Demographics are a workable proxy. Get over it!

Ian Dowds, CEO, UKOM

There is a great deal of huffing and puffing by some sections of the industry about the supposed obsolescence of demographic segmentation now that technology is capable of identifying and segmenting audiences on context/behaviour/interest/intent/other. Especially when, the evangelists for each will claim, those identifications are made with absolute, up-to-the-millisecond-A.I.-driven, certainty. “Why turn back the clock?!” calls one. “Demos dead in the water” states another. And my favourite hysterical wail: “Demographics have turned marketing into a lie!” screams another.

Please be clear, I am no luddite. I recognise the ever-improving capability to apply real-time data driven insight to make programmatic campaign delivery more efficient in reducing waste and driving effective activation. Luddite? Me? Quite the opposite. I am genuinely fascinated to see how the industry will make best use of such capability, because it surely will.

However. Data is not a zero sum game. There is a whole world outside of digital ad campaigns and online activation. A world of insights that drive strategic, communications and media planning. A world where acting one way or another, dependent only on data changing by millisecond, often generated by businesses with significant commercial self-interest, would be madness in the extreme. There is a world of the bigger picture. The longer term. A broader understanding.

Nobody in this world sees demographics as absolute or exact realities. Demographics are proxies. Of course there’s the 105 year old man in Japan who trains for and runs 100m races (albeit quite slowly). Of course, if Mark Zuckerberg walks into a room of under thirty-five year olds, then the average net worth of individuals in that room rises off the scale. But those two are (whisper it) the exceptions. Of course we could put either of those two outliers into non-demographic categorisations with different commonalities in which they would not stand out or distort the cohort.

Demographics are necessarily broad categorisations on top of which should be laid a whole range of other data driven insights. Mark Ritson, responding in order to debunk a Brand Quarterly “Death of Demographics” article back in 2015, wrote: “There have always been shit marketers who don’t do any research or segmentation and just conjure up a broad, stereotypical ‘target segment’ to make their marketing plan look more professional.”

Who knows? We may see the day when there will be an industry agreed definition of what constitutes an audience displaying an interest in buying a car, or intending to book a holiday, along with an independent verification of that audience.

In the meantime, are demographics used only as proxies? Sometimes, of course they are.  Despite the almost limitless supply of digital data and online audience segmentations, demographics are used as the foundation for UKOM’s industry standard for online audience measurement because there are independent and objective establishment surveys for them.

Independent. Objective. Important words not to be dismissed lightly.

The Role of Data in the Advertising Cycle

Liam Corcoran, VP Ad & Audience Measurement EMEA, Research Now SSI


Most businesses – both brands and agencies – recognise that in order to achieve greater ROI on marketing investments, they have to get smarter about how they use data. It would be fair to say that marketers’ and advertisers’ use of data is evolving, and that there is a general awareness that there is room for improvement, both in data quality and application, such as for measuring advertising effectiveness. As companies continue to invest billions in technology to collect and house proprietary data, they need a way to tap into and leverage this investment, as well as enhance it.

Our recent research report ‘Data – The New Oil: Building a Customer View Through Data’ confirmed data quality is indeed important to building a complete customer view, with a business’s own internal customer data as the cornerstone, the core, to data-driven customer engagement. While a company’s own CRM data is truly first-party data, marketers face trade-offs between what they seek to learn about customers and the friction they add into the path to purchase. Fortunately, there are other data sets available – quality data sets – that can be combined and integrated with those company assets to enhance profiles for a truly robust, well-rounded, accurate, “in the moment” customer view.

We are seeing that marketers are increasingly building complementary data partnerships, and turning to trusted second- and third-party data sets. Market research panel data is one kind of complementary, enriching data. Market research data companies that recruit and manage survey panellists collect information from verified, permissioned consumers, and manage it on independent platforms so that it is richly attributed and maintained. Market research data collected in this way offers a different kind of first-party data, authentic and directly from consumers and business professionals. And because the data is offered on an independent, open platform, access to it is not filtered or restricted as it can be through closed platforms (“walled gardens”) that control both the buy side and sell side of their data.

Building a 360° customer view in this way helps get to the “why” as well as the “what” behind brand preference or path to purchase because an actual consumer has provided the data. Ultimately, quality depends on this kind of authenticity. Beyond data quality and a complete view of the customer, integrated data – across channels – is also crossing into the next frontier of cross-media ad measurement and effectiveness. The two – data and channel – go hand-in-hand for accurate assessment and, therefore, optimisation.

