Open Analytics Talk

7/28/2019 Open Analytics Talk

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Developments and Challenges Social Media Measurement


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Agenda

Who is this guy?

Dj vu all over again

A game of Chutes and Ladders

Light at the end of the tunnel?


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Who is this guy?

20 years research/analytics experience

Focus on media: Turner Networks, MySpace,

Yahoo, media/ad agencies

Quantitatively focused:

MMMs

Segmentation Analysis Campaign Attribution

Behavioral Targeting

Fan/Follower Valuation


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Who is this guy?

The Public Relations

discipline took hold of socialmarketing

PorterNovellis client base

is global, which leads to

some interesting social

media analytics

opportunities


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Dirty data in the social space

Inappropriate methodologies

Vendors that do not care about data

quality

No industry standards


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Data is spam laden

All tweets are not created equal

Interactions across social channels

mean something different

Does an emoji connote sentiment?

Does it generate influence? Howmuch influence does it generate?

What is influence worth? What is

reputation worth?


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Because of the sheer volume of data,

trying to make sense of this has led somefirms down very strange roads

A common approach is to sample the

social conversation, and infer quantitative

conclusions

This is in defiance of the Central LimitTheorem


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On my arrival into the public relations

industry, I took as many vendor meetingsas I could. My findings:

All data vendors have the best

sentiment scoring engine though the

criteria for this claim is unknown

Vendor-side spam filtering is ineffective

The interest across vendors is creating

prettier charts with vibrant colors, rather

than data quality

magic beans


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There are several groups trying to develop

some industry standards around social mediameasurement, but as of now, there are no

accepted standards

The best we have at the moment are the

Barcelona Principles

Will social media ever get to the same level ofstandards as the IAB/WAA on online media

measurement?


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Chutes and Ladders

Every thing is measurable

The reason that standards were

developed on the web analytics side was

due to the investment

Public relations wants more marketing

dollars

Standards are coming out, but are they

strong enough?

Where:

E = excused from flyI = insanity

R = requests an evalu


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Chutes and Ladders

Is the objective of the social analytics qualitative insights mining, me

both?

If sampling leads to inappropriate or insufficient conclusions what ar

measurement options?

In the web analytics world, we take spam filtration for granted; in soc

everything.

Every social analytics program is going to have error some known

unknown.


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There are platforms that allow a full

analysis of text some are robust

and offer easy ways to integrate

text and other data into one

reporting platform

The solution that we have

developed is using an open source

text analytics platform, so weeffectively built our own solution


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People talk about brands, products

and services using a specific

ontology

Sick connotes good for some

categories, bad for others

Most vendors who provide

sentiment scoring across the entireuniverse of conversation are not

able to account for these

differences


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Process:

Pull in data from multiple sources

Build dictionary and grammar rules

Categorize text by conversation

category and sentiment based on rules

(human and machine learningalgorithms)

Human scoring and validation

Dump results to UI


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Best Practices

Any vendor who talks about best sentiment engine based on wha

Know your data

Get as close to the source as you can

Solutions custom to your needs are always better than out-of-the-box

Beware of pretty Uis

Good governance of data and analytics


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Questions?

Documents

Open Analytics Talk