Interpreting social media for retailers final

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© 2012 Venuelabs™ | Interpreting Social Media for Retailers | January 31, 2012 | Marshall Sponder Analytics Expert & Author January 31, 2012

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Transcript of Interpreting social media for retailers final

Page 1: Interpreting social media for retailers   final

© 2012 Venuelabs™ | Interpreting Social Media for Retailers | January 31, 2012 |

Marshall Sponder Analytics Expert & Author

January 31, 2012

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Venuelabs Retail & Storefront Analytics

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Venuelabs Measures Local

97,000+ Storefronts

500+ Brands

9 Countries

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Marshall Sponder Analytics Expert & Author

Blog: http://WebMetricsGuru.com

Booksite: http://www.smabook.com

About our Guest

Writing AllAnalytics.com, InternetEvolution.com, MyCustomer.com

Recent Speaking Engagements • Social Media Monitoring – ROI Metrics Forum • Social Media World Forum • Social CRM 2011

Clients & Companies IBM, Prudential, Citibank, Monster Worldwide, Porter Novelli, WCG, Barclays, Reuters, Digital Backpack, and may others.

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Brand Monitoring Overview

• Broad listening across the internet

• Focused on keyword matches

– Mentions of

• Brand name “Starbucks”

• Product names “Frappuccino”

• Produces valuable insights, but is exploratory in nature, as a result, it can not answer tactical questions and is not scalable.

Blogs

Social Networks

News Sites Trade Sites

Forums Press

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Brand Monitoring Providers

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8 Brand Monitoring Challenges

1. Signal to Noise Ratio

2. Not Scalable, too manual

3. Insightful vs. Actionable

4. Poor Geography / Location

5. Local Data Gap / Blind Spot

6. Lack segmentation capabilities

7. Correspondence between Online

chatter vs. Offline Word of Mouth is

Industry dependent

8. No established, universally

acceptable standards for conducting

social listening

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Signal to Noise Ratio in LBS

• 5% of content were

legitimate generated by

real customers

• 95% was spam, spoofs,

coupons &

miscellaneous

• January 2012

• Study impact of

social on retail

• 11.5 million posts

reviewed

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Not Scalable – Too Manual

A lot of work is required to turn real time exploratory data from Social Media into actionable insights (although LBS is a little easier since it has context).

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Insightful vs. Actionable

• Traditional “exploratory” SMM Insights and

outputs don’t match business goals or outcomes – Share of voice

– Sentiment

– Trends

– Topics and Themes

• And … they don’t match critical needs of retail

– What customer experiences need attention?

– Where are my most critical issues?

– Which of my locations are leaders & laggards?

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Geography & Location Existing SMM providers treat location as

country, region, or city and usually fail to

capture LBS data from Facebook, Foursquare and

other geo-local data.

This gap makes existing SMM providers next to

useless for retailers.

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Your customers are at your storefronts!

Retail is about distinct location

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Local Data Gap for Retailers

32% 68% Blind Spot

24% 76% Blind Spot

37% 63% Blind Spot

38% 62% Blind Spot

18% 82% Blind Spot

Local Content Surfaced Local Content Blind Spot

August 2011

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Local Data Gap Example

They mess up here A LOT! If I wasn’t in a rush

nor a coffee addict I would go somewhere else!

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Platforms lack segmentation capabilities

Most existing SMM (and

some LBS) platforms can

not segment and bucket

the data in a customized,

useful way, so it can

become insightful.

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Correspondence between Online chatter vs.

Offline Word of Mouth is Industry dependent

Read more: http://www.webmetricsguru.com/page/4/#ixzz1ewWAjXpj

While SMM platforms typically have poor, uncontrolled sampling bias, geo-location and LBS platforms such as Venuelabs don’t suffer, as much, from the same issues (poor sampling) because their data is generated at the storefront, via check-ins and direct store references, and is therefore, more relevant and authentic.

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No established, universally acceptable

standards for conducting online social listening

Read more: http://www.webmetricsguru.com/page/4/#ixzz1ewWAjXpj

Almost no data

consistency

or interoperability

between platforms and a

clear sense that under

the hood, all of the them

are different.

As a result, there are no

standards in how to go

about, clean and

structure social data,

which makes it harder to

take seriously.

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Retail Storefronts

Going Local

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Critical Value of Local

• Actual Customer Insights

• Actionable Intelligence

• Location specific

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Actual Customer Insights are much

better than SMM for Retailers

• 80% content

from customers

• 15% local staff

• 5% spam

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Actionable Intelligence gleamed from LBS

instead of exploratory insights from SMM

Topic Sentiment

Influence Engagement

They mess up here A LOT! If I wasn’t in a rush

nor a coffee addict I would go somewhere else!

Traditional Insights

Location Date /Time

Staff Working Managers

Local Context Unit Sales

Nearby Competitors

New Insights

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Advice: Start thinking of LBS Tactically

• What Measures in your stores do you want to

track (that can be integrated into LBS)?

• What Decisions would you make differently if

the one of the retails measures was surprisingly

high or low?

• What is the threshold of the measure? In other

words, at what point, if the value was exceeded

or dipped, would an alternative action take place?

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Marshall Sponder

Q&A Session