In a world of heightened awareness about and concern over the proper use of data, particularly in a post-GDPR age, it has never been more important for marketers to find trusted partners to help educate them around the value of their data and the potential to leverage it through integration.

 


View the agenda for more information on the topics being discussed at the Audience Analytics and Insight conference or book now to secure your place.

Q&A with Martyn Bentley, Commercial Director UK, Audience Project

We caught up with Martyn Bentley, Commercial Director UK for Audience Project, ahead of his participation at the Audience Analytics & Insights Conference this October.


AAI: Why is it that more data can lead to fewer insights?

MB: The quality of data output is completely aligned with the quality and to some degree the quantity of data that comes in. If the data quality is too low, then more data does not help, and you will just spend more time trying to understand it and still get crap out.

Additionally, greater amounts of data could lead to worse output, as it will place greater demands on the skill of the data scientist crunching the data.

AAI: Where should analytic & insight teams sit within the business structure?

MB: Close to the money! Just look at how technology and data are giving sports-stars the edge – it’s never been more important in helping business perform at its full potential. 

AAI: Why is it important to break down the silos between data teams and insight teams?

MB: I think it is important so that the data that is supplied to the insight team, will actually be useful to them and solve problems they set out to solve. 

It is, in the end, two sides of the same coin and is 100% a team effort; data teams have the capabilities to crunch the data – Insights teams have the capabilities to structure the data.

AAI: As an industry are we losing the art of interpreting human motivation?

MB: There is a danger of reducing human motivation to sale-able data packages. Human motivation is, I think, well understood at some points in the advertising chain (perhaps at the creative agencies) – but it’s often not aligned with how audiences are sold and targeted – e.g. the creative teams and their insight partners build complex audience personas, which are not targetable in digital other than by proxy data sets. It’s a disconnect that could be reduced by more through-the-chain collaboration.

At AudienceProject and other reputable data companies, human motivation is interpreted through the (machine-led) study of behavioural data – this is then fused with deterministic data, to grow accurate segments – and we also safety-check the segment’s but using a proportion of the deterministic seed to ensure we are hitting the correctly motivated people.

AAI: GDPR should mean cleaner better data – has it?

MB: I think it’s too early to say. What it has done is got the market thinking about cleaner better data and best practice… which can only be a good thing.

AAI: If AI and machine learning could deliver one improved solution to help your understanding and targeting of your audience, what would it be?

MB: True cross-platform measurement! Enhanced cross-media planning and attribution is a highly desired outcome for everyone in the market. Whether AI could definitely solve it remains to be seen – because the data inputs also need to be very high quality, standards need to be agreed between stakeholders and some very large platforms don’t even allow measurement!

I doubt AI can solve this perfectly but it may be able to be deployed to improve the estimates in the interim.

AAI: Blockchain, could it be the answer to all advertisers’ problems, 100% accurate behavioural data or just the next new shiny thing?

MB: Shiny thing in its present form and capabilities. It has some highly interesting potential and as a technology can provide some of the answers to the problems of the industry, but not all. 

AAI: What’s the biggest single challenge that impedes the successful integration of behavioural and survey data?

MB: Ensuring that the behavioural data is high quality, and transparent – and if possible the mix-level (or dilution of the deterministic data set) of the two types of data should be stated.  Otherwise behavioural data can be used fast and loose by players looking to create scale at the expense of accuracy.

AAI: Why is it important for brands and their agencies to be able to compare online and offline audience and advertising data more effectively?

MB: Because understanding cross-channel attribution is an important key to media effectiveness and thus lowering the cost of sale/increasing margin. It will reduce wastage.  It’s that simple.

AAI: Can you sum up the holy grail of total advertising attribution in one sentence?

MB: The holy grail is to be able to understand the consumer’s need states and motivations, and influence them, cross-channel, in the most efficient way possible.

View Agenda

Exploring Broader Uses for Location Data for Businesses

At Blis, we’re fond of saying that ‘where you go defines who you are.’ While there’s truth to that, it’s even more accurate to say that where you go contributes to a greater understanding of who you are. We’re finding that combining location or movement data with other, complementary data is helping us build rich and valuable pictures of consumers and their behaviours – and that is allowing us to leverage Blis data in ways that go beyond targeted advertising.

