Video: Last Week in Local Jan 2, 2018
Mike Blumenthal

Last week in local 1/2/2018 2018

Join Local U for the latest public episode of Last Week in Local, a weekly conversation about the articles that attracted our attention during the past week ending Dec. 4th. This week features Mary Bowling & Mike Blumenthal.

If you have a special topic you would like us to discuss for our other weekly feature, the Deep Dive in Local, or if you would like to be on one or the other of our segments, reach out and send us the topic and your availability. If you are interested in sponsoring this weekly show also please let us know.


What to watch for in 2018: Mobile SEO predictions

Cindy Krum’s Mobile Search predictions for 2018- overview of where mobile’s going, Apple’s finally okay with PWAs, indexing data w/o URLs, schema becomes even more important

3 inconsistencies in Yelp’s review solicitation crackdown

Yelps reasoning for not asking for reviews is flawed and goes against several studies showing email solicitation does not skew ratings

Microsoft Releases’ Healthbot’ Virtual Assistant For Hospitals |

“…designed to enable hospitals and medical centers to easily create interactive virtual assistants and chatbots to serve patients.”

Mind your business: Our top local search columns of 2017

Bookstore Chains, Long in Decline, Are Undergoing a Final Shakeout – The New York Times

“You can never save enough money through centralization to be able to compete with Amazon,” he said. “Instead, you have to go in the other direction — be so rooted in your community you can turn on a dime.”

Tsunami of retail store closings expected to hit US, hurt malls

More than 12,000 stores are expected to close in 2018 – up from roughly 9,000 in 2017. The closings would push hundreds of shopping malls to the brink of death.

How Do You Vote? 50 Million Google Images Give a Clue – The New York Times

The Stanford project gives a glimpse at the potential of image AI by pulling the vehicles’ makes, models and years from the images, and then linking that information with other data sources, the project was able to predict factors like pollution and voting patterns at the neighborhood level.

Google Maps’s Moat

Google is creating “data from data”. AOIS (areas of commercial interest are being auto created from googles other data sources).

Case Study: How a Brooklyn Retailer Brings E-Commerce Shoppers In-Store | Street Fight

As a local store selling gifts and other mid-century modern vintage items in Brooklyn, New York, Woods Grove has a hard time competing with national chains. But the local retailer has managed to piggyback on the success of e-commerce heavyweights like Everlane and Tradesy by accepting customer returns on their behalf.

How Long Should Your Meta Description Be? (2018 Edition) – Moz

Competitor Tracking, Visibility Score & More Local Rank Tracker Features – Whitespark

Google Makes 14 Changes to the Review Guidelines – Sterling Sky Inc

Pay-to-Play Local Search Will Be Increasing Thanks to Google Assistant

A few takeaways from these changes:

  • With a low price point and a lot of promotion, Google is is making a strong push to get Google Home products into everyone’s home. There may not be a lot of people searching for contractors this way currently but it will increase.
  • If you’re in one of the impacted categories and your business is located in a Google Local Services ads market, this is another reason to sign up
  • If you’re in one of the impacted categories but NOT located in a Google Local Services ads market, becoming a Porch guaranteed or HomeAdvisor Screened & Approved contractor is worth serious consideration

Analyzing Review Snippets in the Local Pack | Local SEO | Brian Barwig

  • The average number of Snippets with Reviews in the Local Pack was 1
  • On average, a business was ranking in the 2nd position within the Pack when a review snippet was present
  • There were no businesses in the Local Pack which had snippets, which did not have snippets in the Local Finder
  • There were many businesses in the Local Finder which had snippets, but were not in the Local Pack. The Raleigh example above shows this.
    • Several of the businesses which ranked in the Local Pack had snippets in the Finder, but not in the Pack
    • Look at this example of salons in St. Louis:
  • All of the review snippets came from reviews left on Google
  • Most of the review snippets are from reviews which were at least 6 months old
  • There were no negative reviews in the snippets
    • There were several review snippets being pulled from 3 star reviews
  • Google was highlighting keywords in the snippets. Many of the keywords were directly related to the query, though some were not.

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