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Getting the edge/ The Magic of Blended Data Will | @willmcinnes
Let’s get this straight.
Simple idea #1 As social moves from a silo to being better through its connection to everything else in an organisation, so does social data.
Simple idea #2 That getting an edge matters. And the best way to get an edge with social data is to blend it.
For every beer and nappies… @willmcinnes
...there are 17 spurious correlations. @willmcinnes @brandwatch
3 main risks with social data 1. Sample/selection bias Assuming people on social are representative of the people you're interested Assuming the people you're interested in are posting on social 2. Inference problems Things like sentiment, gender, location, etc. are inferred with less than 100% accuracy 3. Being creepy @willmcinnes @brandwatch
Pic of mike
Raw ingredients Great quality social data you can manipulate Great quality other data Analyst or data science resource
7 stories to Enlightenment
Be real. These are all real examples. All but one are Brandwatch customers.
Goal: Blend social data with weather data to find insights How: Got social data for customers talking about consuming their ice cream product using Got weather data for the same period Outcome: Found there were meaningful increases in people talking about eating ice cream when the weather was bad. Used that to inform their future advertising strategy @willmcinnes @brandwatch
Goal: Jump in to conversations about test drives to signpost potential buyers to local dealers How: Queries set up to locate social mentions that mention car model names with ‘test drive’, dealers names. Using Rules, Categories and Tags to automatically filter these conversations by Colour, Model, Brand, Dealer etc. Then matching CRM details of known customers with social handles to explore the potential of social CRM at scale (they already have a database of >1m customers on social). Outcome: Increase in car sales from test drives.
Goal: More effective ad spend and return visits to their parks How: Identified people who met demographic criteria in each of their theme park DMA region. Identified topical areas of interest in those demographic segments, by region Fed those topics into tailored regional advertising campaigns Outcome: Uplift in ticket sales + increase in per ticket revenues @willmcinnes @brandwatch
Goal: Change and Adapt Brand Perception How: Matching offline physical event check-in data with the social conversations around each of the physical events Matching social handles to offline identities and then observing and learning Outcome: New evidence and insight into which events drive the most brand favourability change. @willmcinnes @brandwatch
Goal: Understand which brands and items their existing customers were talking about publicly How: Acquired mentions for the key brands that they sell Worked with a third party vendor to match social identities to their own CRM database Outcome: Used information to promote those brands and items via the website and email. ROI ‘made the leaderships’ jaws drop’ @willmcinnes @brandwatch
The point is that it’s not just about social anymore It’s about the business The customers The market Social is just part of it
What about the plateau of productivity?
Come and say hello
now you know