We’ve been engaging in some exciting studies in crossover competition across a few different vertical markets, including automotive, retail fashion, and supermarkets. In the case of automotive, which was our first study, we were interested in learning about the behaviours of luxury car shoppers. Specifically, if three luxury car dealers are located in close proximity to each other, will a shopper go to all three? We set up geo-fences around several dealerships to observe.

What we found by tracking mobile device IDs was that if a shopper went to one luxury car dealership, they were unlikely to visit a direct competitor. In fact, they were more likely to visit a mid-market dealership than look at another high-end car. In other words, a shopper who looks at a Lexus is more likely to look at a Toyota Camry than Jaguar.

Another study we conducted observes High Street fashion retailers with a focus on physical store location. Our question was this: If a fashion retailer is planning to open a store in a new spot, should they be concerned about opening in the same precinct as competitors? Should they be concerned about the competition, or does it make more sense to be opportunistic, and count on the traffic they’re likely to receive from consumers shopping across the street? By establishing geo-fences around shops in a number of busy High Street areas, we’re able to track and analyze how consumers tend to shop for fashion.

Additionally, another study focuses on the new discount supermarkets rapidly gaining traction in the British grocery retail landscape. Legacy supermarket chains are deeply concerned by the proliferation of these new players and how much market share they appear to be losing to them. But are they? Are consumers ditching these established chains completely, or are they just testing out the new stores? Perhaps they’re only visiting the discount shops once a month, but generally remaining loyal to the older chains. It’s been difficult to accurately quantify this promiscuity in the past, but mobile data gives us the ability to see which consumers are shopping where, and when, and how frequently.

For us, this is an exciting new way to leverage mobile data – to measure the size and level of threat in an amongst brick-and-mortar businesses. We can offer a level of insight, analysis and consultation to these companies that goes far beyond advertising, and can affect their bottom line in even more meaningful ways. As head of insight, I believe this represents a huge opportunity for Blis and our customers, and my hope is that it’s only the beginning. There are so many potential uses for the rich, detailed data that comes to us every day. I’m optimistic that we’ll discover even more ways to help businesses and consumers enjoy and thrive in the era of mobile disruption.

 


View the agenda for more information on the topics being discussed at the Audience Analytics and Insight conference or book now to secure your place.

Q&A with Nic Pietersma, Business Director UK, Ebiquity

We caught up with Nic Pietersma, Business Director UK for Ebiquity, ahead of his participation on our panel on metrics and finding the balance between short-term ROI & long-term brand success.


AAI: Why is it that more data can lead to fewer insights?

NP: I think with an abundance of data and increasingly powerful algorithms sometimes we forget the old tricks like the 80/20 rule. It is amazing how often the simple act of adding things up and ranking them can help us focus the mind on what really matters. 

A related challenge is that practitioners in marketing analytics tend to oversell what they can do with data. Decision makers value confidence, clarity and optimism in their advisors and this leads to a tendency to overstate the accuracy of predictions and perhaps understate the width of the confidence intervals.

AAI: Where should analytic & insight teams sit within the business structure?

NP: That’s a really interesting question. Most of our client contacts sit within the marketing function, but ROI is so central to what we do at Ebiquity that we have always felt reporting lines into the CFO or Commercial Director could work well.

Probably more important than the reporting line is the mandate given to insight and analytics people. Personally, I always feel a bit uncomfortable when I see “making the case for marketing” spelt out as a bullet point in project goals. I think in our field identifying failure, and honestly identifying what does not work is just as important as identifying does work.

Organisations that recognise this tend to make better decisions, but sometimes this requires a cultural shift.

AAI: Why is it important to break down the silos between data teams and insight teams?

NP: Sometimes silos exist for arbitrary reasons; if data teams and insight teams sit in a very different place on the organogram collaborative work and coordination tends to be much harder.

Another barrier is lack of cross-functional skills. In a perfect world insight teams would have better coding and data management skills and data teams, on the other hand, would have more of an appreciation for how consumer and market trends relate to business objectives.

AAI: As an industry are we losing the art of interpreting human motivation?

NP: I’m not sure how to answer this question. What yardstick should we use?

AAI: GDPR should mean cleaner better data – has it?

NP: I think it is too early to say. Most of the measurement strategies we use at Ebiquity, such as econometrics and geotesting, do not rely on personally identifiable data. So the impact on our business has been limited.

