1130 omma metrics joel rubinson
Transcript of 1130 omma metrics joel rubinson
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What’s a researcher like me doing in a place like
this?Joel Rubinson
February 21, [email protected]
Blog.joelrubinson.netTwitter: @joelrubinson
The ideas expressed are the property ofRubinson Partners, Inc.
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Donna is a researcher (now called insights)
Donna is Vice President, Consumer & Market Insights, Unilever, Americas
When I asked what she does for a living, she said…”I sell soap. Research is just what I do”.
What does Donna Goldfarb do for a living?
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The marketing research/insights function answers questions such as…
Research is about informing brand strategy and marketing action in a grounded way…evidence, analytics, insights◦ How do I grow brand sales effectively and
efficiently? ◦ How do I create brand loyalty?◦ How do people use my brand?◦ How do people shop?◦ How do target consumers consume and respond
to media?
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A digital river of information
•fed by numerous tributaries•Reflecting changing societal values, lifestyle trends, and brand engagement•Mostly naturally occurring, on-going 24/7•Some tributaries are “man-made” and proprietary like a brand tracker
•Start research with river rather than projects
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http://www.youtube.com/watch?v=BYfbjZmk2VQ
Time for digital and research to work together
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The value of forensic analysis◦ Massive databases of actual behavior,
native to the process, have been proven to have great insights value…DunnHumby frequent shopper data.
◦ Digital is native to the shopping process for offline purchases (as well as TV is native to online). Must be studied Impact must be measured Rich in forensic insights
Instead of an attribute rating on “dinner ideas”, ask..when people search for “dinner ideas” where do they wind up?
So, what’s a researcher like me doing in a place like this?
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Brand tracking needs to position brands to succeed in the digital future
So, what’s a researcher like me doing in a place like this?
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While valuable for brand imagery and traditional media, brand survey tracking is too slow, sample sizes are insufficient, and recall is inaccurate for owned and earned media.◦ The promise of digital is real time, and you must sense the
naturally occurring digital signals to respond instantly.
Basic premise: if you are tracking a brand with surveys alone, you are not positioned to succeed in social media and more broadly, in digital
With brand tracker data, all measures are strongly correlated to market share. Social media and digital data look nothing like that.
Ex: although Starbucks is only twice as large as Dunkin Donuts, it has more than 10X the buzz
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Marketing is losing a handle on how much brand communication is out there◦ Paid◦ Owned◦ Earned
Hard to measure◦ Marketing mix modeling usually done at a market level◦ Owned media is way undervalued◦ Earned media impressions is way larger than social
media comments (but much less than Facebook would lead you to believe!)
◦ And what about search impressions?
Media measurement
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We need to learn from each other◦ Advanced analytic tools used by research can
benefit digital analytics Loyalty modeling Game theory statistics Conjoint analytics
◦ Understanding digital possibilities will benefit research analytics
So, what’s a researcher like me doing in a place like this?
Loyalty modeling has lawlike patterns
0%
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30%
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70%% category buyers
Share of requirements25% 50% 75%
Share of Requirements distribution for a 10% share brand; avg. category purchase cycle of 12x/year
Loyalty modeling has lawlike patterns
0%
10%
20%
30%
40%
50%
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70%% category buyers
Share of requirements25% 50% 75%
measure fringe Only OK preferred
“engaged”
% Buyers 12 5 4 5
Your brand share of group
2 34 60 90
% sales 19 17 21 43
Share of Requirements distribution for a 10% share brand; avg. category purchase cycle of 12x/year
1. Which loyalty segments grow and which decline as the brand grows?
2. Where do Facebook fans come from?
3. How can digital analytics help to develop strategies for less loyal groups?
Game theory statistics
• Applied in marketing research analytics to TURF analysis problems and regression modeling to determine relative value of different predictor variables in the face of multicollinearity
• A possible tool for attribution analysis?
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Experimental design used to understand the relative impact of an attribute on choice◦ Example:
Brand X, Y, Z Protein, no protein Different price levels (low, medium, high) Packaging (resealable, not resealable)
Couldn’t this work for display advertising?◦ Image, no image◦ Rich, not rich◦ Ad unit format◦ Media placement approach
Conjoint designs
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Judah and I see great value in an open source community for all that we call “arrow” (Analytics Research Organization), http://www.thearo.org/◦ ARO ("arrow") brings together professionals
globally and locally from marketing research, digital analytics, modelers, predictive analytics, business analytics and other experts who are interested in consumer and shopper data, big data, digital analytics, traditional media analytics, media mix modeling and other forms of research and analysis.
Creating a community to facilitate this idea exchange