Jason Falls, the author of No Bullshit Social Media, Stacy Debroff, Stephen Murphy see 2012 as the year of relevancy marketing. Jason Falls predicts that adding customer relationship management and tying social CRM functionality to marketing efforts will improve “our ability as marketers to hit relevant audiences with relevant messages at relevant times and in relevant places.
Facebook’s frictionless sharing has kicked off an interesting discussion about using graphs to filter signal from the ever-increasing noise. There are three types of graphs we learned to use for filtering:
- The Social Graph
- The Social Interest Graph
- The Interest Graph
The Social Graph
In its minimal form the Social Graph only contains people (nodes) and the relationships between them (edges). Usually the relationship between nodes is symmetric: both have to agree on being linked to each other. The by far largest social graph existing today is Facebook’s Open Graph, which also includes virtual objects people shared (photos, videos, links, wall entries …) and the relationship with these objects (posted, commented, liked, disliked, read …).
When using the Social Graph as a filter a node will be presented with content other nodes have entered into the graph. Quantitative measures can be used such as how close a node entering the content is and how many other nodes related to the reader also shared the content.
The Social Interest Graph
While relationships in the Social Graph are symmetric and emerge out of a bi-directional conversation the Social Interest Graph is asymmetric and initiated by one person starting to follow another one. Usually this asymmetric relationship is based on common interests whereby the follower expects to learn relevant content from the person she follows.
Using the Social Interest Graph as a filter delivers more relevant results than the Social Graph as the scope of content is more focused on the follower’s interests (provided the followed person maintains this scope).
The Interest Graph
We define the Interest Graph as the set of relationships one person has to a number of terms she is interested in. These terms can relate to real-world items (car) as well as virtual items (quality) and it’s meaning entirely depends on the person’s individual perception (a person interested in SUV will perceive quality different than a person interested in sports cars). As such the Interest Graph resides within the person itself and is highly individual.
When using the Interest Graph as a filter only content relevant to the individual reader’s interest at that point in time is delivered - independently of the popularity of the content within the Social Graph.
How to Measure Interest
To measure interest we need to give it a price tag. The most precious currency today is attention and therefor the attention a person spends on a term can be used as the measure of relevance. So to identify the terms of interest we need to analyze the content the person has paid attention to.
There are three ways content is analyzed for personalization purposes:
- Human Tagging and Categorizing: The writer, an editor or a curator categorizes content and assigns tags based on a set of rules.
- Algorithmic Tagging: An algorithm analyzes the content and generates a list of tags. Usually this is based on statistical methods.
- Semantic Analysis: An algorithm analyzes the content, detects relationships between terms and uses an ontology to generate a semantic profile of the content.
By nature the first way is labor intensive and inevitably influenced by the categorizer’s perception. This becomes especially problematic when a piece of content can be associated to more than one category. A reader subscribing to one category will miss relevant content put into another one by the categorizer.
The second alternative delivers a deterministic result based on its algorithm. However, missing out on the semantics of the text only terms will be associated explicitly contained in the content. A text on iPad will not be tagged Steve Jobs unless he is explicitly mentioned in there.
The third way will deliver a semantic profile of the content based on an ontology. Searching for iPad will also deliver content on Steve Jobs as the ontology links him to iPad directly and indirectly via Apple.
Human tagging and categorizing alone will not sufficiently reduce noise as there are only so many topics an editor can manage consistently. The second alternative delivers sufficient filtering, but will inevitably create an echo chamber. Semantic analysis delivers better results and is dynamic provided the ontology used for the analysis is maintained.
How to learn the Interest Graph
Lets come back to the iPad example we used before: A reader interested in the term iPad will also be interested in Tablet PC, iOS, iPhone, Apple, Steve Jobs, Android, Google, Samsung, Apps, all the terms directly or indirectly related to iPad. But how do we get to know these relationships?
The answer lies in the content base we have at hand: provided we have enough text to understand the relationships between terms we can build the ontology around iPad from that base. And with the content explosion we witness there is enough text on almost any topic these days free of charge. So all we need to do is aggregate content from trustworthy sources and use it as the base for our ontology, which will then be built in real-time whenever the reader calls for content.
Every new piece of content will influence the ontology and thus make it dynamic: while Amazon’s Kindle used to have a weak relationship with iPad via eReader, the new Kindle Fire all of a sudden established a very strong link to it and as a consequence also the relationship between the terms Apple and Amazon was strengthened significantly.