AAI: If AI and machine learning could deliver one improved solution to help your understanding and targeting of your audience, what would it be?

NP: This really does depend on your business model. For some businesses getting to the right consumer at the right time and the right place is all important. But for most businesses, there isn’t really such a thing as ‘wastage’, at least not in the purest sense of the word. Target audiences are usually broader and more fluid than we recognise in the marketing community. Usually, cost-effective reach is more important than, for example, using AI to target slightly more accurately with some kind of dynamically-served tailored creative.

AI has the potential to be phenomenally disruptive in our economy; self-driving cars, logistics, call-centres, agriculture and so on. I think the impact of AI on targetted advertising is really more of a footnote.

AAI: Blockchain, could it be the answer to all advertisers’ problems, 100% accurate behavioural data or just the next new shiny thing?

NP: ‘New shiny thing’.

Blockchain, which I understand is essentially a decentralised anonymous ledger, often seems to be touted as a solution to problems that are already solved quite nicely by old-fashioned, centralised, non-anonymous ledgers… you know, like the way credit cards work, which are famously inconvenient. 

I have read that blockchain may be a useful technology for digital ad-verification and that certainly is a problem in search of a solution. So for this, it is worth keeping an open mind.

AAI: What’s the biggest single challenge that impedes the successful integration of behavioural and survey data?

NP: It is unlikely that survey data and behavioural data will ever be perfectly aligned, but from a decision-making point of view it makes sense to have both reference points available.

Netflix made a change to their rating algorithm recently that is instructive. In the past they had a five-star rating system, but realised that people would kind of slip into ‘film critic’ mode when asked to rate a movie that way. What they really wanted to train their algorithm on was unguarded feedback on whether people enjoyed the movie or not.

Their new system is simply a thumbs-up or thumbs-down. It is more user-friendly and seems to work well… and arguably this is a small change that more closely aligns real world behaviour with the ‘survey response’ data.

AAI: Why is it important for brands and their agencies to be able to compare online and offline audience and advertising data more effectively?

NP: If a consumer sees the same 30 second advert on an iPad, through a Roku or smart TV, or on traditional channels it is a fair question for advertisers to ask what the relative costs are and where they would get the most cost-effective reach?

The question is complicated by differences in reporting standards and the definition of audience metrics – so far the industry has not done enough to make audience metrics transparent and comparable.

AAI: Can you sum up the holy grail of total advertising attribution in one sentence?

NP: No, we should reject holy grails!

Instead, the rally call should be for a diversified portfolio of research methods and a cultural commitment to openness.

View Agenda

How Businesses Stay Relevant With Rapid Cultural Change

Alex Wright, Head of Insight, Blis

 

There’s an easy answer here, and a harder one. The easy answer is to achieve scale quickly. If your company is a small startup, scale up as fast as possible. Gathering scale leads to even more scale, and the result of that, usually, is a lot of buzz. If your company is larger, the smart move is to buy one of those small, buzzworthy companies. Why build when you can buy – especially when your acquisition target is already getting noticed?

Sometimes, Staying Relevant is Bigger Than Marketing: It’s a Matter of Keeping Markets Stable
Of course, not every company can simply scale up and be acquired, contrary to popular belief. Some industries are slow to adopt; others are losing steam. Financial services is an excellent example of a slow adopter. Strict regulations create obstacles to adoption, so this industry lags in almost every aspect of technology. Considering how younger generations tend to avoid banks, preferring cash machines, online banking and apps like Venmo, the industry’s reluctance to embrace mobile is concerning.

In the case of FinServ, the industry has lost people’s trust, and that’s why businesses risk losing ground to apps like Venmo and PayPal, which work around banking institutions. The first step, in this industry’s case, is to be humble and admit to the mistakes they’ve made in the past. Banks and other financial institutions need to get on side with the generations that are driving today’s changes.

It’s worth noting that these institutions probably belong in the various processes that involve moving funds from one party to another: disintermediation could ultimately harmful for everyone. While I’m personally in favour of democratising things and giving everyone equal access to banking, there’s risk involved. Here in the UK, we’ve had low interest rates for the past few years – since the global financial crisis. With the rising costs and stagnant wages we’re seeing now, a rise in interest rates could lead to financial hardship for many people.

