How IBM uses audience intent modelling

I spoke at, and attended, the Content Strategy Applied 2017 conference last week. One of the keynote speakers, James Mathewson, provided a fascinating description of how IBM uses audience intent modelling to map its content plans. By doing this IBM is able to align its content with the buying cycles for their target personas.

This planning involves the management of 300 million pages and 100,000 marketing assets, and they use a dizzying array of artificial intelligence and software to improve their search engine rankings. However, their strategy is actually very simple.

There are three forms of audience intent

These are informational (learn about a topic), navigational (find information about a topic), and transactional (find a place to buy the solution or get help).

Audience intent

There are two kinds of audience

These are business people and specialists.

There are two kinds of queries

These are branded and unbranded. Most searches are unbranded questions, and people only move to branded questions when they are ready.

There are five stages in the IBM customer journey

Here are the steps and the type of content IBM  provides:

  1. Discover –  What is big data? web page
  2. Learn –  Video on big data (“Four ways big data and analytics transform marketing”)
  3. Solve – A product information page (“14 top big data analytics platforms”)
  4. Try – The offer (Watson Analytics 30 day free trial)
  5. Buy – A whitepaper (“Understanding Watson Analytics”)

IBM has invested heavily in technology

This is used to maintain consistency in the tagging of content, and in the tone and voice. It’s also used to learn what audience want, and are searching for on the web. A lot of searches are in the form of questions, so they mine those questions to discover what they are asking.

audience type

IBM avoids online marketing tricks

James said “clever messages to push people and trick them” rarely work online, and if they do the reader is unlikely to come back. Instead they focus on what the audience wants, and aim to meet that need.

It was the best presentation at the conference, and it provided lots of ideas for Cherryleaf’s website.

Here are my slides from Content Strategy Applied 17

Crossing the Divide between Technical Communication and Content Strategy at Datomia

Technical communicators have many of the skills that overlap with content strategists, but they can find it hard to get involved in this type of strategic work. In this presentation, we’ll explore some of the content projects Cherryleaf been asked to do for Datomia, a software start-up, and whether this case study can provide some answers to how people can break out of the “techcomm box”.

Technical writing builds trust in your organisation

I spoke at, and attended, the Content Strategy Applied 2017 conference last week. One of the keynote speakers, Madi Weland Solomon, explored what the impact of content has on users, and the trends that will inform content strategy in the near future.

She said one of the key challenges for organisations will be dealing with the loss of trust in information. She quoted a survey that stated over 50% of Americans have no trust in mainstream news. Her suggestions to fix this was to become more active at representing the public (and end users). Organisations should use more human-centric data and focus on helping users. Referring back to Dale Carnegie, Madi said being useful, and being seen to be an advocate for users, was vital. She suggested the law of reciprocity would play a part in users returning the favour of being helped by the company.

Help and other forms of user assistance meets this type of need. It is already seen at some of the most trustworthy content on the web, and it is useful. The should not be hidden about behind a firewall, but helping to build and sustain the trust between the organisation and the their users.

She also looked at which type of content is read the most: blog articles that have roughly a seven minute reading time. This fact is more problematic for technical communicators, as the trend is to write short chunks of information. Perhaps there is a need to rethink this style, in some situations.

Can a Technical Author be a master of more than one trade?

Technical Authors are normally seen as masters of writing user documentation, but their skills are not often applied to other areas of the business. For example, it’s usually the case our clients for software documentation are different from our procedures writing clients.

However, we’re currently working for a client where we began by editing a white paper, and this has led us on to other projects across departments. Work has included developing customer journey maps, a terminology database, as well as the online Help. The role is morphing into that of a content editor role: checking for consistency, spotting errors in marketing copy, rewriting copy, and so on.

So what is different? What has led to this wider scope? It may be due to us being recommended to them by word of mouth, and they had greater confidence in our abilities. It may be because they are a start up. It could be because many of the staff are not native English speakers.

We suspect it’s because the first project was the white paper. They had something that was very useful to them, for promoting the company. They also included us in their in-house chat system, which meant we could see other areas where they had issues with content. This led to us intervening more than usual, making suggestions in a proactive way. The growth of chat systems, such as Slack and Socialcast, within companies could open up other opportunities for other Technical Authors, as long as they take the initiative.

Towards content lakes

One of the trends in both data and content management is the move away from silos. In data management circles, there is a trend towards the collection and aggregation of customer data into “data lakes”. According to Margaret Rouse, a data lake is:

A storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data, and that smaller set of data can then be analyzed to help answer the question…Like big data, the term data lake is sometimes disparaged as being simply a marketing label for a product that supports Hadoop. Increasingly, however, the term is being accepted as a way to describe any large data pool in which the schema and data requirements are not defined until the data is queried.

(source: what is a data lake?)

“Content lake” isn’t a word that’s used in the content management or technical communication sectors yet, and whilst it seems unlikely end user content will grow at the same rate as other forms of data, there’s a fair chance this phrase could catch on.

A content lake is likely to have similar attributes to a data lake:

  • Content will be stored in a native format that is then changed into other formats.
  • It will use a flat architecture to store data.
  • Content will be stored in some type of structured format. Perhaps XML, JSON or plain text (with AsciiDoc-like attributes assigned to certain sections). However, user documentation does not require the rigorous structure of other forms of content.
  • The content lake can be queried for relevant content, and that a smaller set of information can then be extracted to help answer questions. This might not mean publishing content on-the-fly, but generating PDFs, CHM files and web-based content from a single source.
  • Rather than content being simply archived, it will deliver the right information in very short timeframes.

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Cutting and pasting content into Word documents – Is there a better way?

Earlier this week, we were helping a large company finalise a bid document where they were required to use a Word file sent by their client. This involved taking content from the company’s repository of standard documents on SharePoint, and from emails, plus writing down information provided verbally by the Subject Matter Experts. The bid writing team had to cut the relevant content from a Word document (and emails, Excel spreadsheets, Visio files, Microsoft Project files and PowerPoint presentations), and then paste it into the bid document.

Before we started to work on the document, this had resulted in it containing a large amount of different formatting styles. For example, the content pasted from emails was in Calibri 10pt. font, and the content posted from Word was in Arial 11pt. This meant the bid writing team had to spend a lot of time remedying the formatting.

This method also meant there was no reliable way to embed content, like there is, for example, in Excel – if you change a cell in Excel, related cells in other places can update themselves automatically to reflect that change. For the bid document, any changes to the source content could trigger a further round of copying and pasting into our master document.

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