What’s the future of data driven advertising? I spoke with Paola Furlanetto, entrepreneur and longtime expert in the field of media and advertising audit.
This is the 4th episode of “The Unbearable Lightness of Data Sharing”, a series of short talks about the future of data driven society and business.
Hello Paola, thanks for your time! You have an extensive experience in the advertising industry and I am extremely curious to know your thoughts.
With economy increasingly plugged into the “personalization age”, the portfolio of the advertising professional seems to be changing, but I suspect that this challenge is far more complex than just swiping a card here or there.
There was a time when platforms sold their targeting capabilities to advertisers because they had access to data. Now, brands are progressively the owners of the most relevant targeting data (let’s think about digital service platforms), so the targeting capabilities might soon evolve into algorithms either plugged into the brand big data or making relevant data match. Do you think the world of advertising is ready for this big transition?
Paola: «You said “there was a time when platforms sold their targeting capabilities to advertisers because they had access to data”, but this scenario is not yet completely outdated, at least in some countries. The evolutionary state of companies is quite different, and there are still those who are accessing that system for the first time. Efficient targeting requires three conditions:
- Companies must find the relevant targeting variables;
- Data and touchpoint/media must define the consumer with the same variables identified by the company;
- All this must generate value.
In short, a targeting system must have a return, if not greater, then at least equal to that generated by a random communication.
The situation of advertisers with respect to targeting and data is full of nuances. For example, here in Italy some companies are very advanced, others have hard times matching targeting variables with those offered by the market (third party data/touchpoint profile).
Among them, many suffer from maintaining a bimodal system, where online and traditional touchpoints are still forced to work together to achieve maximum results in building sales and equity.
For some companies, targeting platforms are still to be explored. There are advertisers stuck in the phase where investments exceed measurable benefits. If we exclude the supply chain mark-up, the answer probably lies in procedures.
A good use of algorithms cannot exist without control and fine tuning, which is often neglected.
Moving to your second point “brands are progressively the owners of the most relevant targeting data”: if we exclude e-commerce companies, in most cases, advertisers today have data related to only a part of their customers.
Sometimes they know really well just one or two types of personas (on an average of 5) and limited to only some phase of the customer journey. Many are in the analysis/paralysis scenario.
So back to your question “do you think the world of advertising is ready for the big transition?”, my answer is yes: the market may be ready. But for this to bring the expected results, a key piece is missing.
Today, it is not a lack of data — or a shortage of — nor the willingness, nor the micro-optimization algorithms that characterize the individual platforms.
Today, what companies probably lack is the “Holy Grail” of a meta-platform that takes into consideration different channels’ responses to operate real-time control and fine tuning.
The meta-platform is the only one that can solve the challenges of a bimodal management, integrating data from different sources to operate effective targeting through fragmented touch-points.
The great opportunity lies in a global vision that embraces granularity. This should be the top priority for 2019.
Because unfortunately, and despite of appearances, advertising still substantially operates in very traditional ways.»
You said “granurality”. Well, when personalization is automated, the border with “personalinvasion” can be very thin. What do you think should be the right choice for a brand willing to explore the opportunities of programmatic personalization without the risk to be wiped out by “personalinvasion”?
Paola: «If we look at “personalinvasion” from the point of view of both business and consumer, there are two directions that can lead to non-invasive effectiveness.
One is paying more attention to client sentiment, the other is a genuine personalization of advertising. For the latter, 3 opportunities are emerging:
- The first — still in its early stage of development — is the use of Artificial Intelligence in the co-creation of almost-one-to-one advertising content;
- The second is a truly personalized omni-channel communication, both for the content and for the frequency of exposure;
- The third refers to a deep understanding of the consumer’s reaction, delivering content that generates constant appreciation and interest.
However, the challenge for data segmentation remains open.
Data challenge is straightforward for some industries that can meet consumers through highly profiled touchpoints (I’m thinking of game players, foodies, beauty addicts).
Similarly it is also easy for brands that need simple segmentation (such as, geolocation, age, or gender) or for those with a small target and modest market penetration, that can work with similar profile brands in non-competing industries.
All this takes more effort when the company has large targets and high market share. If the profile is reflected in lifestyles or consumption clusters, then data lakes can make the consumer reachable through programmatic advertising, but the real challenge is two-way communication, which will be a long and difficult journey to acquire and maintain.
The data challenge requires great determination when segmentation parameters are not reflected in variables describing IDs, third-party lists or touchpoints and when the target is huge.
Companies are forced to start a direct relationship (lead) through an expensive “try and learn” system or by implementing imaginative and visionary solutions to process personal data.
Is this game worth it? Maybe. It depends largely on the company short/long terms goals and, of course, on the market evolution.
