Six step website word check

Have you recently checked your website has the message you want?

It can be the first place prospects see you, so you want make sure it’s sending the right message.

Just reading your website is a good way to start. Check consistency of phrasing between the website and what you say when you talk about your business, as it might’ve changed since you wrote the website copy.

Most people scan a website, jumping across words, so a great way to check the message of your website is to have a list of the words, ordered by frequency. Are the most common words the ones you want people to remember? Are there any typos in the list?

Here is a simple way to check the words on a website page using CloudMaker in Tribal Tool-Kit:

1. Login to Tribal Tool-Kit. You can easily create an account if you don’t have one. You get 3 credits for CloudMaker and this only uses 1, so the check is free.
2. Click on in the red banner along the top.
3. Click on in the gray menu on the left.
4. Fill in the form with:
   • The name of your business
   • Your website address
   • Change the 4 next to “Minimum word length” to 1 (then all words are loaded into Tribal Tool-Kit and you can see if you have too many small joining words making your website difficult to read).
   • Click “Get Page Words”
5. You are told whether the page loaded successfully or not. The first line will tell you how many words have been retrieved from your website. Click “Accept Dataset”.
6. You will see a table with the words and their frequency. The most frequent at the top.

Readers of your site will leave remembering prominent words. Make sure they are the ones you want them to have.

Hyperlinked word clouds

CloudMaker feature explained: How to create a hyperlinked word cloud to place on a webpage, so that when the words are clicked they take you to a specific website page. An explanation of word clouds is on the CloudMaker webpage.

Case study background: Tony Cosentino started a guest book of people attending North Side Coffee Mornings (NSCM or #NCSM) that he has been posting to the North Side Coffee Mornings Posterous site. He wanted to create a word cloud of the people who have come along so the more often they have been to NSCM the larger their name is.

Before he got started we had a bit of a chat, the napkin shows our discussion about it.

Firstly, he set up a spreadsheet with the following format:

• A : Twitter name (eg: @katetribe).
• B : Formula of the sum of columns D, E, F, G etc.
• C : Website address for the twitter name (eg: http://www.twitter.com/katetribe).
• D : Date 1, then column E is date 2, etc. If a person attended an NSCM then they had a 1 put in cell for the dates they attended.

Secondly, the formula column B needs to be copied then paste special with only the values pasted (remember to not save the spreadsheet file as you will then loose the formulas). Then delete the date columns.

Thirdly, save the file with the 3 columns and no column headings as a csv.

Then it is time to play with CloudMaker in Tribal Tool-Kit.

• Click on Upload to CloudMaker.
• The title in this example is: NSCM Guestbook the last 6 weeks…
• The description in this example is: All guestbook data between 29 October 2009 and 3 December 2009.
• Select the file and then click upload.
• As the spreadsheet in this case is already edited there is no need to use the CloudMaker editing features.
• Click on Create word cloud from dataset.
• In the display options section change, Show HTML source to ‘yes’.
• Click ‘re-draw word cloud’ at the bottom of the screen.

Finally, copy the HTML code and paste it into the webpage you would like it to appear.

The outcome: The CloudMaker word cloud below is also on the Posterous site. They look slightly different due to website styling on each site.

Easy isn’t it!  Now other coffee mornings (as well as other events) can start a guest book from the start so that they can progressively create their own word clouds.

Mocks Facebook fan views of the weekend

The Mocks fan page is very active, so I thought it would be interesting to create a word cloud of the comments from: What kind of weekend did you and your Mock have – in one word?

They weren’t all one word, some had a little story, and we still included these. For example:

This is what we did to get the word cloud below:

• Copy and pasted the text into a spreadsheet
• Deleted the profile pic, time and ‘comment’. This left the comments.
• Did a few find & replaces.
  • Took out all the symbols by finding . , ) [ ! etc and replacing with nothing
  • To find spaces and replace with comma and space. This allows CloudMaker to make a series of words into separate words for the word cloud.
• Saved as a CSV file.
• Uploaded the CSV to Tribal Tool-Kit.
• Clicked on the ‘Amalgamate similar terms’ link (this will merge the same words so your words are easier to edit).
• Added a list of words to the stopword list. These were: i; my; to; and; the; a; are; comment; dont; for; im; in; it; of; they; still; is; come; with. This means that these words were still in the list of data, but won’t appear in the word cloud.
• Deleted from the list: don’t
• Merged some words that were similar so that they had a higher frequency and therefore appeared bigger:
  • Supercalifragilisticexpialidocious and supercalifragolisticexpialidociousi
  • BORING and boringi
  • disappointing and disappoint
  • Mockariffic and Mockorific
  • Mocktastic and mocktasstic
• Clicked on ‘Create word cloud from dataset’.
• Changed the font to: Comic Sans MS (Bold Regular).
• Changed ‘Convert case’ to ‘all lower case’
• Made the maximum frequency colour black (#000000)
• Made the minimum frequency colour pink (#CC3399)
• Changed the ‘Save options and formatting’ to ‘new template’
• Clicked on ‘re-draw Word Cloud’
• Gave the template the name ‘Mocks’ and clicked ‘Save’. There is an option to make the settings the default template so future word clouds have this format as soon as you click on the ‘Create word cloud from dataset’.
• Then clicked on ‘Save as image options’. You can save the word cloud as an SVG, PNG, or JPEG image format. JPEG is the lowest quality but opens in the most applications. The word cloud to the right is a JPEG format.