So now that we got our ontology, how do we get to the interest graph? As we stated above attention is the currency: the Interest Graph is build from the content the person paid attention to. Reading an article on iPad will result in an Interest Graph containing iPad and its related terms via the ontology.
Using the Interest Graph for Marketing
One application of the Interest Graph is offering relevant content to a buyer. To do so we score content against his Interest Graph. The more overlap the semantic profile of the content has with the buyer’s Interest Graph the more relevance it carries for him. The interest graph is updated with every new piece of content consumed by the reader. This way our content recommendations follow the reader, click-through rates are improved, spam effects are eliminated and we become the go-to source of relevant, valuable content.
We can also use the Interest Graph for content creation. Running new content against the Interest Graphs we collected will tell us, how the new content will score versus other content and provide feedback to the writer even without having to display the content to anybody. This way alternative drafts can be tested in real-time using the entire recipient base or selected segments of it.
Another application is to use the Interest Graph to detect a buyer’s intent and to automatically drive lead nurturing. Rather than deterministically defining lots of “if-then-else” rules trying to automate marketing we can use the development of hot spots within the buyer’s Interest Graph to trigger action: after the value of a specific term exceeded a threshold we approach him to take our relationship to the next level.
We can also use the Interest Graph to peer buyers with our own resources: if the Interest Graph shows a very narrow, yet very deep profile we are likely to talk to a specialist and want to peer him with one of our own best resources carrying a similar Interest Graph.
These are just a couple of ideas on how to use Interest Graphs within sales and marketing. Managers will have to become familiar with the concept to be able to capitalize on it.
There is no killer approach to Relevance. Henry Nothhaft, Jr., CMO of TrapIt, described it as “the myth of the sweet spot”. The competitive edge will be with services that support multiple discovery methods, multiple filtering approaches, have flexibility, and support multiple mobile platforms.
The Age Of Relevance, March 3rd, 2011
Marketing will have to use a combination of Social and Interest Graphs to drive lead generation and nurturing. By default using 3rd party Social (Interest) Graphs (Facebook, Google+) will not provide competitive differentiation, as they are available to anybody willing to pay for their usage. In contrast a marketing organization can build her own customers’ Interest Graphs by providing valuable and relevant content and tracking its consumption.
Combined with PLM, CRM and Marketing Automation these Interest Graphs can then become the source of sustainable competitive advantage from product and service definition all the way through aftersales. Especially for B2B marketing Interest Graphs will provide deep insight into buyer’s expectations, decision criteria and buying cycle progress.
Marketing is moving away from targeting generalized audiences, and will increasingly target specific individuals with specific interests.
Though this shift is already evident, it will become commonplace over the next three to five years, as advertising firms take advantage of large amounts of customer data plus increasingly mobile platforms to deliver their message.
Marketing automation has a tremendous amount of potential as a way to scale personal attention, but you also have to be careful with it. Any time you are relying on technology to scale communications, you need to be certain that (1) those communications do in fact reflect the interests of your leads, and (2) they are genuinely helpful and not just spam in sheep’s clothing.
The B2B buying process has fundamentally changed. Prospects are spending more time on the Web doing independent research, obtaining information from their peers and other third parties. That’s why companies are meeting prospective buyers earlier than ever, and is a key reason why having sales attempt to engage with every early-stage lead is premature.
So lets look at a typical B2B Buying Cycle:
- An individual within an organization becomes aware of a business problem negatively impacting his organization’s performance. It is important to understand that this happens quite frequently and at this point in time does NOT imply a desire to solve the problem. It is just another entry on a long list of business problems the organization has to deal with.
- Investigating solutions for identified business problems costs time and money, hence NOT for every problem a resolution will be searched for. The organization’s management has to take a conscious decision which problems to investigate and how much budget to set aside for this. This decision will be driven by perceived ROI, but also by general industry trends, peer discussions, investor expectations and so forth.
- Now a multi-disciplinary team is established to define a solution based on the parameters set by the resolution decision (scope, costs, timeline, risk ..) of the prior phase. The team will formulate one or more potential solutions and analyze them in terms of ROI, risk, TCO etc.
- Once the solution has been defined potential vendors are contacted and RFPs are submitted. The different vendor offerings are compared and the final decision by management is prepared. Management then takes the decision based on the vendor evaluation, peer conversations and general industry trends.
In each phase there is demand for information supporting the process. Obviously the more relevant information a vendor can provide and the earlier in the process the better his chances to influence the decision criteria in his favor.