With that in mind, should disintermediated peer-to-peer or micro lending grow, there’s potential for people and businesses to take advantage of the less fortunate and less savvy. For the greater good, banks need to get up-to-speed with these mobile trends to oversee transactions and put themselves back into the process. Ironically, the same regulations that currently hold them back could ultimately make mobile banker safer for everyone moving forward.

Retailers Need Mobile to Complete Their Omnichannel Strategies and Keep Customers Happy
In the UK, the growth of ecommerce is a hot topic. And yet, the rumours of retail’s decline appear to be greatly overstated. Only 16 percent of retail pounds are spent online. While that’s an increase, it’s not exactly killing the High Street. The “digital natives” may love to shop on their phones and tablets, but they love to hang out and shop at stores, as well. Gen Y and Gen Z remain active offline shoppers, despite their many daily hours of screen time.

Retailers are not in bad shape, but they do need to get on board with omnichannel if they want to avoid future problems. For starters, shoppers now expect 24/7 store access, so retailers need to have online and mobile stores that work well. These shoppers also expect to be able to purchase something online and, if necessary, return it to the store – without getting a hard time. After all, they bought the product from your store, regardless of the channel by which they completed the purchase.

It’s a paradigm shift, ultimately: the brand is now the destination, not the local store. Online and offline do not exist in the customers perspective, so they shouldn’t exist in the retailer’s view either.

The Real Mobile Impact

The various channels by which consumers now access content have resulted in fragmentation across social, economic and political demographic groups. It’s important for marketers to try to understand these fragments and what they mean. There are so many more nuanced behavioural segments, and they’re revealing differences between ourselves and our peers that we hadn’t realised were present before. To try to understand cohorts like this at such massive scale is going to become increasingly challenging.

To add to the confusion, we have a tendency as an industry to divide audiences into age brackets and assign behaviours en masse to these “generations.” But generations are too wide to encompass the change that can occur within just a few years’ time. The brackets should be half what they are; for example, if you were at school with someone, you are of the same generation. That’s a bracket of just a few years, but people within those brackets will have the same frames of reference. They’ll have watched the same TV shows, read the same books, played the same video games, bought the same brand of jeans or socks.

Cultural frames of reference are now shifting so quickly because of the immediacy of everything. This is precipitated by access to media, which is now both very personal and very portable. It’s something brands should be acutely aware of: the consumer now has a touchpoint with your brand available at all times, either in their hand or in their pocket. That not only means that brand has to be always on, but also always relevant. Study those fragments and learn who your users really are, and relevance will be less of a struggle.

Q&A with Mathew Knight, Strategy & Innovation Partner, Foxlark Strategy Limited

We caught up with Mathew Knight, Strategy & Innovation Partner for Foxlark Strategy Limited, ahead of his participation at this year’s Audience Analytics & Insight Forum.  Matthew’s role at the Forum will be to provoke our speakers and our delegates to think harder about all the topics under discussion – to ensure that we come up with new ideas and answers!


AAI: Why is it that more data can lead to fewer insights?
MK: Data paralysis creates a sense that you have to deal with all of the data you have available, and the task becomes one of managing the data – rather than using it effectively. For too long, people have been hoarding data, and trying to make sense of it, rather than trying to create an effective way of focusing on specific areas they want to explore. Worry less about the data, and more about the customer challenges you’re trying to solve – then connect the data you have to that.

AAI: Where should analytic & insight teams sit within the business structure?
MK: More and more businesses are moving to hybrid project teams, rather than discipline-based models, and I think this is essential for insights and analytics to be embedded throughout a project, not just at the start or end. If reorganisation isn’t possible – I’ve always felt that insight teams should be sat most integrated with strategy and business planning.

AAI: Why is it important to break down the silos between data teams and insight teams?
MK: Hybrid teams create better work, because they understand the needs and approach of the other people in their group. Any silos only create narrow focus on a ‘task’, rather than solutions for the bigger collective challenge.

AAI: As an industry are we losing the art of interpreting human motivation?
MK: I think that we’re so focused on the optimisation of processes, we’ve forgotten to look up and think about the whole a little more often – asking the why, not just the what and when. Insight has never been about data, but always the underlying motivations and behaviours of people. I think just using ‘data’ to understand people is lazy.

AAI: GDPR should mean cleaner better data, has it?
MK:Time will tell whether GDPR means cleaner data. It has forced organisations to think more actively about how and why they collect data, and hopefully the ‘cost’ of that data collection means people will be more thoughtful about how they approach it.

