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Product Launch Analysis
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Introduc)ons
– Semphonic provides full-‐service web analy<cs consul<ng and advanced online measurement to leading Enterprises in the United States. Semphonic’s prac<ce is heavily focused on advanced digital customer analy<cs in the warehouse and with Web Analy<cs solu<ons. Clients include American Express, Charles Schwab, Genentech, JP Morgan Chase, Kohler, MarrioJ, MicrosoK, Nokia, Samsung, Turner and Walmart. Gary blogs at hJp://semphonic.blogs.com/semangel
Gary Angel Co-‐Founder and President of Semphonic, the leading independent web analy<cs consultancy in the United States.
The Problem
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Product Launch
What we were dealing with:
A new sub-‐category of electronics
Significant Mass Media and Social Media Efforts
A soK-‐launch trial with a small set of “fans”
Our goal:
To quickly (with two weeks) measure the top factors driving true consumer aZtudes about the product
The Upfront
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Our Pre-‐Launch Prepara)on
Because of the short turn-‐around on the project we tried to do significant pre-‐prepara<on for the launch analysis. This included:
Descrip<ves on exis<ng social media chaJer in the larger category
Analysis of the soK launch conversa<ons
Crea<on of an ini<al classifica<on scheme for the product
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Basic Descrip)ves
Pre-‐launch culling of feature sets gave us a sense of the broader market and helped us tune our classifica<on system.
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Conversa)on Topic by Manufacturer
Top Laptop Feature Men)ons
Any Top Feature
BaJery
Dimensions
Hard drive
Processor
Screen quality
Screen size
Touch pad
Weight
Wi-‐fi
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SoG Launch Analysis
The short soK-‐launch was for enthusiasts only – so it wasn’t a useful sample – but it did help us see how conversa<ons might trend.
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Ini)al Classifica)on
We started with Clarabridge’s pre-‐built Compu<ng classificaitons:
Where appropriate, pre-‐built classifica<ons exist, they greatly simplify the ini<al startup.
The Process
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Profile Crea)on
NMIncite Buzzmetrics • We used previously created and tuned profiles to create the initial data pulls from the API. • Why this step is critical to the process:
• If you miss relevant posts it will likely be systematic – ruining subsequent analysis
• We use Clarabridge on a per-verbatim basis so there is a real cost to loading useless verbatims
• For one-time analysis, heavy use of exclusions in profile rules is acceptable. For ongoing analysis this can create maintenance problems.
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External Cleaning
Data Cleansing • We scrubbed product sellers and “owned” and campaign media • Eliminated extraneous text (“Source:” labels for example) • Eliminated URLs • De-Duplicated Full Text Matches after these 3 steps
• Clarabridge will de-duplicate but you get charged for loaded verbatims
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Classifica)on Tuning
Defini)ons
• Each area is defined by a set of keywords and then further classified.
• Here, for example, Form Factor is divided into Style and Quality Words.
• Each sub-‐division then has a Brand classifica<on so that we can compare how oKen (and how favorably) our key Brands were men<oned within the group.
• This is important because posi<ve sen<ment might be aJached to a comparison (i.e. X has great style. Y is rather boring).
The Pres)ge
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Establishing Baselines
At the top-‐level, it is important to know how oKen different features are men<oned. Especially for a new “category” or “sub-‐category” there can be real learnings even at this basic level.
Reliability Gameplay
Product -‐ Boo<ng Applica<on Availability
Netbook Men<on Speed
Tablet Men<on Cloud
Pricing/Value Laptop Men<on Connec<vity1
Product -‐ Hardware Use and Care
Product -‐ Form Factor and Style Product -‐ Opera<ng System
Brand
0 500 1,000 1,500 2,000 2,500 3,000 3,500
Category Men)on Counts
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Segmenta)on of the Audiences
Wordcloud for Laptop Subset Wordcloud for Tablet Subset
Academic Focus Battery Focus
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Presen)ng the Whole Picture
GeZng a reasonable sen<ment analysis took LOTS of cleaning:
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Trending
One significant advantage to this approach is that it allowed us to trend the Product Launch over subsequent weeks.
The Post Mortem
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Goods and Bads
The good: Clarabridge’s sample classifica<ons made the ini<al setup much easier
The itera<ve classifica<on process made it fairly easy to tune the analysis
The pre-‐analysis helped A LOT to get the final analysis done on <me The analysis was impacqul in tuning the post-‐launch social campaigns and cost-‐effec<ve enough to do repeatedly.
The Bad: Extrac<on of the data was frustra<ng – the API is throJled so we had to make repeated calls
GeZng the sen<ment classifica<ons clean took A LOT of work – much more itera<on than we’d hoped
Tuning sen<ment words was manual, arduous and subjec<ve
Discussion