1. Problem Awareness
At this stage the person realizing the business problem is not interested in talking to sales. Instead she is looking for relevant information to judge whether the problem is a) worthwhile investing time in and b) there are proven resolutions to the problem.
To attract the buyer’s attention in this early stage we provide curated social media, blog and trade publication content together with relevant white papers and case studies via a topic oriented landing page together with an RSS feed. This way we associate our brand with the business problem increasing the likelihood of being considered in the next phase.
2. Resolution Investigation
We support the client’s investigation with success stories, self-assessments and ROI calculations as well as 3rd party content such as studies and infographics. Our aim is to be perceived as the provider of a value proposition addressing the identified business problem.
3. Solution Evaluation
In this phase we want to learn the buyer in person and for this reason we invite him to our own events, point him towards industry events such as fairs, seminars and conventions or to our virtual events like webinars. We also start establishing the superiority of our products and services by providing data sheets, interactive demos, charts etc.
4. Vendor Evaluation
Finally we highlight our USPs, influence evaluation criteria in our favor, lower the perceived risk of our products and services by pointing towards favorable industry reports, benchmarks and customer testimonials.
When done correctly we start our relationship with an unknown, anonymous prospect and grow it into a very intimate one adopting constantly to his individual buying cycle, avoiding push where he wants to pull, by always providing valuable own and 3rd party content relevant to the buyer within his individual context.
This is where Algorithmic Content Curation comes into play: providing individually relevant information especially during the early phases of the buying process can be performed neither via static content offerings nor by human curation. Instead the individual buyer’s interest graph has to determine content supply, the appropriate delivery channel and the most suitable format. Using applications we described before Algorithmic Content Curation provides for low cost, highly effective content marketing for organizations of any size.
1. Topic-focused Landing Page
Here we provide our white papers and case studies complemented by curated social media, blogs and trade publications regarding a common industry problem. This way the site is highly dynamic, presents new content with every visit and generates SEO visibility. The visitor stays anonymous, nevertheless we can dynamically curate content based on his reading behavior, which in turn increases time spent on the site and number of pages per visit.
Zooming in on the anonymous visitor’s interest graph increases our chances to motivate him to subscribe to an RSS feed, newsletter or even register for our user portal.
2. Individual RSS feed
Rather than providing one standard RSS feed to all visitors we offer a personalized feed automatically adapting to the reader’s interest graph by following which own and 3rd party content he consumes. The reader can view the feed’s content on one or more front-ends of his choice.
3. Individual Newsletter
Just like with the RSS feed we can provide our own and curated content via an individualized newsletter adapting to the reader’s interest graph, but here we need the visitor to disclose his identity for the first time by providing his email address. As email newsletters live and die with relevance it is important to use the visitor’s interest graph he built during the visit on our landing page or by reading our RSS feed.
4. User Portal
After we demonstrated our problem resolution capabilities via the content we provided and thus gained a trusted advisor status we offer the buyer access to premium content via a user portal. Again we will use the interest graph built in the prior phases to achieve relevance from the first moment. Only now that the buyer has qualified by consuming our content we will invest our staff’s time for webinars, interactive demos and direct sales contacts and spend money on analyst reports, eBooks and other premium content.
5. Mobile App, TV-App
Ultimately we provide the buyer with an app on his personal device like a smartphone, tablet or even TV with valuable features like ROI calculators, configurators and self-assessments and thus establish our brand on the most precious properties of all.
There are a host of advantages Algorithmic Content Curation brings to the table when used the way we described above:
- The buyer is always in control of the process. We control which content we offer to him when and where, but he picks time, location and format and this way tells us about the progress his buying process is making.
- We provide 3rd party content just as our own and this way educate the buyer extensively on the topic we claim to have expertise in.
- Via the content he consumes we can watch the buyer establish his vision and tailor our solution for him already before we meet him in person.
- By looking at the 3rd party content the buyer consumes we learn about our content and offering gaps.
- Analyzing the interest graphs we built will allow us to segment our prospects by interests and develop highly focused niche campaigns.
- Using the interest graphs we can simulate the reach of our own content even before releasing it and instruct internal and external writers on which topics to cover to generate awareness.
Great video on using curation for marketing automation
Industry experts agreed that content is a key ingredient to success and suggested future users to take the time to map offers to buying stage and process. “One of the key values of marketing automation/lead nurturing is the ability to establish one to one dialog with prospects,” said Baldwin. “Nurture content is different than demand gen content – it doesn’t talk about ‘shiny new objects.’ It should deliver valuable content to the reader, based on their profile and behavior.