Simple. And interesting. Lipgloss is so big because we kept the 3 times it was said in the one comment shown above. Great to see the number of ways that fans put ‘mock’ into a word and that ’supercalifragilisticexpialidocious’ was used more than once!

The annoying thing about localis(z)ed spelling

Scenario: You have done a survey and you want to get a quick understanding of the words participants used to answer an open response question.

Solution: A perfect way to do this is to make a word cloud – a visual way to understand the frequency of words; where words with a higher frequency are larger, and words with a lower frequency are smaller.

Problem: The English language has two main spelling systems – the British system and the American system. Read more about the differences at Wikipedia.

Implication: The two spelling systems result in a lower overall frequency for essentially the same word, as they are considered 2 words, and therefore a smaller size in a word cloud.

For example, localise and localize are the same word.  If each are used 5 times by participants, the two words would be smaller than if they were combined to have a frequency of 10 using the spelling of your preference.

To show the impact this has on a word cloud, I selected a group of words with different spelling and put them into a spreadsheet. To create a frequency, I used a formula to count the number of characters in the word [In Excel this is LEN(text)].

Word	Frequency	Word	Frequency
aluminium	9	aluminum	8
artefact	8	artifact	8
color	5	colour	6
disc	4	disk	4
flavor	6	flavour	7
honor	5	honour	6
labor	5	labour	6
neighbor	8	neighbour	9
organise	8	organize	8
program	7	programme	9
realise	7	realize	7
recognise	9	recognize	9
rumor	5	rumour	6
speciality	10	specialty	9

Most word cloud software only allows you to paste in a group of words or upload a file of words, before generating the cloud. You can sometimes automatically merge similar words (for example when there is the word, the plural, and end with ‘ing’ they will merge to be one word with the combined frequency). I haven’t found one, other than CloudMaker, that allows you to personally merge similar words, enabling you to handle the problem of British and American English.

Below, the first word cloud is all the words and to the second word cloud is the merged list.

Fewer words makes it easier to understand but also changes the priorities.

All the words

Merged words

Impact: When words with British and American spelling are mixed with words spelt the same in both systems, the first impression views could be inaccurate.

For example, if there was a single spelt word, such as: national, with the frequency of 10 and one of the dual spelt words, such as: localise with the frequency of 7, then also localize with a frequency of 5, merging localise and localize results in a frequency of 12, which is greater than the single spelt word, national, with a frequency of 10.

This could change your thinking about how the question was answered as localise is more frequent than national.

If the question was: What should our regional focus be? Then merging the British and American systems would result in a different first view, than looking at a word cloud without merging – because localise would be greater than national rather than the reverse when not merged.

Social Media Club

I really enjoy #SMCSYD (Social Media Club Sydney) and when I realised that, as I’m in New York, I will miss the next event on building and managing online audiences, I searched for #SMCNYC (Social Media Club New York) to see if I could head there instead.  Last night #SMCNYC had two topics – the FTC Guidelines for bloggers & Google Wave.

Similarities

Yes, you guessed it, they were both about Social Media. The audience was a combination of bloggers, public relations, researchers, communications and consultants.

Differences

• SMCSYD is about 10 times the audience size of SMCNYC. It is rare to say that something in New York isn’t greater in size than in Sydney. Maybe there are just too many options in New York.
• The last two SMCSYDs have been at the Oxford Art Factory with a bar, while SMCNYC was at the offices of PRNewswire, which creates different vibes. With the next SMCSYD at the University of Technology Sydney, University Hall and no alcohol once the event starts maybe those vibes will be similar.
• SMCSYD has become a trending topic on Twitter during the event. Matt Hurst and I were the most prolific twitters during the event. Although I’m a quantitative mind, I haven’t done the math, maybe it is just the difference in audience size, but I think Sydney-siders tweet more at these kinds of events.
• The great thing about SMCNYC is that the smaller group allowed for some great discussion, so that the speakers were more facilitators in a conversation rather than presenters that SMCSYD has.  So depending on your preference – speakers get more air-time in Sydney, there is more debate in New York.
• SMCSYD has a Twitter feed behind the speakers during question time, so that questions are a mix of those coming from the feed and those from the floor.  This means there is a conversation on the floor and a conversation on Twitter – everyone can laugh about a tweet in the feed and the speakers can get a little confused about whether it is something they said. It also means that someone not there can ask a question, or someone at the back can ask questions with the same opportunity to have it answered as someone at the front.
• On the personal side, I walk into SMCSYD knowing people, whereas I walked into SMCNYC without knowing or following anyone there on Twitter. The crowds at both are very friendly and it is easy to meet new people. Sometimes starting cold can be an advantage because you don’t gravitate to those you know.

