Getting the Edge: The Magic of Blended Data

Getting the edge/ The Magic of Blended Data

Will | @willmcinnes

2

@willmcinnes @brandwatch

@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

@willmcinnes @brandwatch

@willmcinnes @brandwatch

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…

@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

1. Great quality social data you can manipulate

2. Great quality other data3. 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?

@willmcinnes @brandwatch

@willmcinnes @brandwatch

@willmcinnes

So what?

@willmcinnes @brandwatch

Come and say hello

now you know