AAI: If AI and machine learning could deliver one improved solution to help your understanding and targeting of your audience, what would it be?
MK: Free up people’s time on mundane tasks, so they can go out and explore and observe the real world, rather than from behind their desk.

AAI: Block chain, could it be the answer to all advertisers problems, 100% accurate behavioural data or just the next new shiny thing?
MK: Blockchain will be transformative for transparency and accountability – but it is by no means the answer to all advertisers’ problems. Hanging your hat on any one technology is never the answer. You need to consider an ecosystem of tools and processes to solve a problem. Technology alone is never the answer.

AAI: Why is it important for brands and their agencies to be able to compare online and offline audience and advertising data more effectively?
MK: I’m not sure ‘compare’ is the right approach – but advertisers need to be able to understand how consumers behave, and there is no real notion of ‘online’ and ‘offline’ any more – people just exist across multiple channels, so we need a better way of evaluating behaviours at an ecosystem level.

Q&A with Nick Manning, Senior Vice President, Medialink

We caught up with Nick Manning, Senior Vice President from Medialink, ahead of his participation at this year’s Audience Analytics & Insight Forum in a session looking at how to achieve maximum ROI in a multi-platform/data world.


AAI: Why is it that more data can lead to fewer insights?
NM: Data is described as the ‘new oil’, and you can drown in oil. But it’s not oil, it’s electricity, and companies need to be able to operate without thinking too much about it. That means having command and control over the technology. Not easy when there are so few common data currencies. The lack of measurement standards lead to insight loss, so companies need to have a data management strategy that handles different kinds of data to create a composite picture.

AAI: Where should analytic & insight teams sit within the business structure?
NM: We live in an evidence-led world, so there is no longer any debate about the role of analytics. So the relevant teams need to operate transversely across multiple disciplines and departments. Flexibility is key, so the right place is where the most productive cross-functional working resides within each company.

AAI: Why is it important to break down the silos between data teams and insight teams?
NM: There shouldn’t be any. There is no value in insight without data and no point collecting data without the right insight processes. They should be two sides of the same coin.

AAI: As an industry are we losing the art of interpreting human motivation?
NM: Only if we allow ourselves to. Marketing is all about persuasion and that means emotional as well as rational triggers, so we mustn’t lose sight of the need for compelling messaging, even in more functional channels.

AAI: GDPR should mean cleaner better data, has it?
NM: Too early to tell, but it should help. Having a ‘consent’ culture must be a step in the right direction, as long as we don’t take it for granted and treat people with respect in how much data is used in the value exchange. GDPR is just the start.

AAI: If AI and machine learning could deliver one improved solution to help your understanding and targeting of your audience, what would it be?
NM: Helping overcome the lack of common measurement by making business results the main currency, and making effectiveness measurement the main measure of success.

AAI: Block chain, could it be the answer to all advertisers problems, 100% accurate behavioural data or just the next new shiny thing?
NM: It can’t be the answer to all advertisers’ problems, can it? Nothing is. And nothing is ever 100% accurate. But if it really does provide more clarity, better process and less friction, then let’s hope it can lead to a better, more accountable eco-system.

AAI: What’s the biggest single challenge that impedes the successful integration of behavioural and survey data?
NM: Incompatible data-sets, so it’s like comparing dogs with elephants. They’re both animals but you can’t cut them in half and glue them together. People just need to use different techniques to answer different questions and then just use their judgement to make the best possible decisions.

AAI: Why is it important for brands and their agencies to be able to compare online and offline audience and advertising data more effectively?
NM: Because anyone who uses both needs to understand how they affect each other. But there is no common currency, so they should use modelling techniques to calculate the combined effect as much as they can.

AAI: Can you sum up the holy grail of total advertising attribution in one sentence?
NM: One word: impossible. There can never be total advertising attribution because there are too many variables, some of them intangible, such as the emotional effect of creative messaging. The best we can do is carry on working towards the fusion of data-sets with the right kind of statistical skill to arrive at a ‘best-fit’. Then we must use common-sense and judgement to make the best possible decisions. This is advertising, not accountancy.

Q&A with Dr Jillian Ney, Dr of Social Media Intelligence & Behavioural Science

We caught up with Dr Jillian Ney, Dr of Social Media Intelligence & Behavioural Science , ahead of her participation at this year’s Audience Analytics & Insight Forum in the opening keynote on understanding the cost of “creepiness”.


