9 Facebook Post Engagement Killers

Have you wondered why you are getting a very low engagement on your Facebook posts?  Here are nine common reasons that result in low Facebook post engagement and tips on how to fix them.

1. You are posting on wrong day and time – You should time your posts according to your audience’s (fans/targets) most active time on social media. If you are posting your messages when majority of your audience is not active then you are not achieving the maximum benefit from your posts. Below is an infograph that is based on analysis conducted by Bridge.com,  which provides general information about the best time for Facebook posts.
   However, rather than blindly accepting these suggestions, you should use your own data to figure out the best day and time to posts. There are several tools that will allow you to see when your audience is most active. For example, simply Measured provides you stats such as “Top Day For Comments” and “Top Time For Comments”.
   
2. You are posting too many or too few posts – As shown by the Track Social (image below), the more you post in a day the lower is the engagement. According to SocialBakers.com, if you post fewer then 2 posts a week, you will not engage your audience enough and you will lose engagement. If you post more then 2 per day (as a brand) you also will typically lose engagement. That means the ideal number is between 5 – 10 posts per week as a brand, and as a media company, this is typically 4 – 10× higher, as news are information people engage with all day long. However, you should not just rely on industry data, instead try to look at your own data to figure out the best frequency for your posts.
   
3. You are not posting engaging messages – Before preparing your post, think about who are you trying to reach and why will they care about what you are posting. Give them a reason to engage with your posts. If you a grocery store and promote a can of beans at regular price, then do you think you will get any engagement? Give them something to engage about. Give them more than just an image of the product. May be show some urgency on why they should click on the image, watch the video, click on the link, share or comment. Think about other ways to prompt then to take actions – Is there a limited time offer? Is this a limited edition product? Is this fresh crop? Here is an example of two post by PCC Market in Redmond:
   

This post had just 22 likes even tough the page has 45,800 fans. Why is that? Likely because there is nothing for fans to get engaged with?




This post on the other hand a lot higher engagement (though it can still be improved). There is a sense of urgency here “Seasons First Catch of fresh halibut” and there is link to halibut recipes (a call to action, see below).  Also, it is possible that PCC has more halibut lovers than mushroom lovers. The key is to understand your audience and give them something relevant and engaging.

4. There is no call to action or links – Call to actions prompts audience to engage. Ask and you shall receive. Ask them to click on a link, like a photo, participate in the giveaway are some of the examples to get you fans/followers to take action. The post of mushrooms above has no call to action. What is PCC expecting from the fans? Just posting a picture is not going to work, provide a call to action.
5. You don’t have the right fans/followers – This generally happens when you pay to acquire fans. People who do not like your page organically are less like to be engaged with your posts. Also, paid Fans not always genuine, according to Huffington Post, you might be getting fake users to like your pages.
6. You are relying on organic reach – Organic reach has been declining in Facebook. According to Oglvy report, the organic reach was about 6% for pages less than 500K fans and 2% for pages with over 500K fans. This reach is a lot lower today. Lower reach results in lower engagement. This means that if you want your fans to see your post then you will have to pay Facebook to put your post in front of them.
7. You are targeting the wrong people – As you decide to promote your post by using paid ads on Facebook, you might be trying to maximize reach (within you budget) and in doing so might be reaching people who are not likely interested in your brand/product/post. In this case the impressions of your paid post are increasing but you are not getting any clicks/like/comments/share.
8. Your content looks too much like an “Ad” – You are pushing ads for your brand and products and your posts look too much like advertisements. People are ad blind, according to Techcrunch article,  6 in 10 people totally ignore the ads. Do not just push ads, follow tip on this page and make people engage with your post.
9. You are not using video – Photos used to be the king of engagement on Facebook, now pretty much everybody is using them. According to analysis done by Quintly, videos generate more engagement followed by photos.
   

Comments? Questions?

Other Social Media posts that you might have missed:

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• Sentiment Indicator: Social Media KPI
• Social Media Sentiment: Don’t Get Caught Up In Raw Counts
• Value of Social Shares
• 3 Tools for Measuring the Virality of Your Content
• Free Social Media Monitoring Tools for Small Businesses
• Social Media Analytics Part I
• Do Number of Likes and Followers Matter?