I generally create a word cloud after SMCSYD using CloudMaker in Tribal Tool-Kit, here is a word cloud created for SMCNYC (as of mid-day the following day).

The benefit of CloudMaker is the editing features. This is what I did:

• The words were imported in the TweetWords section. The Twitter API limits the import to the last 100 tweets. There were 307 terms imported with 772 terms in total. After the editing below there were 289 terms with 695 terms in total.
• Applied a pre-saved stop word list of terms so they don’t appear in the word cloud, but they stay in the data set. The template includes: a; the; and; to; in; on; I; with; My; For; of; Be; Am; As; At; When; It; Your; First; Put; -; All; Are; Is; So; That; An; If; Its; No; &; Any; Do; Go; from; have; here; there; this; what; will; with; about; that; was; want.
• Added to the stop word list new terms relevant to this word cloud as their higher frequencies would make the rest of the cloud very flat: #smcnyc; @smcnyc; smcnyc; @socialmediaclub; #socialmediaclub.
• Deleted the websites imported so that they were no longer in the list and wouldn’t show in the word cloud. This is easy to do by sorting the list alphabetically. This removed 9 terms.
• Also deleted: 11/16
• I merged similar terms and ones with typos:
  • “1994-5″ and “94-5″
  • “blogger”, “bloggers” and “ftc/bloggers”
  • “blog” and “blogs”
  • “giveaway” and “giveaways”
  • “wave” and “waves”
  • “tonight” and “tonght”
• Gravity and Summit were separate terms with a frequency of 12 so I edited “gravity” to be “gravity summit” and then deleted “summit” so that they became one term together.
• Google had a frequency of 33 and Wave 44. These were edited so it was Google Wave 33 and Wave 11.
• Selected a pre-saved template so the word cloud only had terms with a frequency of 3+, there were 4 terms per row, and Tribe Research colours.

The great aspect to chapters of an international organisation is that they have a common goal or theme, but have their own localised flavour. It was great to be able to attend an SMC in both NYC and Sydney.

Visualising rating question feedback

Asking for rating feedback can give you great insights, but only if you look at the data in a few different ways.

Two examples of rating feedback are:

1. Please rate your level of satisfaction on a scale of 1 to 7
2. How likely are you to recommend us on a scale of 0-10?

The average can be similar over time or between aspects that you want feedback. So how can you gain greater insights from the data, if you are not asking other questions at the same time, allowing you to compare the results to other groups, such as demographics?

In the video below we look at the data from 4 time periods when customers were asked, how likely are you to recommend us on a scale of 0-10. There was only 1 batch that had a lower average, making the average result fairly meaningless. However, when the data was put into CloudMaker the results told a different story. Watch the video to see the story…

Read more about CloudMaker, or visit its home on Tribal Tool-Kit.

Register for one of our workshops that will help you understand customer feedback, member feedback, using data in your business, or how to start a tribe, and all show ways to use CloudMaker to grow your organisation.

Launching CloudMaker: the 1st Tribal Tool-Kit tool

After years of development we’re very excited about sharing our software with you.

Tribal Tool-Kit is being filled with tools to help you get to know your tribe. The first tool is CloudMaker.

Visualise the language your tribe uses. Easily.

Uncover language for your marketing and develop business planning priorities.

Idea 1: Words your tribe uses to describe you.

Send an email, or ask in a survey, When you think of us, what are the first 3 words that come to mind?

Tribe Research did this recently. We put the words together and imported them into CloudMaker and developed our cloud.

Idea 2: Words your tribe uses to describe an aspect to your business.

Recently we asked on various social media: When you think of the skills needed in business, what first 3 words come to mind?

We put the words together and imported them into CloudMaker and developed our cloud.

Idea 3: Use existing data about your tribe.

Understand the spatial distribution of your tribe by exporting your contacts and importing into CloudMaker. You might have a hidden group that could use your services that CloudMaker would highlight for you.

CloudMaker allows you to edit your data once you have imported it, allowing you to easily: merge, delete, and edit words. You can export your revised dataset. Your cloud can be saved as an image to be placed in your documents, or HTML code so you can put the cloud on your website. The website option allows you to link the words to relevant pages on your website.

Now you can do the same. Tribal Tool-Kit is at: https://www.tribaltoolkit.com/

To have an account of your own, complete our enquiry form and we will set one up for you. The first 50 accounts we set up will be given 25 CloudMaker credits, valued at almost $100.

Happy exploring!

Visualising the skills needed in business

When you think of the skills needed in business, what are the first 3 words come to mind?

I posted this question to Twitter, Facebook, LinkedIn and Ecademy.

Then I collated the words and uploaded them to our new software. The results are on our Flickr site – have a look for an insight on the skills that came to business owners minds: Cloud of results.

Do you have any to add? Comment below and I will update the cloud soon.