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Half of business leaders ‘don’t have a marketing attribution model’

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Half of business leaders ‘don’t have a marketing attribution model’

Nearly half (46%) of business leaders don’t have a marketing attribution model, with 37% feeling overwhelmed by the complexity of attribution data.

The study, from Marketing Signals, found that the top ways businesses aren’t using marketing attribution has been revealed from not tracking calls into the company (31%), not tracking sales or discount codes (27%) and 21% who don’t measure paid social media activity.

The survey of 1,021 UK workers also revealed that 74% of business leaders said that marketing attribution reporting is a priority for their company.

The post Half of business leaders ‘don’t have a marketing attribution model’ appeared first on Netimperative – latest digital marketing news.

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Marketers need to be polymaths: the modern-day marketing challenges

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The marketing industry is having change thrust upon it, with the capabilities of data and technology upending traditional roles and career paths. For chief marketing officers (CMO) and marketing leaders, whose job it is to make sense of that rapidly changing space, the price of innovation is constant attention and readjustments.

The Drum and Domo invited a table of brand marketers gathered to discuss what keeps marketers up at night and what solutions are available: Anand Narayanun of Panasonic, Monique Aziz of Universal Music, Tom Stancliffe of Tribe, Debarshi Pandit of Sky, Sam Lloyd of Travis Perkins and Mark Johnston of Domo shared insights over coffee and breakfast on winters’ day in the cosy Merchant’s Tavern in London.

What keeps marketers up at night?

The great hope for marketing in 2018 and into 2019 is personalisation. Anand Narayanan, head of digital for Panasonic believes that modern data capture and analysis tools enable marketers to be creative around much more targeted groups:

“If you have a better understanding of who your customer is, what their motivations are, and what will they care about, you can be more creative in delivering that surprise and delight to them, as opposed to coming up with an idea that’s creative [but] for everybody. It's insight as opposed to data. As long as you have impartial insights, that is where the real value lies, in enhancing creativity.”

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His advice speaks to the ongoing trend towards targeted campaigns and advertisements that focus on a subset of the total audience rather than a mass, anonymised group. Tom Stancliffe, co-founder of natural sports nutrition products Tribe, argues that for challenger brands and start-ups, “targeting with insight” can lead to more tangible results and ultimately growth around super-engaged consumers.

Data and audiences

But when customer experience is the new brand and winning requires data, and more of it, how do marketers balance data and technology as a tool for creativity in equal balance to measurement and performance?

The marketers around the table agreed that they were all wary of embracing data for its own sake, arguing that without insight data has the capability to be misused. Debarshi Pandit, head of special projects at Sky advocates for a recognition that while data is useful, it cannot quantify everything about how audiences interact with and relate to brands.

“Digital is very measurable, everything we do in digital is supposed to have a numerical value. You can't do that with a brand. There are things like net promoter score, which, if it is your KPI, is your measure. Delivering that is something that isn't in my advertising campaign or my influencer campaign. At the end of they day, that is the brand experience,” he explained.

Mark Johnston, marketing director at Domo, admitted to a certain envy for sales teams, whose KPIs have not been shifted by the rise of digital as marketing has: “It seems to me that it's very easy to look inwards at marketing and historically it would have been that inward-looking thing of 'we own or we control'. Then this digital thing came along and people become more focused on the added complexity of digital, whether it's the mediums or the channels, where the technology inside of a marketing organisation, analytics and insight now being a core function of every large marketing organisation.”

However, the group also noted that ‘digital’ is necessarily changing the expertise of the people making key marketing decisions. Where once creativity was the be all and end all for rising to the CMO position, now skills that were once considered the domain of tech and finance teams are taking priority. Narayanan believes that it is the responsibility of a modern CMO “to be more financially savvy”, to aid in demonstrating ROI around campaigns to the rest of the organisation.

Marketers as polymaths

As much as the group argued for changes that are responses to changing consumer habits and the rise of digital, they also advocated for internal changes within marketing companies. Especially when trying to keep up with constant innovation and layers of complexity at organisations.

Sam Lloyd, head of science and data analytics at Travis Perkins believes that increasing the permeability of walls between divisions and silos within companies should be a priority for marketers in 2019: “For me it's about being a maven, someone who can connect various parts of the business, really just to break down silos.” In order to do so, he argued, CMOs need to be “polymaths”.

Pandit agreed, noting that the UK marketing industry has traditionally relied upon specialists, whereas what is required today is generalists who can connect each part within a business.