AAI: Why is it that more data can lead to fewer insights?
JN: There is a distinct irony that having too much data can actually lead to fewer insight. The more data that is collected, the less they overlap which creates holes in the data. The data also collected by marketing teams might not be causal. While we can correlate search advertising data with purchase, it does not always follow that ads caused the sales.

There has been too much focus on gathering and collecting data, as an industry we are awash with data. Many data points are useless and hold no value in optimising advertising effectiveness, they serve as a barrier to success with marketers getting too caught up in analytics. To overcome these issues, marketing teams need to first consider what they want to do with the data, and then explore which data they need to do it. While we can all be confored that we are collecting as much data as possible, it actually makes it more difficult to do our job!

AAI: As an industry are we losing the art of interpreting human motivation?
JN: This is a difficult question to answer but I’d say no. We have more ways than ever to interpret and understand human motivation, this is a very exciting time in history where we have the data to answer almost any question, the challenge is getting the right data. As an industry we are becoming more reliant on technology to help us understand and target our customers, mass automation plays a large part in this process but this is not necessarily the right way. We have become reliant on technology providers to determine the key metrics to be measured, and their interpretation of what this means and what should happen next. We need to re-evaluate the effectiveness of this. Many metrics have been taken from old thinking and re-purposed in today’s world, like we measure car speed in terms of horsepower not engine power.

AAI: GDPR should mean cleaner better data, has it?
JN: This is not necessarily true. Unstructured data is always going to be unstructured so it is always going to be messy. I work with social data which has its own challenges even with GDPR. I still don’t believe that the data will be cleaner or better in other areas too. Companies are still collecting data without knowing what they want to do with it or the best way to process or analyse it.

AAI: If AI and machine learning could deliver one improved solution to help your understanding and targeting of your audience, what would it be?
JN: Propensity to purchase and attention.

AAI: Block chain, could it be the answer to all advertisers problems, 100% accurate behavioural data or just the next new shiny thing?
JN: I think we are a way off this solving advertising issues. Yes, we have behavioural data but the biggest challenge is that advertising is sent at the wrong time. We need to get better at understanding behavioural triggers and the optimum time to send adverts. It is.

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Q&A with Debrah Harding, Managing Director, Market Research Society

We caught up with Debrah Harding, Managing Director at Market Research Society, ahead of her participation at this year’s Audience Analytics & Insight Forum in a session that will look at the impact on both personal and research data 6 months on from GDPR.


AAI: Why is it that more data can lead to fewer insights?
DH: Big data does not mean good data – it just means big. With so much information available, we find ourselves in danger of getting lost in the numbers: data is only as good as the questions you ask of it. To gain real insight requires diving into data to determine why certain activities and behaviours are happening. This is where research and insight techniques are key – they unearth the why – leading to great insights which inspire businesses.

AAI: Where should analytic & insight teams sit within the business structure?
DH: Insight and analytics teams should be at the core of any business structure.

In insight driven organisations, insight drives growth, improves customer centricity and reduces business risk. Insight interprets and aggregates increasingly fragmented information coming from a rapidly evolving and expanding customer channels.

AAI: Why is it important to break down the silos between data teams and insight teams?
DH: To harness fully the intellectual capital derived from data and insight requires the best people, methodologies and techniques to work together to bring the maximum strategic benefit to any business. The best way to do this is to break down the silos and create horizontal structures which ensure that data and insight rests at the heart of any business.

AAI: As an industry are we losing the art of interpreting human motivation?
DH: Insight is not losing the art of interpreting human motivation. At its most effective insight acts as a filter, removing the static and noise of information, enabling businesses to focus on what’s important – the voice of the customer.

Market researchers are the bridge between data and its application, asking the right questions to provide actionable insight. It’s vital that we make big data, smart data.

AAI: GDPR should mean cleaner better data, has it?
DH: GDPR means that businesses have a greater awareness of their data, and a recognition that as well as being a huge business asset, data can also be a liability if not processed appropriately.

As businesses focus on reducing data risks in light of GDPR, increasing transparency and understanding about how data is used should result; with a re-balancing of the social data contract between businesses and their customers. In the long-term this should result in better, and hopefully more meaningful, data exchanges between businesses and their customers.

Skilled market research professionals have been handling sensitive data for decades and must remain at the forefront of best practice, helping businesses respect the people whose data they may hold.

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