Under the guise of Web 3.0 a ‘semantic web’ (semantic defined as ‘meaning’) would see intelligent decision engines sifting through the vast quantities of information online, refining it into a more succinct package for human users. Sophisticated decision engines could not only find and retrieve relevant information (as search engines already do), but could combine it to form fresh understanding, delivering conclusions and recommendations depending on the information found.

One Giant Leap

However, for this evolution to occur, a giant leap is needed in the sophistication of the decision engines we create.  Without the reasoning present in the human brain, how could a machine possibly make the subtle yet fundamental distinction between fact and fiction, or even be able to access the jumbled mess of information the Internet is made from? Suddenly, Web 3.0 is more science fiction than science fact.

Whilst a true semantic web seems years or even decades away, the basic principle under discussion – namely the use of decision engines in combining data to create a meaningful outcome – is much more achievable.

Above, Ben Langdon

The Market Value of Semantics

Direct marketers have been using data to make decisions for years. Whether using profiling information to create segmentations or embedding analytical models in campaign selections for individuals skilled data analysts are a key element of the DM process.  The speed of digital transactions has however moved this decision from planning cycles of months to minutes. Digital channels allow consumers and brands to interact with one another in real-time. Instead of the traditional marketing route of analysing customer data, targeting specific individuals and contacting them via the post or phone (which may not take place until weeks later), the opportunity now exists to make relevant communications at a very specific and immediate point in time. In parallel with this reduction in timescale has been the realisation that effective personalisation of communications requires customer data reflecting both online and offline activity. 

Whilst achieving the semantic web is blocked by the unstructured nature of the Internet, marketers benefit from the data they collect and hold being well organised and easily accessible by human or technological approach; marketing databases are some of the most sophisticated in existence, yet built with easy and efficient data retrieval front of mind. Aligning this data with skilled analysis to create real-time decisions will create the personalised experience to which marketers aspire.

The real opportunity for brands that desire a relationship with their customer is to amalgamate key data from both offline and online interactions and transactions. For example, using online data (such as what a consumer is looking at on the site) in combination with known offline data (such as their purchasing history) to generate web content, an email or a mobile message that offers information relevant to the customer at that time. Using real customer knowledge to drive the personal customer experience results in a positive interaction between brand and consumer. 

To achieve this, real-time data generated online needs to be married, in real-time, with data stored and collected offline and used with analytics and decision rules, also in real-time, to determine the next message and deliver it, resulting in an interaction with the consumer that is precisely relevant and timely.

Input from Offline

It is here that the development of a sophisticated decision engine with the ability to combine existing offline data with real-time generated online data begins to illustrate the potential of a semantic approach. Dubbed a form of ‘digital brain’, this concept is designed to mirror the human brain in its ability to consider a variety of data sources when making the decision as to the best way to interact with the consumer at that specific moment.

Firstly the digital brain identifies the customer who is generating fresh behavioural data, perhaps interacting with the brand’s website or telephone agent.  Generally with the customer’s permission, it then mines the offline database to access other information available on the customer, such as measures of their purchasing history, demographic information and channel preferences. Algorithms developed through analytical insight are used to link these data sources together and deliver a decision, resulting in the engine identifying the most appropriate communication in terms of offer, tone, copy, content and channel.

The instant outcome could be any of the following:

• * Presenting personalised web content to an individual user - perhaps highlighting individually available special offers, product bundles or features that the customer favours and that are based on the value of that customer to the brand.  This is a significant step ahead of conventional content management systems that may be able dynamically to amend copy and images by page but not by user;
• * Directing an email to the customer about the product they’re browsing, encouraging them to purchase and offering an incentive based on their past purchasing behaviour;
• * Instigating an outbound phone call or managing an inbound call that offers the customer help and advice on their purchase;
• * Sending a digitally printed and personalised mail pack including a brochure, based on the customer’s ordering habits and mail order preference.

A real-life example for a travel company used offline data about a customer’s future holiday and other product holdings to encourage customers, via DM and email, to log-in to a microsite which carried information specific to their holiday. As the customer engaged with the site this activity, combined with data held offline and decision analytics, directed email and web content selected from hundreds of options, delivering interactions that were both personal and pertinent and that ultimately resulted in cross-sales of other products.

A bank of interactions from differing web content to email executions is vital in truly realising the potential of real-time interactions, and over time brands can increase their variety of approaches to suit every possible outcome.

From Semantic to Synaptic

In a sense, the way this decision engine operates is as much a new marketing philosophy as it is a new piece of software. If a synapse is defined as “the point at which electrical signals move from one nerve cell to another”, this new philosophy is ‘synaptic marketing’ because of the way all available data is instantly linked together to derive a decision, just like in the human brain.  And the decisions that are made and interactions assigned then become a ‘memory’ of how and why the customer has been communicated with, further improving subsequent real-time decisions and interactions.

Synaptic marketing has the potential to revolutionise the way brands interact with their customers.  Consumer apathy to traditional marketing, and the way digital channels and the Internet specifically has brought instant communication to the fore, mean brands must change the way they approach marketing.  And much like the proposed semantic web would aim to make sense of the vastness of the Internet; synaptic marketing offers a means of humanising the interactions between brand and consumer, even if an artificial intelligence is at its heart.