Demographic Trends in Hashtag Tracking

This Infographic by Carlos Monteiro and featured in Adweek shows how the different social networks are growing in relation to their demographics – which is good to know if you are doing any hashtag tracking or other research in a particular market. 

You’ll notice there are a lot more seniors being attracted to Facebook, while Instagram continues to be dominated by people under the age of 34. All networks are predicted to grow around 10% in 2015, except for Facebook, which will have more static subscriber growth.

Social networks are becoming search engines?
88% of consumers who are online are influenced by reviews. They look at YouTube videos, what their friends say in Facebook, and what reviewers are saying in Yelp. This may be a good time to start monitoring what’s happening in your respective markets.

social-media-users-02-2015

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5 Ways to Lower Your Social Listening Costs

One of the reasons why marketers aren’t doing social listening is because of its high perceived costs. Many marketers think that social listening is expensive, as many of the social listening tools out there begin with a pricing plan of hundreds of dollars per month. Why is social listening so expensive (and conversely, why is Emplify so reasonable)?

The answer lies in data access fees. Today we’re going to take a look at 5 ways that you can do Social Listening without breaking the bank by avoiding data access fees.

  1. Use unique hashtags for your campaigns

The costliest component of Social Listening is usually data access fees. Surprisingly, many marketers do not pick a unique hashtag for their campaigns! By not using a unique hashtag, you’re infinitely increasing both the amount of data and the complexity in filtering for post mortem analysis, and hence increasing your costs.

  1. Know your tracking volume

Related to point 1, another mistake marketers make is to go blindly with high-data-volume solutions without realizing that they do not actually need it. Many non fire-hose solutions provide a sufficient amount of data for most campaigns. For example, Emplify supports up to 3,000 messages per hour per track – that’s 500,000 messages per week! If you don’t need to capture more than 500,000 messages in a week, chances are you don’t need to pay for higher-end solutions (given that you know Emplify!).

Emplify track preview provides you an estimated volume to help you save money on Social Listening

Emplify shows you an estimated tracking volume during track creation
  1. Find your best mix of tools

As with many other toolsets, there’s no one-size-fits-all solution for Social Listening either. Emplify is great in providing a high-level overview of your accounts and campaigns weekly and giving you actionable insights to start your week. There are many other tools that help you in other specific areas such as follower growth, automated replies, and trend predictions. Finding the right mix of low cost tools that work for you will save you both time and money.

  1. Setup your monitoring ahead of time

Many marketers have learned this the hard way – social networks make money by making historical data access paid only. So if you want to analyze some tweets from an event a month after it has ended, chances are you will have to pony up quite a bit for them. By leveraging tools like Emplify (which allows you to record and archive data for a considerable amount of time) and setting up your monitoring ahead of time, you can get the same analytics as opposed to paying for the data a month later, less the costs.

  1. Set a sampling rate

Last but not least, not requiring 100% coverage on data is another way to save money on Social Listening. This is applicable when you’re doing trend analysis or general research as random sampling of data should not affect the results. This is one trick that is most often overlooked as many post mortem analyses are trend-based and do not actually need 100% coverage of data. Most high-end Social Listening tools will allow you to define your sampling rate while Emplify automatically applies a sampling when your data volume is high.

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