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The Need for Intelligent Data Modeling


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How rapidly digital transformations have occurred since 2007. A decade ago organizations had not yet turned en masse to leverage social platforms such as Facebook. Consumer technology and its potential went largely ignored and companies were mainly focused on data mining, search technology, and virtual collaboration.

Nowadays, we are directing our energy toward artificial intelligence, machine learning, and the Internet of Things. In our bid to evolve and optimize our use of technology we have made huge advances yet, at the same time, we appear to have also created some sizeable knowledge gaps.

The digital advertising sector is one such example. Despite the increasing amounts of "big data" at our disposal and the growing investment in data management technology, marketers are still failing to harness its full potential.

The current siloed approach to consumer data is preventing marketers from gaining a complete picture of the customer journey. Little wonder they frequently fail to  accurately target the desired audience at the precise moment when they intend to make a purchase. 

Fueling ad blocking

In the greater scheme of things this may appear like a minor detail however the implications are huge. Brands are struggling to get full value for the money they’re investing in advertising and consumers are being presented with irrelevant ads that are ruining their online experience, and potentially fuelling the growing issue of ad blocking. 

And this is having a knock-on effect. Despite the digital advertising industry’s growth, campaign performance has started to stagnate to the point where it’s threatening to undermine a sector that’s predicted to top traditional TV advertising in terms of spend in 2017.

Closer and more relevant

Surely it is time for a new generation of intelligent data modeling to step up and enable advertisers and marketers to target consumers more accurately. Never mind reaching the target audience, how about going deeper and targeting the right audience based on intent?

Currently each data supplier only tracks part of the consumer journey which leaves knowledge gaps. This siloed approach to structuring and purchasing data is hindering the development of a single view of consumers’ buying behaviour, and, as a consequence, brands are unable to develop a full understanding of the consumer decision making process.

The art of interpretation

There also seems be a disconnect between data and the real world. Despite an obsession with targeting ads at the right consumers, we appear to have lost the art of understanding data and interpreting people’s behaviour and identifying the key signals that indicate intention. Advertisers can serve ads to the right audience, but if the data is not showing individuals in the right frame of mind, are they simply wasting their time? 

If we were to flip the situation around and focus instead on how all the data a marketer can draw upon is modelled we could achieve a greater understanding of the consumer’s intent. This would mean that advertising could be used not simply to drive a purchase, but also to guide consumers along their buying journey, providing the information they need when they need it to ultimately make an informed purchase. 

Closing the gap between brands and consumers

This kind of intelligent data modeling moves brands closer to consumers and reverses the current targeting methods. Starting with the “right mindset, right intent signals,” then establishing the right place and right advert drives performance and provides a better understanding of what a consumer is going to buy, directing which product and price point you should communicate.

Advertisers should develop a different strategy for targeting "active buyer" audiences that are looking for a specific product or service compared with “hot prospect” audiences that are ready to purchase.

As an example a finance organization will know a lot about their customer from name, gender and demographic through to their financial status. However, how do they build and strengthen loyalty to their brand?

The power of real-time data

They could go further in understanding what the customer’s current, real-time focus is on. What if they knew that in the last hour the customer has shown multiple “intent” signals from their digital behaviour that indicate their intention to book a major trip right now. This would be an ideal opportunity to change the marketing message in real-time to, say, “Free additional protection while abroad and discounted travel insurance,” thereby, becoming powerfully relevant to the customer.

What’s even more exciting is how the finance organisation can use the intelligence of understanding “intent signals” from data modeling efficiently and effectively when prospecting for new customers. This includes knowing when not to target certain customers - not everyone is booking a 3-month trip to Asia!

The result would be more efficient and effective advertising trading strategies that would drive up campaign performance levels. This also holds the key to closing the current dangerous gap between digital advertising growth and ad effectiveness.   

The smart brands are already working with data modeling companies to help them boost campaign engagement and build stronger relationships with consumers. The problem is that there is currently a severe skills shortage of data modeling experts that understand advertising and programmatic trading.

We certainly have come a long way since 2007 when digital was synonymous with IT and big data really was huge, and clunky. Nowadays, there is no excuse for allowing unwieldy data to handicap us. Intelligent data modeling is poised to re-boot the entire data ecosystem and offer advertisers unparalleled access to their target consumers without infringing on their privacy.

Those brands that find the right partner will steal a march on their rivals, but more importantly the industry as a whole needs to work toward ramping up skill levels in data modeling and those people who can deliver it.


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