Three Tips for Choosing the Colors for Data Visualization

Colors can add to your data visualization and make the story stand out or they can distract the audience from the main message. It all depends on what colors you chose and how you use them. In this post, I am listing three things to keep in mind when creating your next data visualization. I am not guiding you specifically on what colors (hue, value and chroma) to use but three things to keep in mind when selecting the colors:

1. Use conventional colors:  Generally red color means negative or a bad value and green means positive or a good value.  Choosing the default colors as presented by your visualization tool might not convey this meaning in all the cases.  Say, for example in Tableau, when you choose the default “Red-Green Diverging” color from the pallet, the red represents the smallest number while green represents the largest number, and it changes from red to green for the values in between. This generally works fine.  Where this becomes an issue is when smaller numbers are actually better than the larger numbers. For example, when you are showing Cost Per Click, lower is better so it should be Green while higher numbers are bad, hence they should be Red. Leaving the default will show low CPC as red, while higher CPC will be green, this will convey the wrong message to someone who is just glancing at the visual. To fix this issue in this case, all you have to do is just check the box, titled “Reversed” to reverse the colors, now Red will be used for higher value.
2. Don’t use too many colors: Using too many colors can distract the consumer of the visualization from the main message. Minimize the number of colors you use in your visualization. (See guidelines for selecting colors section in “Expert Color Choices for Presenting Data” paper by Maureen Stone)
3. Make main message/data to stand out:  Make sure the color combinations you chose enables you to make the main message to stand out. For example, white on black will stand out while grey on black might be lost.

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5 Data Quality Issues To Watch Out For

We all know that wrong data leads to wrong analysis and hence can cost a lot of money.   In this post I am highlighting 5 issues that lead to wrong data and hence wrong analysis.  Make sure you take care of these issues before spending any time on analyzing and putting your presentation together.

1. Manual entry by end users – When you rely on the end users/customer to enter the data in the free form you will get lot of variations of the data. For example, when you ask them to enter the city, you can get variations such as Redmond, Redmond WA.  Redmond (Seattle), Remond etc.  Doing any analysis on the city level will pose an issues since you have several variation of the same city. Unless you have thoroughly cleaned and accounted for every possible variation you will not have the right analysis. In order to avoid such issues, wherever possible provide the choices via drop down or auto-fill rather than letting users type in the answers.
2. Manual entry by someone in the organization – Though this is more controlled than letting end users enter the data, you still have issues similar to number 1 above.
3. Excel sheets (calculations) – This becomes an issue when you have lot of calculated columns and some of them are dependent on the other calculated columns and sheets.  One simple mistake can cascade to multiple columns and can mess up all your analysis.  Whenever possible, do not rely on calculated columns (prepared by someone else) in excel sheet, just use the raw data and do your own calculations so that you can stand behind them.
4. Data Imports, connections and processing – In most companies the data resides in various places – databases, excel sheets, flat files Hadoop, 3rd parties etc. For you to get a 360 views you will need to collect and combine the data for all the sources. The data corruption can occur at several places including but not limited to mapping wrong keys, missing some key data, writing wrong queries, importing partial data etc.  You should always verify the data that you are getting and make sure that it is clean and complete. Since there are several owners of the data this is not always an easy task, particularly in the large organizations.  There is not much that you can do on daily basis to verify the quality of the data, but make sure you understand the underlying data sources and have good understanding of the process that is used to combine the data.  Make sure that you in loop on any changes that are being made to the data sources and the process. You can not afford for your raw material (data) to be of a low quality.
5. Visualization tools – You expect these tools to work, don’t you? However be careful and double check your calculations before you present your data.  I recently had two issues that made me believe that there are many of you who will run into these issues and might present the wrong analysis.
   • When you use averages makes sure that you are using the right columns.  Average of an average is not the same as average by summing the values in individual rows.  Recently I ran into a situation where CPM (Cost Per Thousand Impressions) was already calculated in the excel sheet (see Issue no. 3 above).  When that data was brought over in Tableau, the analyst, in an effort to find average CPM, used the calculated column to compute average CPM.  Everything looked good on the surface but it was wrong since it calculated average of an average.
   • When using a map make sure you use the correct values of Longitude and Latitude. I saw an example where the map showed up perfectly, however a quick quality check showed that the average value for a state was more than the individual values of all cities in that state, which is not possible.  On further investigation we found that the issue was with the way Longitude and Latitude were used to render that map. Once the issue was fixed, everything worked fine.

I would love to hear from you if you have encountered these or any other sources of data quality issues.