The changing skillset – and the ephemeral nature of digital advertising – requires that marketers feel empowered to make decisions much more swiftly than has historically been the case. For Monique Aziz, the UK marketing manager of Universal Production Music, “It's good to have a fast response. Sometimes it's difficult. You need to talk to so many people along the way – social is too fast and if you don't do it at the right time that's the moment [gone]. I'm really close with sales, with technology [so] if I make a choice I'm confident to do it… you need to build that relationship.”

Monique Aziz

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Despite those upheavals, the group was keen to point out that certain fundamentals of marketing have not changed, and that the evergreen problems that have kept CMOs up at night for decades haven’t disappeared.

Narayanan referred to the consumer marketing dilemma that you can only sell the features of a new product once, so finding new ways of selling it to an audience is paramount. He noted that “there's only so much you can say, whether that's about TV, camera, Blu-Ray, take your pick… unless you have stories that connect with your audience there is nothing to say.”

The rise of digital has created new reasons for CMOs to lie awake worrying at night. From the speed of change and the need for swift deployment of messages to the change in culture within marketing companies, there’s a lot more to consider.

Despite that the panel was optimistic that the CMO role in particular prepares people to adjust to those changes and deliver upon the promise of marketing in 2019.

Find more insights on The CMO Challenge with Domo here.

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Data & Analytics

2019: Data will be the new IP

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Research from Dresner Advisory Services reveals that 53 per cent of organisations are using predictive analytics to help them enhance marketing communications, reduce risk, detect fraud and optimise operations. Airlines use it to set ticket prices, insurers use it to calculate the likelihood of water ingress, credit providers use it to determine spend limits, banks use it to identify account take overs, retailers use it to predict next likely purchase and so on. Clearly predictive modelling is an incredibly useful tool to determine future behaviour.

There are two types of predictive (or supervised) modelling, classification and regression. Fundamentally, classification is about predicting a label and regression is about predicting a quantity. So classification models calculate class membership e.g. determining whether a customer is likely to leave, how someone might respond to an offer or what someone might want to buy next. Whilst regression models predict numbers such as lifetime value of a customer or how many months it will take to acquire a certain amount of customers.

The most widely used predictive modelling techniques include decision trees, regression analysis, neural networks, bayesian analysis and KNN (K nearest neighbour). However, there are many more. What is important to understand about all these techniques, is the fact that today it is possible to download open source algorithms and packages such as keras, scikit-learn, KNIME, Orange : or buy them in from commercial sources such as SPSS Modeler or KXEN. What this means is that ultimately the building block architecture of most predictive models used commercially or otherwise is the same. Therefore what separates one from another is the data that trains it.

Training data is literally just that. It is the data that data scientists use to ‘teach’ the model so that the predictions it makes are as accurate as possible. Training a model is no different to teaching a child. If you keep showing a child a ball and say ‘ball’ eventually the child will understand that the spherical object is a ball. The same is true for a model – only they have a much greater capacity for learning than humans.  So it is possible to model algorithms to find relationships, detect patterns, understand complex problems and make decisions all at speed. Eventually, the quality, variety, and quantity of the training data will determine the success of the predictive model. To do this training data has to be correctly labelled and classified so that the model can learn from it. To make more accurate predictions the more training data there is available the better. This is why organisations such as Google and Amazon have such a great competitive advantage – because they have so much quality data they can use to train their algorithms. They are walled gardens and have never let their valuable data leave their ecosystems or be shared with competitors. The selection and labelling of training data tends to be a long and laborious process. Each piece of data has to be checked that it is relevant, up-to-date and annotated correctly. The more effort that is put in at this stage the more accurate the predictions will be at the end. As with all data-based applications – rubbish in, rubbish out.

It has already been proved that organisations that use predictive models are more successful that those that don’t. However, as increasing number of organisations turn to predictive modelling it is clear that competitive advantage will lie in training data, not the model itself.

 

Emma Duckworth, Data Scientist, Outra

 

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Data & Analytics

D4t4 Solutions plc Continues Global Expansion with New US Leadership

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Analytics Industry Expert Bill Bruno Announced as VP, North AmericaTuesday 11th December 2018, Sunbury-on-Thames, UK: D4t4 Solutions plc has announced new US leadership to drive continued growth in one of its key global markets. Bill Bruno, an experienced executive in the media and analytics industries, has joined the company to accelerate the opportunity that exists in the US for D4t4’s unique range of solutions for customer data capture and enterprise managed services.Bruno joins D4t4 from…

Source: RealWire

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