See also: An introduction to AIM.
Successful content marketing, communications, customer experiences, and even new business pitches, are based on evidence and insight. I supply a wide range of organisations with the evidence and insight they need to make a difference.
When we really understand how a customer articulates their needs, it’s possible to build real affinity with them through content, and other channels, as we’re not just on their “wavelength”, we’re speaking their language.
However, many organisations, for many reasons, find it difficult to see themselves, and their products, as their customers do. And even with a clear view of the customer, finding actionable insights in the mass of data available can be a daunting prospect.
So, I’ve developed a structured approach to uncovering insights related to customer needs.
I call the system AIM – Audience Intent Modelling.
Uses for AIM
I use AIM when working with leading brands, organisations and agencies to solve a number of business problems, including…
Content marketing & communications that resonate
For brands wanting to align their content marketing and communications with customer needs.
The web can seem like a black hole. Companies keep producing this “content” stuff, yet it mysteriously disappears without trace, without ever resonating with its intended audience.
If you’re a smart organisation you’ll have plans to become a “brand as publisher”, you’ll have probably developed a content marketing strategy. Content strategies can be quite straightforward, and satisfying, to put together.
And yet… yet… something’s not quite right. The content is still not connecting.
In my experience, the first thing to check is whether your content strategy is based on insight.
Customer insight. Market insight. The truths that are obvious to your customers, yet much of your company is oblivious to.
With content marketing (also known as inbound marketing), it’s not enough to just research the platforms where your customers can be found. If you really are going to build a relationship with your customers, you’ll have to find a way to help them. So, you’ll also need to understand:
- Why they might be interested in what you have to say.
- Which means: You have to understand customer needs.
- Which means: You should also explore how the customer articulates their needs.
AIM takes the guesswork out of content marketing and digital communications. It combines various types of research and benchmarking to uncover evidence, and actionable insights, about how your customers articulate their need for your products.
We can then use this information to create content that builds affinity, simplifies customer decision making and more.
Enhanced customer experience
For brands wanting to understand more about, and get closer to, their customers.
AIM combines various types of research and benchmarking to uncover evidence, and actionable insights, about how your customers articulate their understanding of your products. This information can then be woven into business plans – from customer segmentation to customer experience.
For agencies (and other companies) looking to win pitches, RFPS etc.
AIM produces actionable insights about a potential client’s customers. Business leaders know that solid research and analysis builds affinity with potential clients.
Many agencies (marketing, communications, advertising, PR etc) have used my AIM service when they’re responding to Request For Proposals (RFPs), are preparing for initial new business meetings, or need a hand with their deck when pitching for new business.
AIM – elements & ingredients
Depending on the client, the AIM process can include a number of elements.
AIM inputs can include
- Competitive intelligence
- Social listening & analysis
- Audience vocabulary
- Sentiment analysis
- Website auditing
- Editorial judgement, and other internalised algorithms
- Various evolving theories & filters, including behavioural economics
- Trends reporting
- Content auditing
- Analytics reporting
- SEO audits
- Keyword research
- Social media audits
- Website benchmarking
- Natural language processing
- Topic modelling
- Machine learning algorithms