott Evans (Senior Project Strategest at Bazaarvoice, USA) presented at MRIA Netgain 7.0. The notes were originally posted on my Tumblr account (paullongsblog.tumblr.com), but I have moved them here and revised them slightly to remove typos.
Yesterday
- People went into a store and looked on the shelf to determine what brand of product they will buy.
Today
- Enormous amount of information available about products, most of it online. People are comfortable with anonymous views, creating a shift in how people buy things.
- People look at something in store and then buy online at Amazon.
- People will make decisions on their car on a smartphone.
ROI of product Experience (much easier to sell than Market Research)
Social Commerce Engine
- What you see: product pages, reviews/comments, q&a, stories
- What you don’t see: behaviour, customer profiles, purchase data, product data
What Social Media monitoring misses:
- Non-public user generated content
- SKU level data
- Behavioural data
- Purchase data
- Network effect
In big data world it is not about more data — there is too much — it is finding the right data.
Question that market research should be thinking of: how do we map customer journey, how do we segment.
Social commerce engine — all about connecting with consumers
People are passionate about different — and sometimes surprising — things: 3M Dish Wands, Dog food etc.
Case 1
- 3M Dish Wands started started to get negative reviews online, problem was that the wand started to leak. 3M looked exhaustively through the production process and it turned out one supplier changed their formula slightly.
- 3M went back to everyone who posted negative reviews, apologized and sent them a new box
Case 2
- 3M Scissors created technologically superior, but sales did not increase.
- 3M remarketed the scissors in several ways: as scrapbooking scissors, scissors for those with arthritis — sales took off.
BazaarVoice Network Totals for 11/12
- 350 million, 16 billion impressions served
Products covered
- 70 million, 65 million reviewers (cumulative)
Those who review usually created a bond with product. In one instance after posting a review 80% of reviewers who were asked took part in a survey, 20% joined a panel.
Integration and Big Data
- Websites I visited
- Actions taken
- Contacted after buying
- My profile
- My loyalty programs
- Read/write Q&A
- Read/Write Ratings and Reviews
- Read/Write Product Stories
- My in-store purchases
Next Gen MR
- Broaden Reach
- Richer Natural Product Experience
- Extensive X-Market Comparison
- SKU-level analysis
- Detailed Behavioural Patterns
MR Expertise Still Needed Though (ex.)
- Generalization (e.g.,weighting)
- MR assets (e.g., segmentations)
- Consumer modelling