The Magic of Blended Data - WOMMA Summit 2015

Connecting Social to Dollars/ The Magic of Blended Data

Will | @willmcinnes

@willmcinnes | @brandwatch

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.

Centralized Distributed Coordinated Multiple Hub & Spoke

Holistic


Simple idea #2

That getting an edge matters.And the best way to get an edge with social data is to blend it.

Caution!

For every beer and nappies…


...there are 17 spurious correlations.


3 main risks with social data1. 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 problemsThings like sentiment, gender, location, etc. are inferred with less than 100% accuracy

3. Being creepy@willmcinnes | @brandwatch

Raw ingredients

1. Great quality social data you can manipulate

2. Great quality other data3. Analyst or data science resource

6 stories to Enlightenme

nt

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 usingGot 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


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 regionFed those topics into tailored regional advertising campaigns

Outcome: Uplift in ticket sales + increase in per ticket revenues


Goal:Change and Adapt Brand Perception

How: Matching offline physical event check-in data with the social conversations around each of the physical eventsMatching social handles to offline identities and then observing and learning

Outcome: New evidence and insight into which events drive the most brand favourability change


Goal:Sell beer in the coolest bars to remain current by giving sales team precise lists of targets

How: Identified bars in each of their target cities across USA with cool, up and coming reputations, using social dataCross-referenced that list with Liqor Licensing Bureau data and own CRM to give sales teams

Outcome: Directly measurable sales uplift


Goal:Understand which brands and items their existing

customers were talking about publicly

How: Acquired mentions for the key brands that they sellWorked 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

?

China rates its own citizens – including online behaviour

So what?

now you know

The Magic of Blended Data - WOMMA Summit 2015

Social Media

Transcript of The Magic of Blended Data - WOMMA Summit 2015