Still on this topic, to what extent will today’s/tomorrow’s brand be able to see ARPU as the result of a personalized medium/long-term conversation and not as the arithmetic sum of transactions?
Paola: «There is an exciting point today, it’s the role of time in the success of advertising. If we consider the conversation only as a sum of transactions, each activated by short-term techniques, we will hardly maintain a satisfactory conversion index over time.
If, on the other hand, the focus is all on brand awareness, it will be harder to convert the relationship into transactions regularly over time.
For this reason, creating value from conversation is possible only by jointly monitoring the impact of communication investments on both transactions and sentiment.
Setting, measuring and controlling both short and long term KPIs is crucial, it goes without saying!»
Thanks Paola. A final, tricky question: advertising has always been perceived as a process of funneling a product down to a specific audience. The more I think of this, I see a reversed model with personalization guiding people to meet and cheer with what they (will) like and enjoy. How will this affect the role of KPIs in the adv/personalization industry?
Paola: «We can look at the relationship between company and consumer as a dating platform.
Sometimes it’s the company that takes the first step. Sometimes it’s the consumer who looks for the company.
But what makes the relationship a win-win is that the parts find each other quickly, efficiently and with a journey that stays comfortable over time.
If we convert this into KPIs to analyze first the flow that starts from the brand, here are some of the indicators that the company may want to track:
- How many people have been contacted (paid + earned) among those potentially affected (target reach);
- Which results were obtained and at what cost (e.g.: ROAS);
- Which results has the relationship produced in the period (LTV and at what cost).
In the reverse model, the flow that goes from the individual to the brand, key performance indicators may want to track:
- The possibilities for consumers to find the product they are looking for (e.g. search share);
- Brand relevance (in terms of engagement, appreciation, TOM, even image profile whatever is suitable for the industry and brand positioning);
- Degree of satisfaction gained during the relationship (e.g.: NPS, CS).
Each specific indicator depend on multiple factors: objective, target, phase of the customer journey and, of course, industry of reference. All aspects for which the use of Artificial Intelligence will soon make the difference.
But if we focus on today, these are the steps that may be useful to take:
- First up is to work on a double model: from the brand to the consumer and from the consumer to the brand;
- Second: always consider both short term and long term objectives (therefore essentially sales and brand equity).
- Finally, put in place a control system that allows timely intervention if results significantly shift from expectations.
We are in a really inspiring period for communication. “Algortising“ is going to be a successful choice for those who dare, combining three magical ingredients: broad vision, granular measurement and -obviously, but not obvious- control.»
Thanks Paola, I think there are many interesting points arising from this conversation.
My personal takeaway is: in a data driven and service dominant economy, brands are asked to do more than just buy targeting capabilities.
Brands need to become conversational engines, sensing the real-time intersection of individual journeys with a global portfolio of opportunities, eventually bigger than the brand’s portfolio itself. A conversation is a journey made together, not a stack of transaction receipts.
Thanks everyone for reading, this was a though piece, but veeeeeeeeery interesting. In the next episode we will talk about creativity and data with the help of a super “green-haired” digital thinker. Have a great 2019 everybody!
Paola Furlanetto’s Bio.
Entrepreneur, founder of two auditing companies, Paola Furlanetto has an extensive background in communication with a professional career in media and advertising auditing, data and insight institutes, media agencies and media companies. She has been collaborating with key multinational players in finance, telecoms, sports, technology & electronics, automotive, energy, pharmaceutical, upmarket goods, food & beverage, beauty & toiletries, retail. Today she works for ELEY Consulting in Italy (digital audit), UPA -the Italian Advertisers Association (KPIs)- and both global and local advertisers (media/digital Pitch and KPIs guidance).
Riccardo Zanardelli’s Bio.
Riccardo is Beretta’s Digital Business Development Manager. Graduated in Engineering, he got a Masters in Business Administration and has done most of his professional career in B2B and B2C marketing. Since 2016, he deals with business transformation and digital services. Passionate about digital economy and informational privacy, in 2018 he published “OPAL and Code-Contract: a model of responsible and efficient data ownership for citizens and businesses”. He is a member of the advisory board of “Quota 8000 – Service Innovation Hub” at TEH Ambrosetti. Since 2000 he deals with digital art as an independent researcher. Some of his projects have been acquired from the permanent ArtBase collection of Rhizome.org – NY (2002) and exhibited at the Montreal Biennial of Contemporary Art (2004), as well as at Interface Monthly (London, 2016, by The Trampery and Barbican). In 2015, he released FAC3, one of the first artworks in the world to experiment the use of artificial intelligence. He is married and father of two.